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| Rapid Identification of Fresh Meat Based on Laser-Induced Breakdown Spectroscopy Combined With Deep Learning Methods |
| SUN Hao-ran1, WANG Si-wen1, ZHAO Chun-yuan1, LIN Xiao-mei2, GAO Xun3, FANG Jian1* |
1. AI&TE Industrial Technology Research Institute，Jilin Communications Polytechnic, Changchun 130015, China
2. School of Electronics and Electrical Engineering, Changchun University of Technology, Changchun 130012, China
3. School of Science, Changchun University of Science and Technology, Changchun 130022, China
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Abstract Meat is an important source of protein and nutrients in the human diet, and the adulteration of meat has become a major problem in the field of food safety in China. To address the problems of complex operations, time-consuming procedures, and high equipment costs in traditional meat detection methods, this paper proposes using Laser-induced Breakdown Spectroscopy (LIBS) combined with a deep learning network to detect and classify multi-variety meat tissues quickly. Spectral data for beef, mutton, and pork were collected using LIBS. Nine spectral lines of five elements were selected as the analysis spectral lines and model input for modeling and recognition. A ResNet18 backbone network was designed, and three machine learning models were designed to model and recognize the spectral data.The results show that the deep learning network achieves the best recognition performance, with an accuracy of 98.1%. Among the 120 groups of spectral data, 117, 119, and 117 groups of beef, mutton, and pork spectral data were identified correctly, respectively. In the horizontal comparison using the same deep learning model, the ResNet18 model was superior to the three deep learning models, GoogLeNet, Vgg16, and ResNet50, in the recognition of meat spectral data. On this basis, the model's generalization was verified using re-collected data, and the accuracy reached 98.9%, indicating that the model maintains strong cross-data-set recognition ability. It has good generalization and consistency. The above research shows that the combination of LIBS and a convolutional neural network can provide objective, quantitative information on differences between meat varieties in multi-variety meat classification and recognition tasks and has the potential to quickly and in situ diagnose different types of meat tissue.
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Received: 2025-03-31
Accepted: 2025-09-19
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Corresponding Authors:
FANG Jian
E-mail: 1419404982@qq.com
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