分析化学2017，Vol.45Issue(3)：336-341,6.DOI:10.11895/j.issn.0253-3820.160570
支持向量机结台主成分分析辅助激光诱导击穿光谱技术识别鲜肉品种
Classification of Fresh Meat Species Using Laser-induced Breakdown Spectroscopy with Support Vector Machine and Principal Component Analysis
摘要
Abstract
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.关键词
激光诱导击穿光谱/支持向量机/主成分分析/组织分类Key words
Laser-induced breakdown spectroscopy/Support vector machine/Principal component analysis/Tissue classification引用本文复制引用
朱毅宁,杨平,杨新艳,李嘉铭,郝中骇,李秋实,郭连波,李祥友,曾晓雁..支持向量机结台主成分分析辅助激光诱导击穿光谱技术识别鲜肉品种[J].分析化学,2017,45(3):336-341,6.基金项目
本文系国家重大科学仪器设备开发专项(No. 2011YQ160017)、国家自然科学基金项目(No. 6157031235)资助 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). （No. 2011YQ160017）
