为实现白酒发酵过程中黄水酒精度的快速检测，研究采用傅里叶近红外光谱（FT-NIR）技术对黄水进行光谱采集，并且采用偏最小二乘回归（PLSR）法建立酒精度预测模型。为减少全光谱的数据冗余降低复杂度，提升建模准确率，将连续投影算法（SPA）与间隔偏最小二乘法（iPLS）联用，对整个谱区进行特征波段筛选，并用决定系数R2与预测均方根误差（RMSEP）评价预测模型。结果表明：与原始数据集相比，经过异常样品剔除、预处理、特征光谱筛选后预测模型，预测集R2也从最开始的0.702变为0.952，提升35.61%；预测RMSEP从3.812变为1.367，降低64.14%；变量数也从2,203逐步下降到99，降低了95.51%。说明在减少非相关信息与噪声的同时，模型的复杂度也得到极大改善，并且模型的稳定性与准确度得到了有效提升，最终实现黄水酒精度的快速无损检测，以期为白酒发酵领域提供一种新的可能性，为近红外在白酒发酵副产物中的检验提供理论基础。

In order to realize the rapid detection of the alcohol content of yellow water in the process of liquor fermentation, Fourier near-infrared spectroscopy (FT-NIR) technique was used to collect the spectrum of yellow water, and the partial least squares regression (PLSR) method was used to establish the alcohol content predictionmodel. In order to reduce the data redundancy of the full spectrum, reduce the complexity, and improve the modeling accuracy, the continuous projection algorithm (SPA) and the interval partial least squares (iPLS) method were used to screen the characteristic bands of the entire spectral region, and the coefficient of determination R2 and root mean squared error of prediction (RMSEP) were used to evaluate the predictive models. The results showed that compared with the original data set, the prediction model after abnormal sample removal, preprocessing and characteristic spectrum screening, the prediction set R2 also changed from initial 0.702 to 0.952 (an increase of 35.61%); the predicted RMSEP changed from 3.812 to 1.367 (a decrease of 64.14%); the number of variables also gradually decreased from 2,203 to 99 (a decrease of 95.51%). It showed that while reducing irrelevant information and noise, the complexity of the model has also been greatly improved, and the stability and accuracy of the model have been effectively increased, and finally, the rapid and non-destructive detection of the alcohol content in yellow water can be realized, in order to provide a new possibility in the liquor fermentation field, and provide a theoretical basis for the detectionof near-infrared in liquor fermentation by-products.

四川省科技计划项目（2022YFS0554）；四川省科技成果转移转化示范项目（2020ZHCG0040）；四川省重大科技专项项目（2018GZDZX0045）；国家自然科学基金项目（42074218）