Citation: ZHU Yi-Ning, YANG Ping, YANG Xin-Yan, LI Jia-Ming, HAO Zhong-Qi, LI Qiu-Shi, GUO Lian-Bo, LI Xiang-You, ZENG Xiao-Yan, LU Yong-Feng. Classification of Fresh Meat Species Using Laser-induced Breakdown Spectroscopy with Support Vector Machine and Principal Component Analysis[J]. Chinese Journal of Analytical Chemistry, ;2017, 45(3): 336-341. doi: 10.11895/j.issn.0253-3820.160570 shu

Classification of Fresh Meat Species Using Laser-induced Breakdown Spectroscopy with Support Vector Machine and Principal Component Analysis

Wuhan National Laboratory for Optoelectronics, Laser and Tera-Hertz Technology Division, Huazhong University of Science and Technology, Wuhan 430074, China

Received Date: 27 July 2016
Revised Date: 23 December 2016

Fund Project: This work was supported by the National Major Scientific Instrument and Equipment Development Project (No. 2011YQ160017)and the National Natural Science Foundation of China (No. 6157031235)

To improve the classification accuracy of fresh meat species using laser-induced breakdown spectroscopy (LIBS), the support vector machine (SVM) and principal component analysis (PCA) were combined to classify fresh meat species (including pork, beef, and chicken). A simple sample preparation to flatten fresh meat by glass slides was proposed. For each meat sample, 150 spectra were recorded and randomly arranged. The first 75 spectra were used to train a model while the others were used for model validation. By analyzing the 49 normalized spectral lines (K, Ca, Na, Mg, Al, H, O, etc.) in the different tissues, the classification model was built. The results showed that the dimensionality of input variables was decreased from 49 to 10 and modeling time was reduced from 89.11 s to 55.52 s using PCA, thus improving the modeling efficiency. The mean classification accuracy of 89.11% was achieved. The method and reference data are provided for further study of fresh meat classification by laser-induced breakdown spectroscopy technique.
- Laser-induced breakdown spectroscopy,
- Support vector machine,
- Principal component analysis,
- Tissue classification
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