以200个不同品种的马铃薯为样品，采用真空微波冷冻干燥技术对样品进行了预处理。用基于偏最小二乘法（PLS）的傅里叶变换近红外光谱技术建立了马铃薯4个主要加工品质指标（水分、还原糖、淀粉和蛋白质）的预测模型。以模型决定系数（R2）、校正标准差（RMSECV）、预测标准差（RMSEP）和相对分析误差（RPD）作为模型精度的评价指标，利用50个未知品种的马铃薯样品对模型预测结果进行了外部检验。外部检验结果表明，利用近红外吸收光谱技术预测马铃薯主要加工品质指标含量是可行的，水分和淀粉模型的预测效果理想，而蛋白质和还原糖模型的精度还有待于进一步提高。

200 different kinds of potato are taken as samples. The samples are preprocessed with the vacuum microwave freeze-dry technique. A model for predicting four main processing quality indexes (water, reducing sugar, starch and protein) of potato is established by using the Fourier transform near-infrared spectroscopy. By taking the determination coefficient (R2), root mean square error of cross validation (RMSECV), root mean square error of prediction (RMSEP) and relative predictive determination (RPD) as the accuracy evaluation indexes of the model, the prediction results of the model are verified with 50 kind-unknown potato samples externally. The external verification result shows that it is feasible to predict the main processing quality indexes of potato. The prediction effectiveness is ideal, but the prediction accuracy should be further improved for protein and reducing sugar.