肉类研究2025，Vol.39Issue(11)：1-8,8.DOI:10.7506/rlyj1001-8123-20250210-032
基于近红外光谱结合网格搜索-随机森林-自适应提升算法无损检测牛肉新鲜度
Non-destructive Detection of Beef Freshness Using Near Infrared Spectroscopy Combined with Grid Search-Random Forest-Adaptive Boosting Algorithm
摘要
Abstract
To improve the prediction accuracy of beef freshness using near-infrared(NIR)spectroscopy,we proposed a predictive model based on the combination of grid search(GS),random forest(RF)and adaptive boosting(AdaBoost).Initially,RF and AdaBoost were employed to establish a NIR spectroscopy prediction model,followed by an analysis of the prediction accuracy for total volatile base nitrogen(TVB-N)content in beef.Subsequently,the RF model,composed of multiple weak learners,was trained using the training set,and AdaBoost was used to integrate these weak learners into a strong learner through varying weights to build an ensemble model.RF was then optimized using GS to develop an AdaBoost model that integrates GS-RF as its weak learner for predicting the TVB-N content in beef.Finally,the prediction performance of the GS-RF-AdaBoost model based on NIR spectroscopy was analyzed and compared with that of the partial least square regression,RF,AdaBoost and RF-AdaBoost models.The results indicated that the GS-RF-AdaBoost model outperformed in predicting the TVB-N content in beef with the lowest root mean square error of predicyion set and the highest correlation coefficient,coefficient of determination and residual prediction deviation of predicyion set,which were 1.731,0.969,0.924 and 4.331,respectively.These findings confirm that integrating GS-RF-AdaBoost model based on NIR spectroscopy can effectively enhance predictive performance regarding TVB-N content in beef.关键词
近红外光谱/网格搜索/随机森林/自适应提升/牛肉新鲜度Key words
near infrared spectroscopy/grid search/random forest/adaptive boosting/beef freshness分类
轻工纺织引用本文复制引用
任智磊,赵霄霄,冯景,毕景然,张公亮,侯红漫..基于近红外光谱结合网格搜索-随机森林-自适应提升算法无损检测牛肉新鲜度[J].肉类研究,2025,39(11):1-8,8.基金项目
"十四五"国家重点研发计划重点专项(2022YFD2100504) （2022YFD2100504）
