Evaluation of the Bitterness of Traditional Chinese Medicines using an E-Tongue Coupled with a Robust Partial Least Squares Regression Method

Sensors
Zhaozhou LinXuelin Li

Abstract

To accurately, safely, and efficiently evaluate the bitterness of Traditional Chinese Medicines (TCMs), a robust predictor was developed using robust partial least squares (RPLS) regression method based on data obtained from an electronic tongue (e-tongue) system. The data quality was verified by the Grubb's test. Moreover, potential outliers were detected based on both the standardized residual and score distance calculated for each sample. The performance of RPLS on the dataset before and after outlier detection was compared to other state-of-the-art methods including multivariate linear regression, least squares support vector machine, and the plain partial least squares regression. Both R² and root-mean-squares error (RMSE) of cross-validation (CV) were recorded for each model. With four latent variables, a robust RMSECV value of 0.3916 with bitterness values ranging from 0.63 to 4.78 were obtained for the RPLS model that was constructed based on the dataset including outliers. Meanwhile, the RMSECV, which was calculated using the models constructed by other methods, was larger than that of the RPLS model. After six outliers were excluded, the performance of all benchmark methods markedly improved, but the difference betwee...Continue Reading

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Citations

Mar 31, 2017·BMC Complementary and Alternative Medicine·Xiaoguang LiangShuang Liang
Dec 5, 2020·Pharmaceutical Development and Technology·Marina Delanni Vitória GuedesIrene Clemes Kulkamp Guerreiro
Aug 25, 2021·Journal of Pharmaceutical and Biomedical Analysis·Xue ZhangHongjun Yang

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Software Mentioned

MATLAB
SVMlab Toolbox
LS
AlphasoftV12
MATLAB Library for Robust Analysis ( LIBRA ) toolbox
RSIMPLS

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