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| Identification of Pork Parts Based on LIBS Technology Combined With PCA-SVM Machine Learning |
| XU Yu-ting1, SUN Hao-ran2, GAO Xun1*, GUO Kai-min3*, LIN Jing-quan1 |
1. College of Science, Changchun University of Science and Technology，Changchun 130022, China
2. College of Mechanical and Electrical Engineering, Changchun University of Technology，Changchun 130012, China
3. College of Physical Science and Technology, Baotou Normal University，Baotou 014030, China |
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Abstract In recent years, laser-induced breakdown spectroscopy (LIBS) is gradually emerging to classify and identify biological organizations by combining them with algorithms. Due to each part of the pork similar spectral characteristics, it is difficult to achieve accurate identifications only through the analysis of the effect of spectral information, so in this paper studied pork from four different parts of the same individual and sliced and planished them, then applied LIBS technology on four parts, i.e., the organization, fillet, plum flower, and front legs. 100 specimens of each sample were collected and the spectrum analysis was conducted. A preliminary analysis of the spectrum was performed on Ca, Na, K and 6 lines. It was found that other tissues were difficult to distinguish except for the C—N tissue of plum flower with more fat content and higher C content than other tissues, so the Principal Component Analysis (PCA) on these 6 principal components was carried out. The cumulative contribution rate of PC1, PC2 and PC3 reached 95%. The Support Vector Machines (SVM) classification model was established by employing feature scores as the input source of SVM model, and the confusion matrix diagram of these samples got obtained. Through observation of the confusion matrix, the classification accuracy of each type of samples could be clearly distinguished. The results showed that the accuracy of the four samples was 96%, 98%, 97% and 100%, respectively, with an average accuracy surpassing 97%. The study proved that LIBS combined with PCA-SVM can be used as a fast identification method for different parts of pork tissues.
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Received: 2020-10-23
Accepted: 2021-02-04
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Corresponding Authors:
GAO Xun, GUO Kai-min
E-mail: lasercust@163.com
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