###### Abstract
The above research indicates that NIR is an effective method for the quantitative and qualitative analysis of alcohol. NIR fingerprint detection and analysis techniques include preprocessing, feature extraction, and reduction to build a prediction model. In the field of liquor detection, increasing numbers of researchers have applied NIR spectroscopy to quantitative analysis and the quality identification of liquor components. The quantitative analysis of liquor components based on NIR spectroscopy is relatively early in its development and mainly uses partial least squares (PLS) [39; 40; 41]. In the qualitative analysis of liquor, methods combining chemometrics and pattern recognition are mainly used, such as the identification of liquor flavor type [38], liquor brand [42], and liquor year [43]. These detection analysis and modeling methods mainly use principal component analysis (PCA), PLS, PLS discriminant analysis, support vector machine (SVM), linear discriminant analysis, and artificial neural network. NIR spectral data have high-dimensional characteristics and usually contain redundant information irrelevant or weakly related to sample characteristics, which affects the accuracy and generalization ability of the prediction model. Therefore, it is very important to mine the characteristic information of spectral data according to the detection purpose. There are many methods for feature extraction and dimensionality reduction in NIR spectroscopy, such as uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the aforementioned PCA. UVE adds a random noise matrix to the original NIR spectral data, establishes a PLS regression model, uses the regression coefficient of the model to calculate the stability coefficient of each spectral wavelength, and screens the characteristic spectrum using a stability coefficient threshold [44]. CARS selects variables with larger absolute values of the regression coefficients in the PLS model using the adaptive reweighted sampling technique and removes variables with smaller absolute values to obtain characteristic spectra [45]. PCA maps high-dimensional data to a new space to obtain mutually orthogonal principal components to achieve dimensionality reduction [46]. Liquor quality detection includes the classification of the base liquor and the quality evaluation of the product liquor. This study uses the base liquor as the research object, which is the main raw material used to blend the product liquor. In the distillation process, the quality of the base liquor changes with distillation time. Therefore, in this study, three distilled base liquors, from the first, middle, and last stages, were collected. In the feature extraction method, CARS is a variable extraction algorithm based on Kaplan-Meier's survival of the fittest principle in Darwinian evolution. The CARS algorithm can effectively screen wavelength variables closely related to the properties of the measured substance, reduce collinearity between variables, and improve the accuracy of the model [47; 48]. However, the spectrum still has a high dimension after component analysis (KPCA), which is conducive to improving the accuracy of the prediction model. KPCA is more suitable for nonlinear spectrum processing than PCA [49; 50]. Here, a liquor quality model based on a multi-classification support vector machine (MSVM) was established using the characteristic spectrum after dimensionality reduction.