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.
Publication Date
4-25-2025
First Page
50
Last Page
56
DOI
10.13652/j.spjx.1003.5788.2024.60140
Recommended Citation
Xinhua, XU; Libo, YANG; and Xiameng, SI
(2025)
"Pork species identification based on near-infrared spectroscopy and PCA-DBN-SVM,"
Food and Machinery: Vol. 41:
Iss.
3, Article 8.
DOI: 10.13652/j.spjx.1003.5788.2024.60140
Available at:
https://www.ifoodmm.cn/journal/vol41/iss3/8
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