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Deep learning based on the Vis-NIR two-dimensional spectroscopy for adulteration identification of beef and mutton

文献类型： 外文期刊

作者： Wang, Li 1 ; Liang, Jing 2 ; Li, Fei 1 ; Guo, Tao 1 ; Shi, Yanli 1 ; Li, Fadi 1 ; Hao, Shengyan 3 ; Xu, Hui 2 ;

作者机构： 1.Lanzhou Univ, Coll Pastoral Agr Sci & Technol, Key Lab Grassland Livestock Ind Innovat, Minist Agr & Rural Affairs,State Key Lab Pastoral, Lanzhou 730000, Peoples R China

2.Gansu Agr Univ, Lanzhou 730070, Peoples R China

3.Gansu Acad Agr Sci, Inst Anim Husb Pasture & Green Agr, Lanzhou 730070, Peoples R China

4.Minqin Cty Defu Agr Technol Co Ltd, Wuwei 733300, Peoples R China

关键词： 2DCOS; Vis-NIR; Adulterated beef and mutton; Deep learning

期刊名称：JOURNAL OF FOOD COMPOSITION AND ANALYSIS （ 影响因子：4.3； 五年影响因子：4.6 ）

ISSN： 0889-1575

年卷期： 2024 年 126 卷

页码：

收录情况： SCI

摘要： Rapid and accurate identification of contaminated meat products represents a global challenge, particularly with regard to minced meat products. This study reports on two methods for analyzing the adulteration of beef and mutton: the deep learning combined with two-dimensional correlation spectroscopy (2DCOS) method, and the PLS-DA method. The 2DCOS method employed in this research substantially improves the resolution of onedimensional Vis-NIR spectra, allowing for the visualization of spectral information changes within the samples. By analyzing the impact of various proportions of chicken, duck, and pork mixed with beef or mutton, the synchronous 2DCOS images unveil distinct patterns of chemical information alteration within the spectra under different adulteration scenarios. Notably, the auto peaks and cross peaks observed within the 400-1400 nm range serve as key indicators of these changing patterns. The ResNet deep learning method possesses the advantageous capability to effectively extract 2DCOS feature information, resulting in the achievement of high accuracy models (100%). In contrast, the accuracy of the PLS-DA model test set, based on either raw or preprocessed spectral data matrices, ranged from 32.97% to 50.64%. These results substantiate the effectiveness of utilizing 2DCOS in combination with deep learning as a powerful tool for discerning beef and mutton adulteration.

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