Non-destructive online detection of early moldy core apples based on Vis/NIR transmission spectroscopy
Apple moldy core is a fungus-infested disease that is extremely insidious, usually occurring inside the fruit, making it very difficult to distinguish from the exterior with the naked eye. Using VIS/NIR transmission spectroscopy, this study successfully detected moldy core apples. By combining four...
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| Published in | Chemical and biological technologies in agriculture Vol. 11; no. 1; p. 63 |
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| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Cham
Springer International Publishing
01.12.2024
Springer Nature B.V SpringerOpen |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2196-5641 2196-5641 |
| DOI | 10.1186/s40538-024-00588-8 |
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| Abstract | Apple moldy core is a fungus-infested disease that is extremely insidious, usually occurring inside the fruit, making it very difficult to distinguish from the exterior with the naked eye. Using VIS/NIR transmission spectroscopy, this study successfully detected moldy core apples. By combining four wavelength selection algorithms (CARS, CARS-SPA, MC-UVE, and MC-UVE-SPA) with four classifiers (SVM, ELM, KNN, and LDA-KNN), discrimination models were established for two-class and three-class classifications. MC-UVE-SPA-LDA-KNN achieved an AUC of 0.99 and an accuracy of 98.82% for two-class classification, while MC-UVE-SPA achieved an AUC of 0.99 and an accuracy of 97.64% for three-class classification. This confirms MC-UVE-SPA as an effective tool for selecting wavelengths specific to moldy core apples, facilitating precise identification and differentiation of apple states. This study advances dynamic online detection of early-stage moldy core conditions in apples, reducing post-harvest disease occurrence and preserving fruit quality effectively.
Graphical Abstract |
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| AbstractList | Apple moldy core is a fungus-infested disease that is extremely insidious, usually occurring inside the fruit, making it very difficult to distinguish from the exterior with the naked eye. Using VIS/NIR transmission spectroscopy, this study successfully detected moldy core apples. By combining four wavelength selection algorithms (CARS, CARS-SPA, MC-UVE, and MC-UVE-SPA) with four classifiers (SVM, ELM, KNN, and LDA-KNN), discrimination models were established for two-class and three-class classifications. MC-UVE-SPA-LDA-KNN achieved an AUC of 0.99 and an accuracy of 98.82% for two-class classification, while MC-UVE-SPA achieved an AUC of 0.99 and an accuracy of 97.64% for three-class classification. This confirms MC-UVE-SPA as an effective tool for selecting wavelengths specific to moldy core apples, facilitating precise identification and differentiation of apple states. This study advances dynamic online detection of early-stage moldy core conditions in apples, reducing post-harvest disease occurrence and preserving fruit quality effectively. Apple moldy core is a fungus-infested disease that is extremely insidious, usually occurring inside the fruit, making it very difficult to distinguish from the exterior with the naked eye. Using VIS/NIR transmission spectroscopy, this study successfully detected moldy core apples. By combining four wavelength selection algorithms (CARS, CARS-SPA, MC-UVE, and MC-UVE-SPA) with four classifiers (SVM, ELM, KNN, and LDA-KNN), discrimination models were established for two-class and three-class classifications. MC-UVE-SPA-LDA-KNN achieved an AUC of 0.99 and an accuracy of 98.82% for two-class classification, while MC-UVE-SPA achieved an AUC of 0.99 and an accuracy of 97.64% for three-class classification. This confirms MC-UVE-SPA as an effective tool for selecting wavelengths specific to moldy core apples, facilitating precise identification and differentiation of apple states. This study advances dynamic online detection of early-stage moldy core conditions in apples, reducing post-harvest disease occurrence and preserving fruit quality effectively. Graphical Abstract Abstract Apple moldy core is a fungus-infested disease that is extremely insidious, usually occurring inside the fruit, making it very difficult to distinguish from the exterior with the naked eye. Using VIS/NIR transmission spectroscopy, this study successfully detected moldy core apples. By combining four wavelength selection algorithms (CARS, CARS-SPA, MC-UVE, and MC-UVE-SPA) with four classifiers (SVM, ELM, KNN, and LDA-KNN), discrimination models were established for two-class and three-class classifications. MC-UVE-SPA-LDA-KNN achieved an AUC of 0.99 and an accuracy of 98.82% for two-class classification, while MC-UVE-SPA achieved an AUC of 0.99 and an accuracy of 97.64% for three-class classification. This confirms MC-UVE-SPA as an effective tool for selecting wavelengths specific to moldy core apples, facilitating precise identification and differentiation of apple states. This study advances dynamic online detection of early-stage moldy core conditions in apples, reducing post-harvest disease occurrence and preserving fruit quality effectively. Graphical Abstract |
| ArticleNumber | 63 |
| Author | Chen, Nan Ouyang, Aiguo Liu, Yande Huang, Yuyang Hu, Mingmao Ge, Kang Liu, Zhi Jiang, Xiaogang Li, Jinghu |
| Author_xml | – sequence: 1 givenname: Xiaogang surname: Jiang fullname: Jiang, Xiaogang organization: School of Mechatronics & Vehicle Engineering East, China Jiaotong University, Institute of Intelligent Mechanical and Electrical Equipment Innovation – sequence: 2 givenname: Kang surname: Ge fullname: Ge, Kang organization: School of Mechatronics & Vehicle Engineering East, China Jiaotong University, Institute of Intelligent Mechanical and Electrical Equipment Innovation – sequence: 3 givenname: Zhi surname: Liu fullname: Liu, Zhi organization: School of Mechatronics & Vehicle Engineering East, China Jiaotong University, Institute of Intelligent Mechanical and Electrical Equipment Innovation – sequence: 4 givenname: Nan surname: Chen fullname: Chen, Nan organization: School of Mechatronics & Vehicle Engineering East, China Jiaotong University, Institute of Intelligent Mechanical and Electrical Equipment Innovation – sequence: 5 givenname: Aiguo surname: Ouyang fullname: Ouyang, Aiguo organization: School of Mechatronics & Vehicle Engineering East, China Jiaotong University, Institute of Intelligent Mechanical and Electrical Equipment Innovation – sequence: 6 givenname: Yande surname: Liu fullname: Liu, Yande email: jxliuyd@163.com organization: School of Mechatronics & Vehicle Engineering East, China Jiaotong University, Institute of Intelligent Mechanical and Electrical Equipment Innovation – sequence: 7 givenname: Yuyang surname: Huang fullname: Huang, Yuyang organization: Suzhou Suna Optoelectronics Company Limited – sequence: 8 givenname: Jinghu surname: Li fullname: Li, Jinghu organization: Xiamen Yixinyuan Semiconductor Technology Company Limited – sequence: 9 givenname: Mingmao surname: Hu fullname: Hu, Mingmao organization: School of Mechanical Engineering, Hubei University of Automotive Technology |
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| CitedBy_id | crossref_primary_10_1016_j_jfca_2024_106812 crossref_primary_10_3390_foods13233761 |
| Cites_doi | 10.1016/S0925-4005(00)00494-9 10.1016/s0925-5214(02)00066-2 10.1016/j.postharvbio.2016.01.009 10.1016/j.postharvbio.2014.03.005 10.1016/j.postharvbio.2012.08.008 10.1016/j.postharvbio.2013.07.005 10.18201/ijisae.270416 10.1016/j.postharvbio.2020.111417 10.1109/IJCNN.2004.1380068 10.1016/j.scienta.2019.109073 10.1007/s12161-014-9832-8 10.1007/978-1-4757-3264-1 10.1016/S0169-7439(01)00119-8 10.3390/agriculture13081642 10.3390/foods11131923 10.1007/s11947-009-0256-1 10.1016/j.infrared.2019.103099 10.1016/j.postharvbio.2016.10.010 10.1007/s12161-015-0251-2 10.1016/j.compag.2021.106638 10.1177/09670335211057235 10.1016/j.postharvbio.2020.111276 10.1016/j.postharvbio.2020.111348 10.1039/c3ay41907j 10.1016/j.infrared.2023.104714 10.1142/s0218001419500204 10.1016/j.aca.2009.06.046 10.1016/s0925-5214(02)00122-9 10.1016/j.aiia.2019.05.001 10.1016/j.jfoodeng.2007.02.041 10.1094/PDIS-01-21-0025-PDN 10.1016/j.foodchem.2016.09.011 10.1016/j.postharvbio.2021.111562 |
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| Snippet | Apple moldy core is a fungus-infested disease that is extremely insidious, usually occurring inside the fruit, making it very difficult to distinguish from the... Abstract Apple moldy core is a fungus-infested disease that is extremely insidious, usually occurring inside the fruit, making it very difficult to distinguish... |
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| SubjectTerms | Accuracy Agriculture Algorithms Apples Bioindication of agroecosystem pollution: integrating biochemical analyses with proximal and remote sensing technologies Biomedical and Life Sciences Classification Disease disease occurrence Food quality fruit quality Fruits Fungal infections Life Sciences molds (fungi) Moldy core Online detection Organic Chemistry Plant Biochemistry Plant Physiology Post-harvest decay postharvest diseases Scientific imaging Soil Science & Conservation Spectroscopy Transmission spectroscopy Wavelength selection Wavelengths |
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| Title | Non-destructive online detection of early moldy core apples based on Vis/NIR transmission spectroscopy |
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