Non-Destructive Brand Identification of Car Wax Using Visible and Near-Infrared Spectroscopy
ZHANG Yu1, 2, TAN Li-hong1, HE Yong2*
1. Zhejiang Technical Institute of Economics, Hangzhou 310018, China 2. College of Biosystems Engineering & Food Science, Zhejiang University, Hangzhou 310058, China
Abstract：Visible and near-infrared (Vis-NIR) spectroscopy was applied to identify brands of car wax. A total of 104 samples were obtained for the analysis, in which 40 samples (calibration set) were used for model calibration, and the remaining 64 samples (prediction set) were used to validate the calibrated model independently. Linear discriminant analysis (LDA) and least square-support vector machine (LS-SVM) were respectively used to establish identification models for car wax with five brands based on their Vis-NIR spectra. Correct rates for prediction sample set were 84% and 97% for LDA and LS-SVM models, respectively. Spectral variable selection was further conducted by successive projections algorithm, (SPA), resulting in seven feature variables (351, 365, 401, 441, 605, 926, and 980 nm) selected from full range spectra that had 751 variables. The new LS-SVM model established using the feature variables selected by SPA also had the correct rate of 97%, showing that the selected variables had the most important information for brand identification, while other variables with no useful information were eliminated efficiently. The use of SPA and LS-SVM could not only obtain a high correct identification rate, but also simplify the model calibration and calculation. SPA-LS-SVM model could extract the useful information from the Vis-NIR spectra of car wax rapidly and accurately for the non-destructive brand identification of car wax.
[1] YAN Yan-lu，ZHAO Long-lian，HAN Dong-hai，et al(严衍禄，赵龙莲，韩东海，等). Infrared Spectroscopy Analysis Fundamentals and Applications(近红外光谱分析基础与应用). Beijing：China Light Industry Press(北京：中国轻工业出版社)，2005. [2] CHEN Bin, MENG Xiang-long, WANG Hao(陈 斌, 孟祥龙, 王 豪). Journal of Instrumental Analysis(分析测试学报), 2007, 26(1): 66. [3] Wu D, Chen X, Zhu X, et al. Analytical Methods, 2011, 3(8): 1790. [4] Wu D, Sun D-W, He Y. Innovative Food Science & Emerging Technologies, 2012, 16: 361. [5] Wang S J, Liu K S, Yu X J, et al. Journal of Food Engineering, 2012, 109(3): 531. [6] Yu X J, Liu K S, Wu D, et al. Food and Bioprocess Technology, 2012, 5(5): 1552. [7] Suykens J A K, Vandewalle J. Least Squares Support Vector Machine Classifiers Neural Processing Letters, 1999, 9(3): 293. [8] Wu D, Sun D W. Talanta, 2013, 111: 39. [9] Wu D, Nie P C, He Y, et al. Food and Bioprocess Technology, 2012, 5(4): 1402. [10] Wu D, Shi H, Wang S, et al. Analytica Chimica Acta, 2012, 726：57.
