Method of Detecting Microorganisms on the Surface of Mandarin Fish Based on Hyperspectral and Information Fusion
Microorganisms play a key role in fish spoilage and quality deterioration, making the development of a rapid, accurate, and efficient technique for detecting surface microbes essential for enhancing freshness and ensuring the safety of mandarin fish consumption. This study focused on the total viabl...
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MDPI AG
2025-04-01
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| Series: | Foods |
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| author | Tao Yuan Yixiao Ma Zuyu Guo Yijian Wang Liqin Kong Yaoze Feng Haopeng Liu Liang Meng |
| author_facet | Tao Yuan Yixiao Ma Zuyu Guo Yijian Wang Liqin Kong Yaoze Feng Haopeng Liu Liang Meng |
| author_sort | Tao Yuan |
| collection | DOAJ |
| description | Microorganisms play a key role in fish spoilage and quality deterioration, making the development of a rapid, accurate, and efficient technique for detecting surface microbes essential for enhancing freshness and ensuring the safety of mandarin fish consumption. This study focused on the total viable count (TVC) and <i>Escherichia coli</i> levels in the dorsal and ventral parts of fish, and we constructed a detection model using hyperspectral imaging and data fusion. The results showed that comprehensive and simplified models were successfully developed for quantitative detection across all wavelengths. The models performed best at predicting microbial growth on the dorsal side, with the RAW-CARS-PLSR model proving the most effective at predicting TVC and <i>E. coli</i> counts in that region. The RAW-PLSR model was identified as the optimal predictor of the <i>E. coli</i> concentration on the ventral side. A fusion model in the decision layer constructed using the Dempster–Shafer theory of evidence outperformed models relying solely on spectral or textural information, making it an optimal approach for detecting surface microbes in mandarin fish. The best prediction accuracy for dorsal TVC concentration achieved an Rp value of 0.9337, whereas that for ventral TVC concentration reached 0.8443. For the <i>E. coli</i> concentration, the optimal <i>R<sub>p</sub></i> values were 0.8180 for the dorsal section and 0.8512 for separate analysis. |
| format | Article |
| id | doaj-art-e4c68d8972304345a1a08d55ea0f6f4e |
| institution | Kabale University |
| issn | 2304-8158 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Foods |
| spelling | doaj-art-e4c68d8972304345a1a08d55ea0f6f4e2025-08-20T03:52:56ZengMDPI AGFoods2304-81582025-04-01149146810.3390/foods14091468Method of Detecting Microorganisms on the Surface of Mandarin Fish Based on Hyperspectral and Information FusionTao Yuan0Yixiao Ma1Zuyu Guo2Yijian Wang3Liqin Kong4Yaoze Feng5Haopeng Liu6Liang Meng7College of Engineering, Huazhong Agricultural University, Wuhan 430070, ChinaSchool of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430070, ChinaSchool of Electrical and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430070, ChinaCollege of Engineering, Huazhong Agricultural University, Wuhan 430070, ChinaCollege of Engineering, Huazhong Agricultural University, Wuhan 430070, ChinaCollege of Engineering, Huazhong Agricultural University, Wuhan 430070, ChinaCollege of Engineering, Huazhong Agricultural University, Wuhan 430070, ChinaCollege of Engineering, Huazhong Agricultural University, Wuhan 430070, ChinaMicroorganisms play a key role in fish spoilage and quality deterioration, making the development of a rapid, accurate, and efficient technique for detecting surface microbes essential for enhancing freshness and ensuring the safety of mandarin fish consumption. This study focused on the total viable count (TVC) and <i>Escherichia coli</i> levels in the dorsal and ventral parts of fish, and we constructed a detection model using hyperspectral imaging and data fusion. The results showed that comprehensive and simplified models were successfully developed for quantitative detection across all wavelengths. The models performed best at predicting microbial growth on the dorsal side, with the RAW-CARS-PLSR model proving the most effective at predicting TVC and <i>E. coli</i> counts in that region. The RAW-PLSR model was identified as the optimal predictor of the <i>E. coli</i> concentration on the ventral side. A fusion model in the decision layer constructed using the Dempster–Shafer theory of evidence outperformed models relying solely on spectral or textural information, making it an optimal approach for detecting surface microbes in mandarin fish. The best prediction accuracy for dorsal TVC concentration achieved an Rp value of 0.9337, whereas that for ventral TVC concentration reached 0.8443. For the <i>E. coli</i> concentration, the optimal <i>R<sub>p</sub></i> values were 0.8180 for the dorsal section and 0.8512 for separate analysis.https://www.mdpi.com/2304-8158/14/9/1468hyperspectral technologymandarin fishmicrobesnon-destructive testingfreshness |
| spellingShingle | Tao Yuan Yixiao Ma Zuyu Guo Yijian Wang Liqin Kong Yaoze Feng Haopeng Liu Liang Meng Method of Detecting Microorganisms on the Surface of Mandarin Fish Based on Hyperspectral and Information Fusion Foods hyperspectral technology mandarin fish microbes non-destructive testing freshness |
| title | Method of Detecting Microorganisms on the Surface of Mandarin Fish Based on Hyperspectral and Information Fusion |
| title_full | Method of Detecting Microorganisms on the Surface of Mandarin Fish Based on Hyperspectral and Information Fusion |
| title_fullStr | Method of Detecting Microorganisms on the Surface of Mandarin Fish Based on Hyperspectral and Information Fusion |
| title_full_unstemmed | Method of Detecting Microorganisms on the Surface of Mandarin Fish Based on Hyperspectral and Information Fusion |
| title_short | Method of Detecting Microorganisms on the Surface of Mandarin Fish Based on Hyperspectral and Information Fusion |
| title_sort | method of detecting microorganisms on the surface of mandarin fish based on hyperspectral and information fusion |
| topic | hyperspectral technology mandarin fish microbes non-destructive testing freshness |
| url | https://www.mdpi.com/2304-8158/14/9/1468 |
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