A classification tool for N-way arrays based on SIMCA methodology

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Overview of 2-<strong>way</strong> SIMCA SIMCA comparison original SIMCA: ‣ score and orthogonal distances are combined as a “rsd” measure Class space PC2 ‣ square distance/rsd of a “new” sample is compared to square rsd of class by F-test ‣ Score distance is computed from the “box” boundaries (but can be taken from average problematic ‣ degree of freedom <strong>for</strong> F-test ‣ scaling factor needed in order to combine score distance with orthogonal one PC1 S p (q) Evolution ‣ using T 2 avoids to define “box” boundaries ‣ using only Orthogonal distances does not take into account deviations inside class space ‣ using MD may avoid scaling factor Rejected by both approaches Accepted by both approaches Rejected by original SIMCA Accepted by this rule alternative SIMCA: ‣ reduced score distance and orthogonal distance are combined, thus being directly comparable ‣ Score distance is computed from the average problematic ‣ two different reference distribution are assumed ‣ degree of freedom () Class space PC2 T 2 lim T 2 PC1 ‣ giving the same weight to both type of distances Q Q Q lim Accepted by rules using only orthogonal distance T 2 TRICAP 2009 Dr. Marina CocchiMORE chemometrics, Department of Chemistry-University of Modena
SIMCA extension to N-<strong>way</strong> ‣ a disjoint N- decomposition model <strong>for</strong> each class (both PARAFAC & Tucker3 implemented) ‣ separate multi<strong>way</strong> centering/scaling ‣ dimensionality (distinct <strong>for</strong> each class): CV (ncrossdecomp) or according to best SENS/ SPEC 1th Class PARAFAC X= A( C ȮB ) ’ + E X1 Model 2nd Class TUCKER3 X= AG X (C B ) ’ +E X2 Model 3st Class X3 Model TRICAP 2009 Dr. Marina CocchiMORE chemometrics, Department of Chemistry-University of Modena
Page 1 and 2: A classification <
Page 3 and 4: Context Why N-way
Page 5 and 6: Overview of 2-way
Page 7 and 8: Overview of 2-way
Page 9: Overview of 2-way
Page 13 and 14: SIMCA extension to N- way</
Page 15 and 16: Simulated data 3 (+ 2) Data sets n
Page 17 and 18: Sim 1 PARAFAC [3 4 4] Sim1 class 3:
Page 19 and 20: Sim 1 small small 1 SENSITIVITY H/Q
Page 21 and 22: Sim 2 PARAFAC [3 4 4] ‣ in this c
Page 23 and 24: Sim 3 PARAFAC [2 3 2] ‣ essential
Page 25 and 26: Sim 3 PARAFAC [2 3 2] Coomans 2 vs
Page 27 and 28: Parma Ham Data set Jens K.S. Mølle
Page 29 and 30: Parma Ham With AP limits the only c
Page 31 and 32: Parma Ham Coomans plots 1 vs. 2 TRI
Page 33 and 34: Extra Vergin Oliv Oil (EVOO) Data s
Page 35 and 36: EVOO Data set Preprocessing ‣ 3 r
Page 37 and 38: EVOO Data set NSIMCA analysis ‣in
Page 39 and 40: Final remarks Perspective ‣ sampl
Page 41 and 42: atio objs/LVs 41

A classification tool for N-way arrays based on SIMCA methodology

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