使用太赫兹时域光谱(THz-TDS)技术对葛粉中掺薯粉的含量进行定性、定量检测。对葛粉中掺薯粉的光谱数据进行采集,利用偏最小二乘法(PLS)建立葛粉掺薯粉定性模型以判断葛粉中是否掺薯粉,得到PLS的总误判率为0%,模型相关系数为0.925。结果表明:PLS可实现葛粉中是否掺薯粉的定性判别。再利用PLS和最小二乘支持向量机(LS-SVM)算法分别建立葛粉中掺薯粉的定量模型。利用PLS建立的模型的相关系数为0.932,预测集的均方根误差(RMSE)为 2.6%;利用LS-SVM建立的模型的相关系数为0.957,预测集的RMSE为 1.6%,结果表明:利用LS-SVM的葛粉掺薯粉定量模型更准确,说明THz-TDS技术可用于对葛粉中掺薯粉进行快速、有效、无损检测。

This paper uses terahertz time-domain spectroscopy (THz-TDS) to analyze the content of seed potato starch in kudzu qualitatively and quantitatively. The spectral data of kudzu powder mixed with seed potato starch was collected, and the qualitative model was established by partial least squares method (PLS) to determine whether the starch powder was mixed with potato powder. The total false positive rate of PLS is 0%, and the model correlation coefficient is 0.925. The results show that PLS can be used to determine whether the starch powder is qualitatively differentiated. PLS and least squares support vector machine (LS-SVM) were used to establish a quantitative prediction model for the content of seed potato starch in kudzu respectively; for PLS prediction model, the determination coefficient is 0.932, and the root mean square error (RMSE) of the predicted set is 2.6%; for LS-SVM prediction model, the determination coefficient is 0.957, and the RMSE of LS-SVM of the predicted set is 1.6%. The results show that the LS-SVM quantitative prediction model is excellent. The research shows that THz-TDS can be used to rapidly and effectively detect the content of seed potato starch in kudzu qualitatively and quantitatively.