Leveraging multiple linear regression for wavelength selection

Chemometrics and Intelligent Laboratory Systems

Volumn 168, Issue , 2017, Pages 121-127

  Lemos, Tony ^a   Kalivas, John H ^a  

^a IDAHO STATE UNIVERSITY   (United States)

Author keywords

Multiple linear regression; Multivariate calibration; Wavelength selection

Indexed keywords

GASOLINE; SUGAR;

ALGORITHM; ARTICLE; CALIBRATION; MAIZE; MONTE CARLO METHOD; MULTIPLE LINEAR REGRESSION ANALYSIS; PARTIAL LEAST SQUARES REGRESSION; PREDICTION; PRIORITY JOURNAL; WAVELENGTH SELECTION;

EID: 85026749760     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2017.07.011     Document Type: Article

References (41)

1. Næs, T., Isaksson, T., Fearn, T., Davies, T., A User Friendly Guide to Multivariate Calibration and Classification. 2002, NIR Publications, Chichester UK.
2. Kalivas, J.H., Calibration methodologies., second ed. Brown, S.D., Tauler, R., Walczak, B., Kalivas, J.H., (eds.) Comprehensive Chemometrics: Chemical and Biochemical Data Analysis, vol. 3, 2009, Elsevier, Amsterdam, 1–32 Chap. 1.
3. Guuon, I., Elisseef, A., An introduction to variable and feature selection. J. Mach. Learn. Res. 3 (2003), 1157–1182.
4. Nørgaard, L., Saudland, A., Wagner, J., Nielsen, J.P., Munck, L., Engelsen, S.B., Interval partial least-squares regression (iPLS): a comparative chemometric study with and example from near-infrared spectroscopy. Appl. Spectrosc. 54 (2000), 413–419.
5. Jouan-Rimbaud, D., Massart, D.L., Leardi, R., de Noord, O.E., Genetic algorithms as a tool for wavelength selection in multivariate calibration. Anal. Chem. 67 (1995), 4295–4304.
6. Arakawa, M., Yamashita, Y., Funatsu, K., Genetic algorithm-based wavelength selection method for spectral calibration. J. Chemom. 25 (2011), 10–19.
7. Lucasius, C.B., Beckers, M.L.M., Kateman, G., Genetic algorithms in wavelength selection: a comparative study. Anal. Chim. Acta 286 (1994), 135–153.
8. Rajalihit, T., Arneberg, R., Kroksveen, A.C., Berle, M., Myher, K.M., Kvalheim, O.M., Discriminating variable test and selectivity ratio plot: quantitative tools for interpretation and variable (biomarker) selection in complex spectral or chromatographic profiles. Anal. Chem. 81 (2009), 2581–2590.
9. Kalivas, J.H., Roberts, N., Sutter, J., Global optimization by simulated annealing with wavelength selection for ultraviolet-visible spectrophotometry. Anal. Chem. 61 (1989), 2024–2030.
10. Brenchley, J.M., Hörchner, U., Kalivas, J.H., Wavelength selection characterization for NIR spectra. Appl. Spectrosc. 51 (1997), 689–699.
11. Stout, F., Kalivas, J.H., Héberger, K., Wavelength selection for multivariate calibration using Tikhonov regularization. Appl. Spectrosc. 61 (2007), 85–95.
12. Ottaway, J., Kalivas, J.H., Andries, E., Spectral multivariate calibration with wavelength selection using variants of Tikhonov regularization. Appl. Spectrosc. 64 (2010), 1388–1395.
13. 1) regularization variants for full wavelength or sparse spectral multivariate calibration models or maintenance. J. Chemom. 26 (2012), 218–230.
14. Andries, E., Martin, S., Sparse methods in spectroscopy: an introduction, overview, and perspective. Appl. Spec., 67, 2013, 579593.
15. Andries, E., Sparse models by iteratively reweighted feature scaling: a framework for wavelength and sample selection. J. Chemom. 27 (2013), 50–62.
16. Fu, G.-H., Xu, Q.-S., Li, H.-D., Cao, D.-S., Liang, Y.-Z., Elastic net grouping variable selection combined with partial least squares regression (EN-PLSR) for the analysis of strongly multi-collinear Spectroscopic data. Appl. Spectrosc. 65 (2011), 402–408.
17. Tibshirani, R.J., Regression and shrinkage and selection via the LASSO. J. R. Stat. Soc. Ser. B 58 (1996), 267–288.
18. Cai, W.S., Li, Y.K., Shao, X.G., A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra. Chemom. Intell. Lab. Syst. 90 (2008), 188–194.
19. Esquerre, C., Gowen, A.A., Downey, G., O'Donnell, C.P., Selection of variables based on most stable normalized partial least squares regression coefficients in an ensemble Monte Carlo procedure. J. Near. Infra. Spectrosc. 19 (2011), 343–350.
20. Esquerre, C.A., Gowen, A.A., O'Gorman, A., Downey, G., O'Donnell, C.P., Evaluation of ensemble Monte Carlo variable selection for identification of metabolite markers in NMR data. Anal. Chim. Acta 964 (2017), 45–54.
21. Li, H.-D., Liang, Y.-Z., Xu, Q.-S., Cao, D.-S., Model-population analysis and its applications in chemical and biological modeling. Trends Anal. Chem. 38 (2012), 154–162.
22. Li, H.-D., Liang, Y.-Z., Xu, Q.-S., Cao, D.-S., Model population analysis for variable selection. J. Chemom. 24 (2010), 418–423.
23. Yun, Y.-H., Wang, W.-T., Deng, B.-C., Lai, G.-B., Liu, X.-B., Ren, D.-B., Liang, Y.-Z., Fan, W., Xu, Q.-S., Using variable combination population analysis for variable selection in multivariate calibration. Anal. Chim. Acta 862 (2015), 14–23.
24. Bin, J., Ali, F., Fan, W., Zhou, J., Li, X., Tang, W., Liang, Y., An efficient variable selection method based on variable permutation and model population analysis for multivariate calibration of NIR spectra. Chemom. Intell. Lab. Syst. 158 (2016), 1–13.
25. Ŝustek, J., Method for the choice of optimal analytical positions in spectrophotometric analysis of multicomponent systems. Anal. Chem. 46 (1974), 1676–1679.
26. Deng, B.C., Yun, Y.H., Liang, Y.Z., Yi, L.Z., A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling. Analyst 139 (2014), 4836–4845.
27. Yun, Y.-H., Liang, Y.-Z., Xie, G.-X., Li, H.-D., Cao, D.-S., Xu, Q.-S., A perspective demonstration on the importance of variable selection in inverse calibration for complex analytical systems. Analyst 138 (2013), 6412–6421.
28. Yun, Y.-H., Cao, D.-S., Tan, M.-L., Yan, J., Ren, D.-B., Xu, Q.-S., Yu, L., Liang, Y.-Z., A simple idea on applying large regression coefficient to improve the genetic algorithm–PLS for variable selection in multivariate calibration. Chemom. Intell. Lab. Syst. 130 (2014), 76–83.
29. Li, H.-D., Xu, Q.-S., Liang, Y.-Z., Random frog: an efficient reversible jump Markov Chain Monte Carlo-like approach for variable selection with applications to gene selection and disease classification. Anal. Chim. Acta 740 (2012), 20–26.
30. Yun, Y.-H., Li, H.-D., Wood, L.R.E., Fan, W., Wang, J.-J., Cao, D.-S., Xu, Q.-S., Liang, Y.-Z., An efficient method of wavelength interval selection based on random frog for multivariate spectral calibration. Spectrochim. Acta A 111 (2013), 31–36.
31. Yun, Y.-H., Wang, W.-T., Tan, M.-L., Liang, Y.-Z., Li, H.-D., Cao, D.-S., Lu, H.-M., Xu, Q.-S., A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration. Anal. Chim. Acta 807 (2014), 36–43.
32. Deng, B.-C., Yun, Y.-H., Liang, Y.-Z., Yi, L.-Z., A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling. Analyst 139 (2014), 4836–4845.
33. Li, H., Liang, Y., Xu, Q., Cao, D., Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Anal. Chim. Acta 648 (2009), 77–84.
34. http://www.eigenvector.com/data/Corn/index.html, last Accessed 25 May 2017.
35. Brown, P.J., Vannucci, M., Fearn, T., Bayes model averaging with selection of regressors. J. Roy. Stat. Soc. Ser. B 27 (2002), 519–536.
36. Kalivas, J.H., Two data sets of near infrared spectra. Chemom. Intell. Lab. Syst. 37 (1997), 255–259.
37. Shariati-Rad, M., Hasani, M., Selection of individual variables versus intervals of variables in PLSR. J. Chemom. 24 (2010), 45–56.
38. Gerretzen, J., Szymańska, E., Bart, J., Davies, A.N., van Manen, H.-J., van den Heuvel, E.R., Jansen, J.J., Buydens, L.M.C., Boosting model performance and interpretation by entangling preprocessing selection and variable selection. Anal. Chim. Acta 938 (2016), 44–52.
39. Andries, J.P.M., Heyden, Y.V., Buydens, L.M.C., Improved variable selection reduction in partial least squares modeling based on Predictive-Prosperity-Ranked Variables and adaption of partial least squares complexity. Anal. Chim. Acta 705 (2011), 292–305.
40. Osborne, B.G., Fearn, T., Hindle, P.H., Practical NIR Spectrsocpy with Applications in Food and Beverage Analysis. second ed., 1993, Longman Scientific & Technical, Essex: UK, 20–33.
41. Brown, C.D., Green, R.L., Critical factors limiting the interpretation of regression vectors in multivariate calibration. Trends Anal. Chem. 28 (2009), 506–514.