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

Chemometrics and Intelligent Laboratory Systems

Volumn 106, Issue 1, 2011, Pages 73-85

  Durante, Caterina ^a   Bro, Rasmus ^b   Cocchi, Marina ^a  

^a UNIVERSITY OF MODENA AND REGGIO EMILIA   (Italy)

^b UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

Class modelling; Discriminant analysis; Multi way classification; PARAFAC; SIMCA; Tucker

Indexed keywords

ARTICLE; CLASSIFICATION ALGORITHM; CLASSIFIER; CONTROLLED STUDY; DISCRIMINANT ANALYSIS; FOOD ANALYSIS; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PARAFAC METHOD; PARTIAL LEAST SQUARES REGRESSION; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; SIMCA METHOD; SIMULATION; TUCKER3 METHOD; VALIDATION PROCESS;

EID: 79952450155     PISSN: 01697439     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.chemolab.2010.09.004     Document Type: Article

References (38)

1. Smilde A., Bro R., Geladi P. Multi-way Analysis. Application in the Chemical Sciences 2004, WILEY, John Wiley & Sons, Ltd, England.
2. Guimet F., Ferré J., Boqué R., Rius F.X. Application of unfold principal component analysis and parallel factor analysis to the exploratory analysis of olive oils by means of excitation-emission matrix fluorescence spectroscopy. Anal. Chim. Acta 2004, 515:75-85.
3. Cocchi M., Durante C., Grandi M., Lambertini P., Manzini D., Marchetti A. Simultaneous determination of sugars and organic acids in aged vinegars and chemometric data analysis. Talanta 2006, 69:1166-1175.
4. Leardi R., Armanino C., Lanteri S., Albertonanza L. Three-way principal component analysis for monitoring data from Venice lagoon. J. Chemometr. 2000, 14:197-211.
5. Astel A., Tsakovski S., Simeonov V., Reisenhofer E., Piselli S., Barbieri P. Multivariate classification and modeling in surface water pollution estimation. Anal. Bioanal. Chem. 2008, 390:1283-1292.
6. Ballabio D., Consonni V., Todeschini R. Classification of multiway analytical data based on MOLMAP approach. Anal. Chim. Acta 2007, 605:134-146.
7. Guimet F., Boque R., Ferre J. Application of non-negative matrix factorization combined with Fisher's linear discriminant analysis for classification of olive oil excitation-emission fluorescence spectra. Chemom. Intell. Lab. Syst. 2006, 81:94-106.
8. Wise B.M., Gallagher N.B., Watts Butler S., White D.D., Barna G.G. A comparison of principal component analysis, multiway principal component analysis, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process. J. Chemometr. 1999, 13:379-396.
9. Khosravi A., Melendez J., Colomer J. Classification of sags gathered in distribution substations based on multiway principal component analysis. Electr. Power Syst. Res. 2009, 79:144-151.
10. Hall G.J., Kenny J.E. Estuarine water classification using EEM spectroscopy and PARAFAC-SIMCA. Anal. Chim. Acta 2007, 581:118-124.
11. Guimet F., Ferré J., Boqué R. Rapid detection of olive-pomace oil adulteration in extra virgin olive oils from the protected denomination of origin 'Siuriana' using excitation-emission fluorescence spectroscopy and three-way methods of analysis. Anal. Chim. Acta 2005, 544:143-152.
12. Arancibia J.A., Boschetti C.E., Olivieri A.C., Escandar G.M. Screening of oil samples on the basis of excitation-emission room-temperature phosphorescence data and multiway chemometric techniques. Introducing the second-order advantage in a classification Study. Anal. Chem. 2008, 80:2789-2798.
13. Hall G.J., Clow K., Kenny J.E. Estuarial fingerprinting through multidimensional fluorescence and multivariate analysis. Environ. Sci. Technol. 2005, 39:7560-7567.
14. Castro C., Manetti C. A multiway approach to analyze metabonomic data: a study of maize seeds development. Anal. Bioanal. Chem. 2007, 371:194-200.
15. Acar E., Aykut-Bingol C., Bingol H., Bro R., Yener B. Multiway analysis of epilepsy tensors. Bioinformatics 2007, 23:i10-i18.
16. Flåten G.R., Grung B., Kvalheim O.M. Quantification of pollution levels by multiway modelling. J. Chemometr. 2004, 18:173-182.
17. Forina M., Oliveri P., Lanteri S., Casale M. Class-modeling techniques, classic and new, for old and new problems. Chemom. Intell. Lab. Syst. 2008, 93:132-148.
18. Massart D.L., Buydens L.M.C., De Jong S., Lewi P.J., Smeyers-Verbeke J. Handbook of Chemometrics and Qualimetrics 1998, Elsevier, Amsterdam.
19. Wold S. Pattern recognition by means of disjoint principal components models. Pattern Recognit. 1976, 8:127-139.
20. Wold S., Sjostrom M. SIMCA: a method for analyzing chemical data in terms of similarity and analogy. Series, Vol. 52, ISBN13: 9780841203792eISBN: 9780841204393. "Chemometrics: Theory and Application" 1977, 243-282. Amer. Chem. Soc., Washington DC. B. Kowalski (Ed.).
21. Branden K.V., Hubert M. Robust classification in high dimensions based on the SIMCA method. Chemom. Intell. Lab. Syst. 2005, 79:10-21.
22. Louwerse D.J., Smilde A.K. Multivariate statistical process control of batch processes based on three-way models. Chem. Eng. Sci. 2000, 55:1225-1235.
23. Maesschalck R.D., Caldolfi A., Massart D.L., Heuerding S. Decision criteria for soft independent modelling of class analogy applied to NIR. Chemom. Intell. Lab. Syst. 1999, 47:65-77.
24. Wold S., Sjostrom M. Letter to the editor-comments on a recent evaluation of the SIMCA method. J. Chemometr. 1987, 1:243-245.
25. Albano C., Blomqvist G., Coomans D., Dunn W.J., Edlund U., Eliasson B., Hellberg S., Johansson E., Nordin B., Jonels D., Sjostrom M., Soderstrom B., Wold H., Wold S. Pattern recognition by means of disjoint principal components models: SIMCA philosophy and methods. Proceedings of the Symposium on Applied Statistics, Copenhagen Jan. 22, 1981.
26. Kvalheim O.M., Oygard K., Grahl-Nielsen O. SIMCA multivariate data analysis of blue mussel components in environ-mental pollution studies. Anal. Chim. Acta 1983, 150:145-152.
27. Gemperline P.J., Webber L.D., Cox F.O. Anal. Chem. 1989, 61:138-144.
28. Daszykowski M., Kaczmarek K., Stanimirova I., Vander Heyden Y., Walczak B. Robust SIMCA-bounding influence of outliers. Chemom. Intell. Lab. Syst. 2006, 47:65-77.
29. PLS-Toolbox Manual 4.0 for MATLAB© (distributed by Eigenvector Research, WA, USA).
30. Andersson C.A., Bro R. The N-way Toolbox for MATLAB. Chemom. Intell. Lab. Syst. 2000, 52:1-4. (http://www.models.life.ku.dk/source/nwaytoolbox/).
31. Ceulemans E., Kiers H.A.L. Selecting among three-mode principal component models of different types and complexities: a numerical convex hull based method. Br. J. Math. Stat. Psychol. 2006, 59:133-150.
32. Bro R., Kjeldahl K., Smilde A.K., Kiers H.A.L. Cross-validation of component models: a critical look at current methods. Anal. Bioanal. Chem. 2008, 390:1241-1251.
33. Louwerse D.J., Smilde A.K. Multivariate statistical process control of batch processes based on three-way models. Chem. Eng. Sci. 2000, 55:1225-1235.
34. Forina M., Casale M., Oliveri P., Lanteri S. CAIMAN brothers: a family of powerful classification and class modeling techniques. Chemom. Intell. Lab. Syst. 2009, 96:239-245.
35. Pomerantsev A.L. Acceptance areas for multivariate classification derived by projection methods. J. Chemometr. 2008, 22:601-609.
36. Todeschini R., Ballabio D., Consonni V., Mauri A., Pavan M. CAIMAN (Classification And Influence Matrix Analysis): a new approach to the classification based on leverage-scaled functions. Chemom. Intell. Lab. Syst. 2005, 87:3-17.
37. Møller J.K.S., Parolari G., Gabba L., Christensen J., Skibsted L.H. J. Agr. Food Chem. 2003, 51:1224-1230.
38. Zunin P., Boggia R., Lanteri S., Leardi R., De Andreis R., Evangelisti P., Chromatogr J. J. Chromatogr. A 2004, 1023:271-276.