Abstract
［Objective］ To propose an early detection method for contaminated food based on the extreme learning machine and crystal structure algorithm.［Methods］ The crystal structure algorithm is used to optimize feature selection,combined with the extreme learning machine for fast and efficient classification and detection,aiming to improve the accuracy and efficiency of early detection of contaminated food.［Results］］ Compared to traditional methods,the proposed approach shows significant improvements in accuracy (94.5%) and F1-score (93.2%).It also outperforms other state -of-the-art methods in recall rate and processing speed.Compared to the latest deep learning methods,the training time is reduced by about 30%,and the detection speed is improved by 25%.［Conclusion］ The early detection method for contaminated food based on the extreme learning machine and crystal structure algorithm demonstrates clear advantages in improving detection accuracy,speeding up detection,and optimizing computational efficiency.It holds promising practical application prospects,especially for rapid and large -scale food safety detection.
Publication Date
7-3-2025
First Page
68
Last Page
74
DOI
10.13652/j.spjx.1003.5788.2025.60011
Recommended Citation
Fu, ZHU; Ruiqing, LIU; Kefeng, PAN; and Rui, ZHAO
(2025)
"Early detection of contaminated food based on extreme learning machine and crystal structure algorithm,"
Food and Machinery: Vol. 41:
Iss.
6, Article 10.
DOI: 10.13652/j.spjx.1003.5788.2025.60011
Available at:
https://www.ifoodmm.cn/journal/vol41/iss6/10
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