Detection of Corn Quality Based on Surface-Enhanced Raman Spectroscopy and Electronic Nose Technology
This study explored a corn quality detection method based on surface-enhanced Raman spectroscopy (SERS) and electronic nose technology. The content of aflatoxin (AFB1) and ochratoxin (OTA) in corn samples was detected by fluorescence immunoassay as the basic data for the experiment. Subsequently, th...
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| Format: | Article |
| Language: | English |
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North Carolina State University
2025-01-01
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| Series: | BioResources |
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| Online Access: | https://ojs.bioresources.com/index.php/BRJ/article/view/24148 |
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| author | HuiHe Yang XiaoYan Wei Guifang Wu PengCheng Qiu JiaNing Di XiangPeng Zhao WenDong Zhong He Ren |
| author_facet | HuiHe Yang XiaoYan Wei Guifang Wu PengCheng Qiu JiaNing Di XiangPeng Zhao WenDong Zhong He Ren |
| author_sort | HuiHe Yang |
| collection | DOAJ |
| description | This study explored a corn quality detection method based on surface-enhanced Raman spectroscopy (SERS) and electronic nose technology. The content of aflatoxin (AFB1) and ochratoxin (OTA) in corn samples was detected by fluorescence immunoassay as the basic data for the experiment. Subsequently, the SERS curve of the corn samples was measured, and the electronic nose was used to analyze the odor of the samples. Combining the relationship between SERS curves, electronic nose data, and the toxin content in corn, a prediction model was established by using the random forest (RF) method. The results showed that the model’s coefficient of determination of the test set for predicting AFB1 reached 0.70, and the model’s coefficient of determination of the test set for predicting OTA reached 0.74. This experiment showed that SERS and electronic nose technology can effectively detect the mycotoxin content in corn samples, which provides a new method to predict the toxin content in corn. |
| format | Article |
| id | doaj-art-8462419a26d041139f752cd3266920f2 |
| institution | Kabale University |
| issn | 1930-2126 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | North Carolina State University |
| record_format | Article |
| series | BioResources |
| spelling | doaj-art-8462419a26d041139f752cd3266920f22025-02-10T23:56:27ZengNorth Carolina State UniversityBioResources1930-21262025-01-01201207120822395Detection of Corn Quality Based on Surface-Enhanced Raman Spectroscopy and Electronic Nose TechnologyHuiHe Yang0XiaoYan Wei1Guifang Wu2PengCheng Qiu3JiaNing Di4XiangPeng Zhao5WenDong Zhong6He Ren7College of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, P.R. ChinaCollege of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, P.R. ChinaCollege of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, P.R. ChinaOrdos Agricultural and Livestock Products Quality and Safety Center, Ordos 017000, P.R. ChinaCollege of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, P.R. ChinaCollege of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, P.R. ChinaCollege of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, P.R. ChinaCollege of Mechanical & Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, P.R. ChinaThis study explored a corn quality detection method based on surface-enhanced Raman spectroscopy (SERS) and electronic nose technology. The content of aflatoxin (AFB1) and ochratoxin (OTA) in corn samples was detected by fluorescence immunoassay as the basic data for the experiment. Subsequently, the SERS curve of the corn samples was measured, and the electronic nose was used to analyze the odor of the samples. Combining the relationship between SERS curves, electronic nose data, and the toxin content in corn, a prediction model was established by using the random forest (RF) method. The results showed that the model’s coefficient of determination of the test set for predicting AFB1 reached 0.70, and the model’s coefficient of determination of the test set for predicting OTA reached 0.74. This experiment showed that SERS and electronic nose technology can effectively detect the mycotoxin content in corn samples, which provides a new method to predict the toxin content in corn.https://ojs.bioresources.com/index.php/BRJ/article/view/24148cornfluorescence immunoassaysurface-enhanced raman spectroscopyelectronic noserandom forest |
| spellingShingle | HuiHe Yang XiaoYan Wei Guifang Wu PengCheng Qiu JiaNing Di XiangPeng Zhao WenDong Zhong He Ren Detection of Corn Quality Based on Surface-Enhanced Raman Spectroscopy and Electronic Nose Technology BioResources corn fluorescence immunoassay surface-enhanced raman spectroscopy electronic nose random forest |
| title | Detection of Corn Quality Based on Surface-Enhanced Raman Spectroscopy and Electronic Nose Technology |
| title_full | Detection of Corn Quality Based on Surface-Enhanced Raman Spectroscopy and Electronic Nose Technology |
| title_fullStr | Detection of Corn Quality Based on Surface-Enhanced Raman Spectroscopy and Electronic Nose Technology |
| title_full_unstemmed | Detection of Corn Quality Based on Surface-Enhanced Raman Spectroscopy and Electronic Nose Technology |
| title_short | Detection of Corn Quality Based on Surface-Enhanced Raman Spectroscopy and Electronic Nose Technology |
| title_sort | detection of corn quality based on surface enhanced raman spectroscopy and electronic nose technology |
| topic | corn fluorescence immunoassay surface-enhanced raman spectroscopy electronic nose random forest |
| url | https://ojs.bioresources.com/index.php/BRJ/article/view/24148 |
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