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Sensors 2016, 16(2), 151; doi:10.3390/s16020151

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

1,2,†
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3,4,5,* , 6
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6,7
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1
Institute of Clinical Pharmacy, Beijing Municipal Health Bureau, Beijing 100035, China
2
School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100102, China
3
Department of Pharmacy, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou 450000, China
4
The Level Three Laboratory of Chinese Traditional Medical Preparation of State Administration of TCM, Zhengzhou 450000, China
5
Key Laboratory of Viral Diseases Prevention and Treatment of TCM of Henan Province, Zhengzhou 450000, China
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School of pharmacy, Henan University of Traditional Chinese Medicine, Zhengzhou 450008, China
7
Department of Computer Science and Statistics, University of Rhode Island, Kingston, RI 02881, USA
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: M. Carmen Horrillo Güemes
Received: 11 October 2015 / Revised: 17 January 2016 / Accepted: 21 January 2016 / Published: 25 January 2016
(This article belongs to the Special Issue E-noses: Sensors and Applications)
View Full-Text   |   Download PDF [669 KB, uploaded 25 January 2016]   |  

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 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. View Full-Text
Keywords: electronic tongue; robust partial least squares; bitterness evaluation; sensors; outlier detection electronic tongue; robust partial least squares; bitterness evaluation; sensors; outlier detection
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lin, Z.; Zhang, Q.; Liu, R.; Gao, X.; Zhang, L.; Kang, B.; Shi, J.; Wu, Z.; Gui, X.; Li, X. Evaluation of the Bitterness of Traditional Chinese Medicines using an E-Tongue Coupled with a Robust Partial Least Squares Regression Method. Sensors 2016, 16, 151.

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