基于STM的三维荧光导数光谱法检测食品中黄曲霉素

基于支持张量机(STM)的三维荧光导数光谱定量分析方法,检测了食品中黄曲霉素。在计算三维荧光导数光谱时,将常规的、只适用于向量光谱数据的Savitzky-Golay方法扩展到由二阶张量描述的三维荧光光谱中。同时,应用了STM方法建立校正模型,对白酒和牛奶中的黄曲霉素进行了检测。在对白酒中的黄曲霉素检测中,复相关系数(CC)和预测误差均方根(RMSEP)分别为0.952 3和14.847 5,与常规的偏最小二乘(PLS)和支持向量机(SVM)方法相比,CC分别提高了2.40%和2.34%,RMSEP分别降低了8.92%和4.36%。在对牛奶中的黄曲霉素检测中,CC和RMSEP分别为0.996 5和5.448 9,与PLS和SVM的方法相比,RMSEP分别提高了0.40%和0.30%,RMSEP分别降低了18.31%和17.18%。检测结果表明,基于STM方法建立的校正模型要优于传统的SVM方法和PLS方法。

Utilizing 3-D first-order derivative fluorescence spectrometry to detect aflat oxin in foods based on STM

Spectral quantitative analysis for thr ee-dimensional first-order derivative fluorescence based on support tensor mac hine (STM) is proposed and applied to detect aflatoxin in foods in this paper.The conventional Savitzky-Golay polynomial smoothing and d ifferentiation methods,only used for vector-based spectral analysis,are extended to three-dim ensional fluorescence spectrometry represented by 2-order tensors.Because the three-di mensional first-order derivative spectrometry is represented as a 3-order tenor,support tensor machines (STMs) are used to build calibration model in order to improve the model prediction accurac y.In the experiments for detecting aflatoxin in liquor,the correlation coefficient (CC) and root mean square error of prediction (RMSEP) are 0.9523and 14.8475,respectively,and compared wit h partial least squares (PLS) and support vector machine (SVM) method,the CC is increased by 2.34% and 2.40% while RMSEP is reduced by 4.36% and 8.92%,respectively.And in the experiments for detecting aflatoxin in milk,CC and RMSEP are 0.9965and 5.4489,and the CC is increased by 2.34% and 2.40% while RMSEP is reduced by 4.36% and 8.92%,respectively with comparison to PLS and SVM.These experimental r esults show that the performance of calibration model used by STM is superior to that by SVM and PLS.