Identification and classification of near-infrared spectrum of adulterated wine based on support vector machine

Although usual chemical methods have more accurate results in detecting the methanol in adulterated wine, they are complex, expensive and requiring rigorous environment condition. A novel identified and classified spectrum of adulterated wine was proposed based on the support vector machine. The spectra of samples were measured by the ASD FieldSpec 3 spectrometer; reflection spectra were pretreated and correlation analysis and univariate regression analysis were carried out, so the peaks of methanol spectra as the characteristic bands which is not over shadowed by the ethanol were obtained; the characteristic bands were used to train classification model, the result was obtained. The result shows that, the classification accuracy is 85% while the content of methanol is less than or equal to 3% as the true wine, and the classification accuracy is 97.5% while the content of methanol is less than or equal to 5% as the true wine. So, this method is available and has higher classification accuracy.