Evaluation of Least Squares Support Vector Machine Regression and other Multivariate Calibrations in Determination of Internal Attributes of Tea Beverages |
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Authors: | Xiao-li Li Yong He |
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Affiliation: | (1) College of Biosystems Engineering and Food Science, Zhejiang University, 268 Kaixuan Road, Hangzhou, 310029, China; |
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Abstract: | This research aimed to explore the relationship between internal attributes (pH and soluble solids content) of tea beverages
and diffuse reflectance spectra. Three multivariate calibrations including least squares support vector machine regression
(LSSVR), partial least squares (PLS), and radial basis function (RBF) neural network were adopted for development of internal
attributes determination models. Ten kinds of tea beverages including green tea and black tea were selected for visible and
near infrared reflectance (Vis/NIR) spectroscopy measurement from 325 to 1,075 nm. As regard the kernel function, least squares–support
vector machine regression models were built with both linear and RBF kernel functions. Grid research and tenfold cross-validation
procedures were adopted for optimization of LSSVR parameters. The generalization ability of LSSVR models were evaluated by
adjusting the number of samples in the training set and testing set, and sensitive wavelengths that were closely correlated
with the internal attributes were explored by analyzing the regression coefficients from linear LSSVR model. Excellent LSSVR
models were built with r = 0.998, standard error of prediction (SEP) = 0.111, for pH and r = 0.997, SEP = 0.256, for soluble solids content, and it can be found that the LSSVR models outperformed the PLS and RBF
neural network models with higher accuracy and lower error. Six individual sensitive wavelengths for pH were obtained, and
the corresponding pH determination model was developed with r = 0.994, SEP = 0.173, based on these six wavelengths. The soluble solids content determination model was also developed with
r = 0.977, SEP = 0.173, based on seven individual sensitive wavelengths. The above results proved that Vis/NIR spectroscopy
could be used to measure the pH and soluble solids content in tea beverages nondestructively, and LSSVR was an effective arithmetic
for multivariate calibration regression and sensitive wavelengths selection. |
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