To address the inefficiency and complexity of traditional chemical methods for measuring the acidity of Huangshui (HS), this study proposed a rapid and non-destructive detection approach using near-infrared (NIR) spectroscopy combined with partial least squares regression (PLSR). The raw spectra were preprocessed by Savitzky-Golay convolution smoothing to reduce noise interference. To simplify the model and enhance the predictive performance, a hybrid feature selection strategy integrating spectral band selection and wavelength optimization was developed. First, interval partial least squares (iPLS), synergy interval partial least squares (SiPLS), and backward interval partial least squares (BiPLS) were employed to preliminarily identify characteristic bands related to acidity. Subsequently, a multi-objective optimization framework was introduced, incorporating an improved non-dominated sorting genetic algorithm Ⅲ (iNSGA-Ⅲ) with chaotic initialization and adaptive mutation operators for secondary wavelength refinement. Results demonstrated that the PLSR model based on 70 optimal wavelengths selected by iPLS combined with iNSGA-Ⅲ had the best predictive performance with higher coefficient of determination of prediction (R_p²) and lower root mean square error of prediction (RMSEP) of 0.9309 and 0.4394 mmol/100 g compared to 0.7576 and 0.8250 mmol/100 g for the full spectral model, respectively. This study provides a theoretical foundation for rapid, non-destructive monitoring of HS acidity during Baijiu fermentation.

Raw Nongxiangxin Baijiu of different grades were collected during the distillation process, and their near infrared spectroscopy (NIR) data and gas chromatography-mass spectrometry (GC-MS) data were acquired. After preprocessing the NIR data through 5-point 2-fold convolutional smoothing, spectral feature wavelengths were selected using the competitive adaptive reweighted sampling (CARS) algorithm; combining Spearman’s rank correlation coefficient, maximum information coefficient (MIC) and random forest (RF) variable importance, the key flavor components (KC) identified by GC-MS affecting the grading of raw Baijiu were determined. Then, extreme gradient boosting tree (XGBoost) was applied to establish three grade identification models for raw Baijui based on NIR, GC-MS and their fused data. The results showed that the prediction accuracy of the model based on the spectral feature variables selected by CARS was 89.66%, the prediction accuracy of the model based on KC after feature selection was 94.83%, and the classification accuracy of the model based on the fused data of CARS + KC reached as high as 98.28%. This study shows that the fusion of effective feature information from GC-MS and NIR data can enable more accurate and stable grade identification of raw Nongxiangxin Baijiu than either analytical technique alone, which provides a new idea and theoretical basis for the grade identification and quality control of raw Baijiu.

In order to improve the accuracy and efficiency of predictive modeling of the starch content of Huangshui, a byproduct of Baijiu production by solid-state fermentation, spectral information of Huangshui was collected using a Fourier transform near-infrared (FTIR) spectrometer and preprocessed by first derivative. Based on the preprocessed spectra, a predictive model for the starch content of Huangshui was developed using partial least squares regression (PLSR), and its performance was evaluated by determination coefficient (R²) and root mean square error of prediction (RMSEP). As the original spectra contained a lot of redundant information, in order to effectively improve the detection accuracy and to optimize the modeling efficiency, the advantages of different feature extraction methods were combined. Finally, it was found that the PLSR model established by using the spectral features extracted by competitive adaptive reweighted sampling (CARS) combined with the successive projections algorithm (SPA) was significantly better than the model built without feature extraction or using single feature extraction. The results showed that the R² and RMSEP of the model established using CARS were 0.9654 and 0.2012%, while those obtained using CARS-SPA were 0.9738 and 0.1748%, respectively. The spectral dimension reduced from 2203 to 126 after the combination of CARS with SPA, which improved both the prediction accuracy and the modeling efficiency. The method proposed in this study provides an effective means to optimize near-infrared spectral quantitative modeling of starch in Huangshui.

The chemical composition of base baijiu is complex and diverse. A classification model for base baijiu of different sensory grades was established based on the gas chromatography-mass spectrometric (GC-MS) data for their volatile composition. In order to improve the accuracy and generalization capacity of the classification model, a method combining genetic algorithm (GA) and bootstrap aggregating (Bagging) was proposed to optimize the support vector machine (SVM) classifier. Using Spearman’s correlation analysis, 36 key substances were selected, and 12 kernel principal components were extracted as input to the model by kernel principal component analysis, which together accounted for 96.06% of the total variance. The radial basis kernel function support vector machine with the best performance was selected, and the parallel computing Bagging ensemble algorithm with strong adaptability to data diversity was used to construct a Bagging-SVM classifier for base baijiu classification. Finally, GA was used to optimize the parameters (C, γ, and N) of the Bagging-SVM classifier to construct a GA-Bagging-SVM model. The results showed that the accuracy, precision, recall rate, and F1-Score of the GA-Bagging-SVM model were 96.77%, 96.90%, 96.77%, and 96.78%, respectively, which were 6.45%, 5.61%, 6.45%, and 6.42% higher than those of the SVM model, and 3.22%, 2.29%, 3.22%, and 3.15% higher than those of the Bagging-SVM model, respectively. This method can be used as an optimization method for the quality evaluation model for base baijiu.