食品与机械2025，Vol.41Issue(12)：51-58,8.DOI:10.13652/j.spjx.1003.5788.2025.60137
基于近红外光谱和FOA-RF的猪肉新鲜度检测
Pork freshness monitoring based on near-infrared spectroscopy and random forest improved by fruit fly optimization algorithm
摘要
Abstract
[Objective]To achieve rapid,non-destructive,and high-precision monitoring of pork freshness,addressing the low efficiency,high destructiveness,and insufficient prediction accuracy of single models in conventional monitoring.[Methods]A pork freshness monitoring model was proposed based on near-infrared spectroscopy(NIRS)combined with random forest(RF)improved by the fruit fly optimization algorithm(FOA).With the total volatile basic nitrogen(TVB-N)content as the freshness indicator,near-infrared spectral data of pork samples at different storage stages are collected(scanning range:1 000～1 800 nm).Spectral noise and baseline drift are eliminated via a preprocessing method combining multiplicative scatter correction(MSC)and first-derivative transformation.Then,FOA is employed to optimize key hyperparameters(number of decision trees,minimum leaf node sample size,and maximum number of features)of RF to construct the FOA-RF model.[Results]Among all the prediction models evaluated,the FOA-RF model demonstrates the highest accuracy for predicting pork TVB-N content.The preprocessing method combining MSC and first-derivative transformation effectively enhances the quality of the spectral data.The FOA-RF model achieves a root mean square error of prediction(RMSEP)of only 1.582 mg/100 g,a correlation coefficient of prediction(Rp)of 0.978,a coefficient of determination of prediction(R2p)as high as 0.956,and a residual prediction deviation of prediction(RPDp)of 4.723,significantly outperforming the other comparative models.The overall predictive performance of partial least squares regression(PLSR),the un-optimized RF model,and the grid search-optimized random forest(GS-RF)model is inferior to that of the FOA-RF model.[Conclusion]The method proposed in this study provides an efficient and accurate new approach for non-destructive monitoring of pork freshness,meeting the demand for rapid testing in the meat industry.关键词
近红外光谱/果蝇优化算法/随机森林/猪肉新鲜度/总挥发性盐基氮/无损检测Key words
near-infrared spectroscopy/fruit fly optimization algorithm/random forest/pork freshness/total volatile basic nitrogen/non-destructive monitoring引用本文复制引用
张丽,李志辉,马明星..基于近红外光谱和FOA-RF的猪肉新鲜度检测[J].食品与机械,2025,41(12):51-58,8.基金项目
河南省科技攻关项目(编号:252102210224,252102210008) （编号:252102210224,252102210008）
