Near-infrared Spectroscopy Combined with Improved Permutation Combination Population Analysis for Identifying Base Liquor Grades of Nongxiangxing Baijiu

Base liquor of Baijiu contains a wide variety of trace components with complex interactions. How to predict its grade rapidly and simply was of great importance. To achieve rapid and accurate identification while accounting for interactions between multi-component substances and reducing computational complexity, this study developed an improved permutation combination population analysis (imPCPA) method for feature selection based on near-infrared spectroscopy data. This study established a near-infrared spectroscopy (NIRS) model to discriminate the quality grades of base liquors in Nongxiangxing Baijiu. The model was developed using 687 samples from four quality grades. A combined feature selection strategy was applied to optimize spectral wavelength selection. The initial screening applied interval partial least squares (iPLS) to remove uninformative variables from preprocessed spectra. The improved permutation combination population analysis (imPCPA) further optimized wave point selection within the retained spectral intervals. Finally, constructed an extreme gradient boosting (XGBoost) classification model for grade prediction. The final selection identified 32 characteristic wavelength points. Compared to the original algorithm, the improved method reduced computational time by 80%. The median prediction accuracy of the classification model reached 95.65% on the prediction set. The results demonstrate that this method addresses key limitations of conventional sensory evaluation, including strong subjectivity and poor reproducibility in liquor analysis. It effectively captures synergistic interactions among multi-component substances while maintaining interpretable feature selection. The approach provides a reliable reference for rapid grade assessment of Baijiu base liquors.

白酒基酒含有种类繁多的微量组分，且各组分间存在复杂的相互作用。如何快速、简便地预测其品质等级具有重要意义。为实现快速准确的品质识别，同时兼顾多组分物质间的相互作用并降低计算复杂度，本研究基于近红外光谱（Near-infrared Spectroscopy, NIRS）数据，开发了一种改进型排列组合种群分析（improved permutation combination population analysis, imPCPA）特征选择方法。本研究构建了近红外光谱（NIRS）模型，用于浓香型（Nongxiangxing）白酒基酒的品质等级判别，该模型以覆盖4个品质等级的687个样本为数据集开发而成。研究采用组合式特征选择策略优化光谱波长选择：首先通过区间偏最小二乘（interval partial least squares, iPLS）对预处理后的光谱剔除无信息变量，再通过改进型排列组合种群分析（imPCPA）对保留的光谱区间内的波长点进行进一步优化筛选。最终构建了极限梯度提升（extreme gradient boosting, XGBoost）分类模型以实现等级预测。最终筛选得到32个特征波长点。与原始算法相比，改进后的方法将计算时间降低了80%，在预测集上该分类模型的中位预测准确率达到95.65%。研究结果表明，该方法有效解决了传统感官评价主观性强、白酒分析重现性差的关键局限，能够精准捕捉多组分物质间的协同相互作用，同时保留特征选择的可解释性，为白酒基酒的快速等级评定提供了可靠的技术参考。