1. 10th Winter Symposium on Chemometrics
29 February 2016
Samara, Russia
Hybrid chemometric approaches to increase efficiency of classification and data fusion techniques
Yulia B. Monakhova, Monika Hohmann, Svetlana P. Mushtakova, Norbert Christoph, Helmut Wachter, Bernd Diehl, Ulrike Holzgrabe, Douglas N. Rutledge
Institute of Chemistry, Saratov State University, Saratov, Russia
Spectral Service AG, Cologne, Germany
Institute of Pharmacy and Food Chemistry, University of Würzburg, Würzburg, Germany
Bavarian Health and Food Safety Authority, Würzburg, Germany
UMR Ingénierie Procédés Aliments, AgroParisTech, Inra, Université Paris-Saclay, France

2. linear classfication models???
Introduction
How can we cope with
cluster overlap in
linear classfication models???
By using synergetic
combination of existing
multivariate approaches

3. ICA + DA
Problem statement
Variable reduction is
necessary for DA
PCA is a routine tool for
dimention reduction
Is ICA an alternative for the DA preprocessing?

4. 5 groups: MSR, PFL, RHH, Sac, Wue
ICA + DA
NMR wine data set
Data set
Classified parameter 1
Riesling (n=334)
Year
5 groups: 2005, 2006, 2007, 2009, 2010 2
Riesling (n=217)
Origin
5 groups: MSR, PFL, RHH, Sac, Wue 3
2009
(n=111)
4 groups: PFL, NAH, MSR, RHH 4
Red wine (n=303)
Grape variety
6 groups: Pinot noir, Dornfelder, Lemberger, Portugieser, Trollinger, Regent

5. ICA + DA
Number of PCs/ICs

6. ICA + DA
Number of PCs/ICs

7. ICA + DA Number of PCs/ICs Data matrix Classified parameter FDA LDA
Data matrix
Classified parameter
FDA
LDA
PCA
ICA 1
Riesling (n=334)
Year 10 7 8 6 2
Riesling (n=217)
Origin 12 3
2009 (n=111) 9 4
Red wine (n=303)
Grape variety 15 13

8. Number of samples for validation
ICA + DA
Classification results
Data matrix
Classified parameter
Number of samples for validation
FDA
LDA
PCA
ICA
Riesling (n=334)
Year 56 70 75 84 90
Riesling (n=217)
Origin 36 69 89 95
2009 (n=111) 22 76 85 87 92
Red wine (n=303)
Grape variety 61 43 46 79

9. Sum of ranking difference
ICA + DA
Sum of ranking difference
Ref.: K. Heberger, Sum of ranking differences compares methods or models fairly. Trends Anal. Chem. 29 (2010)

10. Classification results
ICA + DA
Classification results
PCA-FDA
ICA-FDA

11. Common components and specific weights analysis (CCSWA) Better
CCSWA + PLS-DA
Problem statement
PLS-DA
Common components and specific
weights analysis (CCSWA)
Better
classification???

12. CCSWA + PLS-DA Algorithm Global scores calculation
PLS-DA on global scores
1-Latent Variable PLS regression between WG matrix and binary-coded groups matrix
PCA decomposition of the WG matrix

13. CCSWA + PLS-DA Normally fruited tomatoes CCSWA Wilks' lambda = 0.14
PLSDA-CCSWA
Wilks' lambda = 0.05

14. CCSWA + PLS-DA Small fruited tomatoes MB hierarchical PLS CCSWA
Wilks' lambda = 0.13
CCSWA
Wilks' lambda=0.11
PLSDA-CCSWA
Wilks' lambda = 0.05

15. CCSWA + PLS-DA
Preprocessing

16. CCSWA + PLS-DA
Number of LVs and CCs
CCSWA
CCs=11 / LVs=2
PLSDA-CCSWA
CCs=5-12 / LVs=1-2

17. CCSWA + PLS-DA
Prediction

18. Thank you for
your attention!!!
CCSWA
PLS-DA DA
ICA