Effectiveness of Differentiating Mold Levels in Soybeans with Electronic Nose Detection Technology
This study employed electronic nose technology to assess the mold levels in soybeans, conducting analyses on artificially inoculated soybeans with five strains of fungi and distinguishing them from naturally moldy soybeans. Principal component analysis (PCA) and linear discriminant analysis (LDA) we...
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MDPI AG
2024-12-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/13/24/4064 |
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| author | Xuejian Song Lili Qian Dongjie Zhang Xinhui Wang Lixue Fu Mingming Chen |
| author_facet | Xuejian Song Lili Qian Dongjie Zhang Xinhui Wang Lixue Fu Mingming Chen |
| author_sort | Xuejian Song |
| collection | DOAJ |
| description | This study employed electronic nose technology to assess the mold levels in soybeans, conducting analyses on artificially inoculated soybeans with five strains of fungi and distinguishing them from naturally moldy soybeans. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to evaluate inoculated and naturally moldy samples. The results revealed that the most influential sensor was W2W, which is sensitive to organic sulfur compounds, followed by W1W (primarily responsive to inorganic sulfur compounds), W5S (sensitive to small molecular nitrogen oxides), W1S (responsive to short-chain alkanes such as methane), and W2S (sensitive to alcohols, ethers, aldehydes, and ketones). These findings highlight that variations in volatile substances among the moldy soybean samples were predominantly attributed to organic sulfur compounds, with significant distinctions noted in inorganic sulfur, nitrogen compounds, short-chain alkanes, and alcohols/ethers/aldehydes/ketones. The results of the PCA and LDA analyses indicated that while both methods demonstrated moderate effectiveness in distinguishing between different dominant fungal inoculations and naturally moldy soybeans, they were more successful in differentiating various levels of moldiness, achieving a discriminative accuracy rate of 82.72% in LDA. Overall, the findings suggest that electronic nose detection technology can effectively identify mold levels in soybeans. |
| format | Article |
| id | doaj-art-71d6fa7fc65c4f43b8b2592bcc8d0ca1 |
| institution | Kabale University |
| issn | 2304-8158 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Foods |
| spelling | doaj-art-71d6fa7fc65c4f43b8b2592bcc8d0ca12024-12-27T14:26:24ZengMDPI AGFoods2304-81582024-12-011324406410.3390/foods13244064Effectiveness of Differentiating Mold Levels in Soybeans with Electronic Nose Detection TechnologyXuejian Song0Lili Qian1Dongjie Zhang2Xinhui Wang3Lixue Fu4Mingming Chen5College of Food Science, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaCollege of Food Science, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaCollege of Food Science, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaCollege of Food Science, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaCollege of Food Science, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaCollege of Food Science, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaThis study employed electronic nose technology to assess the mold levels in soybeans, conducting analyses on artificially inoculated soybeans with five strains of fungi and distinguishing them from naturally moldy soybeans. Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to evaluate inoculated and naturally moldy samples. The results revealed that the most influential sensor was W2W, which is sensitive to organic sulfur compounds, followed by W1W (primarily responsive to inorganic sulfur compounds), W5S (sensitive to small molecular nitrogen oxides), W1S (responsive to short-chain alkanes such as methane), and W2S (sensitive to alcohols, ethers, aldehydes, and ketones). These findings highlight that variations in volatile substances among the moldy soybean samples were predominantly attributed to organic sulfur compounds, with significant distinctions noted in inorganic sulfur, nitrogen compounds, short-chain alkanes, and alcohols/ethers/aldehydes/ketones. The results of the PCA and LDA analyses indicated that while both methods demonstrated moderate effectiveness in distinguishing between different dominant fungal inoculations and naturally moldy soybeans, they were more successful in differentiating various levels of moldiness, achieving a discriminative accuracy rate of 82.72% in LDA. Overall, the findings suggest that electronic nose detection technology can effectively identify mold levels in soybeans.https://www.mdpi.com/2304-8158/13/24/4064soybeanelectronic nosemildewprincipal component analysislinear discriminant analysis |
| spellingShingle | Xuejian Song Lili Qian Dongjie Zhang Xinhui Wang Lixue Fu Mingming Chen Effectiveness of Differentiating Mold Levels in Soybeans with Electronic Nose Detection Technology Foods soybean electronic nose mildew principal component analysis linear discriminant analysis |
| title | Effectiveness of Differentiating Mold Levels in Soybeans with Electronic Nose Detection Technology |
| title_full | Effectiveness of Differentiating Mold Levels in Soybeans with Electronic Nose Detection Technology |
| title_fullStr | Effectiveness of Differentiating Mold Levels in Soybeans with Electronic Nose Detection Technology |
| title_full_unstemmed | Effectiveness of Differentiating Mold Levels in Soybeans with Electronic Nose Detection Technology |
| title_short | Effectiveness of Differentiating Mold Levels in Soybeans with Electronic Nose Detection Technology |
| title_sort | effectiveness of differentiating mold levels in soybeans with electronic nose detection technology |
| topic | soybean electronic nose mildew principal component analysis linear discriminant analysis |
| url | https://www.mdpi.com/2304-8158/13/24/4064 |
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