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Rapid and nondestructive THz inspection of unsound kernel of sunflower seed based on SMOTE algorithm

文献类型： 外文期刊

作者： Yuan, Xiyan 1 ; Li, Yang 1 ; Wu, Jingzhu 1 ; Li, Jiangbo 2 ; Chen, Yuanyuan 1 ; Sun, Xiaorong 1 ; Zhang, Shanzhe 1 ;

作者机构： 1.Beijing Technol & Business Univ, Beijing Key Lab Big Data Technol Food Safety, Beijing 100048, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

关键词： Terahertz time-domain spectroscopy; Synthetic minority over-sampling technique; Sunflower seeds; Unsound kernel; Least squares support vector machine

期刊名称：INFRARED PHYSICS & TECHNOLOGY （ 影响因子：3.3； 五年影响因子：3.2 ）

ISSN： 1350-4495

年卷期： 2023 年 133 卷

页码：

收录情况： SCI

摘要： Inspection of unsound kernel of sunflower seed do an important role to the safe storage of sun-flower seeds and the quality of its processed products. A rapid and nondestructive method based on terahertz (THZ) time-domain spectroscopy was studied here to detect unsound kernel of sunflower seed. To explore the feasibility, the THz spectra of 446 sunflower seed samples were obtained. The synthetic minority over-sampling technique (SMOTE) was applied to balance the number of normal and unsound samples in the training set. To test effectiveness of SMOTE, the proportion of normal and unsound samples was set 4:1, 2:1 and 1.5:1 respectively in the experiment, then, new unsound samples was created by SMOTE algorithm till the number of unsound same to the normal, finally, the least squares support vector machine (LS-SVM) was used to construct the discrimination model. Comparing the performance of the model used SMOTE before and after, the sensitivity in the test set was improved from 0 % to 91.04 %, from 34.52 % to 92.86 %, and from 51.85 % to 92.59 % respectively. Five independent experiments with new sets of sample were repeated to verify that the model is robust, and the accuracy fluctuation is not more than & PLUSMN;5 %. THz time-domain spectrum, combined with SMOTE algorithm can effectively solve the problem of sample imbalance, thereby improving model performance. It is expected to provide feasible reference for accurate, nondestructive and rapid inspection of sunflower seeds and other shell agricultural products.

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