本文利用近红外(NIR)透射光谱分析技术结合连续投影算法(SPA)建立了黄酒中酒精度、酸度定量检测模型。为考察不同类黄酒对模型精度的影响，对比研究了干型、半干型黄酒样品的分类及混合偏最小二乘法(PLS)模型，确定了黄酒中酒精度和酸度的最佳建模方式。应用SPA 对样品近红外全波段(800~2 500 nm)进行优化，建立的SPA-PLS 模型效果优于全波段PLS 模型。干黄酒酒精度、半干黄酒酒精度和两类黄酒酸度SPA-PLS 模型的R 分别为0.983、0.993 和0.943，RMSECV 分别为0.174、0.189 和0.258。结果表明，分类建模显著提高了黄酒酒精度模型检测精度，混合建模能改善酸度模型精度；应用SPA 对模型优化，简化了模型且提高了模型精度及稳健性。研究结果为建立稳健的黄酒品质近红外检测应用模型提供了有效方法，对黄酒品质在线检测模型的研究具有参考价值。

The quantitative models for alcohol and acid of Chinese rice wine were established by Near-infrared (NIR) transmission spectroscopy combined with Successive Projection Algorithm (SPA). Considering the influence of different kinds of wine, the optimal modeling method of alcohol and acid was developed by comparing the classification and mixed models for dry and semi-dry wine. SPA-PLS model was established by optimizing wavelengths on the range of 800~2500nm, which was better than full-spectrum PLS model. The correlation coefficient (R) of the model for acid,alcohol in dry wine and alcohol in semi-dry wine were 0.943, 0.983 and 0.993, respectively, and the Root Mean Square Errors of Cross Validation (RMSECV) were 0.258, 0.174 and 0.189, accordingly. The result indicated that the classification modeling significantly increased the precision of alcohol model, and the acid model has been improved by mixed modeling. SPA made the analysis model simple, and promoted the precision and stability. The result provides aneffective way of building robust quantitative model in Chinese rice wine, and can be applied in on-line wines’ quality testing.