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

Liu,Ruixin; Zhang,Xiaodong; Zhang,Lu; Gao,Xiaojie; Li,Huiling; Shi,Junhan; Li,Xuelin

doi:10.3892/etm.2014.1614

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

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
Published online on: March 11, 2014 https://doi.org/10.3892/etm.2014.1614
Pages: 1696-1702

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

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.

View Figures

View References

1	Li L, Naini V and Ahmed SU: Utilization of a modified special-cubic design and an electronic tongue for bitterness masking formulation optimization. J Pharm Sci. 96:2723–2734. 2007. View Article : Google Scholar : PubMed/NCBI
2	Ross CF, Bohlscheid J and Weller K: Influence of visual masking technique on the assessment of 2 red wines by trained and consumer assessors. J Food Sci. 73:S279–S285. 2008. View Article : Google Scholar : PubMed/NCBI
3	Gaudette NJ and Pickering GJ: The efficacy of bitter blockers on health-relevant bitterants. Journal of Functional Foods. 4:177–184. 2012. View Article : Google Scholar
4	Xu J, Bovet LL and Zhao K: Taste masking microspheres for orally disintegrating tablets. Int J Pharm. 359:63–69. 2008. View Article : Google Scholar : PubMed/NCBI
5	Lorenz JK, Reo JP, Hendl O, Worthington JH and Petrossian VD: Evaluation of a taste sensor instrument (electronic tongue) for use in formulation development. Int J Pharm. 367:65–72. 2009. View Article : Google Scholar : PubMed/NCBI
6	Gupta H, Sharma A, Kumar S and Roy SK: E-tongue: a tool for taste evaluation. Recent Pat Drug Deliv Formul. 4:82–89. 2010. View Article : Google Scholar : PubMed/NCBI
7	Woertz K, Tissen C, Kleinebudde P and Breitkreutz J: Taste sensing systems (electronic tongues) for pharmaceutical applications. Int J Pharm. 417:256–271. 2011. View Article : Google Scholar : PubMed/NCBI
8	Zheng JY and Keeney MP: Taste masking analysis in pharmaceutical formulation development using an electronic tongue. Int J Pharm. 310:118–124. 2006. View Article : Google Scholar : PubMed/NCBI
9	Choi DH, Kim NA, Nam TS, Lee S and Jeong SH: Evaluation of taste-masking effects of pharmaceutical sweeteners with an electronic tongue system. Drug Dev Ind Pharm. 40:308–317. 2014. View Article : Google Scholar : PubMed/NCBI
10	Ito M, Ikehama K, Yoshida K, Haraguchi T, Yoshida M, Wada K and Uchida T: Bitterness prediction of H1-antihistamines and prediction of masking effects of artificial sweeteners using an electronic tongue. Int J Pharm. 441:121–127. 2013. View Article : Google Scholar : PubMed/NCBI
11	Kayumba PC, Huyghebaert N, Cordella C, Ntawukuliryayo JD, Vervaet C and Remon JP: Quinine sulphate pellets for flexible pediatric drug dosing: formulation development and evaluation of taste-masking efficiency using the electronic tongue. Eur J Pharm Biopharm. 66:460–465. 2007. View Article : Google Scholar
12	Arrieta ÁA, Rodríguez-Méndez ML, de Saja JA, Blanco CA and Nimubona D: Prediction of bitterness and alcoholic strength in beer using an electronic tongue. Food Chem. 123:642–646. 2010.
13	Liu R, Liu N, Shi X and Qiao Y: Optimization of solubilizing process of Qingkailing injection based on GA-BPNN modeling. Zhongguo Zhong Yao Za Zhi. 34:2469–2472. 2009.(In Chinese).
14	Xiao H and Tian Y: Prediction of mine coal layer spontaneous combustion danger based on genetic algorithm and BP neural networks. Procedia Engineering. 26:139–146. 2011. View Article : Google Scholar
15	Ke L, Wenyan G, Xiaoliu S and Zhongfu T: Research on the forecast model of electricity power industry loan based on GA-BP neural network. Energy Procedia. 14:1918–1924. 2012. View Article : Google Scholar
16	Karaosmanoglu K, Sayar NA, Kurnaz IA and Akbulut BS: Assessment of Berberine as a multi-target antimicrobial: a multi-omics study for drug discovery and repositioning. OMICS. 18:42–53. 2014. View Article : Google Scholar : PubMed/NCBI
17	Apetrei C, Apetrei IM, Villanueva S, de Saja JA, Gutierrez-Rosales F and Rodriguez-Mendez ML: Combination of an e-nose, an e-tongue and an e-eye for the characterisation of olive oils with different degree of bitterness. Anal Chim Acta. 663:91–97. 2010. View Article : Google Scholar : PubMed/NCBI
18	Kawano Y, Ito A, Sasatsu M and Machida Y: Preparation of orally disintegrating tablets with taste-masking function: masking effect in granules prepared with correctives using the dry granulation method and evaluation of tablets prepared using the taste-masked granules. Yakugaku Zasshi. 130:81–86. 2010. View Article : Google Scholar
19	Wang Y, Feng Y, Wu Y, Liang S and Xu D: Sensory evaluation of the taste of berberine hydrochloride using an Electronic Tongue. Fitoterapia. 86:137–143. 2013. View Article : Google Scholar : PubMed/NCBI
20	LI XL, Zhang XF, Liu RX, Li hL, Qiu JX and Wu ZD: Study on quantitation of bitterness intensity and relationship between bitterness intensity. Concentration of bitter drug. World Science and Technology-Modernization of Traditional Chinese Medicine and Materia Medica. 15:667–671. 2013.
21	Fechner GT: Elements of Psychophysics. Adler HE: 1. Holt, Rinehart and Winston, New York, NY: 1860/1966
22	Omür-Ozbek P and Dietrich AM: Developing hexanal as an odor reference standard for sensory analysis of drinking water. Water Res. 42:2598–2604. 2008.PubMed/NCBI