Assessing feature relevance in NPLS models by VIP

Chemometrics and Intelligent Laboratory Systems

Volumn 129, Issue , 2013, Pages 76-86

  Favilla, Stefania ^a   Durante, Caterina ^a   Vigni, Mario Li ^a   Cocchi, Marina ^a  

^a UNIVERSITY OF MODENA AND REGGIO EMILIA   (Italy)

Author keywords

Feature selection; Multi way data; NPLS; NPLS DA; VIP

Indexed keywords

OLIVE OIL;

ARTICLE; BOLD SIGNAL; BRAIN REGION; CONTROLLED STUDY; FLOUR; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; INFRARED SPECTROSCOPY; MASS FRAGMENTOGRAPHY; MATHEMATICAL MODEL; MATHEMATICAL PARAMETERS; PAIN; PAIN ASSESSMENT; PERFORMANCE; PREDICTION; PRIORITY JOURNAL; REPRODUCIBILITY; VARIABLE IMPORTANCE IN PROJECTION;

EID: 84889083637     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2013.05.013     Document Type: Article

References (28)

1. Bro R. Multi-way calibration. Multi-linear PLS. Journal of Chemometrics 1996, 10:47-61.
2. Bro R., Smilde A.K., De Jong S. On the difference between low-rank and subspace approximation: improved model for multi-linear PLS regression. Chemometrics and Intelligent Laboratory Systems 2001, 58:3-13.
3. Smilde A. Comments on multilinear PLS. Journal of Chemometrics 1997, 11:367-377.
4. De Jong S. Regression coefficients in multilinear PLS. Journal of Chemometrics 1998, 12:77-81.
5. Smilde A., Bro R., Geladi P. Multi-way analysis with applications. Multi-way Analysis in the Chemical Sciences, Chpt 3.1 2004, 35-45. John Wiley & Sons.
6. Salvatore E., Bevilacqua M., Bro R., Marini F., Cocchi M. Classification methods for multi-way arrays as a basic tool for food PDO authentication. Wilson & Wilson's Comprehensive Analytical Chemistry 2013, vol. 60. Elsevier B.V. M. De La Guardia, A. Gonzalvez Illueca (Eds.).
7. Nomikos P., MacGregor J.F. Multivariate SPC charts for monitoring batch processes. Technometrics 1995, 37:41-59.
8. Durante C., Cocchi M., Grandi M., Marchetti A., Bro R. Application of N-PLS to gas chromatographic and sensory of traditional balsamic vinegars of Modena. Chemometrics and Intelligent Laboratory Systems 2006, 83:54-65.
9. Rubingh C.M., van Erk M.J., Wopereis S., van Vliet T., Verheij E.R., Cnubben N.H.P., van Ommen B., van der Greef J., Hendriks H.F.J., Smilde A.K. Discovery of subtle effects in a human intervention trial through multilevel modeling. Chemometrics and Intelligent Laboratory Systems 2011, 106:108-114.
10. Conesa A., Prats-Montalbán J.M., Tarazona S., Nueda M.J., Ferrer A. A multiway approach to data integration in systems biology based on Tucker3 and N-PLS. Chemometrics and Intelligent Laboratory Systems 2010, 104:101-111.
11. Mehmood T., Liland K.H., Snipen L., Sæbø S. A review of variable selection methods in partial least squares regression. Chemometrics and Intelligent Laboratory Systems 2012, 118:62-69.
12. Norgaard L., Saudland A., Wagner J., Nielsen J.P., Munck L., Engelsen S.B. Interval partial least squares regression (iPLS): a comparative chemometric study with an example from near-infrared spectroscopy. Applied Spectroscopy 2000, 54:413-419.
13. Leardi R., Gonzales A.L. Genetic algorithms applied to feature selection in PLS regression: how to use them. Chemometrics and Intelligent Laboratory Systems 1998, 41:195-207.
14. Stordrange L., Rajalahtia T., Libnau F.O. Multiway methods to explore and model NIR data from a batch process. Chemometrics and Intelligent Laboratory Systems 2004, 70:137-145.
15. Odman P.J., Lindvald C., Olsson L., et al. Sensor combination and chemometric variable selection for online monitoring of Streptomyces coelicolor fed-batch cultivations. Applied Microbiology and Biotechnology 2010, 86:1745-1759.
16. Lorho G., Westad F., Bro R. Generalized correlation loadings. Extending correlation loadings to congruence and to multi-way models. Chemometrics and Intelligent Laboratory Systems 2006, 84:119-125.
17. Wold S., Johansson E., Cocchi M. PLS: partial least squares projections to latent structures. 3D QSAR in Drug Design: Theory, Methods and Applications 1993, 523-550. ESCOM Science Publishers, Leiden. H. Kubinyi (Ed.).
18. Wold S., Sjöström M., Eriksson L. PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 2001, 58:109-130.
19. Chong I.G., Jun C.H. Performance of some variable selection methods when multicollinearity is present. Chemometrics and Intelligent Laboratory Systems 2005, 78:103-112.
20. Gosselin R., Rodrigue D., Duchesne C. A bootstrap-VIP approach for selecting wavelength intervals in spectral imaging applications. Chemometrics and Intelligent Laboratory Systems 2010, 100:12-21.
21. Rajalahti T., Arneberg R., Berven F.S., Myhr K.M., Ulvik R.J., Kvalheim O.M. Biomarker discovery in mass spectral profiles by means of selectivity ratio plot. Chemometrics and Intelligent Laboratory Systems 2009, 95:35-48.
22. Acar E., Aykut-Bingol C., Bingol H., Bro R., Ritaccio A.L., Yener B. Modeling and detection of epileptic seizures using multi-modal data construction and analysis. Technical Report 2008, (Computer Science Department at RPI (http://www.cs.rpi.edu/research/tr.html)).
23. Nilsson J., De Jong S., Smilde A.K. Multiway calibration in 3D QSAR. Journal of Chemometrics 1997, 11:511.
24. Li Vigni M., Cocchi M. Near infrared spectroscopy and multivariate analysis to evaluate wheat flour doughs leavening and bread properties. Analytica Chimica Acta 2013, 764:17-23.
25. Durante C., Bro R., Cocchi M. A classification tool for N-way array based on SIMCA methodology. Chemometrics and Intelligent Laboratory Systems 2011, 106:73-85.
26. Prato M., Favilla S., Zanni L., Porro C.A., Baraldi P. A regularisation algorithm for decoding perceptual temporal profiles from fMRI data. NeuroImage 2011, 56-1:258-267.
27. Burnham A.J., MacGregor J.F., Viveros R. Interpretation of regression coefficients under a latent variable regression model. Journal of Chemometrics 2001, 15:265-284.
28. Trygg J., Wold S. Journal of Chemometrics 2003, 17:53-64.