###### Abstract
Although these traditional detection and analysis methods have high accuracy, they involve a series of time-consuming experimental preparations, such as sampling, extraction, and chromatography, which cannot be applied in rapid nondestructive detection [22]. These detection methods are quantitative analyses of monomer compounds with high content, which cannot reflect the coordination and comprehensive effects of multiple components in liquor. Currently, it is impossible to determine the quality of liquor by quantitative analysis because the complex coordination relationship of multi-components has not been studied clearly [23; 24]. In the quality control system of most liquor production enterprises, artificial sensory evaluation still occupies a dominant position. The inevitable defects of sensory evaluation, such as strong subjectivity, arbitrariness, and poor reproducibility, render quality control personnel unable to obtain an accurate quantitative evaluation result. Therefore, a rapid method for liquor quality identification is urgently needed. At present, near-infrared (NIR) spectroscopy is considered one of the most suitable methods for multi-component detection, with the advantages of a rapid, nondestructive identification of complex mixed objects and good reproducibility [25; 26]. NIR spectroscopy is widely used in food safety, the quality control of agricultural products, and drug analysis. There has been a lot of research on NIR detection technology in the quantitative and qualitative analysis of wine composition and classification. In terms of quantitative analysis, [PERSON] used visible light and NIR to analyze amino acids and total soluble solids in the ripening process of intact grape berries. Calibration models, cross-validation models, and prediction models were established by the partial least square method. The results showed that non-contact and nondestructive spectroscopy could be used as an alternative method to provide amino acid composition information on grapes [27]. Pascoa used the Savitzky-Golay preprocessing method to divide the spectrum into five bands. After an exhaustive search and the cross-verification of 31 combined bands, the optimal spectral region was determined. Then, partial least squares regression (PLSR) was used to establish a model for different parameters [28]. BaiX adopted Fourier near-infrared to solve the problem of band distribution and the quantitative prediction of rose oxides in Donglu wine. In order to improve the accuracy of model prediction, principal component analysis (PCA) was used to eliminate outliers. Synergical interval partial least square regression (Si-PLSR) was used to select eight synergical subintervals reflecting rose oxide content, which had good predictive performance [29]. In addition, near-infrared spectroscopy technology has also been studied in the quantitative analysis of wine alcohol and phenols [30; 31; 32; 33]. In terms of qualitative analysis, [PERSON] used mid-infrared and NIR combined with chemometrics to identify -wine origins. PCA, analog soft independent modeling (SIMCA), and discriminant analysis (DA) based on mid-infrared and NIR were used for modeling [34]. [PERSON] used NIR to classify wines aged with different woods. PCA was first used to study the relationship between samples, and then orthogonal partial least squares (OPLS) regression was applied to establish a discrimination model [35]. [PERSON] proposed a Bayesian classifier algorithm based on wavelength optimization and applied it to the recognition of wine brands by NIR, and the results showed that this method has potential in terms of food characterization, traceability, and the authenticity of the food substrate [36]. [PERSON] traced the origin of wines based on an NIR-integrated machine learning method [37]. [PERSON] identified and analyzed 730 Chinese liquors using various methods, and the prediction ability of the established PCA-LDA model reached 98.9% [38].