Application of multispectral imaging combined with machine learning methods for rapid and non-destructive detection of zearalenone (ZEN) in maize
•GA-BPNN is an efficient method to pretreat high dimension spectral signals.•Multispectral imaging combined with machine learning is a promising technique to detect the quality of maize. Maize is inevitably contaminated by zearalenone (ZEN) that will cause serious harm to human beings. In this study...
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| Published in | Measurement : journal of the International Measurement Confederation Vol. 203; p. 111944 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.11.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0263-2241 1873-412X |
| DOI | 10.1016/j.measurement.2022.111944 |
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| Abstract | •GA-BPNN is an efficient method to pretreat high dimension spectral signals.•Multispectral imaging combined with machine learning is a promising technique to detect the quality of maize.
Maize is inevitably contaminated by zearalenone (ZEN) that will cause serious harm to human beings. In this study, multispectral imaging (MSI) technology combined with different machine learning methods were used to detect ZEN content in maize. The wavelengths that were most related to ZEN content in maize could be selected by genetic algorithm with back-propagation neural network (GA-BPNN). Our results showed that ZEN contamination level could be detected with the accuracy of 93.33 % by GA-BPNN method. In addition, for quantitative prediction of ZEN content GA-BPNN algorithm was the best method with the correlation coefficient (Rp), the root means square error (RMSEP), residual predictive deviation (RPD) and bias achieved to 0.95, 3.66 μg/kg, 5.39 and 1.55 μg/kg, respectively in prediction set. It can be concluded that multispectral imaging combined with machine learning was applicable for rapid measurement of ZEN content in maize. |
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| AbstractList | •GA-BPNN is an efficient method to pretreat high dimension spectral signals.•Multispectral imaging combined with machine learning is a promising technique to detect the quality of maize.
Maize is inevitably contaminated by zearalenone (ZEN) that will cause serious harm to human beings. In this study, multispectral imaging (MSI) technology combined with different machine learning methods were used to detect ZEN content in maize. The wavelengths that were most related to ZEN content in maize could be selected by genetic algorithm with back-propagation neural network (GA-BPNN). Our results showed that ZEN contamination level could be detected with the accuracy of 93.33 % by GA-BPNN method. In addition, for quantitative prediction of ZEN content GA-BPNN algorithm was the best method with the correlation coefficient (Rp), the root means square error (RMSEP), residual predictive deviation (RPD) and bias achieved to 0.95, 3.66 μg/kg, 5.39 and 1.55 μg/kg, respectively in prediction set. It can be concluded that multispectral imaging combined with machine learning was applicable for rapid measurement of ZEN content in maize. |
| ArticleNumber | 111944 |
| Author | Liu, Wei Liu, Changhong Zheng, Lei Deng, Haiyang Shi, Yule |
| Author_xml | – sequence: 1 givenname: Wei surname: Liu fullname: Liu, Wei organization: Intelligent Control and Compute Vision Lab, Hefei University, Hefei 230601, China – sequence: 2 givenname: Haiyang surname: Deng fullname: Deng, Haiyang organization: Intelligent Control and Compute Vision Lab, Hefei University, Hefei 230601, China – sequence: 3 givenname: Yule surname: Shi fullname: Shi, Yule organization: School of Food and Biological Engineering, Hefei University of Technology, Hefei, 230009, China – sequence: 4 givenname: Changhong surname: Liu fullname: Liu, Changhong email: changhong22@hfut.edu.cn organization: School of Food and Biological Engineering, Hefei University of Technology, Hefei, 230009, China – sequence: 5 givenname: Lei surname: Zheng fullname: Zheng, Lei email: lzheng@hfut.edu.cn, lei.zheng@aliyun.com organization: School of Food and Biological Engineering, Hefei University of Technology, Hefei, 230009, China |
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| Keywords | Machine learning method Multispectral imaging Non-destructive detection Zearalenone in maize |
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