食品与机械2025，Vol.41Issue(6)：68-74,7.DOI:10.13652/j.spjx.1003.5788.2025.60011
基于极限学习机和晶体结构算法的污染食品早期检测
Early detection of contaminated food based on extreme learning machine and crystal structure algorithm
摘要
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.关键词
极限学习机/晶体结构算法/污染食品/早期检测/特征选择/食品安全Key words
extreme learning machine/crystal structure algorithm/contaminated food/early detection/feature selection/food safety引用本文复制引用
祝福,刘瑞卿,潘克锋,赵蕊..基于极限学习机和晶体结构算法的污染食品早期检测[J].食品与机械,2025,41(6):68-74,7.基金项目
国家自然科学基金资助项目(编号:11501525) （编号:11501525）
河南省高等学校重点科研项目(编号:20ZX003) （编号:20ZX003）
河南省自然科学基金项目(编号:222300420579) （编号:222300420579）
