Portable Raman spectroscopy coupled with partial least squares regression (PLSR) model was performed for monitoring and predicting four quality indicators, moisture content, water activity, polysaccharide content and microbial content of the fresh-cut Chinese yam at different storage temperatures. The variations in the four key indicators were first depicted through a spider web diagram as the storage temperature changed. More importantly, the four key indicators can be accurately monitored and predicted through optimized PLSR models combining with Raman spectroscopy. Among all of the PLSR models for the four indicators, the regression model for moisture content was relatively the best. In addition, storage temperature played a significant role on the model performance of PLSR. The model performance for all indicators at room temperature and high temperature was better than the corresponding PLSR models at refrigeration and freezing conditions. Especially at 25 ℃, the R² in the calibration set basically reached 0.9. These observations indicated that portable Raman spectroscopy, a simple and easy-to-use detection technique, can monitor and predict the multiple quality indicators of fresh-cut Chinese yam combined with effectively PLSR model, which would be conducive to their applications in food industry.

采用便携式拉曼光谱结合偏最小二乘回归（PLSR）模型对不同贮藏温度下鲜切山药的水分含量、水分活度、多糖含量和微生物含量4项品质指标进行监测和预测。首先通过蜘蛛网图描绘了四个关键指标随储存温度变化的变化。更重要的是，通过优化的PLSR模型结合拉曼光谱可以准确监测和预测这四个关键指标。在四个指标的PLSR模型中，水分含量的回归模型相对最好。此外，存储温度对PLSR的模型性能也起着重要作用。模型在室温和高温条件下各项指标的表现均优于相应的冷藏和冷冻条件下的PLSR模型。特别是在25℃时，校准组中的R ²基本达到0.9。这些结果表明，便携式拉曼光谱作为一种简单易用的检测技术，结合有效的PLSR模型，可以对鲜切山药的多项品质指标进行监测和预测，有利于其在食品工业中的应用。