Boosting partial least-squares discriminant analysis with application to near infrared spectroscopic tea variety discrimination

• Published in: Journal of chemometrics 2012-01, Vol.26 (1-2), p.34-39
• Main Authors: Tan, Shi-Miao, Luo, Rui-Min, Zhou, Yan-Ping, Xu, Hui, Song, Dan-Dan, Ze, Tan, Yang, Tian-Ming, Nie, Yan
• Format: Article
• Language: English
• Subjects: boosting partial least-squares discriminant analysis; Discriminant analysis; near infrared spectroscopy; Predictions; Spectrum analysis; Tea; tea variety discrimination
• ISSN: 0886-9383; 1099-128X
• DOI: 10.1002/cem.1423

In the present study, boosting has been combined with partial least‐squares discriminant analysis (PLS‐DA) to develop a new pattern recognition method called boosting partial least‐squares discriminant analysis (BPLS‐DA). BPLS‐DA is implemented by firstly constructing a series of PLS‐DA models on the various weighted versions of the original calibration set and then combining the predictions from the constructed PLS‐DA models to obtain the integrative results by weighted majority vote. Coupled with near infrared (NIR) spectroscopy, BPLS‐DA has been applied to discriminate different kinds of tea varieties. As comparisons to BPLS‐DA, the conventional principal component analysis, linear discriminant analysis (LDA), and PLS‐DA have also been investigated. Experimental results have shown that the inter‐variety difference can be accurately and rapidly distinguished via NIR spectroscopy coupled with BPLS‐DA. Moreover, the introduction of boosting drastically enhances the performance of an individual PLS‐DA, and BPLS‐DA is a well‐performed pattern recognition technique superior to LDA. Copyright © 2012 John Wiley & Sons, Ltd. In the present study, boosting partial least‐squares discriminant analysis (BPLS‐DA), as a new pattern recognition technique, has been designed via combining boosting and partial least‐squares discriminant analysis (PLS‐DA). This technique, compared with principal component analysis, PLS‐DA, and linear discriminant analysis (LDA), has been employed to the NIR spectroscopic tea variety discrimination analysis. Experimental results have shown that NIR spectroscopy combined with BPLS‐DA holds great potential as an accurate, rapid, and noninvasive strategy for identifying the tea quality. In addition, BPLS‐DA is a well‐performed pattern recognition technique superior to LDA and PLS‐DA.