Application of electronic tongue in pattern study about bitterness inhibition by hydroxypropyl-β-cyclodextrin
10.7501/j.issn.0253-2670.2017.20.018
- Author:
Xue-Lin LI
1
Author Information
1. Henan University of Chinese Medicine
- Publication Type:Journal Article
- Keywords:
Andrographis paniculata (Burm. f.) Nees;
Berberine;
Bitterness inhibition law;
Cross-validation;
Electronic tongue;
HP-β-CD;
Oxymatrine;
Prediction model of bitterness inhibition effect;
Sophora flavescens Ait.;
Taste masking
- From:
Chinese Traditional and Herbal Drugs
2017;48(20):4235-4244
- CountryChina
- Language:Chinese
-
Abstract:
Objective To study the bitterness inhibition law of hydroxypropyl-β-cyclodextrin (HP-β-CD) concentration (C) on the bitter compounds and bitter Chinese herbal medicine, and to explore the application of electronic tongue in this study. Methods Berberine, oxymatrine, Sophora flavescens, and Andrographis paniculata decoction were used as bitterness vectors to establish two models of bitterness inhibition law about ΔI-C and ΔIe-C, and to explore the prediction model of bitterness inhibition effect about ΔI-ΔIe, based on the oral taste evaluation results (ΔI) and electronic tongue information (ΔIe). Then, fitting precision and fitting goodness of the prediction model were evaluated with cross-validation and residual analysis. Results In this study, good Weibull model of bitterness inhibition pattern about ΔI-C were established for all the four bitterness vectors, the decision coefficient R2 are as followed: 0.999 6, 0.987 9, 0.996 4, and 0.998 4 (P < 0.01); The decision coefficient R2 of six (two sensors per vector) models of bitterness inhibition law about ΔIe-C of berberine, S. flavescens, and A. paniculata decoctions were as followed: 0.996 5, 0.991 6, 0.997 3, 0.989 3, 0.999 6, and 0.999 1 (P < 0.01); The decision coefficient R2 of six corresponding linear prediction models of bitterness inhibition effect about ΔI-ΔIe were as followed: 0.989 1, 0.968 3, 0.989 0, 0.982 0, 0.977 9, and 0.986 1 (P < 0.01); The correlation coefficient R calculated by correlation coefficient of six prediction models above were as followed: 0.986 0, 0.997 3, 0.988 4, 0.960 8, 0.980 2, and 0.983 9 (P < 0.01); No model was established for oxymatrine within the range of tested concentration in this research, as it didn’t respond to the four sensors with varied concentration. Conclusion Based on this method, the bitterness inhibition law of HP-β-CD with changed concentration was obtained. Prediction models based on HP-β-CD concentration or electronic tongue data were also established, they can be used to predict the relative bitterness inhibition effect. Part of the bitter compounds didn't response to the electronic tongue regularly, remain further research and development of electronic tongue technology.
