Development and Optimization of the Brand Traceability Model of Vinegar Based on Near-infrared Spectroscopy
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Abstract:
In this paper, near-infrared spectroscopy (NIRS) and pattern recognition technology were used to establish a brand traceability model for vinegar. NIR diffuse reflectance spectra of 160 vinegar samples from four different brands (SICHUAN BAONING vinegar, SHANXI EASTLAKE vinegar, HENGSHUN CHINKIANG vinegar, and CHINKIANG vinegar), were collected. After the spectral variables were compressed and eight abnormal sample data were removed using principal component analysis (PCA), 114 samples were randomly selected to form a training set for the construction of a traceability model, and the remaining 38 samples were used to form a test set for model validation. The effects of different pretreatment methods of the NIR spectra including multiplication signal correction (MSC), second derivative (SD), standard normal variate (SNV) etc., and different combinations of these methods on the traceability model were compared, and the effects of two modeling methods, partial least squares-discriminant analysis (PLS-DA) and soft independent modeling by class analogy (SIMCA)were examined. The results demonstrated that the correct recognition rates of the four different brands of vinegars could reach 100%, 100%, 91.7%, and 90%, respectively, when the spectral data were pretreated by MSC combined with SD and the model was developed using SIMCA. Therefore, the brand traceability of vinegar could be effectively achieved using NIR and a pattern recognition technique.
