| 光谱学与光谱分析 |
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| NIR Spectroscopy Based on Least Square Support Vector Machines for Quality Prediction of Tomato Juice |
| HUANG Kang, WANG Hui-jun, XU Hui-rong*,WANG Jian-ping, YING Yi-bin |
| College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China |
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Abstract The application of least square support vector machines (LS-SVM) regression method based on statistics study theory to the analysis with near infrared (NIR) spectra of tomato juice was introduced in the present paper. In this method, LS-SVM was used for establishing model of spectral analysis, and was applied to predict the sugar contents (SC) and available acid (VA) in tomato juice samples. NIR transmission spectra of tomato juice were measured in the spectral range of 800-2 500 nm using InGaAs detector. The radial basis function (RBF) was adopted as a kernel function of LS-SVM. Sixty seven tomato juice samples were used as calibration set, and thirty three samples were used as validation set. The results of the method for sugar contents (SC) and available acid (VA) prediction were: a high correlation coefficient of 0.990 3 and 0.967 5, and a low root mean square error of prediction (RMSEP) of 0.005 6° Brix and 0.024 5, respectively. And compared to PLS and PCR methods, the performance of the LS-SVM method was better. The results indicated that it was possible to built statistic models to quantify some common components in tomato juice using near-infrared (NIR) spectroscopy and least square support vector machines (LS-SVM) regression method as a nonlinear multivariate calibration procedure, and LS-SVM could be a rapid and accurate method for juice components determination based on NIR spectra.
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Received: 2007-10-16
Accepted: 2008-01-22
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Corresponding Authors:
XU Hui-rong
E-mail: hrxu@zju.edu.cn
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