RAMAN TECHNOLOGYfiles.alfresco.mjh.group/alfresco_images/pharma/... · Raman • AFM SNOM RISE MADE...

June 2019 Volume 34 Number s6 wwwspectroscopyonlinecom

RAMANTECHNOLOGY

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4 Raman Technology for Todayrsquos Spectroscopists June 2019

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Articles

8 Criteria for High-Quality Raman Microscopy Damon Strom Sonja Breuninger Harald Fischer Andrea Richter Ute Schmidt and Olaf Hollricher

Five key qualitative factorsndashspeed sensitivity resolution modularity and upgradeability and combinabilityndashcontribute to the quality of confocal Raman imaging microscopes Using application examples this ar ticle introduces modern Raman imaging and correlative imaging techniques and presents state-of-the-ar t practice examples from polymer research pharmaceutics low-dimensional materials research and life sciences

20 Rapid Portable Pathogen Detection with Multiplexed SERS-based NanosensorsHayleigh Kearns Lauren E Jamieson Duncan Graham Karen Faulds and Cicely Rathmell

A new application of surface-enhanced Raman spectroscopy (SERS) is described for quantifying low concentrations of pathogens with high reproducibility In this novel assay bacteria are captured and isolated using functionalized metal nanoparticles for rapid optical identification via SERS Initial tests with a portable SERS system validated the ability to identify the presence of Escherichia coli and methicillin-resistant Staphylococcus aureus bacteria

32 Characterizing Microplastic Fibers Using Raman SpectroscopyBridget OrsquoDonnell and Eunah Lee

In this study macro- and microscopic Raman spectroscopy were used to identify dif ferent commercial microplastic fibers using measured spectra with database searches Raman microscopy is demonstrated as a powerful technique for microplastic fiber characterization especially for samples that contain mixtures of components including multiple polymers or additives

Cover image courtesy of pitjuAdobe Stock Ioan PanaiteAdobe Stock

r t13 t13rsquosj 2019


It was 90 years ago that Chan-d r a s ek ha r a Ven k at a R a ma n and Kariamanickam Srinivasa

K r ishna n f i rs t docu mented ldquoA New Type of Secondary Radiationrdquo which then became known as the Raman ef fect (12) Raman spec-troscopy is based on this ef fect and it is used for qualitative and quantitative analysis of the chemi-cal components and molecules of a sample It is a nondestructive method that requires little if any sample preparation

Nevertheless Raman spectros-copy long remained a technique that was only performed in spe-

cia l ized laboratories In recent yea rs however it has been in-creasingly losing its outsider sta-tus One reason for this is the de-velopment of the confocal Raman microscope with which not only indiv idua l Raman spectra but a lso complete images generated from thousands of spectra can be acquired Through continuous development commercially avail-able Raman microscopes are also becoming more user-friendly For example modern software inter-faces guide the user through the Raman measurement and the sub-sequent data analysis

Criteria for High-Quality Raman MicroscopyIn recent years confocal Raman imaging and related techniques have become more and more popular in many fields of application such as materials sci-ence pharmaceutics life sciences geoscience food technology and many others The available Raman microscopes are focused on user-friendly and intuitive operation Additionally several key qualitative factors of confocal Raman imaging microscopes should be considered to guarantee clear and substantial results In this article the following five criteriandashspeed sensitivity resolution modularity and upgradeability and combinabilityndashare explained and factors that influence them positively or negatively are examined On the basis of application examples modern Raman imaging and correlative imaging techniques are introduced and state-of-the-art practice examples from polymer research pharmaceutics low-dimensional materials research and life sciences are presented

Damon Strom Sonja Breuninger Harald Fischer Andrea Richter Ute Schmidt and Olaf Hollricher

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There are severa l key factors that can be used as criteria for de-termining the quality of confocal Raman microscopes which are ex-plained as follows

Speed

In the past exposure times of min-utes to hours were common for ac-quiring single Raman spectra but today the process generally takes fractions of a second to less than one mil l isecond In one second more than 1000 Raman spectra can be recorded Thus a Raman image can be generated within a few minutes To achieve this acqui-sition speed the Raman imaging system should be equipped with optimized optics and an electron multiplying charge coupled device (EMCCD) camera

High acquisition speeds are par-ticularly important for measure-ments on sensit ive and valuable samples in which the excitation energy must be as low as possible Time-resolved invest igat ions of dynamic processes can also benefit from rapid Raman spectral acqui-sition Operating costs can also be reduced by shorter analysis times concurrent with increased data rates Having a high system speed is also advantageous for time-crit-ical work

Sensitivity

The signal sensitivity of a system is critical for the quality of the re-sults and is especially important when weak Raman signals are to be detected

To achieve the best possible sen-sitivity a confocal beam path such

as using a diaphragm aper ture must be employed to el iminate light from outside the focal plane to increase t he signa l-to-noise ratio The entire Raman imaging system should also be optimized for high light throughput This in-cludes a spectrometer that ensures throughput of over 70 and is de-signed for measurements with low light and signal intensity Charge-coupled dev ice (CCD) cameras optimized for spectroscopy which exhibit more than 90 quantum efficiency in the visible range are most commonly used as detectors Finally the use of almost lossless photonic f ibers ensures eff icient light and signal transmission

Resolution

The resolution of a Raman system comprises both spatial and spec-tral resolution The spatial resolu-tion includes the lateral resolution (x- and y-directions) and the depth resolution (z-direction) The spa-tia l resolution is determined by the numerical aperture (NA) of the objective used and the excitation wavelength In addition a confo-cal microscope produces images with a higher contrast because the background signal is reduced The smaller the aperture of a confocal microscope the higher its reso-lution In a confocal Raman mi-croscope the lateral resolution is about 200ndash300 nm and the depth resolution below 1 micrometer A confocal microscope can also cre-ate optical sections from different focal planes which can be used with transparent samples for depth profiles and 3D images

June 2019 Raman Technology for Todayrsquos Spectroscopists 11

Spectral resolution defines the ability of a spectroscopic system to separate Raman lines near one another Symmetric peaks in the spectrum are ensured by a spec-trometer design that operates free of coma and ast igmat ism The grating used the focal length of the spectrometer the pixel size of the CCD camera and the size of the aperture also affect the spec-tral resolution

At room temperature the width of the Raman l ines is ty pica l ly greater than 3 cm-1 but some appli-cations (gases low temperature or stress analysis) may require signifi-cantly higher resolution (Figure 1)

Modularity and UpgradeabilityThe introduct ion of Raman mi-croscopy into laboratories puts new demands on commercia l ly available systems These require-ments can sometimes appear con-

tradictory easy operat ion with d iver s e f u nc t iona l i t y a w ide range of applicat ions with opti-mized sensit iv ity low cost and high performance To offer users a Raman system tailored to their individual requirements it is par-ticularly important that systems have a modular design that can be adapted to new conditions through being reconfigured or upgraded A system can be optimized for spe-cific requirements by individually combining suitable lasers f i lters lenses spec t rometers a nd de-tectors With such a customized Raman imaging system the user is able to obtain meaningful Raman images perform 3D volume scans and create depth profiles

Combinability

Confocal Raman microscopy can be combined with other micros-copy techniques By using differ-

Figure 1 Speed sensitivity and resolution are some of the characteristics that can be used to identify a high quality Raman microscope These three characteristics should not be mutually exclusive Ideally the Raman imaging system should be configured in such a way that high-resolution images with a high signal-to-noise ratio can be acquired in a short period of time


ent methods and correlating the data the user attains a more com-prehensive understanding of the sample Common examples of cor-relative microscopy techniques are Raman-atomic force microscopy (AFM) Ra manndash sca nning near-field optical microscopy (SNOM) and Ramanndashscanning electron mi-croscopy (SEM) To correlate the data of these disparate technolo-gies the exact same sample loca-t ion must be examined by each approach If different instruments are to be used finding this sample location can be very difficult and t ime-consuming This is made much easier with a hybrid system that combines the different analy-sis methods in one instrument so that the sample can remain in place during all measurements

Applications

Some applications of correlative Raman microscopy are

Raman and ProfilometryFor R a ma n m ic ros c opy mos t samples do not need to be treated

sta ined or other wise prepared prior to measurement The com-bi nat ion of a confoca l R a ma n microscope with a prof i lometer module for focus stabilization al-lows rough or inclined surfaces to be examined (34) During Raman analysis the examination area is kept constantly in focus by the si-multaneously acquired profilom-etry data This also compensates for t herma l shi f t s a nd enables long-ter m measu rements T he application example in Figure 2 shows the analysis of a microstruc-tured silicon sample The chemical image of the Raman measurement was overlaid onto the topographic profile measurement

Raman and FluorescenceFluorescence microscopy has been a widespread imaging method for the analysis of biological cells and organisms for decades Samples are stained with f luorescent dyes or organisms are genetica l ly en-gineered to express f luorescent proteins The f luorescence signal is usually much stronger than the

Figure 2 Topographic Raman image of a silicon microstructure

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Raman signal Nevertheless cor-relative Raman f luorescence mea-surements are possible with an ap-propriate system Figure 3 shows a Raman f luorescence image of a live cell culture of eukaryotic cells An inverted confocal Raman mi-croscope was used to examine the cells in their aqueous cell culture medium in the Petri dish The cell nuclei were stained with the f luo-rescent dye 4rsquo6-diamidino-2-phe-nylindole (DAPI) An excitation wavelength of 532 nm was used for the Raman measurement An image with 50 x 40 μmsup2 and 150 x 120 pixels was acquired A Raman spectrum was recorded at each pixel The recording time was 02 s per spectrum In the correlative Raman f luorescence image the nuclei are shown in blue (recorded with f luorescence microscopy) the nucleoli in green and the endo-plasmic reticula in red (recorded w it h Ra ma n microscopy) The

corresponding Raman spectra are shown in the same colors

Raman and AFMThe combination of Raman micros-copy which provides information about the type and distribution of molecules in a sample and the high-resolution AFM technique which determines the surface char-acteristics of a sample enables the visualization of both chemical and morphological properties

Here the analysis of a 111 mix-ture of polystyrene (PS) 2-ethyl-hexyl acrylate (EHA) and styrene-butadiene rubber (SBR) is shown For t h is a correlat ive Ra ma nndashA FM microscope was used in which Raman microscopy and AFM technologies are fully integrated

The measurement with AFM in intermittent contact or a lternat-ing current (AC) mode documents t he topography of t he poly mer mixture (Figure 4a) The simul-

Figure 3 Showing (a) correlative Raman fluorescence image of primate cells in a cell culture Blue objects are nuclei recorded by fluorescence microscopy red objects are endoplasmic reticula and green circles are nucleoli recorded by Raman microscopy (b) Raman spectra associated with the image


taneously recorded phase image (Figure 4b) provides information on the viscosity and elasticity of the individual components of the polymer mixture The confoca l Raman image (Figure 4c) shows that PS (red) and EHA (green) are present separately SBR (purple) partly mixes with EHA (mixture shown in blue) By correlating the Raman image with the AFM image the chemical information can be linked to the structural informa-tion (Figure 4d)

Raman and SEMSca nning elec t ron microscopy (SEM) is a well-established method

for structural surface analysis By combining Raman imaging with SEM in a correlative microscope it is possible to combine results of SEM structural analysis with chemical and molecular informa-t ion f rom confoca l Ra ma n mi-croscopy (5) The sample is placed in t he vacuum chamber of t he electron microscope Both analy-sis methods are then carried out automatically at the same sample location The obtained SEM and Raman images can then be super-imposed

In Figure 5 a structure several atoms in thickness comprised of graphene layers was analyzed by

Figure 4 Correlative high resolution Raman-atomic force microscopy (AFM) image of a 111 mixture of polystyrene (PS) 2-ethylhexyl acrylate (EHA) and styrene-butadiene rubber (SBR) The image shows (a) the topography of the polymer mixture determined with AFM in the AC mode with (b) the phase of the AFM image showing the fine structure of the compound In (c) a color-coded confocal Raman image is shown as generated from the Raman spectra showing the distribution of the polymers PS (red) EHA (green) SBR (purple) and SBR-EHA mixture (blue) In (d) a correlative Raman-AFM image is shown where the topography and distribution of the different polymers can be visualized


correlative RamanndashSEM micros-copy The Raman image consists of 22500 spectra with 50 ms re-cording time per spectrum While in the SEM image the contrast bet ween t he subst rate a nd t he graphene f lake is v isible in the Raman image the number of gra-phene layers and their dif ferent orientations can be analyzed This is not possible with SEM alone

Raman Particle Identificationand CharacterizationHigh-resolution investigations of particles are of great interest in many fields of application such as environmental science pharma-ceutical research and many oth-ers Combining a particle analysis tool with the fast label-free and nondestruct ive Raman imaging technique makes it possible to find classify and identify particles in a sample according to their size shape and chemical characteristics

The physical and molecular attri-butes of the particles in a sample may be correlated and qualitatively and quantitatively evaluated Fig-ure 6 shows the results of particle analysis carried out on a cosmetic peeling cream sample Figure 6a shows the overlay of an optica l br ight f ie ld microscope image w it h t he correspond ing confo-ca l Raman image Par t icles are identif ied according to their size and shape and further character-ized by their molecular properties through confocal Raman imaging The chemical analysis revealed an-atase and boron nitride particles in an oil matrix (Raman spectra shown in Figure 6b) Further eval-uation of the results determines the quantitative prevalence of the molecular sample components in the particles (Figure 6c) and also the distribution of chemical com-pounds correlated to particle size (Figure 6d) In extended analyses

Figure 5 Correlative Raman-scanning electron microscopy (SEM) image of a multilayer graphene flake The different colors show folds and orientations in the graphene that can be identified by Raman spectroscopic analysis

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the chemical characteristics of par-ticles could also be linked to param-eters such as area perimeter bound-ing box Feret diameter aspect ratio equivalent diameter and many others This illustrates the potential for com-prehensive investigations of particles in many fields of application

References(1) CV Raman and KS Krishnan Nature

121 501 (1928)

(2) J Toporski T Dieing and O Hollricher

E d s C o n f o c a l R a m a n M i c r o s c o p y

(Spr inger Internat ional Publ ishing

New York New York 2nd ed 2018)

(3) A Masic and J C Weaver J StructBiol

189 269ndash275 (2015)

(4) B Kann M W indbergs The A APS

Journal 15 505ndash510 (2013)

(5) G Wille C Lerouge and U Schmidt

J Microsc (Oxford UK ) 270 309ndash317

(2018)

Damon Strom Sonja Breuninger Harald Fischer Andrea Richter Ute Schmidt and Olaf Hollricher are with WITec GmbH in Ulm Germany Direct correspondence to karinhollricherwitecde

For more information on this topic please visit our homepage at wwwspectroscopyonlinecom

Figure 6 Particles in a cosmetic peeling cream sample (a) the optical bright field image overlaid with the confocal Raman image (b) the corresponding Raman spectra of the molecular components in the sample (c) a pie chart of the quantitative compound distribution in the sample and (d) the graphical representation of the correlation between chemical characteristics and particle size

Raman spectroscopy holds great potential to provide answers about

the world around us As you unlock these connections wersquore creating

the products to bring them to life from off -the-shelf modular systems

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to learn how wersquore pushing the limits of compact Raman ndash in speed

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Bringing Raman to Life


T he prol i fer at ion of mu lt i -drug-resistant bacteria is a si lent but growing crisis ac-

knowledged by public health au-thorities and regulatory agencies as an emergent global malady (1) Although resistance to antibiotic drugs may develop naturally via mutation or by transfer of mobile genet ic elements encoded wit h resistance genes it is the selec-

tive pressure exerted through the widespread use and misuse of antimicrobial agents that causes these bacteria to selectively f lour-ish relative to weaker strains

Both the absolute number and proportion of antimicrobial-resis-tant pathogens have increased this century Although the prevalence of methicil lin-resistant Staphylo-coccus aureus (commonly known

Rapid Portable Pathogen Detection with Multiplexed SERS-based Nanosensors

Antibiotic resistance rates are rising for many common pathogens putting added pressure on healthcare providers to rapidly and accurately diagnose the cause of bacterial infections New diagnostic methods must be sensitive selective and capable of multiplexed detection as well as rapid dependable and portable enough to operate in a variety of settings A new application of surface-enhanced Raman spectroscopy (SERS) shows great promise to meet these criteria In this novel assay bacteria are captured and isolated using functionalized metal nanoparticles for rapid optical identification via SERS Initial tests with a portable SERS system validated the ability to identify the presence of Escherichia coli and methicillin-resistant Staphylococcus aureus bacteria quantifying concentrations down to 10 colony-forming units per mL (cfumL) with high reproducibility The system was also able to discriminate between the bacteria within the same sample matrix at clinically relevant concentrations (103 cfumL) demonstrating its potential as a rapid reliable on-site diagnostic for bacterial pathogens

Hayleigh Kearns Lauren E Jamieson Duncan Graham Karen Faulds and Cicely Rathmell


as MRSA) is stabilizing or decreas-ing in Europe (2) infections due to ant imicrobia l-resistant Esch-erichia coli (E coli) and Klebsiella pneumoniae (K pneumoniae) are increasing across the continent In the United States Clostridium dif-ficile Enterobacteriaceae (includ-ing E coli and K pneumoniae) and Neisseria gonorrhoeae have been ident i f ied as the most ur-gent threats in a l ist of 18 drug-resistant pathogens (3) The rise of multi-drug resistance in E coli is particularly concerning as it is the most common Gram-negative

pathogen in humans causing a variety of maladies ranging from general digestive upset to urinary tract infections and life-threaten-ing bloodstream infections Unlike MRSA which is often acquired in a healthcare setting most multidrug-resistant strains of E coli originate in the general community In one Icelandic study increased preva-lence of quinolone-resistant E coli has even been traced back through the food chain to its presence in chicken feed (4)

As the costs to our healthcare systems grow and the points of

Figure 1 Schematic illustrating the sandwich SERS assay used for (a) single-pathogen and (b) multiplexed pathogen detection (c) Lectin-functionalized silver-coated magnetic nanoparticles facilitate magnetic separation of all bacterial strains from the sample matrix while SERS active conjugates composed of silver nanoparticles functionalized with strain-specific antibodies and unique Raman reporter molecules facilitate optical detection of each pathogen via SERS


origin increase so does the need for rapid specif ic and sensitive pathogen detect ion Given that infections due to antimicrobia l-resistant pathogens may stem from the food chain the environment or the clinic detection methods must be f lex ible a nd por table enough to be deployed in a wide variety of f ield conditions and by nonexperts The vast majority of technologies current ly used for the task fail on these counts due

to long analysis t imes (most ap-proaches require culture prior to further analysis to increase bac-teria concentration) the need for specia l ized equipment or sta f f high cost or addit ional sample preparation steps these include staining culturing and biochemi-cal assays real-time polymerase chain reaction (RT-PCR) and mi-croarray assays like enzyme-linked immunosorbent assay (ELISA) Lab-on-a-chip technology seeks

Figure 2 SERS spectra obtained from single pathogen detection using the sandwich SERS assay (a) SERS spectrum of MGITC observed when detecting E coli (green) and the control spectrum representing when no bacteria was present (red) (b) SERS peak intensities at 1620 cmminus1 for assay and control when detecting E coli (c) SERS spectrum of PPY observed when detecting MRSA (blue) and the control spectrum (red) (d) SERS peak intensities at 960 cmminus1 for assay and control when detecting MRSA Raman intensity is given in arbitrary units (au)

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IQ Frametrade

A unique feature that can be added

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Origin coordinates are registered

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imaging analysis is used to exactly

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QRI

Using a high speed high

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can be imaged 50x faster than

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Raman imaging can be difficult and

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to st reamline sample hand l ing and concentration steps to reduce human error and improve accu-racy in a portable format but the technology still lacks the simplic-ity of our technique

SERS for Rapid Pathogen Detection Surface enhanced Raman spec-troscopy (SERS) offers a rapid and portable alternative to the existing methods for bacteria l detection SERS leverages the specif icity of Raman signals with their sharp wel l-def ined peaks to provide a f ingerprint unique to each ana-lyte with enhanced sensitivity due to interaction with silver or gold nanostructures When in close proximity surface plasmon reso-nance in the noble metal generates an amplified electromagnetic field that enhances the Raman signal If the laser frequency used is tuned to the absorbance maxima of the analyte additional enhancement can be observed an effect known as sur face enhanced re sonance

Raman scatter ing (SERRS) The combined enhancement can yield up to ~1014 in total signal ampli-fication Using silver-coated gold nanorods SERRS has been used by Wang and associates to differenti-ate between strains of carbapenem-resistant E coli and carbapenem-sensitive E coli with almost 100 accuracy using orthogonal partial least squares (OPLS) discriminant analysis of their spectra (5)

SE R S u s i n g f u nc t ion a l i z e d nanoparticles (NPs) tagged with resona nt Ra ma n repor ter mol-ecules has proven ideal for mul-tiplexed detection of bacteria of-fering excel lent selectivity high photostability and lack of spectral crosstalk The addition of a ldquomag-netic separationrdquo step to isolate bacteria from the host matrix has been shown to improve detection limits to within clinically relevant levels (103 colony-forming units [c f u]) T h is approach enabled Guven and col leagues to report detection limits of eight cfumL in water for E coli (6) and Najafi

Figure 3 Concentration series for E coli showing (a) Comparison of SERS spectra for concentrations 101ndash104 cfumL versus the control and (b) peak intensity at 1620 cm-1 as a function of concentration The dashed line gives visual clarification of the SERS peak intensity of the control Raman intensity is given in arbitrary units (au)


and associates 102 cfumL in apple ju ice (7) Wang and col leagues were able to simultaneously de-tect Salmonel la enter ica and S aureus in spiked spinach solution and peanut butter at concentra-tions of 103 cfumL demonstrat-ing the potential of the approach in complex matrices (8) Although these studies meet the threshold for speed and minimum detection limit they leave opportunities for

further improvement in sensitivity and ease of sample preparation

A Novel SERS AssayTo this end we developed a highly sensit ive ef f icient and easy-to-use sandwich SERS assay for the isolation and multiplexed detec-tion of bacteria (Figure 1) Lectin- functionalized magnetic nanopar-ticles (MNPs) are used to capture and isolate bacter ia within the

Figure 4 Duplex pathogen detection using the SERS assay for E coli and MRSA at 103 cfumL (a) Stacked SERS spectra showing the spectra obtained from the detection of both bacterial pathogens simultaneously (black) versus each pathogen separately and a control sample (red) MGITC spectrum (green) represents E coli and PPY spectrum (blue) represents MRSA The black dotted lines show peaks unique to each Raman reporter used to identify the presence of the bacterial targets (b) Comparative peak intensities at 1620 cmminus1 for assay and control (c) PCA scores plot showing the relationship between the multiplex spectra (black) and the E coli (green) and MRSA (blue) single pathogen spectra Raman intensity is given in arbitrary units (au)


sample while SERS-active si lver nanoparticles (AgNPs) function-alized with bacteria-specific anti-bodies and Raman reporter mol-ecules bind to each specific strain for interrogation

L e c t i n s were chos en a s t he functional group for the magnetic nanoparticles because of their af-finity for the sugars expressed on the surface of bacteria the MNPs then act as a handle by which mag-netic separat ion can be used to capture bacteria indiscriminately from the sample matrix This ap-proach allows any unbound mate-rial to be washed away concentrat-ing the sample for increased signal less background and elimination of false positives

SERS-active conjugates facilitate optical detection of the bacterial pathogens Each conjugate type consists of si lver nanopart icles functionalized with strain-specific bacterial antibodies and a unique Raman repor ter molecu le pro-viding each bacterial strain under interrogation with a bespoke opti-cal f ingerprint when investigated using SERS The small size of the AgNPs relat ive to the bacter ia

surface permits multiple binding sites allowing the AgNPs to come in close proximity to one another and form SERS hot spots for in-creased Raman signal Unbound AgNPs are washed away during the concentration stage further benef iting signal to noise of the detection technique

The use of lectin-functionalized MNPs makes this separation step novel a nd ef f ic ient i n c apt u r-ing and isolating bacteria from a sample matrix while concurrent addition of bacteria-specific SERS conjugates simplif ies and speeds s a mple prepa r at ion Pre v iou s work by our group has shown this detection assay to be effective in isolating and detecting E coli S typhimurium and MRSA down to concentrations of 101 cfumL indi-vidually using a benchtop Raman microscope system for SERS detec-tion We have also demonstrated t r iplex detec t ion successf u l ly identifying each bacterial strain within the same sample matrix (9) The present study establishes transferabi l ity of the technique to a more compact cost-effective and portable system without com-

Table I Percent relative standard deviation ( RSD) comparison for different E

coli concentrations (in cfumL) for SERS assay detection using the microscope system versus the portable system

E coli Concentration (cfumL)Microscope System

( RSD)Portable System

( RSD)

105 42 09

104 39 10

103 26 12

102 51 13

101 43 19

Control 65 22

Renishaw Inc 1001 Wesemann Drive West Dundee Illinois 60118 United States

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Next generation

Raman imaging

bull if a specific material or species is present

bull if any unknown materials are present in the

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bull the variation in a parameter of a material

such as crystallinity or stress state

bull the distribution of the material or species

bull the size of any particles or domains

bull the thickness and composition of layered

materials such as polymer laminates from

micrometres to millimetres thick

bull the relative amounts of materials or species

Raman spectroscopy produces chemical and structural images to help you understand more about

the material being analysed You can determine

Renishaw has decades of experience developing flexible Raman systems that give reliable results

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promise to detection limit or mul-tiplexing ability greatly expanding its potential for practical use

ExperimentalSy nt he s i s of s i lver na nopa r t i-cles and si lver coated magnetic nanopart icles and the prepara-t ion of t he biomolecu le-AgNP conjugates that were used in these studies have previously been re-ported by our group (9) Detailed protocols for the preparation and characterization of these colloidal suspensions are provided in the supporting information (ESI) of this paper and prev iously pub-lished in the journal Analytical Chemistry in December of 2017

Bacterial Strains

E coli ATCC 25922 and Saureus (me t h ic i l l i n re s i s t a nt) ATC C BAA-1766 were used in this study E coli and MRSA were grown on Luria-Ber tani (LB) Mi l ler agar in an aerobic atmosphere for 24 h at 37 oC Harvested cel ls were suspended in LB broth to obtain an optical (OD600 nm) of 06 Cells were t hen plated onto LB agar plates as triplicates and incubated for 24 hours as described previ-ously All the strains were grown using the same batch of culturing plates to reduce any potential un-wanted phenotypic variation

Bacterial Sample Preparation

Bacterial slurries were prepared by harvesting the biomass from the surface of each plate using sterile inoculating loops and resuspend-ing in physiological saline solution (1 mL 09 NaCl) The prepared

bacterial slurries were washed by centrifugation (E coli 650 g for 5 min MRSA 1600 g for 5 min) and the pellets resuspended in 1 mL sa-line The wash step was repeated a further twice with the final re-sus-pension being in deionized water (1 mL dH2O) Note the OD600 nm

was recorded for all samples and bacterial concentrations were ob-tained by serial dilutions based on plate-counting results All samples were stored at minus80 degC until further analysis

Detection Assay

The novel method for bacteria l detect ion descr ibed prev iously by Kearns and colleagues (9) was modif ied slightly for use in this study

Single Pathogen DetectionFor each of the bacteria l patho-gens the following procedure was carried out SERS active antibody functionalized silver nanoparticles (200 μL Ab-AgNP) were added to-gether with lectin functionalized si lver coated magnetic nanopar-ticles (200 μL Con A-AgMNP) a nd a speci f ic bac ter ia l s t ra in (50 μL 104 cfumL) Note for the control sample the bacteria was replaced with dH2O (50 μL) only The sample was mixed thoroughly for 30 min before being placed in a magnetic rack for a further 30 min to allow the sample to collect The clear supernatant was removed and the sample re-suspended in dH2O (600 μL) ready for analysis The same procedure was followed for the concentration study of E coli except the bacteria concentra-


tion was varied from 105 to 101 cfumL It should be noted that each sample was prepared in triplicate

Multiple Pathogen DetectionThe two sets of antibody function-alized silver nanoparticles (100 μL of each conjugate) were added to-gether with lectin-functionalized si lver-coated magnetic nanopar-ticles (200 μL) plus the two bacte-rial strains (50 μL of each patho-gen 103 c f umL) Note for t he control sample the bacteria was replaced with dH2O (100 μL) only The same procedure as described above (for the single pathogen de-tection) was employed

SERS and Multivariate Analysis

R a m a n r e p o r t e r s u s e d w e r e malachite green isothiocyanate (MGITC) and 4-(1H-pyrazol-4-yl)-py ridine (PPY) and these were used to detect E coli and MRSA respectively

The samples were analyzed im-mediately after preparation using a WP 532 compact Raman spectrom-eter (50 μm slit temperature-regu-lated detector set at 15 degC) and fiber optic probe (Wasatch Photonics Morrisville North Carolina) with 532 nm laser excitat ion (InPho-tonics Norwood Massachusetts) The probe was focused direct ly into a 1 mL glass vial containing 600 μL of the bacteriandashNP con-jugate solution All the measure-ments had a 1 s acquisition time and a laser power operating at 77 mW For all Raman spectra data handling was carried out in Excel sof tware and no preprocessing was required The peak intensities

were obtained by scanning three replicate samples f ive times and in all plots the error bars represent one standard deviation The peak heights were calculated by averag-ing the peak intensities acquired from the 15 scans and then sub-tracting the maximum intensity at 1620 cm-1 (E coli) and 960 cm-1 (MRSA) from the base of the peak Following this the signal enhance-ments were calculated by dividing the peak heights of the sample by the peak height of the control Fur-thermore the relative standard de-viations were calculated ( RSD) by dividing the average standard deviation by the mean peak inten-sity and multiplying by 100

All multivariate statistical anal-ysis was carried out in MATLAB software (The MathWorks Natick Massachusetts) Prior to conduct-ing principal component analysis (PCA) the data was scaled PCA was performed on three different data sets two consisting of spectra obtained from single pathogen de-tection experiments and one data set obtained for the multiplex de-tection of the two pathogens (10)

Results and DiscussionValidation of Single

Pathogen Detection

To va l idate t he SERS assay for simplex detect ion of each indi-v idua l bac ter ia l pat hogen we compa red spec t ra of bac ter ia l samples treated with the E coli assay (SERS act ive conjugates + MNPs) to control samples The total analysis time was ~1 h The distinct Raman spectrum seen for 104 cfumL E coli samples treated


with the assay is due to the Raman reporter MGITC (selected for use with E coli antibodies) and thus confirms the presence of E coli in the sample (Figure 2a) In contrast the control (water + assay) showed only very weak signal attributable to unbound residual SERS conju-gates not washed away during the magnetic separation step Compar-ing the characteristic MGITC peak at 1620 cm-1 a signal enhancement of 145 times was observed for the E coli bacteria over the control (Figure 2b) matching the 10 times enhance-ment observed previously using a Raman microscope for detection (9)

Simi la rly 10 4 c f umL MRSA samples treated with the MRSA assay containing the Raman re-por ter PPY c lea r ly con f i r med presence of MRSA in the sample versus t he cont rol (Fig u re 2c) E x a m i n i ng t he c h a r ac ter i s t ic PPY pea k at 960 cm-1 a signa l enhancement of 125 t imes was obser ved for t he M R SA bac te-ria over the control (Figure 2d) matching the 11 t imes enhance-ment observed previously using a Raman microscope Transfer of the method to detection using a portable Raman system did not compromise the level of discrimi-nation which could be achieved and may have increased it some-what We believe the sampling ge-ometry with the portable system may be more conducive to this type of assay because it accessed a larger optical area and therefore more conjugates Fur t her work will directly compare the two de-tection methods using the same sample set

Limit of Detection

Single Pathogen

Having established the ability of our portable bionanosensor sys-tem to clearly identify pathogens within a matrix we characterized the detection limit for E coli using a concentration series from 105 to 101 cfumL Spectra for concentra-tions up to 104 cfumL showed a monotonic increase in signal with clear discrimination at all concen-trations versus the control (Figure 3a) Comparing signal intensity for the characteristic MGITC peak at 1620 cm-1 E coli could be clearly detected down to 101 cfumL at which concentration a signal en-hancement of 3 t imes over t he control was observed (Figure 3b) Above 104 cfumL the signal de-creased we believe it was damp-ened due to sel f-absorbance of Raman light when large numbers of conjugated bacteria are present If a high concentration of bacteria is present the sample could be di-luted and reanalyzed for accurate signal response

Comparing t he relat ive stan-dard dev iat ion ( RSD) of t he portable system to results from t he ea rl ier microscope system study the portable system showed much lower variance at a l l con-centrations (Table I) This result indicates improvement in repro-ducibi l ity which may be due to the larger sampling area and in-creased laser power and should b e e x plore d i n f u r t he r work

Multiplex Detection

For a bionanosensor to be of prac-tical use in healthcare food safety


or environmental testing it must be capable of multiplexed patho-gen detection both Gram-negative and Gram-positive To this end we tested the performance of our assay when both E coli (Gram-negative) and MRSA (Gram-positive) were present at a clinically relevant con-centration 103 cfumL SERS con-jugates for both pathogens were included in the assay The result-ing duplex sample spectrum shared features of both the E coli and MRSA reference spectra and clear discrimination versus the control (Figure 4a) Given that the peak previously used to identify E coli overlaps a SERS peak for the MRSA Raman reporter (1620 cm-1 shown with a green star) we instead used the peak at 1180 cm-1 to confirm the presence of E coli Both peaks were clearly present in the duplex sample Furthermore comparison of the duplex sample to the control using the 1620 cm-1 peak present in both Raman reporters yielded a signal enhancement of 84 times clearly demonstrating a unique bio-recognition interaction between the SERS conjugates and the two bacte-rial strains (Figure 4b)

To conf irm detect ion of each pathogen principa l component analysis (PCA) was performed on the multiplex spectrum and two single pathogen spectra (Figure 4c) The tight clustering observed for the fifteen scans taken for each individual pathogen as well as the duplex sample shows the excellent reproducibility and discrimination in the identification of pathogens that is possible with a portable system

ConclusionA compact SERS system providing all the requirements for a field-de-ployable assay (sensitivity speci-f icity reproducibi l ity cost por-tability ease of use and speed of analysis) has been demonstrated This novel approach employs a sandwich-type nanoparticle assay in which magnetic separation is used to capture SERS active bac-teria complexes for isolation and concentrat ion f rom the sample matrix Using this detection assay cel l concentrations as low as 10 cfumL were readily detected in single pathogen tests for E coli and MRSA using a compact por-table Raman detection system Du-plex detection clearly and simulta-neously identified the presence of both pathogens with discrimina-tion being made using both PCA and SERS analysis

T he abi l i t y of t h i s p or t able biona nosensor to detec t bac te-r ia l conc ent r at ions be low t he r e q u i r e m e nt s f o r c l i n i c a l d i -a g no s i s a nd t o d i s c r i m i n at e b e t we e n b a c t e r i a l p a t h o g e n s within a duplex system in ~1 h w it h m i n i ma l sa mple prepa ra-t ion demonstrates its potentia l as a f ield-deployable technique for use in hea lthcare food and environmental testing As sample preparation time and system size are further reduced its potential to provide rapid sensitive detec-tion in a f ield-deployable format wi l l surely play a role in g loba l ef forts to curb the spread of an-timicrobial-resistant pathogens

Continued on page 40


T he large-scale production and use of plastic dates back to the mid-20th century Since then

there has been a rapid growth in the product ion and use of plas-tics With the shift from reusable to sing le-use appl icat ions t he amount of plast ic produced has only continued to increase over time In a study by Geyer and asso-ciates it is estimated that to date 8300 million metric tons (Mt) of virgin plastics have been produced Of that amount only 2500 Mt are still in use Of the remainder only 600 Mt have been recycled while 800 Mt have been incinerated and 4900 Mt have been discarded This

means that 60 of the plastics ever produced are either accumulating in landfills or polluting the natu-ral environment (1)

The same character ist ic t hat makes plastic an appealing choice for many dif ferent applicat ions is also what is ultimately causing harm to the natural environment namely its long lifetime and dura-bility While materials like paper cardboard and wood decompose on a month or year t ime sca le plastics take tens to hundreds of years to ful ly decompose (2) In the meantime weathering causes plastics to break down into smaller fragments exposing marine l ife

Characterizing Microplastic Fibers Using Raman Spectroscopy

Microplastics have become a growing area of research as concern over harm to the environment and potential harm to humans has increased Recently legislation passed in California has mandated the testing for and reporting of plastics in drinking water (SB 1422) In this study Raman spectroscopy is used to characterize two different commercial fabric fiber samples that include synthetic polymer materials Both macro- and microscopic Raman measurements are carried out and chemical identification of the individual components is made through database searches Raman microscopy is demonstrated as a powerful technique for microplastic fiber characterization especially for samples that contain mixtures of components including multiple polymers or additives

Bridget OrsquoDonnell and Eunah Lee


at all stages of the food web from small organisms like plankton (3) to large mammals like whales (4) Plastics have also been observed in many different marine environ-ments including coastlines (5) in Arctic sea ice (6) at the sea surface (7) and on the sea f loor (8) In an effort to understand the ecological impact of plastics on marine life research has been undertaken to identify and quantify plastic debris

A lt houg h pla s t ic debr i s c a n come in many sizes microplastics are defined as plastic particulates that are smaller than 5 mm in size In addition to a range of sizes they can take on many forms includ-ing fragments f i lms and f ibers Fibers have been identif ied as a

signif icant portion of the micro-plastics found in the environment They are shed from textiles includ-ing consumer clothing during the manufacturing stage and in the wearing and washing of clothing (9) Studies have been undertaken to tackle the practice of sample col-lection preparation and filtering of microplastics which is a critical topic (10) but this study focuses on the chemical identification of mi-croplastics

Many dif ferent methods have been used to characterize micro-plastics including standard mi-croscopy techniques to character-ize size shape and morphology staining thermogravimetric anal-ysis pyrolysis gas chromatogra-

Figure 1 Optical layouts of typical Raman systems (a) A typical Raman microscope including objective filtering and confocal aperture with focusing optics (Generally confocal Raman microscopes are coupled to scanning spectrometers with wavelength tuning capability and multiple lasers and gratings) (b) A typical benchtop Raman system with immersion or touch probe including fiber optic coupling of laser excitation source with Raman collection optics (generally benchtop Raman systems are coupled to fixed grating spectrographs for use with single wavelength laser excitation)


phyndashmass spectrometry (GCminusMS) Raman and Fourier transformndashin-frared spectroscopy (FT-IR) (11) Both Raman and FT-IR techniques are spectroscopic techniques sen-sitive to the vibrational modes of the molecule under study Selec-t ion rules for Raman and infra-red (IR) dictate which vibrational modes are observable using each technique but generally Raman spectroscopy wi l l provide more v ibrat iona l informat ion on the poly mer backbone whereas IR spectroscopy wi l l provide more i n for m at ion on p oly mer s ide chains Because Raman and IR spectroscopy are complementary techniques ideal ly a laboratory

would be equipped with both tech-niques if resources are available This ar t ic le focuses on Raman spectroscopy speci f ica l ly as an analysis technique for character-izing plastics

Because microplast ics encom-pass a wide variety of sample sizes different sampling techniques can be employed for characterization using Raman spectroscopy For example for samples larger than 1 mm macroscopic measurements can be carried out The use of a fixed spectrograph with a Raman probe enables measurement in the field because experimental setups are t y pica l ly l ight weight com-pact and rugged making them

Figure 2 Macroscopic Raman spectrum recorded using a macroscopic Raman system described in the Experimental section for sample 1 (red) and reference spectrum of polyethylene terephthalate (blue)

Band-pass Filtersbull Pass band gt 90 transmittancebull Narrow bandwidth as low as 1 nmbull Blocking (ODgt5) from 300 nm to

1200 nmbull Multiple band-pass filters in the

UV-VIS-IR region available

Edge Filtersbull Pass band gt 93 transmittancebull Cut-off values 25-100 cm-1bull Deep Blocking (ODgt6)

Wide Angle Ultra-Steep Edge

Filtersbull Pass band gt 90 transmittancebull AOI 0-2 degree cone half anglebull (CHA) 5 degreebull Cut-off values 05 of the wave-

length (635-935 cm-1)bull Deep Blocking (ODgt6)

Dichroic Filtersbull Pass band gt 90 transmittancebull High reflection gt 95bull Tunable AOI 40-50 degree available

Notch Filtersbull Pass band gt 90 transmittancebull Narrow notch width as low as 10nm bull at 50 widthbull Deep Blocking (ODgt6)bull Multiple notch available

wwwiridianca

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easily transportable For samples smaller than 1 mm or for hetero-geneous samples with mixtures of polymers or other additives a Raman microscope is the preferred option because of its high spatial selectivity

Once a spectrum is acquired the next crit ica l step is to iden-tify exactly which polymers make up the unknown sample Having a complete high quality spectral database is crit ica l for carrying out accurate spectra l searching and matching In a typical data-base spectra of reference materials are recorded and stored with their chemical and physical properties in the sample record metadata For

microplastics it is advantageous to build an application library that includes spectra of weathered plas-tics with their location prepara-tion and analysis methods and to include environmental and health impacts within the metadata In addition spectra of different col-ored plastics or plastics with ad-dit ives such as ca lcium carbon-ate (CaCO3) or titanium anatase (TiO2) are also critical to the cor-rect identification of unknowns

ExperimentalIn this study two different fabric (fiber) samples are measured both acquired from Amazon Sample 1 is white in color and identified by

Figure 3 Confocal Raman microscope spectrum of sample 1 acquired using a Raman microscope described in the Experimental section with a 785 nm laser excitation (red) reference spectrum of microcrystalline cellulose (green) and a reference spectrum of PET (blue) The inset shows the video image of the fiber under study


its description as ldquopolyester soft felt rdquo comprised of ldquo fabr ic and rayon high quality acrylic craf t feltrdquo Sample 2 is a patterned fabric sample with white and red areas identified as being ldquo100 cottonrdquo

The Raman spectra presented in this paper are collected using two different systems For macro-scopic measurements a MacroRam benchtop Ra ma n spect rometer (Horiba) equipped with a Touch-Raman Bal lProbe (Marqmetrix) is used The use of the TouchRa-man Ba l lProbe a l lows spectra l measurements to be carried out without the requirement to mount the sample or align the probe The probe is pressed onto the sample under measurement and a spec-trum is recorded The MacroRam

uses an excitation wavelength of 785 nm which helps to suppress f luorescence that would otherwise be present at shorter laser wave-length excitat ion (12) A simpli-f ied opt ica l layout of a t y pica l Raman benchtop system with an immersion-touch probe is shown in Figure 1b

For microscopic measurements an XploRA Plus (Horiba) confocal Raman microscope was used This system is equipped with a combi-nation of three dif ferent excita-tion lasers (532 nm 638 nm and 785 nm) and four gratings (600 gr 1200 gr 1800 gr and 2400 gr) A 100x magnification objective (nu-merica l aperture of 09) is used for a l l micro-R a ma n measu re-ments described here The system

Figure 4 (a) Raman spectra recorded from sample 2 PET is shown in red red dye spectrum is shown in green and cellulose is shown in blue (b) Pseudo color rendered Raman image of 100 μm x 100 μm area of the sample with color coding corresponding to the three spectra in (a) (c) Video image of sample 2 used for the Raman measurement


is equipped with a deep-cooled SynapseEM electron multiplying charge coupled device (EMCCD) for sensit ivity and speed which is especial ly critical when carry-ing out Raman imaging measure-ments A simplified optical layout of a typical Raman microscope is shown in Figure 1a

Results and DiscussionFor Sample 1 measurements were first carried out using the macro-scopic conf iguration previously described The recorded Raman spectrum is baseline subtracted to account for f luorescence back-ground and fast Fourier transform (FFT) smoothing was carried out to further refine the spectral quality The resulting processed spectrum is shown in Figure 2 A database search using KnowItAll libraries (BioRad) resulted in a one compo-nent match to polyethylene tere-phthalate (PET) This is not sur-prising since polyester is defined by 16 CFR sect 3037 (c) as ldquoany long chain synthetic polymer composed of at least 85 by weight of an ester of a substituted aromatic carboxylic acid including but not restricted to substituted terephthalate unitsrdquo

Although the macroscopic mea-surement confirms that sample 1 contains PET if there is a combi-nation of different components in the fabric as advertised Raman m icroscopy w i l l prov ide add i-tional spatial information Using the microscopic configuration de-scribed in the Experimental sec-tion 785 nm excitation is used to carry out a line scan measurement across a fiber located in sample 2

An image of the fiber measured is shown in the inset of Figure 3 and the resulting average spectrum is shown in red The presence of PET in the sample is confirmed (PET reference spectrum in blue Fig-ure 3) but there is also evidence of cel lulose in the sample (cel lu-lose reference spectrum in green Figure 3) Specif ically the sharp band observed at approximately 1100 cm-1 in sample 1 overlaps with the same vibrational band in cellulose Although this band also overlaps with spectral features in the PET reference spectrum (blue) the relative magnitudes of the two bands around 1100 cm-1 in sam-ple 1 are not consistent with PET alone This result agrees with the fabric description that includes rayon According to 16 CFR sect 3037 (d) rayon is def ined as a ldquomanu-factured fiber composed of regen-erated celluloserdquo

What is not observed in sample 1 but is mentioned in the descrip-tion of the fabric is acrylic Ac-cord ing to 16 CFR sect 3037 (a) acrylic is def ined as being ldquocom-posed of at least 85 by weight of acrylonitri le unitsrdquo There is no evidence of acrylonitri le (mono-mer or polymer) in sample 1 If acr yl ic were present a band at 2240 cm-1 would be observable in the Raman spectrum correspond-ing to the nitrile stretching mode in acrylonitri le No such bands are observed in sample 1 despite a thorough survey of the fabric area

For sample 2 macroscopic measure-ments result in spectra that are too congested for straightforward analysis Microscopic measurements yield much


more information Preliminary single point measurements taken at vari-ous spots on the sample indicate the presence of different components To account for these spatial differences a Raman map is carried out over a 100 μm times 100 μm area The results show that there are at least three different components in the fabric shown as red green and blue spectra in Figure 4 Database searches for the three un-known components reveal matches to PET (red) cellulose (blue) and a red pigment (multiple possible database matches green)

To assess the spatial heterogeneity in sample 2 the representative spectra recorded from the sample for PET cel-lulose and the unknown red pigment are used as references in a classical least squares (CLS) treatment of the hyper-spectral Raman data cube The result-ing rendered Raman image is shown in Figure 4b When comparing the Raman image with the optical image (Figure 4c) one can see good correlation of the Raman signal with individual fibers in the fabric Sample 2 was described by the seller as 100 cotton however it is read-ily apparent from the Raman image that there are PET fibers present as indicated by the bright red areas in Figure 4b

ConclusionsThe samples studied here serve as a demonstration of how Raman spec-troscopy can be applied to the study of microplastics specifically fiber sam-ples Both synthetic (PET) and natural (cellulose) fibers are easily identifiable In addition Raman spectroscopy can be used to identify additives including pigments or dyes as demonstrated in sample 2 Finally the spatial selectivity that is provided by Raman microscopy

allows for characterization of hetero-geneous samples that might otherwise be misidentified or mischaracterized using macroscopic techniques

References(1) R Geyer JR Jambeck and KL Law Sci

Adv 3 E1700782 (2017)

(2) Ocean Conservancy International Coastal

Cleanup (2010 Report Trash Travels 2010)

(3) M Cole P Lindeque E Fileman C

Halsband R Goodhead J Moger and TS

Galloway Environ Sci Technol 47 6646-

6655 (2013)

(4) AL Lusher G Hernandez-Milian J OrsquoBrien

S Berrow I OrsquoConnor and R Officer

Environ Pollut 199 185-191 (2015)

(5) SL Whitmire SJ Van Bloem and CA

Toline Marine Debris Program Office

of Response and Restoration (National

Oceanic and Atmospheric Administration

Contract GSI-CU-1505 2017) pg 23

(6) I Peeken S Primpke B Beyer J

Gutermann C Katlein T Krumpen M

Bergmann L Hehemann and G Gerdts

Nat Commun 9 1505 (2018)

(7) National Oceanic and Atmospheric

Administration Marine Debris Program

2016 Report on Modeling Oceanic

Transport of Floating Marine Debris (NOAA

Marine Debris Program Silver Springs

Maryland 2016) pg 21

(8) AJ Jamieson LSR Brooks WDK Reid

SB Piertney BE Narayanaswamy and TD

Linley Roy Soc Open Sci 6 180667 (2019)

(9) BM Carney Almroth L Astrom S Roslund

H Petersson M Johansson and N-K

Persson Environ Sci Pollut R 25 1191-

1199 (2018)

(10) GESAMP Sources Fate and Effects of

Microplastics in the Marine Environment

Part Two of a Global Assessment P J

Kershaw and C M Rochman Eds IMO

FAOUNESCO-IOCUNIDOWMOIAEA

UNUNEPUNDP (Joint Group of Experts


on the Scientific Aspects of Marine

Environmental Protection Rep Stud

GESAMP No 93 220 2016) pp 104ndash109

(11) MGJ Loumlder and G Gerdts Marine

Anthropogenic Litter M Bergmann L

Gutow and M Klages Eds (Springer New

York New York 2015) pp 201ndash207

(12) D Tuschel Spectroscopy 3114-23 (2016)

Bridget OrsquoDonnell is the Manager of Raman Applications at Horiba Scientific in

Piscataway New Jersey Eunah Lee is the Raman Project Manager of Raman Applica-tions at Horiba Scientific in Sunnyvale Califor-nia Direct correspondence to bridgetodon-nellhoribacom


References(1) I Roca M Akova F Baquero J Carlet

M Cavaleri S Coenen J Cohen D

Findlay I Gyssens OE Heure G

Kahlmeter H Kruse R Laxminarayan

E L ieacutebana L Loacutepez-Cerero A

MacGowan M Martins J Rodriacuteguez-

Bantildeo J-M Rolain C Segovia B

Sigauque E Taconelli E Wellington

and J Vila New Microbes New Infect 6 22-29 (2015)

(2) European Antimicrobial Resistance

Surveillance Network Surveillance of

Antimicrobial Resistance in Europe

( European Cent re fo r D i sease

Prevention and Control Solna Sweden

2017)

(3) Centers for Disease Control and

Prevent ion Antibiotic Resistance

Threats in the United States (United

States Depar tment of Health and

Human Se r v i c e s Wash ing ton

DC2013)

(4) TR Thorsteinsdottir G Haraldsson

V Fridriksdottir KG Kristinsson and

E Gunnarsson Emerg Infect Dis 16 133ndash135 (2010)

(5) J Li C Wang H Kang L Shao L Hu

R Xiao S Wang and B Gu RSC Adv

8 4761ndash4765 (2018)

(6) B Guven N Basaran-Akgul E Temur

U Tamer and IH Boyaci Analyst 136 740minus748 (2011)

(7) R Najafi S Mukherjee J Hudson A

Sharma and P Banerjee Int J Food

Microbiol 189 89minus97 (2014)

(8) Y Wang S Rav indranath and J

Irudayaraj Anal Bioanal Chem 399 1271minus1278 (2011)

(9) H Kearns R Goodacre LE Jamieson

D Graham K Faulds Anal Chem

89(23) 12666-12673 (2017)

(10) DH Kim RM Jarvis Y Xu AW Oliver

JW Allwood L Hampson I Hampson

and R Goodacre Analyst 135 1235-

44 (2010)

Hayleigh Kearns Lauren E Jamieson Duncan Graham and Karen Faulds are with the Department of Pure and Applied Chemistry at the University of Strathclyde in Strathclyde United Kingdom Cicely Rathmell is the Vice President of Marketing at Wasatch Photonics in Morrisville North Carolina Direct correspondence to mar-ketingwastachphotonicscom


Continued from page 31

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An Executive

Summary

Raman Spectroscopy as a PAT Tool for Continuous Manufacturing of Solid Dose Pharmaceutical Products

In-line monitoring of blend uniformity in

powder streams by Raman spectroscopy

Introduction

Traditional pharmaceutical manufacturing uses a batch approach in

which product is collected after each unit operation Material is sent to

an off-line laboratory for analysis and the product is held up until the

test results are received This stopstart system is very time-consuming

and inefficient as the entire process may take days or even weeks In

contrast continuous manufacturing connects all of the individual unit

operations so that they continuously feed into one another Material can

be monitored during the process which can then be adjusted based

upon the in-process measurements The reduced process time is

minutes to hours resulting in a significant boost in production

An important consideration

in successful continuous manu-

facturing is integrating analytical

tools into the reaction flow In

batch reaction monitoring on-

line and at-line analyses enable

Qual i ty by Design (QbD) and

ensure stable operations There

is a clear need for rugged and

validated analytical tools that

will meet the specific needs for

continuous reaction monitoring

Intense react ion condi t ions

non-traditional chemistries and

miniatur ized reactors of con-

tinuous reactions are challenging

environments for analytical tools

originally developed for batch

reaction monitoring

Process analytical technology

(PAT) plays a vital role in con-

tinuous manufacturing where

monitoring and control functions

ensure quality in real time and

help to keep the process running

uninterrupted Near-infrared (NIR)

and Raman spectroscopy are

commonly used for process

monitoring and control because

they are both fast and easy to

implement However NIR can be

affected by physical characteris-

tics of a sample such as particle

size which makes quantification

Douglas B Hausner Associate Director

Center for Structured Organic Particulate

Systems Rutgers University

RAMAN SPECTROSCOPY AS A PAT TOOL FOR CONTINUOUS MANUFACTURING

Sponsored Content

of low concentrations challenging Raman

spectroscopy offers an advantage over NIR

because it has higher chemical specificity

h igher accuracy when quanti f y ing low

concentration materials and is less affected

by the physical at tr ibutes of a sample

Active pharmaceutical ingredients (APIs)

are excellent Raman scatterers making the

technology more amenable to monitoring in

pharmaceutical manufacturing Development

of continuous processes and incorporation of

PAT into these processes is a topic of intense

research involving academic industrial and

regulatory scientists


Systems Continuous Manufacturing Facility

The Center for Structured Organic Particulate

Systems (C-SOPS) at Rutgers University is a

research hub where academia industry and

regulators meet (virtually) to develop and refine

the next generation of advanced pharmaceu-

tical manufacturing The C-SOPS continuous

manufacturing line is currently being used

for the development and implementation of

a continuous process for pharmaceutical

tablet production The C-SOPS continuous

direct compression manufacturing line is

shown in Figure 1 Ingredients flow from the

feeders through the mill and blender to the

tablet press Critical quality attributes (CQAs)

of a continuous manufacturing line call for

monitoring the final product and intermediates

and blend uniformity (BU) must be measured

as part of ensuring the final product quality

A Kaiser PhAT Raman probe was mounted

between the blender and tablet press to

measure the composition of the blend before

tableting A NIR probe may also be installed

instead of a Raman probe

ldquoActive pharmaceutical

ingredients are excellent

Raman scatterers making

the techno logy more

amenable to monitoring

i n p h a r m a c e u t i c a l

manufacturingrdquo

Figure 1 Rutgers Direct Compression (DC) continuous manufacturing line


Sponsored Content

Model Development and Transfer

Model development validation and verification

are important aspects in regulatory guidance

on PAT as noted in ICH Q8Q9Q10 The regu-

latory framework ensures that the PAT and the

analytical model are fit for purpose and well-

integrated into a risk-based manufacturing

approach whether it is a batch or continuous

approach Calibration of the in-line sensors

is essential to ensure that critical quality

attributes (CQAs) are manufactured within the

design space ensuring safety and efficacy

In five-second measurements of a 30-kghr

production line the amount of sample being

interrogated using Raman spectroscopy is

165 mg while NIR analyzes 90 mg Thus for

a 350-mg tablet Raman measures almost

half a tablet and NIR only about one-quarter

Thus Raman leads to a higher level of process

monitoring and understanding which can be

a benefit for regulatory compliance

Calibration of the mass flow and spectros-

copy sensors using the manufacturing would

be difficult with the typical flow of 45 kghr

Thus the C-SOPS continuous manufacturing

line was calibrated using an off-line proce-

dure that mimics the flow within the actual

line A laboratory model was developed as

shown in Figure 2 A stainless-steel chute of

the off-line calibration mechanism contained

windows for mounting Raman and NIR sen-

sors and a window allowed visual inspection

of the powder fill level The Raman window

was opaque to reduce stray l ight f rom

reaching the detector A feed frame on the

apparatus controlled the rate of powder flow

The of f- l ine model was bui l t using a

standard blend of acetaminophen (APAP)

with magnesium stearate lubricant and

lactose filler then transferred to the C-SOPS

continuous manufacturing line A calibration

and validation model dataset was performed

using the laboratory system then the ana-

lytical model transferred to the continuous

line and tested with a prediction set The

API ranged from 1 ww to 11 ww for

the calibration and validation sets and the

prediction set was run at 2 ww API and

10 ww API For the calibration set the

flow rate was 25ndash30 kghr for the validation

Figure 2 Off-line setup

Chute

bull Stainless steel pipe

bull Windows for visual inspection of the powder

level and for mounting the probes

Sensors

bull Mounted on the chute

bull Multiple tests for light interference

conducted

Feed framerotary valve

bull Placed at the exit of the chute

bull Enables powder to flow at a desired speed

bull Mass of the exiting powder weighed with an

analytical balance


Sponsored Content

set the flow rate was 9ndash15 kghr for the

prediction set the f low rate was 15 kg

hr and 30 kghr Raman and NIR spectra

were collected throughout the f low and

optimized to balance collection frequency

and signal to noise Reference UV-Vis and

HPLC measurements were collected offline

for the calibration validation and prediction

samples

Univariate or multivariate data analysis

is simplif ied when peaks from the active

pharmaceutical ingredient (API) are well

resolved from excipients The standard

formulation of APAP magnesium stearate

and lactose was chosen not only for its flow

and blending properties but also for their

well-resolved spectral features The spectral

resolution of APAP from excipients is shown

in Figure 3 This figure also shows how the

high specificity of Raman spectroscopy can

improve material identification and quanti-

f ication The Raman spectrum of APAP

(bottom spectra blue) have unique bands

in the 600ndash850 cm-1 and 1500ndash1700 cm-1

spectra l reg ions d is t ingu ish ing APAP

from the excipients The NIR spectrum

of APAP has many overlapping spectral

regions but there was a weak envelope at

1040ndash1210 cm-1 unique to APAP

Process conditions such as feed rate mill

screen blending speed and chute height

were varied to test the model under realistic

conditions Process conditions were varied

to ensure that the model variables were

independent Taking these parameters

under consideration enabled a robust design

space for PCA calculations and improved

confidence in the calibration method Slight

spreading of the data points was observed

in the NIR data compared to Raman data

in the principal component analysis (PCA)

This was likely caused by subtle changes in

the material properties which do not affect

Raman results Therefore the calibration

model for the formulation based on Raman

spectroscopy was more robust

NIR- and Raman-predicted API concen-

tration measurements post-blending were

compared against with reference of f-line

NIR and dissolutionUV measurements (1) A

Figure 3 NIR and raman spectra


Sponsored Content

comparison of on-line and off-line quantifica-

tion results can be seen in Figure 4 The plot

on the left shows that on-line predictions are

in good agreement with the reference off-line

measurement and show that the Raman

and NIR in-line measurements provide the

same data but without needing to collect a

sample from the line The plot on the right

side of Figure 4 shows Raman-predicted

API concentration tracks with changes in

the feed rate

Figure 4 Line run data

ampŝŐŽŵƉĂƌŝƐŽŶŽĨWйĨƌŽŵĨŽƵƌŵĞĂƐƵƌĞŵĞŶƚƐĚƵƌŝŶŐĂ

ĐŽŶƚŝŶƵŽƵƐůŝŶĞƌƵŶŝŶůŝŶĞZĂŵĂŶĂŶĚEZƐĞŶƐŽƌƐƉŽƐƚ

ďůĞŶĚŝŶŐŽĨĨůŝŶĞƚĂďůĞƚŵĞĂƐƵƌĞŵĞŶƚƐƵƐŝŶŐhssŝƐĂŶĚEZ

ampŝŐŽŵƉĂƌŝƐŽŶŽĨWйŝŶŝƚŝĂƚĞĚďǇĨĞĞĚĞƌƐĂŶĚ

ƉƌĞĚŝĐƚĞĚƵƐŝŶŐŝŶůŝŶĞZĂŵĂŶĂŶĚEZƐĞŶƐŽƌƐƉŽƐƚƚŚĞ

ďůĞŶĚŝŶŐƐƚĂŐĞ

Figure 5 Automation and RTR

dĂďůĞƚWƌĞƐƐ

ůĞŶĚĞƌ

ampĞĞĚĞƌƐ

ŵŝůů

W

WWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWWM

M

M

M

M

M

M

M

ŽŶƚĞŶƚĞŶƐŝƚǇ

ůĞŶĚhŶŝĨŽƌŵŝƚǇ

EZZĂŵĂŶ

gtd

dŚŝĐŬŶĞƐƐ

ĞŶƐŝƚǇ

dd h^

ĂƌĚŶĞƐƐ

EZ

ŝƐƐŽůƵƚŝŽŶ

ĐŚĞĐŬ

ampĞĞĚ

ĨŽƌǁĂƌĚ

ĐŽŶƚƌŽů

ampŽƌĐĞ

tĞŝŐŚƚ

ŽŵƉƌĞƐƐŝŽŶ

ĂƉ

ƌŽƐƐĐŚĞĐŬ

ŽŶƚĞŶƚ

ĐŚĞĐŬ


Sponsored Content

Automated Process Monitoring and Control

Feedback and feedforward loops are two

benefits of continuous manufacturing over

batch processes Automated feedback loops

incorporate process sensors and process

control functions to enable in-process correc-

tions and improve process efficiency Control

loops in the C-SOPS process were designed

to send a signal to the feeder control if a

sensor detected an error in blend composi-

tion For example if the blend appears to be

sub-potent a signal was sent to the feeder to

increase the API feed rate and insert more API

into the process A schematic of the automa-

tion and real-time release (RTR) process is

shown in Figure 5 Continuous manufacturing

with automated sensing and feedback-based

process control saved considerable time and

materials compared to a batch process

Predicted Dissolution

The standard technique for measuring a

tabletrsquos efficacy and API concentration is dis-

solution Conventional dissolution measure-

ments require materials to be removed from

the process line is a destructive technique

and the relatively small amount of tablets

sampled may not be representative of the

batch Moreover dissolution can take up to

an hour and that timescale is not compatible

ldquoThe regulatory framework

ensures that the PAT and

the analytical model are

fit for purpose and well-

integrated into a risk-based

manufacturing approach

whether it is a batch or

continuous approachrdquo

ldquoRaman and NIR in-line

measurements provide

the same data but without

need ing to co l lec t a

sample from the linerdquo

Figure 6 General dissolution prediction methodology

ĞĨŝŶĞƚĂƌŐĞƚĐŽŶĚŝƚŝŽŶƐ

ʹ

ͺ

ʹ

ʹ ͺ ʹ

Ψ

13Ȁ13

[5eth

ĮSUHGLFWHG

ĮUHIHUHQFH

5HIHUHQFHYVSUHGLFWHG

Ϭ

ϮϬ

ϰϬ

ϲϬ

Ϭ

ϭϬϬ

Ϭ ϮϬ ϰϬ ϲϬ Ϭ ϭϬϬ ϭϮϬ

й

ƌƵŐ

ŝƐ

ƐŽůǀ

ĞĚ

dŝŵĞŵŝŶ

ƌĞĨĞƌĞŶĐĞ

ƉƌĞĚŝĐƚŝŽŶ

ĨϮсϳ+ϭϯ

ĚĞŶƚŝĨǇĚŝƐƐŽůƵƚŝŽŶŵĞĐŚĂŶŝƐŵ


Sponsored Content

with continuous manufacturing rates To

suppor t a real-t ime release strategy of

continuously-produced tablets a dissolution

prediction model was developed to support

the C-SOPS process line Figure 6 shows

an overview of the dissolution prediction

model First the dissolution mechanism

was identified such as immediate release

or controlled release It was important to

understand enough about the dissolution

mechanism to set-up a program to explore

the variability around it Using experimental

design several tablets were produced with

varying amounts of

super-disintegrant

API and compres-

sion force These

model tablets were

used to build data

sets for s tudy ing

the variability

The tablets were

a n a l y z e d u s i n g

Raman (or NIR) as

wel l as traditional

d i s so l u t i on me a-

s u r e m e n t s Tw o

data sets were then correlated using mul-

tilinear regression which revealed how the

dissolution profiles and variances related to

changes in the Raman spectra From that

information an equation was defined that

yielded a simulated dissolution profile

The advantage of continuous manufac-

turing is that the dissolution model data

set can be obtained concurrently with

process development Producing tablets

with dif ferent process conditions every

five minutes allows the generation of many

different levels of process conditions which

facilitates rapid exploration of the process

Conclusion

Continuous manufacturing has been success-

fully extended to solid materials used for the

production of pharmaceutical tablets at the

C-SOPS facility The line utilized a unique

system for building calibration models for

moving powders Raman spectroscopy and

NIR was used to monitor blend uniformity

post-blend Both in-line spectroscopic models

w e r e v a l i d a t e d

with traditional wet

chemistry techniques

Feedback and feed-

forward control loops

detected defects in

the powder stream to

make immediate cor-

rections and minimize

loss of materials and

t ime The demon-

strated traceabil ity

of materials through

the process reveals

the effect of process variables which leads

to greater understanding of the process The

addition of Raman spectroscopy as a PAT tool

improved process knowledge efficiency and

robustness for a continuous manufacturing

approach to pharmaceutical tablets

Reference

1 P Pawar et al Int J Pharm

512 (1) 96ndash107 (2016)

ldquoThe addition of Raman

spectroscopy as a PAT

tool improved process

knowledge efficiency and

robustness for a continu-

ous manufacturing ap-

proach to pharmaceutical

tabletsrdquo

THE APPLICATION NOTEBOOK ndash JUNE 2019 Molecular Spectroscopy 49

Visual image analysis can drive rapid microplastic investigations on etched silicon fi lters Th e smooth

consistent wafer surface provides an ideal substrateMicroplastic contamination has become highly visible

Investigations require particle counts sizes and identities Raman micro-spectroscopic analysis can provide all three automatically by analyzing particles directly on a fi lter

Th e entire fi lter can be imaged but this produces a huge dataset Imaging a 1-cm2 fi lter using a pixel size of 10 μm results in 1 million spectra Critically a signifi cant fraction of the dataset is ldquoemptyrdquo (fi lter only no particle see Figure 1)

Ideally Raman collection should focus on the particles Visual imaging provides the tools to locate and select particles Sensitivity and particle size fi ltering enable a refi ned selection process Th is provides the particle location and size enabling spectra to be collected only from particles skipping the empty space

Th e visual and Raman analyses will depend upon the fi lter Irregular topography could generate false particle locations aff ecting particle counts sizing or causing wasted time during Raman data collection Ideally the fi lter should lie fl at removing complex focusing steps Th e substrate should exhibit minimal Raman spectral contributions

Etched silicon wafer fi lters off er excellent performance Flat and rigid the wafer surface is visually uniform Th e known silicon Raman signals are well defi ned and consistent and can be easily accounted for in the analysis Additionally silicon fi lters can be used in transmission FT-IR microscopy for added analysis capabilities

Experimental and Results

Five hundred milliliters of bottled water was fi ltered through etched silicon fi lters Th e fi lter was analyzed using a Th ermo Scientifi ctrade DXRtrade2 Raman microscope equipped with OMNICtrade and particle analysis software A visual mosaic was collected (Figure 1) Automated visual analysis identifi ed targets With the digital fi lters adjusted 1439 possible particles were identifi ed in this sample Automatic data collection from each was followed by an automated removal of the silicon Raman signal Searching yielded a table showing counts for each type of particle sizes and identities Export of this information to a report or to Exceltrade provides a fi nal summary

Conclusion

Combining the DXR2 Raman microscope the silicon fi lter and the particle analysis software leads to a complete microplastics analysis Automation minimizes the need for expertise

Automated Microscopic Analysis of Microplastics on Silicon FiltersMike Bradley and Robert Heintz Thermo Fisher Scientifi c

Thermo Fisher Scientifi c5225 Verona Road Madison WI 53711

tel (800) 532-4752Website wwwthermofi shercomspectroscopy

Figure 1 Top Left Visual mosaic with red dots indi-

cating image analysis location of particles Note the

fi bers and the absence of artifacts along the mosaic

stitching lines Top Right The specifi c targeted lo-

cations numbered in sequence for data collection

(1439 points) Bottom First portion of the report

from the analysis Count and percent of total area

added to identifi cation Rank is ordered by area

percentage

THE APPLICATION NOTEBOOK ndash JUNE 201950 Molecular Spectroscopy

Several pesticides were detected at parts-per-

million levels on apple skin using a unique

swabbing method and surface enhanced Raman

spectroscopy (SERS)

Commonly used pesticides such as organothiophosphates and fungicides can attack the central nervous system

posing a risk to humans and other animals upon exposure (1) SERS has been investigated as a technique to detect trace levels of pesticides on food items and off ers reduced instrument size fast measurement times non-destructive sampling and simple implementation compared to the traditionally used methods such as HPLC and GCndashMS (2) In this study we utilize a swabbing technique that has the potential to be a testing platform for fi eld use to detect trace levels of pesticides on apple skin

Experimental Conditions

Pesticide-containing apple skin pieces are swabbed and added to colloidal gold nanoparticles Th e mixtures are interrogated with 785-nm excitation laser with 3 s integration time and 350 mW laser power and the Ocean Optics QE Pro spectrometer

Results

Th e pesticides examined in this study were thiram malathion acetamiprid and phosmet Th e SERS spectra of each pesticide after being applied to apple skin and swabbed are presented in Figure 1 Th e observed peaks are consistent with reports in the literature

Conclusions

We present a swabbing technique that has the potential to be a testing platform for fi eld use and utilizes colloidal gold to detect trace levels of several pesticides on apple skin Th is technique can detect each pesticide down to 1 ppm where the pesticide residue tolerances on apples are 5 ppm 8 ppm 1 ppm and 10 ppm for thiram malathion acetamiprid and phosmet respectively (3) Th e results presented here indicate that SERS coupled with the swab method is a valuable tool and has signifi cant potential for identifying pesticide residues on the surface of fruits for food quality and safety control

References

(1) M Stoytcheva InTech 30ndash48 (2011)(2) ML Xu Y Gao XX Han and B Zhao J Agric Food

Chem 65 6719ndash6726 (2017) (3) Office of the Federal Register Electronic Code of Federal

Regulations 2018 httpswwwecfrgov (8 January 2019)

Trace-Level Pesticide Detection Utilizing Surface-Enhanced Raman Spectroscopy (SERS)Anne-Marie Dowgiallo Ocean Optics Inc

Ocean Optics Inc8060 Bryan Dairy Road Largo FL 33777

tel +1 (727) 733-2447Website wwwoceanopticscom

Figure 1 SERS spectra of swabs of 1 ppm (a)

malathion (b) acetamiprid (c) phosmet and (d)

thiram on apple skin

+ 1 ( 4 0 7 ) 5 4 2 - 7 7 0 4i n f o o p t i g r a t e c o mw w w o p t i g r a t e c o m

Ultra-Low Frequency Raman SpectroscopyldquoExtend your Raman system into THz frequency range (5-200 cm-1)rdquo

BragGratetrade Bandpass and Notch Filters Spectral and spatial laser line cleaning filters and ultra-narrow line notch filters

for low frequency Raman Spectroscopy

Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550

tSFRVFODJFTCFMPXDN-1 with single stage spectrometer

tOHMFUVOBCMFGPSQSFDJTFXBWFMFOHUIBEKVTUNFOU

t4UPLFTBOEBOUJ4UPLFT3BNBOCBOET

t6OMJNJUFEPQUJDBMMJGFUJNF

t$VTUPNXBWFMFOHUITJOSBOHFoON

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Ϯ

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ZĂŵĂŶ^ŚŝĨƚ13Đŵϭ

ϰϮϰϮ^ŝĞ^ƵƉĞƌůĂƚƚŝĐĞ

ΛϱϯϮ Ŷŵ

Ϭ

Measured with

LabRAM HR Evolution

(data courtesy of

HORIBA Jobin Yvon SAS)

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ϭϬ ϱ Ϭ ϱ ϭϬ

ZĂŵĂŶ^ŚŝĨƚĐŵϭ

ϰϮ ϱϯϰϮϱϯ

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true PDFXNoTrimBoxError false PDFXTrimBoxToMediaBoxOffset [ 000000 000000 000000 000000 ] PDFXSetBleedBoxToMediaBox true PDFXBleedBoxToTrimBoxOffset [ 000000 000000 000000 000000 ] PDFXOutputIntentProfile (None) PDFXOutputConditionIdentifier (CGATS TR 001) PDFXOutputCondition () PDFXRegistryName (httpwwwcolororg) PDFXTrapped False CreateJDFFile false Description ltlt ENU ([Based on [PDFX-1a2001]] Use these settings to create Adobe PDF documents that are to be checked or must conform to PDFX-1a2001 an ISO standard for graphic content exchange For more information on creating PDFX-1a compliant PDF documents please refer to the Acrobat User Guide Created PDF documents can be opened with Acrobat and Adobe Reader 40 and later) gtgt Namespace [ (Adobe) (Common) (10) ] OtherNamespaces [ ltlt AsReaderSpreads false CropImagesToFrames true ErrorControl WarnAndContinue FlattenerIgnoreSpreadOverrides false IncludeGuidesGrids false IncludeNonPrinting false IncludeSlug false Namespace [ (Adobe) (InDesign) (40) ] OmitPlacedBitmaps false OmitPlacedEPS false OmitPlacedPDF false SimulateOverprint Legacy gtgt ltlt AddBleedMarks false AddColorBars false AddCropMarks false AddPageInfo false AddRegMarks false BleedOffset [ 9 9 9 9 ] ConvertColors NoConversion DestinationProfileName () DestinationProfileSelector DocumentCMYK Downsample16BitImages true FlattenerPreset ltlt PresetSelector HighResolution gtgt FormElements false GenerateStructure false IncludeBookmarks false IncludeHyperlinks false IncludeInteractive false IncludeLayers false IncludeProfiles false MarksOffset 6 MarksWeight 0250000 MultimediaHandling UseObjectSettings Namespace [ (Adobe) (CreativeSuite) (20) ] PDFXOutputIntentProfileSelector DocumentCMYK PageMarksFile RomanDefault PreserveEditing true UntaggedCMYKHandling LeaveUntagged UntaggedRGBHandling UseDocumentProfile UseDocumentBleed false gtgt ltlt AllowImageBreaks true AllowTableBreaks true ExpandPage false HonorBaseURL true HonorRolloverEffect false IgnoreHTMLPageBreaks false IncludeHeaderFooter false MarginOffset [ 0 0 0 0 ] MetadataAuthor () MetadataKeywords () MetadataSubject () MetadataTitle () MetricPageSize [ 0 0 ] MetricUnit inch MobileCompatible 0 Namespace [ (Adobe) (GoLive) (80) ] OpenZoomToHTMLFontSize false PageOrientation Portrait RemoveBackground false ShrinkContent true TreatColorsAs MainMonitorColors UseEmbeddedProfiles false UseHTMLTitleAsMetadata true gtgt ]gtgt setdistillerparamsltlt HWResolution [300 300] PageSize [612000 792000]gtgt setpagedevice

Novel SERS Substrate

Boosts Weak Signals

Measure samples such as

pesticides at parts-per-million

and parts-per-billion using

our modular surface

enhanced Raman

spectroscopy systems

X SERS substrate uses gold

nanoparticles to increase

weak Raman signals

X Systems comprise a

spectrometer 785 nm excitation

laser probe and substrate holder

X Measure trace levels of pesticides

narcotics fuel markers and food additives

APPLIED SPECTRAL KNOWLEDGE bull wwwoceanopticscom bull infooceanopticscom

US +1 727-733-2447 bull EUROPE +31 26-3190500 bull ASIA +86 21-6295-6600

Raman for TraceLevel AnalyteDetection


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HORIBA Scientific

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into solutions for




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CO

MP

LE

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SO

LU

TI

ON

SMira

Raman

Handheld


Laboratory

START

SET

SETUP

EXIT

STATS


Process



Speed



Sensitivity




Resolution







Combinability





Applications







Coherence Matters




same footprint



























loading dock





121 501 (1928)






189 269ndash275 (2015)





(2018)

































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105 42 09

104 39 10

103 26 12

102 51 13

101 43 19

Control 65 22



wwwrenishawcom


applications


Next generation

Raman imaging



sample


















Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



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Chute




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conducted





analytical balance


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the feed rate










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varying amounts of

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used to build data

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Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











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percentage





spectroscopy (SERS)





Results


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Wavelengths

in Production (nm)

405 442 458 473 488

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785 830 980 1064 1550






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MADE IN GERMANY








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HORIBA Scientific

the Leader in

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horibacomafmupgrade






reg


Articles






























into solutions for




bull And more

Find out more at


CO

MP

LE

TE

SO

LU

TI

ON

SMira

Raman

Handheld


Laboratory

START

SET

SETUP

EXIT

STATS


Process



Speed



Sensitivity




Resolution







Combinability





Applications







Coherence Matters




same footprint



























loading dock





121 501 (1928)






189 269ndash275 (2015)





(2018)

































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( RSD)

105 42 09

104 39 10

103 26 12

102 51 13

101 43 19

Control 65 22



wwwrenishawcom


applications


Next generation

Raman imaging



sample


















Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



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Chute




Sensors



conducted





analytical balance


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samples












































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the feed rate










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Sponsored Content














varying amounts of

super-disintegrant

API and compres-

sion force These

model tablets were

used to build data

sets for s tudy ing

the variability

The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






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Ϯ

ϰ

ϲ

ϭϬ

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regregregreg















Fax (732) 647-1235



SShaffermmhgroupcom





LBushmmhgroupcom


JChassemmhgroupcom


JWorkmanmmhgroupcom


CDelonasmmhgroupcom


KMooremmhgroupcom





KEbnermmhgroupcom


dwardhclcom


BPangarommhgroupcom




TEhardtmmhgroupcom




JFrommermmhgroupcom


jsingerhclcom


WBongmmhgroupcom


HORIBA Scientific

the Leader in

Raman Spectroscopy


horibacomafmupgrade






reg


Articles






























into solutions for




bull And more

Find out more at


CO

MP

LE

TE

SO

LU

TI

ON

SMira

Raman

Handheld


Laboratory

START

SET

SETUP

EXIT

STATS


Process



Speed



Sensitivity




Resolution







Combinability





Applications







Coherence Matters




same footprint



























loading dock





121 501 (1928)






189 269ndash275 (2015)





(2018)

































jascoinccomfreebook


jascoinccomfreebook

Download your

FREE RAMAN EBOOK

IQ Frametrade












measurement

QRI












SSI











Raman spectrum

jascoinccomfreebook






















( RSD)

105 42 09

104 39 10

103 26 12

102 51 13

101 43 19

Control 65 22



wwwrenishawcom


applications


Next generation

Raman imaging



sample


















Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































Sponsored Content











the feed rate










ůĞŶĚĞƌ

ampĞĞĚĞƌƐ

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M

M

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dŝŵĞŵŝŶ

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Sponsored Content














varying amounts of

super-disintegrant

API and compres-

sion force These

model tablets were

used to build data

sets for s tudy ing

the variability

The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ


ŶƚĞ

ŶƐŝƚǇ



ΛϱϯϮ Ŷŵ

Ϭ

Measured with

LabRAM HR Evolution

(data courtesy of


Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ

ϭϬ ϱ Ϭ ϱ ϭϬ


ϰϮ ϱϯϰϮϱϯ


HORIBA Scientific

the Leader in

Raman Spectroscopy


horibacomafmupgrade






reg


Articles






























into solutions for




bull And more

Find out more at


CO

MP

LE

TE

SO

LU

TI

ON

SMira

Raman

Handheld


Laboratory

START

SET

SETUP

EXIT

STATS


Process



Speed



Sensitivity




Resolution







Combinability





Applications







Coherence Matters




same footprint



























loading dock





121 501 (1928)






189 269ndash275 (2015)





(2018)

































jascoinccomfreebook


jascoinccomfreebook

Download your

FREE RAMAN EBOOK

IQ Frametrade












measurement

QRI












SSI











Raman spectrum

jascoinccomfreebook






















( RSD)

105 42 09

104 39 10

103 26 12

102 51 13

101 43 19

Control 65 22



wwwrenishawcom


applications


Next generation

Raman imaging



sample


















Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































Sponsored Content











the feed rate










ůĞŶĚĞƌ

ampĞĞĚĞƌƐ

ŵŝůů

W


M

M

M

M

M

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dŚŝĐŬŶĞƐƐ

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ĂƉ

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ĐŚĞĐŬ


Sponsored Content













































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dŝŵĞŵŝŶ

ƌĞĨĞƌĞŶĐĞ


ĨϮсϳ+ϭϯ



Sponsored Content














varying amounts of

super-disintegrant

API and compres-

sion force These

model tablets were

used to build data

sets for s tudy ing

the variability

The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ


ŶƚĞ

ŶƐŝƚǇ



ΛϱϯϮ Ŷŵ

Ϭ

Measured with

LabRAM HR Evolution

(data courtesy of


Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ

ϭϬ ϱ Ϭ ϱ ϭϬ


ϰϮ ϱϯϰϮϱϯ


reg


Articles






























into solutions for




bull And more

Find out more at


CO

MP

LE

TE

SO

LU

TI

ON

SMira

Raman

Handheld


Laboratory

START

SET

SETUP

EXIT

STATS


Process



Speed



Sensitivity




Resolution







Combinability





Applications







Coherence Matters




same footprint



























loading dock





121 501 (1928)






189 269ndash275 (2015)





(2018)

































jascoinccomfreebook


jascoinccomfreebook

Download your

FREE RAMAN EBOOK

IQ Frametrade












measurement

QRI












SSI











Raman spectrum

jascoinccomfreebook






















( RSD)

105 42 09

104 39 10

103 26 12

102 51 13

101 43 19

Control 65 22



wwwrenishawcom


applications


Next generation

Raman imaging



sample


















Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































Sponsored Content











the feed rate










ůĞŶĚĞƌ

ampĞĞĚĞƌƐ

ŵŝůů

W


M

M

M

M

M

M

M



EZZĂŵĂŶ

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dŚŝĐŬŶĞƐƐ

ĞŶƐŝƚǇ

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EZ


ĐŚĞĐŬ

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ĐŽŶƚƌŽů

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ĂƉ

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ŽŶƚĞŶƚ

ĐŚĞĐŬ


Sponsored Content













































ʹ

ͺ

ʹ

ʹ ͺ ʹ

Ψ

13Ȁ13

[5eth

ĮSUHGLFWHG

ĮUHIHUHQFH


Ϭ

ϮϬ

ϰϬ

ϲϬ

Ϭ

ϭϬϬ


й

ƌƵŐ

ŝƐ

ƐŽůǀ

ĞĚ

dŝŵĞŵŝŶ

ƌĞĨĞƌĞŶĐĞ


ĨϮсϳ+ϭϯ



Sponsored Content














varying amounts of

super-disintegrant

API and compres-

sion force These

model tablets were

used to build data

sets for s tudy ing

the variability

The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ


ŶƚĞ

ŶƐŝƚǇ



ΛϱϯϮ Ŷŵ

Ϭ

Measured with

LabRAM HR Evolution

(data courtesy of


Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ

ϭϬ ϱ Ϭ ϱ ϭϬ


ϰϮ ϱϯϰϮϱϯ























into solutions for




bull And more

Find out more at


CO

MP

LE

TE

SO

LU

TI

ON

SMira

Raman

Handheld


Laboratory

START

SET

SETUP

EXIT

STATS


Process



Speed



Sensitivity




Resolution







Combinability





Applications







Coherence Matters




same footprint



























loading dock





121 501 (1928)






189 269ndash275 (2015)





(2018)

































jascoinccomfreebook


jascoinccomfreebook

Download your

FREE RAMAN EBOOK

IQ Frametrade












measurement

QRI












SSI











Raman spectrum

jascoinccomfreebook






















( RSD)

105 42 09

104 39 10

103 26 12

102 51 13

101 43 19

Control 65 22



wwwrenishawcom


applications


Next generation

Raman imaging



sample


















Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































Sponsored Content











the feed rate










ůĞŶĚĞƌ

ampĞĞĚĞƌƐ

ŵŝůů

W


M

M

M

M

M

M

M



EZZĂŵĂŶ

gtd

dŚŝĐŬŶĞƐƐ

ĞŶƐŝƚǇ

dd h^

ĂƌĚŶĞƐƐ

EZ


ĐŚĞĐŬ

ampĞĞĚ

ĨŽƌǁĂƌĚ

ĐŽŶƚƌŽů

ampŽƌĐĞ

tĞŝŐŚƚ


ĂƉ

ƌŽƐƐĐŚĞĐŬ

ŽŶƚĞŶƚ

ĐŚĞĐŬ


Sponsored Content













































ʹ

ͺ

ʹ

ʹ ͺ ʹ

Ψ

13Ȁ13

[5eth

ĮSUHGLFWHG

ĮUHIHUHQFH


Ϭ

ϮϬ

ϰϬ

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Reference


512 (1) 96ndash107 (2016)








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percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

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Bacterial Strains






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Pathogen Detection





Limit of Detection

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Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















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varying amounts of

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used to build data

sets for s tudy ing

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Raman (or NIR) as


















Conclusion












Feedback and feed-


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Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

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568 588 594 632 660

785 830 980 1064 1550






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Speed



Sensitivity




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Combinability





Applications







Coherence Matters




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104 39 10

103 26 12

102 51 13

101 43 19

Control 65 22



wwwrenishawcom


applications


Next generation

Raman imaging



sample


















Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


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samples












































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Sponsored Content














varying amounts of

super-disintegrant

API and compres-

sion force These

model tablets were

used to build data

sets for s tudy ing

the variability

The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






Ϭ

Ϯ

ϰ

ϲ

ϭϬ

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Combinability





Applications







Coherence Matters




same footprint



























loading dock





121 501 (1928)






189 269ndash275 (2015)





(2018)

































jascoinccomfreebook


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Download your

FREE RAMAN EBOOK

IQ Frametrade












measurement

QRI












SSI











Raman spectrum

jascoinccomfreebook






















( RSD)

105 42 09

104 39 10

103 26 12

102 51 13

101 43 19

Control 65 22



wwwrenishawcom


applications


Next generation

Raman imaging



sample


















Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































Sponsored Content











the feed rate










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Sponsored Content














varying amounts of

super-disintegrant

API and compres-

sion force These

model tablets were

used to build data

sets for s tudy ing

the variability

The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






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Ϯ

ϰ

ϲ

ϭϬ

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ŶƚĞ

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LabRAM HR Evolution

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Applications







Coherence Matters




same footprint



























loading dock





121 501 (1928)






189 269ndash275 (2015)





(2018)

































jascoinccomfreebook


jascoinccomfreebook

Download your

FREE RAMAN EBOOK

IQ Frametrade












measurement

QRI












SSI











Raman spectrum

jascoinccomfreebook






















( RSD)

105 42 09

104 39 10

103 26 12

102 51 13

101 43 19

Control 65 22



wwwrenishawcom


applications


Next generation

Raman imaging



sample


















Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































Sponsored Content











the feed rate










ůĞŶĚĞƌ

ampĞĞĚĞƌƐ

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M

M

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Sponsored Content













































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Sponsored Content














varying amounts of

super-disintegrant

API and compres-

sion force These

model tablets were

used to build data

sets for s tudy ing

the variability

The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






Ϭ

Ϯ

ϰ

ϲ

ϭϬ

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ŶƚĞ

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LabRAM HR Evolution

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ϲ

ϭϬ

ϭϮ

ϭϬ ϱ Ϭ ϱ ϭϬ


ϰϮ ϱϯϰϮϱϯ



Coherence Matters




same footprint



























loading dock





121 501 (1928)






189 269ndash275 (2015)





(2018)

































jascoinccomfreebook


jascoinccomfreebook

Download your

FREE RAMAN EBOOK

IQ Frametrade












measurement

QRI












SSI











Raman spectrum

jascoinccomfreebook






















( RSD)

105 42 09

104 39 10

103 26 12

102 51 13

101 43 19

Control 65 22



wwwrenishawcom


applications


Next generation

Raman imaging



sample


















Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































Sponsored Content











the feed rate










ůĞŶĚĞƌ

ampĞĞĚĞƌƐ

ŵŝůů

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M

M

M

M

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ĐŚĞĐŬ


Sponsored Content













































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Ψ

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Sponsored Content














varying amounts of

super-disintegrant

API and compres-

sion force These

model tablets were

used to build data

sets for s tudy ing

the variability

The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ


ŶƚĞ

ŶƐŝƚǇ



ΛϱϯϮ Ŷŵ

Ϭ

Measured with

LabRAM HR Evolution

(data courtesy of


Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ

ϭϬ ϱ Ϭ ϱ ϭϬ


ϰϮ ϱϯϰϮϱϯ




























loading dock





121 501 (1928)






189 269ndash275 (2015)





(2018)

































jascoinccomfreebook


jascoinccomfreebook

Download your

FREE RAMAN EBOOK

IQ Frametrade












measurement

QRI












SSI











Raman spectrum

jascoinccomfreebook






















( RSD)

105 42 09

104 39 10

103 26 12

102 51 13

101 43 19

Control 65 22



wwwrenishawcom


applications


Next generation

Raman imaging



sample


















Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































Sponsored Content











the feed rate










ůĞŶĚĞƌ

ampĞĞĚĞƌƐ

ŵŝůů

W


M

M

M

M

M

M

M



EZZĂŵĂŶ

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ĂƉ

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Sponsored Content













































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Ψ

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[5eth

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ĮUHIHUHQFH


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ƌƵŐ

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dŝŵĞŵŝŶ

ƌĞĨĞƌĞŶĐĞ


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Sponsored Content














varying amounts of

super-disintegrant

API and compres-

sion force These

model tablets were

used to build data

sets for s tudy ing

the variability

The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ


ŶƚĞ

ŶƐŝƚǇ



ΛϱϯϮ Ŷŵ

Ϭ

Measured with

LabRAM HR Evolution

(data courtesy of


Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ

ϭϬ ϱ Ϭ ϱ ϭϬ


ϰϮ ϱϯϰϮϱϯ





















loading dock





121 501 (1928)






189 269ndash275 (2015)





(2018)

































jascoinccomfreebook


jascoinccomfreebook

Download your

FREE RAMAN EBOOK

IQ Frametrade












measurement

QRI












SSI











Raman spectrum

jascoinccomfreebook






















( RSD)

105 42 09

104 39 10

103 26 12

102 51 13

101 43 19

Control 65 22



wwwrenishawcom


applications


Next generation

Raman imaging



sample


















Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























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512 (1) 96ndash107 (2016)








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percentage





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Wavelengths

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measurement

QRI












SSI











Raman spectrum

jascoinccomfreebook






















( RSD)

105 42 09

104 39 10

103 26 12

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Limit of Detection

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wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































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Sensors



conducted





analytical balance


Sponsored Content









samples












































Sponsored Content











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varying amounts of

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sion force These

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used to build data

sets for s tudy ing

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The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






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121 501 (1928)






189 269ndash275 (2015)





(2018)

































jascoinccomfreebook


jascoinccomfreebook

Download your

FREE RAMAN EBOOK

IQ Frametrade












measurement

QRI












SSI











Raman spectrum

jascoinccomfreebook






















( RSD)

105 42 09

104 39 10

103 26 12

102 51 13

101 43 19

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wwwrenishawcom


applications


Next generation

Raman imaging



sample


















Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

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wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































Sponsored Content











the feed rate










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varying amounts of

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sion force These

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sets for s tudy ing

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The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






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Ϯ

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121 501 (1928)






189 269ndash275 (2015)





(2018)

































jascoinccomfreebook


jascoinccomfreebook

Download your

FREE RAMAN EBOOK

IQ Frametrade












measurement

QRI












SSI











Raman spectrum

jascoinccomfreebook






















( RSD)

105 42 09

104 39 10

103 26 12

102 51 13

101 43 19

Control 65 22



wwwrenishawcom


applications


Next generation

Raman imaging



sample


















Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































Sponsored Content











the feed rate










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varying amounts of

super-disintegrant

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sion force These

model tablets were

used to build data

sets for s tudy ing

the variability

The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






Ϭ

Ϯ

ϰ

ϲ

ϭϬ

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ϰϮ ϱϯϰϮϱϯ































jascoinccomfreebook


jascoinccomfreebook

Download your

FREE RAMAN EBOOK

IQ Frametrade












measurement

QRI












SSI











Raman spectrum

jascoinccomfreebook






















( RSD)

105 42 09

104 39 10

103 26 12

102 51 13

101 43 19

Control 65 22



wwwrenishawcom


applications


Next generation

Raman imaging



sample


















Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































Sponsored Content











the feed rate










ůĞŶĚĞƌ

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M

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Sponsored Content














varying amounts of

super-disintegrant

API and compres-

sion force These

model tablets were

used to build data

sets for s tudy ing

the variability

The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ


ŶƚĞ

ŶƐŝƚǇ



ΛϱϯϮ Ŷŵ

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Measured with

LabRAM HR Evolution

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ϰ

ϲ

ϭϬ

ϭϮ

ϭϬ ϱ Ϭ ϱ ϭϬ


ϰϮ ϱϯϰϮϱϯ























jascoinccomfreebook


jascoinccomfreebook

Download your

FREE RAMAN EBOOK

IQ Frametrade












measurement

QRI












SSI











Raman spectrum

jascoinccomfreebook






















( RSD)

105 42 09

104 39 10

103 26 12

102 51 13

101 43 19

Control 65 22



wwwrenishawcom


applications


Next generation

Raman imaging



sample


















Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































Sponsored Content











the feed rate










ůĞŶĚĞƌ

ampĞĞĚĞƌƐ

ŵŝůů

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M

M

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Sponsored Content













































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Sponsored Content














varying amounts of

super-disintegrant

API and compres-

sion force These

model tablets were

used to build data

sets for s tudy ing

the variability

The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ


ŶƚĞ

ŶƐŝƚǇ



ΛϱϯϮ Ŷŵ

Ϭ

Measured with

LabRAM HR Evolution

(data courtesy of


Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ

ϭϬ ϱ Ϭ ϱ ϭϬ


ϰϮ ϱϯϰϮϱϯ















jascoinccomfreebook


jascoinccomfreebook

Download your

FREE RAMAN EBOOK

IQ Frametrade












measurement

QRI












SSI











Raman spectrum

jascoinccomfreebook






















( RSD)

105 42 09

104 39 10

103 26 12

102 51 13

101 43 19

Control 65 22



wwwrenishawcom


applications


Next generation

Raman imaging



sample


















Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































Sponsored Content











the feed rate










ůĞŶĚĞƌ

ampĞĞĚĞƌƐ

ŵŝůů

W


M

M

M

M

M

M

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Sponsored Content













































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ƌĞĨĞƌĞŶĐĞ


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Sponsored Content














varying amounts of

super-disintegrant

API and compres-

sion force These

model tablets were

used to build data

sets for s tudy ing

the variability

The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ


ŶƚĞ

ŶƐŝƚǇ



ΛϱϯϮ Ŷŵ

Ϭ

Measured with

LabRAM HR Evolution

(data courtesy of


Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ

ϭϬ ϱ Ϭ ϱ ϭϬ


ϰϮ ϱϯϰϮϱϯ










jascoinccomfreebook


jascoinccomfreebook

Download your

FREE RAMAN EBOOK

IQ Frametrade












measurement

QRI












SSI











Raman spectrum

jascoinccomfreebook






















( RSD)

105 42 09

104 39 10

103 26 12

102 51 13

101 43 19

Control 65 22



wwwrenishawcom


applications


Next generation

Raman imaging



sample


















Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






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Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






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8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















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Pathogen Detection





Limit of Detection

Single Pathogen



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wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































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conducted





analytical balance


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512 (1) 96ndash107 (2016)








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percentage





spectroscopy (SERS)





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Wavelengths

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785 830 980 1064 1550






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105 42 09

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103 26 12

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101 43 19

Control 65 22



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applications


Next generation

Raman imaging



sample


















Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































Sponsored Content











the feed rate










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super-disintegrant

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Conclusion












Feedback and feed-


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Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






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( RSD)

105 42 09

104 39 10

103 26 12

102 51 13

101 43 19

Control 65 22



wwwrenishawcom


applications


Next generation

Raman imaging



sample


















Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































Sponsored Content











the feed rate










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varying amounts of

super-disintegrant

API and compres-

sion force These

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used to build data

sets for s tudy ing

the variability

The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






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Ϯ

ϰ

ϲ

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ϭϮ

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ϰϮ ϱϯϰϮϱϯ












( RSD)

105 42 09

104 39 10

103 26 12

102 51 13

101 43 19

Control 65 22



wwwrenishawcom


applications


Next generation

Raman imaging



sample


















Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































Sponsored Content











the feed rate










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varying amounts of

super-disintegrant

API and compres-

sion force These

model tablets were

used to build data

sets for s tudy ing

the variability

The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ


ŶƚĞ

ŶƐŝƚǇ



ΛϱϯϮ Ŷŵ

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LabRAM HR Evolution

(data courtesy of


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Ϯ

ϰ

ϲ

ϭϬ

ϭϮ

ϭϬ ϱ Ϭ ϱ ϭϬ


ϰϮ ϱϯϰϮϱϯ




wwwrenishawcom


applications


Next generation

Raman imaging



sample


















Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































Sponsored Content











the feed rate










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Sponsored Content














varying amounts of

super-disintegrant

API and compres-

sion force These

model tablets were

used to build data

sets for s tudy ing

the variability

The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ


ŶƚĞ

ŶƐŝƚǇ



ΛϱϯϮ Ŷŵ

Ϭ

Measured with

LabRAM HR Evolution

(data courtesy of


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Ϯ

ϰ

ϲ

ϭϬ

ϭϮ

ϭϬ ϱ Ϭ ϱ ϭϬ


ϰϮ ϱϯϰϮϱϯ





Bacterial Strains






Detection Assay












Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































Sponsored Content











the feed rate










ůĞŶĚĞƌ

ampĞĞĚĞƌƐ

ŵŝůů

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M

M

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Sponsored Content













































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Sponsored Content














varying amounts of

super-disintegrant

API and compres-

sion force These

model tablets were

used to build data

sets for s tudy ing

the variability

The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ


ŶƚĞ

ŶƐŝƚǇ



ΛϱϯϮ Ŷŵ

Ϭ

Measured with

LabRAM HR Evolution

(data courtesy of


Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ

ϭϬ ϱ Ϭ ϱ ϭϬ


ϰϮ ϱϯϰϮϱϯ











Pathogen Detection





Limit of Detection

Single Pathogen



Multiplex Detection




































wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































Sponsored Content











the feed rate










ůĞŶĚĞƌ

ampĞĞĚĞƌƐ

ŵŝůů

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M

M

M

M

M

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M



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Reference


512 (1) 96ndash107 (2016)








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percentage





spectroscopy (SERS)





Results


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References














Wavelengths

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405 442 458 473 488

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wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































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conducted





analytical balance


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Reference


512 (1) 96ndash107 (2016)








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percentage





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Wavelengths

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Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























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Sponsored Content














































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Conclusion












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Reference


512 (1) 96ndash107 (2016)








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Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

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Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




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Conclusion












Feedback and feed-


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Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






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wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































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Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






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wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































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sets for s tudy ing

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Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






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Ϯ

ϰ

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wwwiridianca





























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































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varying amounts of

super-disintegrant

API and compres-

sion force These

model tablets were

used to build data

sets for s tudy ing

the variability

The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






Ϭ

Ϯ

ϰ

ϲ

ϭϬ

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ŶƚĞ

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ΛϱϯϮ Ŷŵ

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ϭϮ

ϭϬ ϱ Ϭ ϱ ϭϬ


ϰϮ ϱϯϰϮϱϯ



























Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































Sponsored Content











the feed rate










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varying amounts of

super-disintegrant

API and compres-

sion force These

model tablets were

used to build data

sets for s tudy ing

the variability

The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ


ŶƚĞ

ŶƐŝƚǇ



ΛϱϯϮ Ŷŵ

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Measured with

LabRAM HR Evolution

(data courtesy of


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ϰ

ϲ

ϭϬ

ϭϮ

ϭϬ ϱ Ϭ ϱ ϭϬ


ϰϮ ϱϯϰϮϱϯ





















Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































manufacturingrdquo



Sponsored Content




















































Chute




Sensors



conducted





analytical balance


Sponsored Content









samples












































Sponsored Content











the feed rate










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Reference


512 (1) 96ndash107 (2016)








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Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

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405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






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Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






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8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































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Reference


512 (1) 96ndash107 (2016)








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Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

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Adv 3 E1700782 (2017)






6655 (2013)



Environ Pollut 199 185-191 (2015)









Nat Commun 9 1505 (2018)













1199 (2018)

































2017)






DC2013)






8 4761ndash4765 (2018)










89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























reaction monitoring




















Sponsored Content














































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Reference


512 (1) 96ndash107 (2016)








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percentage





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Results


Conclusions


References














Wavelengths

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2017)






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89(23) 12666-12673 (2017)




44 (2010)











An Executive

Summary




Introduction





























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Sponsored Content














































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512 (1) 96ndash107 (2016)








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percentage





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Results


Conclusions


References














Wavelengths

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Reference


512 (1) 96ndash107 (2016)








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percentage





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Results


Conclusions


References














Wavelengths

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Reference


512 (1) 96ndash107 (2016)








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Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

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Reference


512 (1) 96ndash107 (2016)








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percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

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Reference


512 (1) 96ndash107 (2016)








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Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

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Reference


512 (1) 96ndash107 (2016)








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Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

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varying amounts of

super-disintegrant

API and compres-

sion force These

model tablets were

used to build data

sets for s tudy ing

the variability

The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






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Ϯ

ϰ

ϲ

ϭϬ

ϭϮ


ŶƚĞ

ŶƐŝƚǇ



ΛϱϯϮ Ŷŵ

Ϭ

Measured with

LabRAM HR Evolution

(data courtesy of


Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ

ϭϬ ϱ Ϭ ϱ ϭϬ


ϰϮ ϱϯϰϮϱϯ



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Sponsored Content














varying amounts of

super-disintegrant

API and compres-

sion force These

model tablets were

used to build data

sets for s tudy ing

the variability

The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ


ŶƚĞ

ŶƐŝƚǇ



ΛϱϯϮ Ŷŵ

Ϭ

Measured with

LabRAM HR Evolution

(data courtesy of


Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ

ϭϬ ϱ Ϭ ϱ ϭϬ


ϰϮ ϱϯϰϮϱϯ



Sponsored Content














varying amounts of

super-disintegrant

API and compres-

sion force These

model tablets were

used to build data

sets for s tudy ing

the variability

The tablets were


Raman (or NIR) as


















Conclusion












Feedback and feed-


detected defects in


make immediate cor-



t ime The demon-



the process reveals







Reference


512 (1) 96ndash107 (2016)








tabletsrdquo











Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ


ŶƚĞ

ŶƐŝƚǇ



ΛϱϯϮ Ŷŵ

Ϭ

Measured with

LabRAM HR Evolution

(data courtesy of


Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ

ϭϬ ϱ Ϭ ϱ ϭϬ


ϰϮ ϱϯϰϮϱϯ












Conclusion













percentage





spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ


ŶƚĞ

ŶƐŝƚǇ



ΛϱϯϮ Ŷŵ

Ϭ

Measured with

LabRAM HR Evolution

(data courtesy of


Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ

ϭϬ ϱ Ϭ ϱ ϭϬ


ϰϮ ϱϯϰϮϱϯ






spectroscopy (SERS)





Results


Conclusions


References














Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ


ŶƚĞ

ŶƐŝƚǇ



ΛϱϯϮ Ŷŵ

Ϭ

Measured with

LabRAM HR Evolution

(data courtesy of


Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ

ϭϬ ϱ Ϭ ϱ ϭϬ


ϰϮ ϱϯϰϮϱϯ






Wavelengths

in Production (nm)

405 442 458 473 488

491 514 532 552 561

568 588 594 632 660

785 830 980 1064 1550






Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ


ŶƚĞ

ŶƐŝƚǇ



ΛϱϯϮ Ŷŵ

Ϭ

Measured with

LabRAM HR Evolution

(data courtesy of


Ϭ

Ϯ

ϰ

ϲ

ϭϬ

ϭϮ

ϭϬ ϱ Ϭ ϱ ϭϬ


ϰϮ ϱϯϰϮϱϯ



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