Presented a fast and non-destructive method for the discrimination of vinegar brands by near-infrared spectroscopy technology. One hundred and fifty-two representative samples of vinegar including Bao Ning, East Lake, Heng Shun, Zhenjiang were collected from market. Multiplicative Scatter Correction (MSC) was used to handle the original near infrared spectrum (NIR) data and Principal Component Analysis (PCA) was used to process the spectral data after pretreatment according to the accumulative contribution rate of principal components to select principal components. Support Vector Machine (SVM) was then applied to build the brand traceability model with proper kernel function. Particle Swarm Optimization was applied to optimize the parameters of the model. The experiments indicated that the method combing near infrared spectroscopy with support vector machine could classify the vinegar brand with 100% accuracy.

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