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

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 (C) 2012 John Wiley & Sons, Ltd.