Non-destructive online detection of early moldy core apples based on Vis/NIR transmission spectroscopy

• Published in: Chemical and biological technologies in agriculture Vol. 11; no. 1; p. 63
• Main Authors: Jiang, Xiaogang, Ge, Kang, Liu, Zhi, Chen, Nan, Ouyang, Aiguo, Liu, Yande, Huang, Yuyang, Li, Jinghu, Hu, Mingmao
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2024; Springer Nature B.V; SpringerOpen
• Subjects: 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; Wavelength selection; Moldy core; Transmission spectroscopy; Online detection
• ISSN: 2196-5641; 2196-5641
• DOI: 10.1186/s40538-024-00588-8

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