食品与机械2025，Vol.41Issue(3)：50-56,7.DOI:10.13652/j.spjx.1003.5788.2024.60140
基于近红外光谱和PCA-DBN-SVM的猪肉种类识别
Pork species identification based on near-infrared spectroscopy and PCA-DBN-SVM
摘要
Abstract
[Objective]To improve the classification accuracy of pork species by building an identification model based on near-infrared spectroscopy and PCA-DBN-SVM.[Methods]Combining the near-infrared spectroscopy characteristics of pork,principal component analysis(PCA)is used for dimensionality reduction and feature extraction,and DBN-SVM is then applied for classification and recognition to construct a pork species identification method that integrates near-infrared spectroscopy characteristics with PCA-DBN-SVM model.[Results]Compared with the KNN model,RF model,ELM,and DBN combination model,the PCA-DBN-SVM model has the highest classification accuracy of pork species,which reaches 99.91%.[Conclusion]The PCA-DBN-SVM model exhibits superior classification accuracy.关键词
支持向量机/主成分分析/近红外光谱/深度置信网络Key words
support vector machine/principal component analysis/near-infrared spectroscopy/deep belief network引用本文复制引用
许新华,杨礼波,司夏萌..基于近红外光谱和PCA-DBN-SVM的猪肉种类识别[J].食品与机械,2025,41(3):50-56,7.基金项目
河南省科技攻关项目(编号:242102210021) （编号:242102210021）
河南省高等学校重点科研项目(编号:24B510016) （编号:24B510016）
