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

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Sim 2 PARAFAC [3 4 4] ‣ class 1 can be modeled because of small rsd (even if lies among the other two) ‣ CV 95% limits seem too tight (however only 2 train sample are rejected, i.e. balance Q/H ) ‣ CV AP seems best in this case Q Model of Class 1 Factor 3 H TRICAP 2009 Dr. Marina CocchiMORE chemometrics, Department of Chemistry-University of Modena
Sim 2 PARAFAC [3 4 4] ‣ in this case with higher class rsd and strong overlap fit limits seems better, difference is in Qlim not much in Hlim Q Model of Class 2 Factors 4 Model of Class 3 Factors 4 + + + + + + + + H TRICAP 2009 H 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 11 and 12: SIMCA extension to N-way</s
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: Sim 1 small small 1 SENSITIVITY H/Q
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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