High-precision Discrimination of Maize Silage Based on Olfactory Visualization Technology Integrated with Chemometrics Analysis
Rapid, reliable and non-destructive detection of the quality of maize silage is essential to high-efficiency animal husbandry and food safety. In this study, the colorimetric sensor array (CSA) integrated with chemometric methods is innovatively proposed for qualitative discrimination of maize silag...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
North Carolina State University
2024-04-01
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| Series: | BioResources |
| Subjects: | |
| Online Access: | https://ojs.cnr.ncsu.edu/index.php/BRJ/article/view/23138 |
| _version_ | 1797197617021583360 |
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| author | Yan Tao Haiqing Tian Kai Zhao Yang Yu Li'na Guo Genhao Liu Xin Bai |
| author_facet | Yan Tao Haiqing Tian Kai Zhao Yang Yu Li'na Guo Genhao Liu Xin Bai |
| author_sort | Yan Tao |
| collection | DOAJ |
| description | Rapid, reliable and non-destructive detection of the quality of maize silage is essential to high-efficiency animal husbandry and food safety. In this study, the colorimetric sensor array (CSA) integrated with chemometric methods is innovatively proposed for qualitative discrimination of maize silage. First, 12 color-sensitive dyes were selected to fabricate colorimetric sensor arrays to be used as artificial olfactory sensors for obtaining odor fingerprints of maize silage. Machine vision algorithms were utilized to extract the color features, and principal component analysis was applied to reduce the dimensionality of the obtained data. Finally, the PCA results were input variables to develop different qualitative discrimination models. These models involve support vector machines (SVM), extreme learning machine (ELM), and random forest (RF). The analysis results show the 100% correct identification rate for independent samples. The general results sufficiently reveal that olfactory visualization technology integrated with chemometrics analysis has promising applications for high-precision discrimination of maize silage. |
| first_indexed | 2024-04-24T06:46:48Z |
| format | Article |
| id | doaj.art-a1d0d844d2164d35af809f0266802970 |
| institution | Directory Open Access Journal |
| issn | 1930-2126 |
| language | English |
| last_indexed | 2024-04-24T06:46:48Z |
| publishDate | 2024-04-01 |
| publisher | North Carolina State University |
| record_format | Article |
| series | BioResources |
| spelling | doaj.art-a1d0d844d2164d35af809f02668029702024-04-22T18:43:32ZengNorth Carolina State UniversityBioResources1930-21262024-04-01192359736131256High-precision Discrimination of Maize Silage Based on Olfactory Visualization Technology Integrated with Chemometrics AnalysisYan Tao0Haiqing Tian1Kai Zhao2Yang Yu3Li'na Guo4Genhao Liu5Xin Bai6College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, PR ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, PR ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, PR ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, PR ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, PR ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, PR ChinaCollege of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot, PR ChinaRapid, reliable and non-destructive detection of the quality of maize silage is essential to high-efficiency animal husbandry and food safety. In this study, the colorimetric sensor array (CSA) integrated with chemometric methods is innovatively proposed for qualitative discrimination of maize silage. First, 12 color-sensitive dyes were selected to fabricate colorimetric sensor arrays to be used as artificial olfactory sensors for obtaining odor fingerprints of maize silage. Machine vision algorithms were utilized to extract the color features, and principal component analysis was applied to reduce the dimensionality of the obtained data. Finally, the PCA results were input variables to develop different qualitative discrimination models. These models involve support vector machines (SVM), extreme learning machine (ELM), and random forest (RF). The analysis results show the 100% correct identification rate for independent samples. The general results sufficiently reveal that olfactory visualization technology integrated with chemometrics analysis has promising applications for high-precision discrimination of maize silage.https://ojs.cnr.ncsu.edu/index.php/BRJ/article/view/23138maize silageolfactory visualizationcolorimetric sensors arrayqualitative discrimination |
| spellingShingle | Yan Tao Haiqing Tian Kai Zhao Yang Yu Li'na Guo Genhao Liu Xin Bai High-precision Discrimination of Maize Silage Based on Olfactory Visualization Technology Integrated with Chemometrics Analysis BioResources maize silage olfactory visualization colorimetric sensors array qualitative discrimination |
| title | High-precision Discrimination of Maize Silage Based on Olfactory Visualization Technology Integrated with Chemometrics Analysis |
| title_full | High-precision Discrimination of Maize Silage Based on Olfactory Visualization Technology Integrated with Chemometrics Analysis |
| title_fullStr | High-precision Discrimination of Maize Silage Based on Olfactory Visualization Technology Integrated with Chemometrics Analysis |
| title_full_unstemmed | High-precision Discrimination of Maize Silage Based on Olfactory Visualization Technology Integrated with Chemometrics Analysis |
| title_short | High-precision Discrimination of Maize Silage Based on Olfactory Visualization Technology Integrated with Chemometrics Analysis |
| title_sort | high precision discrimination of maize silage based on olfactory visualization technology integrated with chemometrics analysis |
| topic | maize silage olfactory visualization colorimetric sensors array qualitative discrimination |
| url | https://ojs.cnr.ncsu.edu/index.php/BRJ/article/view/23138 |
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