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Article

Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA‑BP neural network

  • Authors:
    • Ruixin Liu
    • Xiaodong Zhang
    • Lu Zhang
    • Xiaojie Gao
    • Huiling Li
    • Junhan Shi
    • Xuelin Li
  • View Affiliations / Copyright

    Affiliations: Department of Pharmacy, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, Henan 450000, P.R. China, College of Pharmacy, Henan University of Traditional Chinese Medicine, Zhengzhou, Henan 450008, P.R. China
  • Pages: 1696-1702
    |
    Published online on: March 11, 2014
       https://doi.org/10.3892/etm.2014.1614
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Abstract

The aim of this study was to predict the bitterness intensity of a drug using an electronic tongue (e‑tongue). The model drug of berberine hydrochloride was used to establish a bitterness prediction model (BPM), based on the taste evaluation of bitterness intensity by a taste panel, the data provided by the e‑tongue and a genetic algorithm‑back‑propagation neural network (GA‑BP) modeling method. The modeling characteristics of the GA‑BP were compared with those of multiple linear regression, partial least square regression and BP methods. The determination coefficient of the BPM was 0.99965±0.00004, the root mean square error of cross‑validation was 0.1398±0.0488 and the correlation coefficient of the cross‑validation between the true and predicted values was 0.9959±0.0027. The model is superior to the other three models based on these indicators. In conclusion, the model established in this study has a high fitting degree and may be used for the bitterness prediction modeling of berberine hydrochloride of different concentrations. The model also provides a reference for the generation of BPMs of other drugs. Additionally, the algorithm of the study is able to conduct a rapid and accurate quantitative analysis of the data provided by the e‑tongue.
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Copy and paste a formatted citation
Spandidos Publications style
Liu R, Zhang X, Zhang L, Gao X, Li H, Shi J and Li X: Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA‑BP neural network. Exp Ther Med 7: 1696-1702, 2014.
APA
Liu, R., Zhang, X., Zhang, L., Gao, X., Li, H., Shi, J., & Li, X. (2014). Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA‑BP neural network. Experimental and Therapeutic Medicine, 7, 1696-1702. https://doi.org/10.3892/etm.2014.1614
MLA
Liu, R., Zhang, X., Zhang, L., Gao, X., Li, H., Shi, J., Li, X."Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA‑BP neural network". Experimental and Therapeutic Medicine 7.6 (2014): 1696-1702.
Chicago
Liu, R., Zhang, X., Zhang, L., Gao, X., Li, H., Shi, J., Li, X."Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA‑BP neural network". Experimental and Therapeutic Medicine 7, no. 6 (2014): 1696-1702. https://doi.org/10.3892/etm.2014.1614
Copy and paste a formatted citation
x
Spandidos Publications style
Liu R, Zhang X, Zhang L, Gao X, Li H, Shi J and Li X: Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA‑BP neural network. Exp Ther Med 7: 1696-1702, 2014.
APA
Liu, R., Zhang, X., Zhang, L., Gao, X., Li, H., Shi, J., & Li, X. (2014). Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA‑BP neural network. Experimental and Therapeutic Medicine, 7, 1696-1702. https://doi.org/10.3892/etm.2014.1614
MLA
Liu, R., Zhang, X., Zhang, L., Gao, X., Li, H., Shi, J., Li, X."Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA‑BP neural network". Experimental and Therapeutic Medicine 7.6 (2014): 1696-1702.
Chicago
Liu, R., Zhang, X., Zhang, L., Gao, X., Li, H., Shi, J., Li, X."Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA‑BP neural network". Experimental and Therapeutic Medicine 7, no. 6 (2014): 1696-1702. https://doi.org/10.3892/etm.2014.1614
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