Abstract:To classify baijiu base wine, reduce the classification error of baijiu base wine, and reduce the harm of base wine to the body of Baijiu Based Liquor pickers, 18 pretreatment methods and three characteristic wave screening methods were selected to reduce irrelevant interference information in the spectrum and complexity of the modeling data. The Fourier-transform near infrared spectra of baijiu base wine were divided into datasets using SPXY and preprocessed, and then subjected to Mahalanobis distance anomaly elimination, eigenwave screening, and support vector machine regression prediction. After multiplicative scatter correction, the classification accuracy of training set prediction was 100%. Principal component analysis can be combined with specific algorithms to achieve accurate classification; studies are needed to combine this analysis with other algorithms. Uninformative variables elimination and competitive adaptive reweighted sampling can achieve efficient feature wavelength extraction, with an average accuracy of prediction of close to 90%. The experimental results showed that the processed spectral data accounted for up to 47.57% of the original data, the complexity of the regression model was reduced, and the accuracy of the model was improved after pretreatment and characteristic wavelength selection of the near-infrared spectrum of the base wine.
