Study on wine quality evaluation based on extreme learning machine improved by whale optimization algorithm

Authors

DOU Li, Taiyuan Tourism College, Taiyuan, Shanxi 030032, ChinaFollow
ZHENG Wei, Shenyang Pharmaceutical University, Shenyang, Liaoning 110015, China
LI Baiqiu, Shanxi Datong University, Datong, Shanxi 037009, China
LI Fei, North University of China, Taiyuan, Shanxi 030051, China

Abstract

[Objective] In order to solve the issue of excessive redundant information in near-infrared spectroscopy, enhance the accuracy of wine quality evaluation models, a rapid and non-destructive method was established for wine quality evaluation. [Methods] A wine quality evaluation model was proposed based on competitive adaptive reweighting sampling method for feature wavelength screening and extreme learning machine improved by whale optimization algorithm. Various feature wavelength screening methods such as competitive adaptive reweighting sampling was used, and the most suitable method for wine spectral feature wavelength screening was determined. In response to the problem of initial value and hidden layer bias in ELM, the whale optimization method was used to optimize the initial value and hidden layer bias of ELM, and an wine quality evaluation model based on extreme learning machine improved by whale optimization algorithm was constructed. [Results] Compared with GA-ELM, PSO-ELM, and the traditional ELM model, the accuracy of WOA-ELM was the highest, reaching 0.944 5, which was better than GA-ELM (0.929 0), PSO-ELM (0.906 1) and traditional ELM (0.817 7). [Conclusion] The parameters of the ELM model optimized by intelligent algorithms can effectively improve the accuracy of wine quality evaluation.

Publication Date

7-22-2024

First Page

62

Last Page

68

DOI

10.13652/j.spjx.1003.5788.2024.60035

Recommended Citation

Li, DOU; Wei, ZHENG; Baiqiu, LI; and Fei, LI (2024) "Study on wine quality evaluation based on extreme learning machine improved by whale optimization algorithm," Food and Machinery: Vol. 40: Iss. 6, Article 8.
DOI: 10.13652/j.spjx.1003.5788.2024.60035
Available at: https://www.ifoodmm.cn/journal/vol40/iss6/8

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