Ge Hongyi, Ji Xiaodi, Lu Xuejing, Lv Ming, Jiang Yuying, Jia Zhiyuan, Zhang Yuan
Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, Henan, China; Henan Provincial Key Laboratory of Grain Photoelectric Detection and Control, Zhengzhou 450001, Henan, China; College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, Henan, China.
PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, Henan, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2023 Dec 15;303:123206. doi: 10.1016/j.saa.2023.123206. Epub 2023 Jul 25.
This paper proposes to detect heavy metal pollutants in wheat using terahertz spectroscopy and deep support vector machine (DSVM). Five heavy metal pollutants, arsenic, lead, mercury, chromium, and cadmium, were considered for detection in wheat samples. THz spectral data were pre-processed by wavelet denoising. DSVM was introduced to further enhance the accuracy of the SVM classification model. According to the relationship between the accuracy and the training time with the number of hidden layers ranging from 1 to 4, the model performs the best when the hidden layer network has three layers. Besides, using the back-propagation algorithm to optimize the entire DSVM network. Compared with Deep neural network (DNN) and SVM models, the comprehensive evaluation index of the proposed model optimized by DSVM has the highest accuracy of 91.3 %. It realized the exploration enhanced the classification accuracy of the heavy metal pollutants in wheat.
本文提出利用太赫兹光谱和深度支持向量机（DSVM）检测小麦中的重金属污染物。研究考虑对小麦样品中的五种重金属污染物，即砷、铅、汞、铬和镉进行检测。太赫兹光谱数据通过小波去噪进行预处理。引入DSVM以进一步提高支持向量机分类模型的准确性。根据隐藏层数量从1到4时准确率与训练时间的关系，当隐藏层网络为三层时模型表现最佳。此外，使用反向传播算法对整个DSVM网络进行优化。与深度神经网络（DNN）和支持向量机模型相比，经DSVM优化后的所提模型综合评价指标准确率最高，达91.3%。实现了提高小麦中重金属污染物分类准确率的探索。
