A method of flavors and fragrances identification based on hybrid neural network

Yun Zou; Yan Chen; Xiao Zhou; Hailing Liang

doi:10.1117/12.3026820

8 April 2024 A method of flavors and fragrances identification based on hybrid neural network

Yun Zou, Yan Chen, Xiao Zhou, Hailing Liang

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130902Y (2024) https://doi.org/10.1117/12.3026820
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

Traditional spices quality testing methods face problems such as long testing latency, high testing cost, complicated testing process, environment-dependent inconsistency and great influence of human factors on the testing results. In order to solve these problems, this paper proposes a hybrid model based on Shuffled Frog Leaping Algorithm (Shuffled Frog Leaping Algorithm, SFLA) and Back Propagation (Back Propagation, BP) neural network, namely SFLA-BP. The model first obtains the weights and biases as initial parameters through the BP neural network, then uses the SFLA to optimize the parameters, and finally feeds the optimized parameters back into the BP neural network for model training. Experiments show that the hybrid neural network model can effectively improve the accuracy of identifying fragrances and flavors, and reduce the interference of environmental and subjective factors, which has significant practical application value.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yun Zou, Yan Chen, Xiao Zhou, and Hailing Liang "A method of flavors and fragrances identification based on hybrid neural network", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130902Y (8 April 2024); https://doi.org/10.1117/12.3026820

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