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Li X.,Henan University of Traditional Chinese Medicine | Gao X.,Henan University of Traditional Chinese Medicine | Liu R.,Henan University of Traditional Chinese Medicine | Liu R.,The Level Three Laboratory of Chinese Traditional Medical Preparation of State Administration of TCM | And 15 more authors.
Experimental and Therapeutic Medicine | Year: 2016

Tools to define the active ingredients and flavors of Traditional Chinese Medicines (TCMs) are limited by long analysis times, complex sample preparation and a lack of multiplexed analysis. The aim of the present study was to optimize and validate an electronic tongue (E-tongue) methodology to analyze the bitterness of TCMs. To test the protocol, 35 different TCM concoctions were measured using an E-tongue, and seven replicate measurements of each sample were taken to evaluate reproducibility and precision. E-tongue sensor information was identified and classified using analysis approaches including least squares support vector machine (LS-SVM), support vector machine (SVM), discriminant analysis (DA) and partial least squares (PLS). A benefit of this analytical protocol was that the analysis of a single sample took <15 min for all seven sensors. The results identified that the LS-SVM approach provided the best bitterness classification accuracy (binary classification accuracy, 100%; ternary classification accuracy, 89.66%). The E-tongue protocol developed showed good reproducibility and high precision within a 6 h measurement cycle. To the best of our knowledge, this is the first study of an E-tongue being applied to assay the bitterness of TCMs. This approach could be applied in the classification of the taste of TCMs, and serve important roles in other fields, including foods and beverages. © 2016, Spandidos Publications. All rights reserved.

Lin Z.,Institute of Clinical Pharmacy | Lin Z.,Beijing University of Chinese Medicine | Zhang Q.,Beijing University of Chinese Medicine | Liu R.,Henan University of Traditional Chinese Medicine | And 16 more authors.
Sensors (Switzerland) | Year: 2016

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 between the RPLS model constructed before and after outlier exclusion was negligible. In conclusion, the bitterness of TCM decoctions can be accurately evaluated with the RPLS model constructed using e-tongue data. © 2016 by the authors; licensee MDPI, Basel, Switzerland.

Loading Key Laboratory of Viral Diseases Prevention and Treatment of TCM of Henan Province collaborators
Loading Key Laboratory of Viral Diseases Prevention and Treatment of TCM of Henan Province collaborators