PAT EXPERT platform
1
Presentation & goals of PAT-Expert
Partners
QbD
PAT
Validation
2
First PAT-EXPERT platform
• To increase knowledge in real time
• To ensure compliance with FDA and EMA standards
• To control the manufacturing process
• To deliver a drug product in a state of control
• To reduce the costs of poor quality
• To increase efficiency
We create the PAT EXPERT platform, the first complementary, innovative and operational competencies center for R & D, Industrialization and Production in Pharmaceutical and Chemical sector.It brings together the skills of • Inhalexpert, expert of QbD development,
• Nir-Industry, specialist of PAT
• Galenisys Pharmaceutical Consultants, leader in the validation and transfer of analytical methods.
3
QbDIdentify and explain the critical sources of variability that impact DP CQAs,
•to control the variability of raw materials,
•to understand, validate and control the manufacturing process,
•to predict and control the drug product
PAT
•reduce production cycling time
•prevent rejection of batches
•enable real time release
•increase automation and control
•improve energy and material use
•facilitate continuous processing
Development& analyticalvalidation
Process Validation
Our strategy: From R&D to production up to final release
4
Presentation & goals of Pat-Expert
Partners
QbD
PAT
Validation
5
Page 6
INHALEXPERT
Founded in 2012, by Pascal CAVAILLON and Grégoire LERY, INHALEXPERT provides:
References:
Inhalexpert brings 29 years of experience and expertise to provide consulting and training for the development of any type of complex product including inhaled product
Inhalexpert stands QbDtraining and project support for development strategyand concept understanding, integration of pragmaticapproach including tools, validation and regulatorydossier
We had led successful projects in the EU, US and international markets.
NIR-INDUSTRY
• More than 15 years of expertise in process analyticalcontrol & monitoring, off-line (laboratory) and on-field, within several companies:
SEPPAL, ISITEC-LAB and now NIR-INDUSTRY (since 2014)
• Focused on Consulting, sale & installation of analyticalkey-hand solution for Pharma, chemical & polymere, food & feed, beverages and environment.
• Installation, training and follow up in R&D, up-scale and routine production of spectroscopic technologies: NIR, NIR AOTF, RAMAN and FTIR…
• Sales, set-up and training on chemometrics database, with Unscrambler (CAMO).
Some Pharma References:
Galenisys Solutions
Galenisys consultants are THE EXPERTS in:
• Aseptic Manufacturing and Control, Sterility Assurance and Sterilisation
• Quality Management Systems
• Quality and Regulatory compliance
• Qualification and Validation
• FDA & EMEA inspections / Audits
8
Qualification & Validation:
• Processes
• Cleaning, IT, Equipment, facilities and utilities
• VMPs for New/modified products and routine Q&V programmes
Presentation & goals of Pat-Expert
Partners
QbD
PAT
Validation
9
Objective and benefit
Higher Drug Product control i.e. control of all pDP CQAs• Throughout product lifecycle• Throughout product supply
chain
COPQ* = Cost of Poor Quality
Benefit for the Patient• Higher level of assurance of product Quality linked to:
➔ Efficacy➔ Safety
Benefit for the Pharmaceutical Company:
• Higher Quality and Capability
• Cost saving and efficiency for industry
• More efficient regulatory oversight
• Higher regulatory flexibility linked to Design Space
• To enhance root cause analysis and postapproval change management
10
Situation in 2002?
Dr. Doug Dean & Frances BruttinPriceWaterhouseCoopers
Sigma level ppm defects Yield Cost of Poor Quality
2s 308 537 69,2% 25 – 35%
3s 66 807 93,3% 20 – 25%
4s 6 210 99,4% 12 – 18%
5s 233 99,98% 4 – 8%
6s 3,4 99,99966% 1 – 3%Targeted Quality
to patients
Observed➔ 5 to 15%
Rejects
QA InspectionOOS
Rework
2000s Mfg
Design ExploratoryDevelopment
Official Development
Production
LOW
High
X
X
X
X
€1€10
Cost of making changes
€1000
€100
Ability to Implementchanges
TIME
Rule of 10s
Hidden• Lost customer loyalty• Excess inventory• Cost of engineering changeovers ➔ 15 to 25%• Extra equipment• Extra headcount
Move everything early on in the design stage
The only way to fix that is by design
Cost of Poor Quality
11
Voice of Customer: FDA/EMAA process is generally considered well understood when:
• All critical sources of variability i.e. API CQAs, CMAs, CDAs, QCPPs, CQAs are identified and explained ➔ Level 1
• Variability is managed by the process ➔ Level 2
• DP CQAs can be accurately and reliably predicted over the ranges of acceptance criteria established for API CQAs, CMAs, CDAs, QCPPs, CQAs and manufacturing environmental and other conditions ➔ Level 3➔ The ability to predict reflects a high degree of process understanding
Drug Product
Before QbD Variable Input
Fixed Process
Variable Output
Consistent output
Variable Input
Adapted Process
QbD/PAT/Design space
Control the Inputs (X's)………….……...Monitor the Output (Y's) 12
How to reach the objective?
Process step/unit operation
pCMAs, pCDAs and
API CQAs
pCQAs
pQCPPs
Process step/unitoperation
Process step/unitoperation
…..
pQCPPs pQCPPs
pCQAsDrug
ProductpDP CQAs
Mathematicalmodel, correlation➔ predictive
Need to understand and control raw materials and manufacturing process
To control Drug Product
i.e. all DP CQAs
Need to identifyand control linkbetween input
and output and DP CQAs
Understandand control
each unit operation
Understandand control
each unit operation
Understandand control
each unit operation
Understandand control
raw material
13
CQA1
CQA2
CQA3
Critical Quality Attributes
DP CQAs
DP CQA1
DP CQA2
DP CQA3
Drug Product
Intermediate CQAs
Objectives: identifying critical sources of variability and mapping the linkage to control DP CQAs
Inputs of unit operation Outputs of unit operation
Using prior knowledge, models and risk assessmentprocesses, relationships between input and output are
analyzed :CQA1 = function (API CQA1, CMA1)CQA2 = function (QCPP1, QCPP3)CQA3 = function (CMA1, CMA2, QCPP1)
QPP2 might not be needed in the establishment of design space
QCPP1
QPP2
QCPP3
Quality CriticalProcessParameters
CQA1
CQA2
CQA3
Critical Quality Attributes
Using prior knowledge, models and riskassessment processes, pCMAs, pCDAs, pAPICQAs and pQCPPs which could have an impact upon DP CQAs are analyzed.
CMA1
CMA2
Critical MaterialAttributes
API CQA1
Unit operation
Process variables:1. PSD ➔ API CQA12. PSD ➔ CMA13. Density➔ CMA24. Mixing speed ➔ QCPP15. Mxing time ➔ QCPP2
Intermediate CQAs:Blend uniformity
DP CQA 1: Content uniformityDP CQA 2: AssayDP CQA 3: Dissolution
DOE
Control strategy
Analyse de risques
14
OUTPUT = CQAs or DP CQAs
y
Inputs to the processcontrol variability
of the Output
People
Equipment
Measurement
Process
Materials
Environment
Observation
Ind
ivid
ua
l V
alu
e
4038363432302826242220
120
115
110
105
100
95
90
_X=102.37
UCL=116.68
LCL=88.05
I Chart
Observation
Ind
ivid
ua
l V
alu
e
6058565452504846444240
115
110
105
100
95
90
85
80
_X=97.94
UCL=112.65
LCL=83.23
I Chart
Observation
Ind
ivid
ua
l V
alu
e
8078767472706866646260
115
110
105
100
95
90
_X=99.63
UCL=111.55
LCL=87.71
I Chart
Observation
Ind
ivid
ua
l V
alu
e
10098969492908886848280
110
105
100
95
90
85
_X=98.76
UCL=111.17
LCL=86.35
I Chart
Observation
Ind
ivid
ua
l V
alu
e
6058565452504846444240
115
110
105
100
95
90
85
80
_X=97.94
UCL=112.65
LCL=83.23
I Chart
Observation
Ind
ivid
ua
l V
alu
e
8078767472706866646260
115
110
105
100
95
90
_X=99.63
UCL=111.55
LCL=87.71
I Chart
INPUTS
(X)High risk variables
y = ƒ(x)
DevelopmentDesign phase
Prior knowledgeRisk assessment
Experimental phaseDOEs
Mutivariate analysis
Production• Manufacturing transfer• Quality and cost
optimisation• Quality investigation• Validation
CAPABILITY = 3s
OUTPUT = CQAs or DP CQAs
y
Inputs to the processcontrol variability
of the Output
CAPABILITY = 6s
Process Robustness: ability of a process to tolerate variability of materials and changes of the process and equipment without negative impact on quality.
15
How do we measure the control of drug product?The output of this measurement i.e. DP CQA, if normal distribution, is usually illustrated by a histogram and calculations that predict how many parts will be produced with specific mean (m) and standard deviation (s)
68,3
95,4
99,7
m
LCL UCL
• Two parts of process capability are: 1) measure the variability of the output of a process➔ voice of the process
Voice of the process
Variability around the target, about 1,5s ?
2) compare that variability with a proposed specification➔ voice of the customer
LSL USLVoice of the customer
3,5 s level
16
Presentation & goals of Pat-Expert
Partners
QbD
PAT
Validation
17
MONITORING SUPERVISION
CONTROL DIAGNOSTIC
“A system for designing, analyzing, and controlling manufacturing through timely measurements (i.e., during processing) of critical quality and performance attributes of raw and in-process materials and processes with the goal of ensuring final product quality”
ICH, ASTM and FDA definitions
➢PAT= Think global + act locally
➢PAT are focused on process stepsand analysis within a global process approach
Les PAT: Process Analytical Technology
More than only install online sensors
18
Process Quality Management System in Pharmaceutical facility
BENEFITS:• Facilitates process understanding.• Causes of product variability can be identified and
managed.• The increase in process understanding enables the
reduction in development and scale-up time.• Increases patent life of new and existing products.• Reduces production cycle times.• Reduces work in progress.• Enables the use of alternative (less expensive) raw
materials.
19
• Reduces wastage and rejects.
• Improves in quality and consistency of quality.• Enables rapid release of product.• Improves energy and material use.• Facilitates continuous processing.• Reduces manufacturing cycle times and staffing.• Reduces the manufacturing facilities required.• Creates potential for skid-/cellbased manufacturing for
rapid deployment as pre-built systems.• Meets the new FDA Process Validation Guidance.• Reduces development, manufacturing and quality costs
ACTIVE
DISP. 1EXCIPIENT
DISP.
ACTIVE
DISP. 2TABLET
PRESS1
Brimrose
SPECT1
FBD
Ethernet
Moisture
Optic Fibre
Ethernet
Optic Fiber
Optic Fiber
EthernetEthernet
NIRA SERVER
PACMAN
SERVER
Routeur
Site Ethernet
Optic Fiber
Weight
Management
Panel PC Panel PC Panel PC Panel PC Panel PC Panel PC
Barcode
Reader
EthernetEthernet
BLEND
SPECT
BLENDER
BIN
TABLET
ANALYZERLAB TABLET
ANALYZER
Panel PC
WIRELESS
RECEIVER
ETHERNET
Total quality control: Complete solution compliant with CFR21 part 11
20
Different industries, same QbD and PAT approaches
Biopharma (Mab) Chemistry (CPI)
Pharma Petrochemical➢ Formes solides➢ Formes Liquides➢ Formes semi-pâteuses
21
PROCESS & PAT: solid formsNIR
NIR
NIR
NIR
NIR
NIR
NIR
RAMAN
RAMAN
RAMAN
RAMAN
22
PAT application: solid form
Raw Material& API
Spray-DryerBlender, Mixing
FBD & Wurster
Tablet compression
Freeze drying monitoring
Solvant recovery
Control & sorting
23
PAT application : liquid & semi solid form
Raw Material& API
MixingSolvant
recoveryControl &
sorting
24
SUMMARY OF POSSIBILITIES
Step NIR & RAMAN Option 1 Option 2 Option 3
Raw materials Identification of API & excipient, (ok)Water contentPolymorphism state (ok)
Luminar 5030 with probes
Luminar 6000 lab with probe
Luminar 3060 multi channel
Blend Blend uniformityWater content
Luminar 4030
“Free Space”
Luminar 5030
in “Free Space”
mode
Wet granulation and drying
Moisture end pointMoisture profile controlBlend uniformityGranule size and distribution (ok)Polymorphism state (ok)
Luminar 4030
“Free Space”
Luminar 5030
in “Free Space”
mode
Luminar 3060
Multi channel with probes
Tablet analysis Identification of API & excipient (ok)Active & excipient content (ok)MoistureDissolutionHardnessDensity
Luminar 3070 for transmission and reflectance
Luminar 4030
For reflectance only(*)
(*) Diffuse reflectance may be used for high dosage tablets where distribution of active in the tablet can be safely assumed to be uniform. At low dosage, where active may be distributed non-uniformly, transmission is critical
25
SUMMARY OF POSSIBILITIES
Step NIR & RAMAN Option 1 Option 2 Option 3
Suspensions Identification of API & excipient, (ok)ContentPolymorphism state (ok)
Luminar 5030 with probes
Luminar 6000 lab with probe
Luminar 3060 multi channel
Creams, pastes Identification of API & excipient, (ok)Content
Luminar 4030
“Free Space”
Luminar 5030
in “Free Space”
mode
Clear liquids
Eye drops, syrups
Identification of API & excipient, (ok)Content
Luminar 4030
“Free Space”
Luminar 5030
in “Free Space”
mode
Luminar 3060
Multi channel with probes
Transdermal patches
Identification of API & excipient, (ok)Content
Luminar 3070 for transmission and reflectance
Luminar 4030
For reflectance only(*)
26
RAW MATERIALS
27
• Same Multiplexed Analyzer can be used for:
- Multiple Excipient and ActiveDispensaries
- Fluid Bed Dryer
- Warehouses
ID Raw Materials – Receiving and Dispensaries operations
28
Absorption spectra of some raw material
Solid & liquid
PlasdoneK-90, Lactose anhydrous, Lactose Mono-hydrous,
Sulfamethoxazole, Atenolol, Sulfasalzine,Trazodone-HCl,
Metronidazole, Metroformin-HCl, Doxycycline-HCl
1 - Anhydrous IP
2. Ethanol
3. Methanol
4. Acetone
5. Ethanol
6. Glycerin
29
Absorption Spectra - Pure Polymorphs
1461
1477
1970
1978
2164
2181
1732
1736
Similar chemically
Activities different…
Crystallisation form different
Polymorph H
Increasing amount
Polymorph A
Increasing amount
polymorph A
1st Derivative - Polymorphs in Tablets
WET GRANULATION: FBD, WURSTER, etc…
31
CQAs for wet granulation unit operationPotential CQA
End point measurement (e.g. power consumption, torque)
Blend uniformity
Flow
Moisture content
Granule size and distribution
Granule strength and uniformity
Bulk/tapped/true density
API polymorphic form
Cohesive/ adhesive properties
Electrostatic properties
Granule brittleness
Granule elasticity
PAT monitoring with NIR &/or RAMAN
End point measurement (e.g. power consumption, torque)
Blend uniformity
Flow
Moisture content
Granule size and distribution
Granule strength and uniformity
Bulk/tapped/true density
API polymorphic form
Cohesive/ adhesive properties
Electrostatic properties
Granule brittleness
Granule elasticity
PAT
32
Granulation – FBD/LAF
❖ The Simple – moisture end point
❖ The Smart – moisture profile control
❖ The Advanced – Excipient bead coating control
Granulation – FBD/LAF: Granulation Basics
Granulation is a process where two opposing effects take place simultaneously
- Agglomeration due to presence of “adhesive” compounds & moisture.
- Attrition of agglomerates due to collisions with other particles and the chopper. This effect occurs all the time until end point is reached
To achieve a reproducible granulate, we need to control the following:- Moisture profile with time, from beginning to end of spraying.- Rate of drying to end point- End point 33
Granulation – FBDSimple - End point by LOD only
34
Granulation – FBD
Simple - End point by NIR
Granulation – FBDSimple - End point by NIR
36
0.00
0.50
1.00
1.50
2.00
2.50
3.00
13:2
0:35
13:2
9:42
13:3
6:58
13:4
4:44
13:5
2:00
13:5
9:16
14:0
6:17
14:1
3:48
14:2
1:04
14:2
8:19
14:3
5:50
14:4
2:51
14:5
0:43
14:5
8:38
15:0
6:33
15:1
4:11
15:2
1:35
15:2
9:28
15:3
7:08
15:4
4:48
15:5
2:29
15:5
9:26
16:0
6:44
16:1
4:50
16:2
2:26
16:3
1:07
16:3
8:29
16:4
6:37
LOD
Time
Predicted
Reference
Granulation – FBDWhat can go wrong?
37
1. Diffuse Reflectance Probe inserted at same level and penetration as temperature probes2. Real time results during drying cycle3. Operators see moisture value on Glatt control panel, feed back can be used.4. Use multiplexer for up to 12FBD with same unit
Granulation - Glatt FBD, Fluid Air, Probe (Free Space available)
38
Miniature Luminar 5030 Hand Held installed
on a CPCG1
Miniature Luminar 4030 Process Analyzer installed
on a NIRO FBD
Granulation – FBDHow to do it? From R&D to Production
39
Absorption Spectra from Glatt FBD& PLS1 Regression of Absorption Spectra
40
FBD Operation by AOTF NIR via Sapphire window
Analyzer is integrated into Glatt 500 FBD Control system
NIR Moisture
Vacuum Oven LOD
*
Drying Curve: Batch XXXXX, Load X
Removed spectrometer
to inspect window
41
The Smart Way – Active coating on Excipient BeadsThis is typically done on Wurster Coater
Area of change
42
Second Derivative – very useful in Understanding the full Process.
43
Blender & Mixer
44
CQAs for wet granulation unit operation
PAT
Potential CQAs
Blend uniformity
Particle size distribution
Bulk/tapped/true density
Moisture content
Flow properties
Cohesive/adhesive properties
Powder segregation
Electrostatic properties
PAT monitoring with NIR &/or RAMAN
Blend uniformity
Particle size distribution
Bulk/tapped/true density
Moisture content
Flow properties
Cohesive/adhesive properties
Powder segregation
Electrostatic properties
45
FROM LAB TO PRODUCTION SIZE IN ONE EASY STEP
R&D SCALE
PRODUCTION 500 LITER
46
On-line Blend Analysis
4030 on 25cu.ft. double cone
5030 on a 5 liter V-blender
5030 on a lid
47
On-line Blend Analysis - The ultimate way
N = 1-30 N = 31- 60
Blend Run 3
0
2
4
6
8
10
12
0 20 40 60 80 100 120 140 160
Rotation Number
Diff
eren
ces
Differences
48
Blend profiles: moving block spectral standard deviation
Large block size
– Suitable endpoint based on y-axis value (1 point) or profile slope
Small block size
– Suitable endpoint based on consecutive y-axis values (multiple points) by minimal profile variability
Moving block blend profiles using different block sizes
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0 10 20 30 40 50 60 70
Revolutions (block number)
me
an
sp
ec
tra
l s
tan
da
rd d
ev
iati
on
block = 3
block = 20
49
Blending – Quantitative Mg-Stearate
Peak of Mg-St 0.5 to 2%
2nd derivative
50
Blending – Quantitative Mg-Stearate
SEP (standard error of prediction) = 0.06
51
Blend Behavior – Stable or De-blending?
0.00E+00
5.00E-05
1.00E-04
1.50E-04
2.00E-04
2.50E-04
3.00E-04
3.50E-04
4.00E-04
4.50E-04
1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 101 106 111
MB
SD
Rotation
Comparison Phenit vs. CMZ
XR1308 6block MBSD
phen2408 block6 MBSD
PhenetoinCarbamazapine
52
TABLET COMPRESSION
53
CQAs for tablet compression operation
PAT
Potential CQA
Tablet appearance
Target weight
Weight uniformity
Content uniformity
Hardness/tablet breaking force/ tensilestrength
Thickness/dimensions
Tablet porosity/density/solid fraction
Friability
Moisture content
Disintegration
Dissolution
PAT monitoring with NIR
Tablet appearance
Target weight
Weight uniformity
Content uniformity
Hardness/tablet breaking force/ tensilestrength
Thickness/dimensions
Tablet porosity/density/solid fraction
Friability
Moisture content
Disintegration
Dissolution
54
Off the Press Tablet Analyzer
55
Off the Press Tablet Analyzer
Tablet passing over detector
Reflectance detector inside
Software renders spacing between tablets totally insignificant
56
Transmission or Diffuse Reflectance measurements of Tablets
©1999-2000 Brimrose Corp of America
The following parameters can be measured in Tablets:
•I.D. of active (& excipient)
•Concentration of active & excipient)
•Moisture
•Dissolution
•Hardness
•Density
•Dosage Level
Laboratory with Tablet
Carousel
With Tablet Conveyer belt
57
Absorption Reflectance Spectra of Coating on Tablets
1394nm
58
PLS1 Regression of Spectra of Moving Coated Tablets
LOADING WEIGHTS FOR COATING WEIGHT
Bound water in coating oxide
1394nm
59
1st Derivative Spectra of 9 Dosage Levels in Transmission
1138nm
60
PLS1 Regression of 2nd Derivative Spectra
SEP = 0.15
61
LYOPHILISATION
62
CQAs for lyophilisation operation
PAT
Potential CQA
Appearance
Weight mean
Weight uniformity
Content uniformity and mean
Moisture content
API polymorph
Reconstitution time
PAT monitoring with NIR
Appearance
Weight mean
Weight uniformity
Content uniformity and mean
Moisture content
API polymorph
Reconstitution time
63
Lyophilization – Experimental set up
Brimrose Diffuse reflectance Remote sensing head
Rotating table
64
Freeze drying Process: Lyophilisation Monitoring
65
SOLVANT RECOVERY
66
Solvent Recovery Experience - A 10 Channel System
New Solvent Recovery
Plant
Commissioned in 1997
First MDC Recovery Sept. 97
Multipurpose Columns A and B
67
Solvent Recovery Experience - Flow Cell
•Liquid Side Draw Column B
•Flow Cells installed in by-
pass
•Sample take off point close to
probes
•Measure at LSD, Vapor Side
Draw, Reflux and Feed on
each column (8 total)
Flow cell68
82% MDC
15% Toluene
2.5% Acetone
0.2% Water
Trace heptane and methanol (<0. 1%)
Solvent Recovery Experience - A 10 Channel System
Typical MDC Feed Composition Typical Composition - Recovered MDC
99.7% MDC
0.1% Acetone
0.04% Methanol
0.03% Water
69
Solvent Recovery Experience - Methanol Validation
70
Solvent Recovery Experience - Process Screen
71
MDC Liquid Side Draw
98
98.2
98.4
98.6
98.8
99
99.2
99.4
99.6
99.8
100
6/9/99
14:24
6/9/99
16:48
6/9/99
19:12
6/9/99
21:36
6/10/99
0:00
6/10/99
2:24
6/10/99
4:48
6/10/99
7:12
6/10/99
9:36
6/10/99
12:00
Date/Time
%Co
mpo
nent
-0.05
0
0.05
0.1
0.15
0.2
MDC
Methanol
Ethanol
Water
Solvent Recovery Experience - Liquid Side Draw
72
OTHER APPLICATIONS
73
Luminar 3030 - Moving Vials Inspection linked to cap color
Cap color reader
Luminar 3030 “Free Space” optical module
Good separation between
2 products with different
active concentration with
speed up to 200 per
minute
74
PCA Analysis - Blue Caps (1%) vs. Red Caps (2%)1. Excellent separation of the two products at speeds of up to 80 per minute.
2. No need for trigger, system determines presence of sample by itself, and collects only relevant spectra.
75
NIR Project Status 1st production line fully commissioned with 16 channel Luminar 3060
Excellent performance in determining on-line moisture content
5 plants were equipped by 2002
76
Absorbance Spectra - Moving Transdermal Patches
77
1st Derivative Spectra - Moving Transdermal Patches
78
Regression for Active in Moving Transdermal Patches
SEC = 1.33
SEP = 1.65
79
Presentation & goals of Pat-Expert
Partners
QbD
PAT
Validation
80
VALIDATION
81
1) Definition of the Master Plan for the implementation
of the NIR method
2) Input to and Verification of the "Justification of the
Change of Method and the Equivalence"
3) Input to and Verification of the qualification protocol
4) Verification of the completed qualification
protocol
5) Input to, assesment of, and verification of both and the validation protocol for the
NIR method and the report of the equivalence with the
current method
6) Verification of the completed NIR method
validation protocol
7) Verification of the NIR method SOP
8) Verification of the method registration amendment with
health agencies
9) Verification of the completed Change Control
with respect to the NIR Equipment and the new
analytical methods
82
Galenisys expertise in NIR