[NIR spectroscopy based on least square support vector machines for quality prediction of tomato juice].
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 0.9903 and 0.9675, and a low root mean square error of prediction (RMSEP) of 0.0056 degree Brix and 0.0245, respectively. And compared to PLS and PCR methods, the performance of the LSSVM 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.
Huang
Kang
K
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China. huangkang23@hotmail.com
Wang
Hui-jun
HJ
Xu
Hui-rong
HR
Wang
Jian-ping
JP
Ying
Yi-bin
YB
D001628:Beverages / Q000032:analysis*; D002241:Carbohydrates / Q000032:analysis; D016018:Least-Squares Analysis; D018551:Solanum lycopersicum / Q000737:chemistry*; D011786:Quality Control; D019265:Spectroscopy, Near-Infrared / Q000379:methods*
D002241:Carbohydrates