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利用Videometerlab多光谱成像系统预测呕吐毒素-脱氧雪腐镰刀菌烯醇含量
发表时间:2020-06-16 10:37:05点击:1044
较近,科学家利用Videometerlab 多光谱成像系统发表了题为Rapid and nondestructive determination of deoxynivalenol (DON) content in wheat using multispectral imaging (MSI) technology with chemometric methods的文章,研究结果显示多光谱成像技术和化学计量法结合是有效、无损测定小麦中DON-呕吐毒素-脱氧雪腐镰刀菌烯醇的方法。
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Rapid and nondestructive determination of deoxynivalenol (DON) content in wheat using multispectral imaging (MSI) technology with chemometric methods
Article in Analytical methods · June 2020 with 1 Reads
DOI: 10.1039/D0AY00859A
Abstract
Wheat is susceptible to contamination of deoxynivalenol (DON) which is regarded as a class III carcinogen. In this paper, a rapid and nondestructive method for DON content determination and contamination degree discrimination in wheat was developed by using a multispectral imaging (405-970 nm) system. Genetic algorithm (GA) and principal component analysis (PCA), as preprocessing methods, were used to obtain the best spectral characteristics. The determination model was established by combining preprocessing methods and chemometric methods including partial least squares (PLS), support vector machines (SVM) and back propagation neural network (BPNN). The best quantitative determination result was obtained based on GA-SVM with the correlation coefficient of prediction (Rp), the root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) were 0.9988, 365.3 μg/kg and 8.6, respectively. Furthermore, the accuracy of contamination degree classification was up to 94.29 % in the prediction set by using the PCA-PLS model. The results showed that the combination of multispectral imaging technology and chemometrics was an effective and nondestructive method for the determination of DON in wheat.