Spectroscopic

Technique:

Near Infrared (NIR)

Spectroscopy

By Khoa Bui, 03/2015

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Definitions Near-IR (NIR) is a spectroscopic method based on molecular

overtones and combination vibrations of any molecule containing C-H, N-H, S-H or O-H bonds.

Normal mode: For a given molecule, a normal mode of vibration corresponds to internal atomic motions in which all atoms move in phase with same frequency but with different amplitude.

Combinations arise by interaction of two or more vibrations taking place simultaneously.

Overtones: Additionally to these normal vibrations transitions corresponds to be called overtones. Such transitions are forbidden by the selection rules of quantum mechanics.

Transition from the ground state to the second excited state with the absorption of NIR give rise to weak bands called 1st overtone in NIR and so on.

NIR is comprised of combinations and overtones that is anharmonicoscillation. Most molecules contain covalent bonds which share electrons between atoms. Although bonds are elastic, they do not obey Hooke’s law exactly. The model of anharmonic oscillation is more precise.

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Absorption Regions

The NIR spectrum of each sample is unique. Like a fingerprint no two unique molecular structures produce the same infrared spectrum. This makes infrared spectroscopy useful for several types of analysis.

When the near infrared spectrum of unknown compound is scanned numbers of questions come to our mind such as:

1. which groups are present in the compound

2. what environments are influencing it

3. what type of carbon skeleton is present in the compound.

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Principle Features

• Continuous spectrum NIR instruments may include a diffraction grating or be of the diode array, AOTF or Fourier transform near-infrared (FT-NIR) type.

• Such instruments are much more flexible than discrete-wavelength instruments, and can be used in a wider variety of measurements ( McClure, 2001 ; Blanco and Villarroya, 2002 )

=> Usage:a) It can identify unknown materialsb) It can determine the quality or consistency of a samplec) It can determine the amount of components in a mixture

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Fourier Transform vs Dispersive/Filter

Methods

Original infrared instruments were of the dispersive type: separated the individual frequencies of energy emitted from the infrared source. The detector measures the amount of energy at each frequency which has passed through the sample. This results in a spectrum which is a plot of intensity vs. frequency.

FT-IR spectrometry measuring all of the infrared frequencies simultaneously, rather than individually.

The interferometer produces a unique type of signal which has all of the infrared frequencies “encoded” into it. The signal can be measured very quickly, usually on the order of one second or so. Thus, the time element per sample is reduced to a matter of a few seconds rather than several minutes.

The measured interferogram signal can be transformed into frequency spectrum (a plot of intensity vs frequencies) via a well-known mathematical technique called the Fourier transformation

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Sample FT-IR Analysis Process 6

FT-IR Thermo Scientific

4. The Detector: The beam fially passes to the detector for fial measurement. The detectors used are specially designed to measure the special interferogram signal.

5. The Computer: The measured signal is digitized and sent to the computer where the Fourier transformation takes place. The final infrared spectrum is then presented to the user for interpretation and any further manipulation.

1. The Source: Infrared energy is emitted from a glowing black-body source. 2. The Interferometer: The beam enters the interferometer where the “spectral encoding” takes place. The interferometer uses a reference laser for precise wavelength calibration, mirror position control and data acquisition timing. 3. The Sample: The beam enters the sample compartment where it is transmittedthrough or reflected off of the surface of the sample. This is where specific frequencies of energy, which are uniquely characteristic of the sample, are absorbed.

Measuring Modesa) transmittance: is obtained as in conventional UV-VIS spectroscopy.Many substances in solution follow Beer´s law, showing a linearrelationship between concentration and absorbance. This law is validonly for transparent homogeneous materials.b) transflectance: a special way to obtain a transmitancemeasurement which is referred to as transflectance. Doubling theoptical path as the radiation beam passes twice through the sample.c) diffuse reflectance: of solid samples is a distinguishingmeasurement mode employed in NIR spectroscopy.• Both scattering and absorbance by solid granules contribute to

change the signal intensity.• Beer´s law not applicable!d) interactance: a higher probability is given to the incident beam tointeract with the sample. Contains more information on the sampleconstituents and reflects better the actual composition of the sample.e) transmittance: through scattering medium for dense solid samples.Appropriate for quantitative determination of the active principle ofpharmaceutical tablets because the longer optical path, resulting frominternal scattering, can provide information which is better correlatedwith the average sample content than the surface diffuse reflectancesignal.

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Pros Cons1. Speed of analysis: get results in seconds, rather than hours (or days). On-line NIRS analysis during processing enables close control of operations.

2. Accuracy equal to reference testing.

3. Reproducibility equal to (and often better than) reference testing.

4. Low cost per test.

5. Flexibility and efficiency—many constituents (up to 32) can be tested simultaneously.

6. Environmentally clean—no chemicals needed (and therefore none to dispose of after analysis).

7. Easy and cheap to install (no requirements for fume exhaust or drainage).

8. Little or no sample preparation.

9. Simple and totally safe to operate.

10. Stand-alone instruments (no peripherals).

11. Small instrument size.

12. Durable—instruments work well for many years.

13. Networking capability of instruments within a plant or among a few or many plants, all under the control of a single bench-top computer (remote from the operation if necessary)

1. Separate calibration for each commodity and constituent. The mostapparent impediment to NIR application!!!

Although modern methods including “local” calibration and artificialneural networks (ANN) have streamlined large-scale NIRS and near-infrared transmittance (NITS) analysis of the most widely analyzed grains,to be most reliable these methods require large databases.

(200–300 is considered a small sample set for modern commercial orindustrial NIR applications).

2. The need to monitor accuracy and precision.

3. Instruments are expensive to purchase.

4. Skepticism: People don’t trust NIR (yet). Management still tends to adhere to the “tried and trusted” methods, such as forms of the Kjeldahltest for nitrogen or protein.

5. Lack of knowledge of how to operate instruments really efficiently.

Education in the application and interpretation of NIRS technology is the key to its proliferation.

Better education in the use of NIR instruments would enhanceconfidence in the technology.

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Chemometrics

The true value of NIR spectroscopy as an analytical tool rests on the statistical and mathematical manipulation of the spectral data.

NIR spectral data set normally undergoes some type of pre-treatment (i.e., 1st and 2nd derivatives of the spectra) before being used for qualitative or quantitative purposes.

For example, to overcome problems associated with radiation scattering by a solid sample measured by reflectance and other spectrum base-line- affecting phenomena.

The most employed technique for qualitative analysis using NIR spectroscopy is based on Principal Component Analysis (PCA).

Other chemometric tools are Multiple Linear Regression (MLR), Principal Component Regression (PCR) and Partial Least Square Regression (PLS): a linear relationship between the spectral data and the concentration or other property value to be determined. PCR and PLS can be considered standard calibration techniques for NIR spectroscopy.

For quantitative treatment, it is worth mentioning Artificial Neural Networks (ANN) as an emerging alternative for NIR calibration.

Most of the quantitative applications are targeted to determine major constituents in the sample. The detection limit is about 0.1-1%.

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NIR modeling• NIR relies on a multivariate model to quantify a property

or a concentration in complex samples, such as gasoline,and agricultural products, such as soy beans, wheatflour and sugar cane.

• Most of the quantitative models developed by using NIRspectral information are based on the use of sampleswhose analyte concentration or property has beendetermined by a standard, well-accepted analyticalprocedure designated as the reference method.

• Seldom is it possible to produce a set of artificialcalibration samples in the laboratory, an exception ismade for very simple samples as, for example, thoseknown to contain only two or three components, suchas fuel alcohol, which can be viewed as a binary water-ethanol system.

• The number of samples employed for calibration hasbeen considered of great importance (perhaps, evenmore important than the chemometric techniqueemployed for model development), i.e., 50-100 samples.

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How Do I Build a Calibration?

1. Identify your goals and the types of machines available.

2. Scan all samples into machine using appropriate settings (TBD) and under similar conditions.

3. Use integrated software features to select most informative samples for wet chemistry.

4. Re-scan those same samples to make sure you are accurate and have two spectra per sample if wet chemistry is expensive.

5. Submit samples for wet chemistry.

6. Combine actual values from wet chemistry with spectra.

7. Run statistical analysis (YOU CAN NOT TELL FROM LOOKING AT SPECTRA!):

a. Examine derivatives not raw spectrum (usually)

b. Multivariate modeling for multiple wavelengths

c. Partial least-squares regression, principal component analysis (PCA) or other calibration techniques.

8. Use best calibration to select new samples to include (return to step 3).

9. Include validation samples that are not used to develop the calibration to determine the “fit” and usefulness of the calibration.

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An example

A model is being designed to assess the protein content of wheat grains in general:

all types of wheat must be represented in the calibration set.

for each class of wheat the concentration of protein must span the expected values.

factors affecting the NIR spectrum and those related with seasonal variations must be represented in the sample set.

Inclusion of these factors of variability in the model will lead to having the number of samples used for calibration reach 100 or, perhaps, even 1000, depending on sample complexity.

Signal to noise ratio:

The most important measurement according to NIRS sales people is signal to noise ratio, each salesperson say’s their instrument is the best.

The longer the pathlength the more light is lost (usually). The lower the signal the higher the noise.

More scans per sample will average out noise

- Noise adds up as the square root of the number of scans

- Signal adds up linearly

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Geographic Origin Assessment

The NIR spectrum of rice wine spectrum is mainly influenced by:

1- absorption bands of O–H groups in water

2- ethanol absorption bands of C–H and O–H groups (2266 and 2305nm)

3- sugar absorption bands (1790 nm)

29 and 9 wine samples respectively were used for the calibration and validation sets.

PCA allowed the discrimination of samples of the two brands from two different geographic origins in China.

Using PLS regression in order to construct discriminant functions => the wavelength range of 1300–1650nm gives the best calibration results in comparison to those obtained with the full spectral range (i.e. 800–2500 nm).

Of the samples in the validation set, 100% were correctly classified.

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NIR spectra of cheese present several absorption bands characteristic of overtones and combinations of C–H, N–H and O–H bonds

The spectra are mainly influenced by: 1- the O–H groups of water absorption bands (1470 and 1940 nm); 2- the C–H2 groups of lipids and proteins (2173, 2350 and 2380 nm)

Spectra consisted of the means of 64 co-added scans recorded from 1000 to 2500nm with a spectral resolution of 1.25 nm (2 cm-1 ).

PCA was chosen in order to reduce the number of variables, since principal component scores were used as input for the LDA (linear discriminant analysis) technique. The stepwise backward procedure was used with the jackknife classification test.

The analyzed samples ranged in age from 12 to 16 weeks, and reflected the normal ageing time of commercial cheeses.

Spectra were normalized by reducing the area under each spectrum to 1.

Factoral discriminant analysis (FDA) and common component and specific weights analysis (CCSWA) were applied.

Using all the discriminant factors, 100% of the cheeses from Austria were correctly classified, followed by 94.7%, 83.3%, 76.9% and 66.7% correct classification for cheeses from Switzerland, France, Germany and Finland, respectively. Modern Techniques for Food Authentication - ISBN: 978-0-12-374085-4

Modern Techniques for Food Authentication - ISBN: 978-0-12-

374085-4

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Modern Techniques for Food Authentication - ISBN: 978-0-12-374085-4

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J. Braz. Chem. Soc., Vol. 14, No. 2, 198-219, 2003.

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QUESTIONS? 17