Nonlinear partial least squares regressions for spectral quantitative analysis

Chemometrics and Intelligent Laboratory Systems

Volumn 148, Issue , 2015, Pages 32-50

  Wang, Yanxia ^a   Cao, Hui ^a   Zhou, Yan ^a   Zhang, Yanbin ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Spectral quantitative analysis; The external NPLS; The internal NPLS; The kernel NPLS; The nonlinear components extracted NPLS; The NPLS model

Indexed keywords

CARBON DIOXIDE; CARBON MONOXIDE; METHANE;

ARTICLE; COAL POWER; FAT CONTENT; FLUE GAS; GAS TURBINE POWER; KERNEL METHOD; MEAT; MOISTURE; NONLINEAR PARTIAL LEAST SQUARES REGRESSION; NONLINEAR REGRESSION ANALYSIS; PARTIAL LEAST SQUARES REGRESSION; PREDICTION; PRIORITY JOURNAL; PROTEIN CONTENT; QUANTITATIVE ANALYSIS; RADIAL BASED FUNCTION; SPECTROSCOPY;

EID: 84942254320     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2015.08.024     Document Type: Article

References (61)

1. Stenlund H., Johansson E., Gottfries J., Trygg J. Unlocking interpretation in near infrared multivariate calibrations by orthogonal partial least squares. Anal. Chem. 2009, 81:203-209.
2. Zou X.B., Zhao J.W., Povey M.J.W., Holmes M., Hanpin M. Variables selection methods in near-infrared spectroscopy. Anal. Chim. Acta 2010, 667:14-32.
3. Mou H.Y., Wang X.J., Tian L.V., Xie L., Xie H.P. On-line dissolution determination of Baicalin in solid dispersion based on near infrared spectroscopy and circulation dissolution system. Chemom. Intell. Lab. Syst. 2011, 105:38-42.
4. Xiong C., Li G., Lin L. Composition analysis of scattering liquids based on spatially offset visible-near-infrared spectroscopy. Appl. Spectrosc. 2012, 66:1347-1352.
5. Shao X.G., Ning Y., Liu F.X. Application of near-infrared spectroscopy in micro inorganic analysis. Acta Chim. Sin. 2012, 70:2109-2114.
6. Kim N.Y., Miao Y., Young L.D. Rapid determination of betulin in betula platyphylla outer bark using near-infrared spectroscopy. Anal. Lett. 2013, 46:1289-1298.
7. Luo R.-M., Tan S.-M., Zhou Y.-P. Quantitative analysis of tea using ytterbium-based internal standard near-infrared spectroscopy coupled with boosting least-squares support vector regression. J. Chem. Theory Comput. 2013, 27:198-206.
8. Hsieh C.H., Julien B., Mary M.N., Ellen P. Kinetic modeling of dissolution and crystallization of slurries with attenuated total reflectance uv-visible absorbance and near-infrared reflectance measurements. Anal. Chem. 2013, 85:5367-5375.
9. Perez-Guaita D., Kuligowski J., Quintas G., Garrigues S., Guardia M.D. Modified locally weighted-Partial least squares regression improving clinical predictions from infrared spectra of human serum samples. Talanta 2013, 107:368-375.
10. Geladi1 P., Kowalski B.R. Partial least-squares regression: a tutorial. Anal. Chim. Acta 1986, 185:1-17.
11. Beatriz A.S., Feliciano P.C., Juan G.O. Near-infrared spectroscopy and partial least squares-class modeling (PLS-CM) for metabolomics fingerprinting discrimination of intervention breakfasts ingested by obese individuals. J. Chem. Theory Comput. 2013, 27:221-232.
12. Cao D.S., Liang Y.Z., Xu Q.S., Hu Q.N., Zhang L.X., Fu G.H. Exploring nonlinear relationships in chemical data using kernel-based methods. Chemom. Intell. Lab. Syst. 2011, 107:106-115.
13. Wold S., Kettaneh-Wold N., Skagerberg B. Nonlinear PLS modeling. Chemom. Intell. Lab. Syst. 1989, 7:53-65.
14. Yacoub F., Gregor J.F.M. Product optimization and control in the latent variable space ofnonlinear PLS models. Chemom. Intell. Lab. Syst. 2004, 70:63-74.
15. Frank I.E. A nonlinear PLS model. Chemom. Intell. Lab. Syst. 1990, 8:109-119.
16. Baffi G., Martin E.B., Morris A. Non-linear projection to latent structures revisited: the quadratic PLS algorithm. Comput. Chem. Eng. 1999, 23:395-411.
17. Rahman A., Araby I., Lim J.Gino A nonlinear partial least squares algorithm using quadratic fuzzy inference system. J. Chem. Theory Comput. 2009, 23:9-10. (530-537).
18. Zhou Y.P., Jiang J.H., Lin W.Q., Xu L., Wu H.L., Shen G.L., Yu R.Q. Artificial neural network-based transformation for nonlinear partialleast-square regression with application to QSAR studies. Talanta 2007, 71:848-853.
19. Wold S. Nonlinear partial least squares modeling II. Spline inner relation. Chemom. Intell. Lab. Syst. 1992, 14:71-84.
20. Frank I.E. Modern nonlinear regression methods. Chemom. Intell. Lab. Syst. 1995, 27:1-9.
21. Shan P., Peng S.L., Bi Y.M., Tang L., Yang C.X., Xie Q., Li C.W. Partial least squares-slice transform hybrid model for nonlinear calibration. Chemom. Intell. Lab. Syst. 2014, 138:72-83.
22. Bar1czek T., Bucinski A., Ivanov A.R., Kaliszan R. Artificial neural network analysis for evaluation of peptide MS/MS spectra in proteomics. Anal. Chem. 2004, 76:1726-1732.
23. Song X.H., Hopke P.K. Classification of single particles analyzed by ATOFMS using an artificial neural network, ART-2A. Anal. Chem. 1999, 71:860-865.
24. Hornik K., Stinchcombe M., White H. Multilayer feedforward networks areuniversal approximators. Neural Netw. 1989, 2:359-366.
25. Melssen W.J., Buydens L.M.C. Aspects of multi-layer feed-forward neural networks influencing the quality of the fit of univariate non-linear relationships. Anal. Proc. 1995, 32:53-56.
26. Schulz H., Derrick M., Stulik D. Simple encoding of infrared spectra for pattern recognition Part 2. Neural network approach using back-propagation and associative Hopfield memory. Anal. Chim. Acta 1995, 316:145-159.
27. Ishihara S., Ikeda A., Citterio D., Maruyama K., Hagiwara M., Suzuki K. Smart chemical taste sensor for determination and prediction of taste qualities based on a two-phase optimized radial basis function network. Anal. Chem. 2005, 77:7908-7915.
28. Qin S.J., McAvoy T.J. Nonlinear PLS modeling using neural networks. Comput. Chem. Eng. 1992, 16:379-391.
29. Baffi G., Martin E., Morris A. Non-linear projection to latent structures revisited (the neural network PLS algorithm). Comput. Chem. Eng. 1999, 23:1293-1307.
30. Janik L.J., Forrester S.T., Rawson A. The prediction of soil chemical and physical properties from mid-infrared spectroscopy and combined partial least-squares regression and neural networks (PLS-NN) analysis. Chemom. Intell. Lab. Syst. 2009, 97:179-188. (2).
31. Holcomb T.R., Morari M. PLS/neural networks. Comput. Chem. Eng. 1992, 16:393.
32. Wold S., Trygg J., Berglund A., Antti H. Some recent developments in PLS modeling. Chemom. Intell. Lab. Syst. 2001, 58:131-150.
33. Qin S.J. Recursive PLS algorithms for adaptivedata modeling. Comput. Chem. Eng. 1998, 503-514.
34. Zhou Y.P., Jiang J.H., Lin W.Q., Xu L., Wu H.L., Shen G.L., Yu R.Q. Artificial neural network-based transformation for nonlinear partial least-square regression with application to QSAR studies. Talanta 2007, 71:848-853.
35. Kramer M.A. Nonlinear principal component analysis using autoassociative neural networks. AICHE J. 1991, 37:233-243.
36. Walczak1 B., Wegscheider W. Non-linear modelling of chemical data by combinations of linear and neural net methods. Anal. Chim. Acta 1993, 283:508-517.
37. Malthouse E.C., Tamhane A.C., Mah R.S.H. Nonlinear partial least squares. Comput. Chem. Eng. 1997, 21:875-890.
38. Sylvester J.J. On the reduction of a biline quantic of the nth order to form of a sum of n products by a doubles orthogonal substitution. Mess. Math. 1889, 19:42-46.
39. DONG D., McAvovt T.J. Nonlinear principal component analysis-based on principal curves and neural networks. Comput. Chem. Eng. 1996, 65-78.
40. Liu J., Min K., Han C., Chang K.S. Robust nonlinear PLS based on neural networks and application to composition estimator for high-purity distillation columns. Korean J. Chem. Eng. 2000, 184-192.
41. Haykin S. Neural Networks: A comprehensive foundation 1999, Prentice Hall International, New Jersey.
42. Lindgren F., Geladi P., Wold S. The kernel algorithm for PLS. J. Chemom. 1993, 7:45-59.
43. Rosipal R., Trejo L.J. Kernel partial least squares regression in reproducing kernel Hibert space. J. Mach. Learn. Res. 2001, 2:97-123.
44. Jong S.d. SIMPLS: an alternative approach to partial least square regression. Chemom. Intell. Lab. Syst. 1993, 18:251-263.
45. Nicola B.M., Theron K.I., Lammertyn J. Kernel PLS regression on wavelet transformed NIR spectra for prediction of sugar content of apple. Chemom. Intell. Lab. Syst. 2007, 85:243-252.
46. Rantalainen M., Bylesj M., Cloarec O., Nicholson J.K., Trygg E.H.J. Kernel-based orthogonal projections to latent structures (K-OPLS). J. Chemom. 2007, 21:376-385.
47. Jia R., Mao Z.Z., Chang Y.Q., Zhang S.N. Kernel partial robust M-regression as a flexible robust nonlinear modeling technique. Chemom. Intell. Lab. Syst. 2010, 100:91-98.
48. Shinzawa H., Jiang J.H., Ritthiruangdej P., Ozaki Y. Investigations of bagged kernel partial least squares(KPLS) and boosting KPLS with applications tonear-infrared (NIR) spectra. J. Chemom. 2006, 20:436-444.
49. Hubert M., Branden K.V. Robust methods for partial least squares regression. J. Chemom. 2003, 17:537-549.
50. Singh K.P., Ojha P., Malik A., Jain G. Partial least squares and artificial neural networks modeling for predicting chlorophenol removal from aqueous solution. Chemom. Intell. Lab. Syst. 2009, 99:150-160.
51. Boger Z. Selection of quasi-optimal inputs in chemometrics modeling by arti? Cial neural network analysis. Anal. Chim. Acta 2003, 490:31-40.
52. Franco V.G., Perin J.C., Mantovani V.E., Goicoechea H.C. Talanta 2006, 68:1005-1012.
53. WalczakaT B., Massartb D.L. The Radial Basis Functions-Partial Least Squares approach as a flexible non-linear regression technique. Anal. Chim. Acta 1996, 331:177-185.
54. Walczak B., Massart D.L. Local modelling with radial basis function networks. Chemom. Intell. Lab. Syst. 2000, 50:179-198.
55. Lei X., Krzyzak A., Oja E. Rival penalized competitive learning for clustering analysis, RBFnet, and curve detection. IEEE Trans. Neural Netw. 1993, 4:636-649.
56. Moody J., Darken C.J. Fast Learning in Networks of Locally-Tuned Processing Units. Neural Comput. 1989, 1:281-294.
57. Borggaard C., Thodberg H.H. Optimal minimal neural interpretation of spectra. Anal. Chem. 1992, 64:545-551.
58. Naes T., Isaksson T., Kowalski B. Locally weighted regression and scatter correction for near-infrared reflectance data. Anal. Chem. 1990, 62:664-673.
59. Thissen U., Üstr1n B., Melssen W.J., Buydens L.M.C. Anal. Chem. 2004, 76:3099-3105.
60. Pierna J.A.F., Lecler B., Conzen J.P., Niemoeller A., Baeten B., Dardenne P. Anal. Chim. Acta 2011, 705:30-34.
61. Ni D., Nørgaard L., Mrup M. Non-linear calibration models for near infrared spectroscopy. Anal. Chimica. Acta 2014, 813:1-14.