Recognition of old and new shells based on terahertz time-domain spectroscopy and machine learning
BAI Xuejie¹, LIAN Feiyu^1,2, FU Maixia^1,2
1. College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China;
2. Ministry of Education Key Laboratory of Grain Information Technology & Control, Henan University of Technology, Zhengzhou 450001, China
Abstract: The classification of old and new shells by traditional methods is inaccurate and unstable, which will cause damage to the sample. In addition, the terahertz spectrum lacks features that can be directly resolved manually. Therefore, a new shell recognition method based on terahertz time-domain spectroscopy and machine learning is proposed. Firstly, the terahertz time-domain spectroscopy system (THz-TDS) is adopted to study the terahertz time domain spectrum, frequency domain spectrum, refractive index spectrum and absorption spectrum characteristics of new and old shells. Secondly, spectral characteristic data were obtained by using principal component analysis (PCA) and this process accord with the requirement that all principal components contain more than 80% of the original data. Four kinds of spectrum can be used to extract four kinds of principal components, whose number is respectively 4, 4, 5 and 4. Finally, Adaboost was used to fuse the principal components and support vector machine (SVM) model was used to identify the new and old shell species. Through the comparative analysis of three kinds of kernel functions (Linear, Polynomial and Radial Basis Function), the Radial Basis Function has the best results and can be selected as the optimal kernel function. The results show that the PCA-Adaboost-SVM model has a 98% correct recognition rate of old and new shells when the Radial Basis Function kernel is used and the parameter C is 2.1 and σ is 4.4. PCA-Adaboost-SVM has higher accuracy and more stable performance than BP neural network, partial least squares regression (PLS) and principal component regression (PCR).In addition, terahertz time-domain spectroscopy combined with machine learning is feasible to identify new and old shell species.
Keywords: shell;terahertz time-domain spectroscopy;PCA;SVM;multidimensional feature fusion
2022, 48(12):172-180  收稿日期: 2022-06-15;收到修改稿日期: 2022-07-15
基金项目: 粮食信息处理与控制教育部重点实验室开放基金（KFJJ-2021-103）；河南省高等学校重点科研项目（22A510014）
作者简介: 白雪杰(1999-),男,河北张家口市人,专业方向为电子信息工程
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