Analysis of Corn Deterioration Due to Molding Using Surface-Enhanced Raman Spectroscopy
Efficient and rapid identification of corn mildew levels is essential for proper storage and transportation. This study utilized surface-enhanced Raman spectroscopy (SERS) to obtain Raman spectral fingerprints of moldy corn, combined with multi-class support vector machines (SVM) for rapid detection...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
North Carolina State University
2025-01-01
|
| Series: | BioResources |
| Subjects: | |
| Online Access: | https://ojs.bioresources.com/index.php/BRJ/article/view/24125 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823859610591690752 |
|---|---|
| author | Xiaoyan Wei Guifang Wu Pengcheng Qiu Huihe Yang Shubin Yan Jianing Di Xiangpeng Zhao Feixu Zhang Hongda Zhang |
| author_facet | Xiaoyan Wei Guifang Wu Pengcheng Qiu Huihe Yang Shubin Yan Jianing Di Xiangpeng Zhao Feixu Zhang Hongda Zhang |
| author_sort | Xiaoyan Wei |
| collection | DOAJ |
| description | Efficient and rapid identification of corn mildew levels is essential for proper storage and transportation. This study utilized surface-enhanced Raman spectroscopy (SERS) to obtain Raman spectral fingerprints of moldy corn, combined with multi-class support vector machines (SVM) for rapid detection. Spectral data were preprocessed using the Savitzky-Golay smoothing method, and principal component analysis (PCA) was applied to extract the top five components. Feature peaks were identified using partial least squares discriminant analysis (PLS-DA) regression coefficients, supplemented by manual selection, resulting in eight characteristic wavenumber peaks (482, 878, 1046, 1082, 1220, 1276, 1452, and 1590 cm-¹). These features were used for clustering analysis, followed by SVM classification to distinguish mildew levels. The model achieved a 100% recognition rate, validated by cross-validation and confusion matrix analysis. The findings demonstrate that SERS combined with SVM enables precise differentiation of mildew levels, providing reliable support for Raman spectroscopy in fungal detection and grain safety monitoring. |
| format | Article |
| id | doaj-art-5985fe24403941b0a2a52b66665d337b |
| 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-5985fe24403941b0a2a52b66665d337b2025-02-10T23:56:27ZengNorth Carolina State UniversityBioResources1930-21262025-01-01201186018712366Analysis of Corn Deterioration Due to Molding Using Surface-Enhanced Raman SpectroscopyXiaoyan Wei0Guifang Wu1Pengcheng Qiu2Huihe Yang3Shubin Yan4Jianing Di5Xiangpeng Zhao6Feixu Zhang7Hongda Zhang8College 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. China Ordos 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. 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. ChinaEfficient and rapid identification of corn mildew levels is essential for proper storage and transportation. This study utilized surface-enhanced Raman spectroscopy (SERS) to obtain Raman spectral fingerprints of moldy corn, combined with multi-class support vector machines (SVM) for rapid detection. Spectral data were preprocessed using the Savitzky-Golay smoothing method, and principal component analysis (PCA) was applied to extract the top five components. Feature peaks were identified using partial least squares discriminant analysis (PLS-DA) regression coefficients, supplemented by manual selection, resulting in eight characteristic wavenumber peaks (482, 878, 1046, 1082, 1220, 1276, 1452, and 1590 cm-¹). These features were used for clustering analysis, followed by SVM classification to distinguish mildew levels. The model achieved a 100% recognition rate, validated by cross-validation and confusion matrix analysis. The findings demonstrate that SERS combined with SVM enables precise differentiation of mildew levels, providing reliable support for Raman spectroscopy in fungal detection and grain safety monitoring.https://ojs.bioresources.com/index.php/BRJ/article/view/24125moldy cornraman spectroscopyprincipal component analysis (pca)partial least squares regression (pls-regression)support vector machine (svm) |
| spellingShingle | Xiaoyan Wei Guifang Wu Pengcheng Qiu Huihe Yang Shubin Yan Jianing Di Xiangpeng Zhao Feixu Zhang Hongda Zhang Analysis of Corn Deterioration Due to Molding Using Surface-Enhanced Raman Spectroscopy BioResources moldy corn raman spectroscopy principal component analysis (pca) partial least squares regression (pls-regression) support vector machine (svm) |
| title | Analysis of Corn Deterioration Due to Molding Using Surface-Enhanced Raman Spectroscopy |
| title_full | Analysis of Corn Deterioration Due to Molding Using Surface-Enhanced Raman Spectroscopy |
| title_fullStr | Analysis of Corn Deterioration Due to Molding Using Surface-Enhanced Raman Spectroscopy |
| title_full_unstemmed | Analysis of Corn Deterioration Due to Molding Using Surface-Enhanced Raman Spectroscopy |
| title_short | Analysis of Corn Deterioration Due to Molding Using Surface-Enhanced Raman Spectroscopy |
| title_sort | analysis of corn deterioration due to molding using surface enhanced raman spectroscopy |
| topic | moldy corn raman spectroscopy principal component analysis (pca) partial least squares regression (pls-regression) support vector machine (svm) |
| url | https://ojs.bioresources.com/index.php/BRJ/article/view/24125 |
| work_keys_str_mv | AT xiaoyanwei analysisofcorndeteriorationduetomoldingusingsurfaceenhancedramanspectroscopy AT guifangwu analysisofcorndeteriorationduetomoldingusingsurfaceenhancedramanspectroscopy AT pengchengqiu analysisofcorndeteriorationduetomoldingusingsurfaceenhancedramanspectroscopy AT huiheyang analysisofcorndeteriorationduetomoldingusingsurfaceenhancedramanspectroscopy AT shubinyan analysisofcorndeteriorationduetomoldingusingsurfaceenhancedramanspectroscopy AT jianingdi analysisofcorndeteriorationduetomoldingusingsurfaceenhancedramanspectroscopy AT xiangpengzhao analysisofcorndeteriorationduetomoldingusingsurfaceenhancedramanspectroscopy AT feixuzhang analysisofcorndeteriorationduetomoldingusingsurfaceenhancedramanspectroscopy AT hongdazhang analysisofcorndeteriorationduetomoldingusingsurfaceenhancedramanspectroscopy |