平滑的
特征选择
Lasso(编程语言)
支持向量机
残余物
相关系数
数学
模式识别(心理学)
均方误差
人工智能
生物系统
计算机科学
算法
统计
万维网
生物
作者
Hongwei Ning,Jiawei Wang,Hui Jiang,Quansheng Chen
标识
DOI:10.1016/j.saa.2022.121545
摘要
Zearalenone (ZEN) can easily contaminate wheat, seriously affecting the quality and safety of wheat grains. In this study, a near-infrared (NIR) spectroscopy detection method for rapid detection of ZEN in wheat grains was proposed. First, the collected original near-infrared spectra were denoised, smoothed and scatter corrected by Savitzky-Golay smoothing (SG-smoothing) and multiple scattering correction (MSC), and then normalized. Three wavelength variable selection algorithms were used to select variables from the preprocessed NIR spectra, which were random frog (RF), successive projections algorithm (SPA), least absolute shrinkage and selection operator (LASSO). Finally, based on the feature variables extracted by the above algorithms, support vector machine (SVM) models were established respectively to realize the quantitative detection of the ZEN in wheat grains. Eventually, the prediction effect of the LASSO-SVM model was the best, the prediction correlation coefficient (RP) was 0.99, the root mean square error of prediction (RMSEP) was 2.1 μg·kg-1, and the residual prediction deviation (RPD) was 6.0. This research shows that the NIR spectroscopy can be used for high-precision quantitative detection of the ZEN in grains, and the research gives a new technical solution for the in-situ detection of mycotoxins in stored grains.
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