SVM Applied to Trace Elements Analysis of Cigarettes

SVC method has been used to classify the cigarettes from the factories of Yunnan province and those from Henan province. The kernel type has been selected in SVC computation. After several trials, the linear function has been found to be the best one in this case. By feature selection based on the prediction ability of SVC, a data subset including the contents of Cu, Zn, Mn and Cr is used for the mathematical modeling, with 100% rate of correctness of prediction in LOO cross-validation test for this data set. A criterion for the classification of Yunnan cigarettes from Henan ones can be obtained by SVC (linear kernel, C =100) as follows  

Although the classification by Fisher method is also very good, but the LOO cross-validation test of Fisher method leads to some misclassification results. 

The classification by Fisher method is also clear-cut, but both the result of LOO cross-validation test of Fisher method and the results of K-Nearest Neighbor (KNN) methods (k=l, 3 or 5) cause some misclassification, while the result of the LOO cross-validation test of SVC (linear kernel, C =100) shows 100% rate of correctness. 

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