chemometrics in method validation why? - eurachem · chemometrics in method validation è why? ......
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Joint Research Centre the European Commission's
in-house science service
Chemometrics in
method validation
– why?
Jone Omar
10th May 2016, Gent
Eurachem 2016
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PhD in analytical chemistry in 2013 New analytical strategies for the
characterization of bioactive compounds
Early career scientist – Who am I?
Bilbao
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Early career scientist – Who am I?
Since 2013 Research fellow in the JRC-IRMM
Geel
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Outline
• Chemometrics in method validation-why?
• Briefly, what is chemometrics
• Where can we apply chemometrics, how?
• Applicability of chemometrics, some examples
• Conclusions
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Chemometrics in method
validation – why?
Method validation
Accuracy
Precision
Specificity
LOD/LOQ
Linearity
Etc… Sample
preparatio
n
Instrumental
setup
Data
treatment
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Where can we apply chemometrics?
Sample preparation
Instrumentamethod
Data treatment
DoE for getting the best
/optimum analyte
disposition
DoE for getting the best
working conditions in
our method & validate Multivariate data or
image analysis
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Design of Experiments = DoE
Screening designs – Full Factorial Design (FFD)
Information of the statistically significant parameters
Optimisation Design – Central Composite Design (CCD)
Optimum conditions of the system and the interactions
among the parameters
What is DoE? And what advantages does
Maximum information - minimum Nº of experiments
Interaction between parameters
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Chemometrics useful in many fields/matrixes
Characterisation of of nanomaterials
nanomaterials
Extraction of volatiles
Quantitative method
development in olive oil
Deterpenation of of cannabis
Authentication method for
coccidiostats
Extraction & digestion of
allergens in cookies cookies
Applicability of chemometrics, some examples
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Smallest particle size
Example 1: characterisation of nanomaterial
Focused Ultrasound Value of interest
Optimise the dispersion of TiO2 into minimum dispersible units
Submitted to JCA, March 2016
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Optimise an AF4 method that can separate a polydisperse TiO2 material
FFD - Pareto diagram with the significant variables
Variables to be studied in a CCD
Example 1: characterisation of nanomaterials
Fractogram of TiO2 under optimised conditions
Particle size 200nm= Value of interest
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Example 2: volatiles
1) Screening - FFD
2) Optimisation - CCD
Develop & optimise a quantitative method for extracting aromas from plants by means of SFE or FUSE
15 volatiles
Highest amount = Value of interest
J.Omar, et al. Food Analytical Methods 6 (2013), 1611-1620, Optimization of FUSE
and SFE of Volatile Compounds and Antioxidants from Aromatic Plants
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Optimise a GCxGC-MS separation method that suits all volatiles
GCxGC-MS microfluidic modulator
Example 2: volatiles & antioxidants
Highest intensity = Value of interest
J.Omar, et al. Talanta 88 (2012) 145– 151, Optimization of comprehensive two-dimensional
gas-chromatography (GC×GC) mass spectrometry for the determination of essential oils
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Optimise the measuring conditions of a Raman method
We look for the highest signal of the spectra Without burning the sample The shortest acquisition time with an acceptable signal/noise ratio
Example 3: aromas in olive oil
J.Omar, et al. Journal of Raman Spectroscopy 43 (2012) 1151-1156, Quantitative analysis of
Essential oils from rosemary in virgin olive oil using Raman spectroscopy and chemometrics
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630-660 cm-1
Spectral windows of interest
Develop a quantitative Raman method for volatiles in olive oil
Example 3: aromas in olive oil
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RM
SE
P
Develop a quantitative Raman method for volatiles in olive oil
Example 3: aromas in olive oil
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Validate the model
Compare real sample concentrations between two techniques
Example 3: aromas in olive oil
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Example 4: deterpenation of cannabis
1) Screening to see feasibility - FFD
2) Optimisation to get quantitative conditions - CCD
2 fractions: aromas & cannabinoids
Develop & optimise a deterpenation method for extracting aromas & cannabinoids from cannabis by means of SFE or FUSE
Highest amount = Value of interest
J.Omar, et al. Journal of Separation Science 36 (2013), 1397-1404, Optimisation and
Characterisation of marihuana extracts obtained by SFE and FUSE and RTL-GC-MS
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MCR-ALS to deconvolute the co-elutions
by means of MS information
Deconvolute co-eluting sesquiterpenes and cannabinoids in GCxGC-MS
Example 4: deterpenation of cannabis
J.Omar, et al. Talanta, 121 (2014) 273-280, Resolution of co-eluting compounds of Cannabis
Sativa in GCxGC-MS detection with Multivariate Curve Resolution-Alternating Least Squares
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Example 5: authentication of coccidiostats
Difficult to distinguish with the naked
eye, model created by PCA and
validated NIR spectra of coccidiostats
Develop a model for authentication of coccidiostats in NIR & MIR
J.Omar, et al. Journal of Food Additives and Contaminants, Part A, 32 (2015) 1464-1474,
Differentiation of coccidiostats-containing feed additives by Mid and Near Infra-Red Microscopy
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Example 5: authentication of coccidiostats
Difficult to distinguish with the naked
eye, model created by PCA and
validated NIR spectra of coccidiostats
Develop a model for authentication of coccidiostats in NIR & MIR
J.Omar, et al. Journal of Food Additives and Contaminants, Part A, 32 (2015) 1464-1474,
Differentiation of coccidiostats-containing feed additives by Mid and Near Infra-Red Microscopy
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Example 6: allergens in cookies
Develop & optimise an extraction + digestion method for MS based quantification of milk & egg allergens in food products.
DoE 1
Extraction
DoE 2
Digestion
Method validation
(coming soon)
Can one method suit all ?
18 peptides to monitor
compromise needed
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Conclusions
-Time and money saving
-Interactions of parameters visible
-Applicability to many fields / matrixes
-Optimised methods will lead to better
figures of merit
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Stay in touch
JRC Science Hub:
ec.europa.eu/jrc
@Jone_OO
Acknowledgements
University of the Basque Country
Joint Research Centre
Standards for Food Bioscience