近红外光谱温度修正定量分析模型的研究

以小麦粉末样品为实验材料，研究了环境温度对近红外光谱定量分析结果的影响。将环境温度作为外部变量，使用不同温度下的45个样品建立了测定小麦蛋白质含量的温度修正模型，预测不同温度下的小麦样品的蛋白质含量，结果同以22℃恒温下45个样品建立的模型进行了比较。分析结果表明：温度修正模型的预测标准差(SEP)平均为0．333，而恒温模型(22℃)的预测标准差随着环境温度与建模时温度差的增大而增大，当环境温度4℃时，SEP=0．6016。温度修正模型可以有效的提高近红外光谱定量分析精度。

Study on Temperature Correctional Models of Quantitative Analysis with Near Infrared Spectroscopy

Effect of enviroment temperature on near infrared spectroscopic quantitative analysis was studied. The temperature correction model was calibrated with 45 wheat samples at different environment temperaturs and with the temperature as an external variable. The constant temperature model was calibated with 45 wheat samples at the same temperature. The predicted results of two models for the protein contents of wheat samples at different temperatures were compared. The results showed that the mean standard error of prediction (SEP) of the temperature correction model was 0.333, but the SEP of constant temperature (22 degrees C) model increased as the temperature difference enlarged, and the SEP is up to 0.602 when using this model at 4 degrees C. It was suggested that the temperature correctional model improves the analysis precision.