Construction of Quantitative Evaluation Model of Baoqing Guiding Black Tea Based on Chromatic Aberration Analysis

DU Shaolong; ZHOU Zhimei; LEI Yalan; LI Jin; TIAN Rongji; TANG Han

doi:10.13386/j.issn1002-0306.2021100164

Volume 43 Issue 14

Jul. 2022

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Science and Technology of Food Industry > 2022 > 43(14): 329-335. > DOI: 10.13386/j.issn1002-0306.2021100164

DU Shaolong, ZHOU Zhimei, LEI Yalan, et al. Construction of Quantitative Evaluation Model of Baoqing Guiding Black Tea Based on Chromatic Aberration Analysis[J]. Science and Technology of Food Industry, 2022, 43(14): 329−335. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2021100164.

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Construction of Quantitative Evaluation Model of Baoqing Guiding Black Tea Based on Chromatic Aberration Analysis

1.
Hunan Xinsheng Tea Sci-tech Co., Ltd., Shaoyang 422900, China
2.
College of Chemistry and Chemical Engineering, Hunan Normal University, Changsha 410083, China
3.
Shaoyang Academy of Agricultural Sciences, Shaoyang 422000, China

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Received Date: October 17, 2021
Available Online: May 11, 2022

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Abstract

Abstract

Objective: In order to construct a quantitative evaluation model of Baoqing Guiding black tea based on chromatic aberration. Methods: Baoqing Guiding black tea were used as materials. On the basis of sensory evaluation, the chromatic aberration value of dry tea, tea infusion and infused leaf were measured respectively. The correlation between chromatic aberration value and tea quality were analyzed, and the quality evaluation model were constructed by GA-BP neural networks. Results: The quality of Baoqing Guiding black tea was extremely significant correlated with the Lab value of tea infusion and leaves (P<0.01), and significantly correlated with the a value of dry tea (P<0.05). The introduction of genetic algorithm (GA) obviously improved the fitting accuracy of BP neural network, the coefficient of determination (R²) of GA-BP model was obviously higher than BP model. By comparing different hidden layer structures, the GA-BP neural network with structure of 9-5-1 were selected. The determination coefficients (R²) of training, verification, test and prediction of optimized GA-BP model were 0.988, 0.976, 0.933 and 0.95 respectively. Conclusion: The quantitative quality evaluation method of Baoqing Guiding black tea based on chromatic aberration and GA-BP neural network was feasible.
- chromatic aberration system,
- GA-BP neural networks,
- Baoqing Guiding black tea,
- quality evaluation model

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References

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