Quantitative analysis of tea using ytterbium-based internal standard near-infrared spectroscopy coupled with boosting least-squares support vector regression

Journal of Chemometrics

Volumn 27, Issue 7-8, 2013, Pages 198-206

  Luo, Rui Min ^a   Tan, Shi Miao ^a   Zhou, Yan Ping ^a   Liu, Shu Juan ^b   Xu, Hui ^a   Song, Dan Dan ^a   Cui, Yan Fang ^a   Fu, Hai Yan ^c   Yang, Tian Ming ^c  

^a CENTRAL CHINA NORMAL UNIVERSITY   (China)

^b Hunan Academy of Agricultural Sciences   (China)

^c SOUTH CENTRAL UNIVERSITY FOR NATIONALITIES   (China)

Author keywords

Boosting least squares support vector regression; Near infrared spectroscopy; Total free amino acids; Total polyphenols; Ytterbium

Indexed keywords

AMINO ACIDS; CALIBRATION; INFRARED DEVICES; LEAST SQUARES APPROXIMATIONS; RARE EARTHS; REGRESSION ANALYSIS; SPECTROSCOPIC ANALYSIS; SPECTRUM ANALYSIS; VECTORS; YTTERBIUM; YTTERBIUM COMPOUNDS;

BOOSTING LEAST-SQUARE SUPPORT VECTOR REGRESSION; FREE AMINO ACIDS; INFRARED SPECTROSCOPIC; INTERNAL STANDARDS; LEAST SQUARES SUPPORT VECTOR REGRESSION; MULTIVARIATE CALIBRATION; NEAR INFRARED; NEAR-INFRARED; TOTAL FREE AMINO ACID; TOTAL POLYPHENOLS;

NEAR INFRARED SPECTROSCOPY;

EID: 84881609100     PISSN: 08869383     EISSN: 1099128X     Source Type: Journal    
DOI: 10.1002/cem.2518     Document Type: Article

References (26)

1. Yang CS, Maliakal P, Meng XF. Inhibition of carcinogenesis by tea. Annu. Rev. Pharmacol. Toxicol. 2002; 42: 25-54.
2. Hall MN, Robertson A, Scotter CNG. Near-infrared reflectance prediction of quality, theaflavin content and moisture content of black tea. Food Chem. 1988; 27: 61-75.
3. Schulz H, Engelhardt UH, Wegent A, Drews HH, Lapczynski S. Application of near-infrared reflectance spectroscopy to the simultaneous prediction of alkaloids and phenolic substances in green tea leaves. J. Agric. Food Chem. 1999; 47: 5064-5067.
4. Luypaert J, Zhang MH, Massart DL. Feasibility study for the use of near-infrared spectroscopy in the qualitative and quantitative analysis of green tea. Anal. Chim. Acta 2003; 478: 303-312.
5. Zhang MH, Luypaert J, Fernández Pierna JA, Xu QS, Massart DL. Determination of total antioxidant capacity in green tea by near-infrared spectroscopy and multivariate calibration. Talanta 2004; 62: 25-35.
6. Chen QS, Zhao JW, Huang XY, Zhang HD, Liu MH. Simultaneous determination of total polyphenols and caffeine contents of green tea by near-infrared reflectance spectroscopy. Microchem. J. 2006; 83: 42-47.
7. Zhao JW, Chen QS, Huang XY, Fang CH. Qualitative identification of tea categories by near infrared spectroscopy and support vector machine. J. Pharm. Biomed. Anal. 2006; 41: 1198-1204.
8. He Y, Li XL, Deng XF. Discrimination of varieties of tea using near-infrared spectroscopy by principal component analysis and BP model. J. Food Eng. 2007; 79: 1238-1242.
9. Airy A. Sub-committee on ISO tea to meet in Calcutta next month. http://www.expressindia.com/fe/daily/19991004/fco04012.html.
10. Zhou YP, Jiang JH, Wu HL, Shen GL, Yu RQ, Ozaki Y. Dry film method with ytterbium as the internal standard for near infrared spectroscopic plasma glucose assay coupled with boosting support vector regression. J. Chemometr. 2006; 20: 13-21.
11. Sjöström M, Wold S, Lindberg W, Persson JA, Martens HA. Multivariate calibration problem in analytical chemistry solved by partial least-squares models in latent variables. Anal. Chim. Acta 1983; 150: 61-70.
12. Suykens JAK, Vandewalle J. Least-squares support vector machine classifiers. Neural Process Lett. 1999; 9: 293-300.
13. Suykens JAK, Van Gestel T, De Brabanter J, De Moor B, Vandewalle J. Least-squares support vector machines. World Scientific: Singapore, 2002.
14. Thissen U, Üstün B, Melssen WJ, Buydens LMC. Multivariate calibration with least-squares support vector machines. Anal. Chem. 2004; 76: 3099-3105.
15. Xu L, Deng DH, Jiang JH, Yu RQ, Wu XM, Zhao Y. Developing novel and general descriptors for traditional Chinese medicine (TCM) formulas: a case study of quantitative formula-activity relationship (QFAR) model for hypertension prescriptions. Chemom. Intell. Lab. Syst. 2011; 109: 186-191.
16. Liu F, He Y. Use of visible and near infrared spectroscopy and least squares-support vector machine to determine soluble solids content and pH of cola beverage. J. Agric. Food Chem. 2007; 55: 8883-8888.
17. Schapire RE. The strength of weak learnability. Mach. Learning 1990; 5: 197-227.
18. Jiao J, Tan SM, Luo RM, Zhou YP. A robust boosting regression tree with applications in quantitative structure activity relationship studies of organic compounds. J. Chem. Inf. Model. 2011; 51: 816-828.
19. Culp M. The ensemble bridge algorithm: a new modeling tool for drug discovery problems. J. Chem. Inf. Model. 2010; 50: 309-316.
20. Shao XG, Bian XH, Cai WS. An improved boosting partial least squares method for near-infrared spectroscopic quantitative analysis. Anal. Chim. Acta 2010; 666: 32-37.
21. Tan SM, Luo RM, Zhou YP, Xu H, Song DD, Tan Z, Yang TM, Nie Y. Boosting partial least-squares discriminant analysis with application to near infrared spectroscopic tea variety discrimination. J. Chemometr. 2012; 26: 34-39.
22. Liu CM, Zhu LK. Constructing least square support vector machines ensemble based on fuzzy integral. Proceedings of the 2006 International Conference on Machine Learning and Cybernetics. Dalian, China, 13-16 Aug 2006; 5.
23. Cristianini N, Shawe-Taylor J. An Introduction to Support Vector Machines. Cambridge University Press: Cambridge, MA, 2000.
24. Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 1997; 55: 119-139.
25. Friedman J. Stochastic gradient boosting. Comput. Stat. Data Anal. 2002; 38: 367-378.
26. Jiang JH, James BR, Siesler HW, Ozaki Y. Wavelength interval selection in multicomponent spectral analysis by moving window partial least-squares regression with applications to mid-infrared and near-infrared spectroscopic data. Anal. Chem. 2002; 74: 3555-3565.