Non-destructive hyperspectral imaging in both olive fruit and powder for aflatoxin detection and estimation by machine learning
Year: 2025
Type: article
Abstract: Aflatoxins are toxic and carcinogenic mycotoxins posing a significant threat to human health and food safety. A non-destructive hyperspectral imaging (HSI) system to automatically detect aflatoxin contamination in olive fruit and powder by machine learning was proposed. Imaging was conducted in the 418 to 1072 nm wavelength range. For whole fruit analysis, Linear Discriminant Analysis (LDA) achieved 100% accuracy in binary classifying healthy and contaminated samples. The Support Vector Machine method also reached 98.75% accuracy for the same purpose. For powdered olive samples, PLSR based on full spectral data, yielded coefficient of determination (R 2 ) values of 0.9986 and 0.9858, for calibration and validation disjoint data sets, respectively. Furthermore, combining Decision Tree with a Learning Automata algorithm extracted the 15 optimal most discriminant (effective) wavelength (EW) values, enabling data dimension reduction without a significant loss of discrimination power. Using 15 effective wavelengths, LDA model had a maximum accuracy of 100%. PLSR model developed using the selected effective wavelengths also had robust performance, with R 2 of 0.89, validation set. Findings confirm the high potential of hyperspectral imaging for non-destructive and accurate detection of fungal toxin contamination in plant food, suggesting its potential as a rapid and reliable method in food industry.
Authors: Ahmad Moradi-Chekan, Tarahom Mesri-Gundoshmian, Meysam Latifi-Amoghin, Gholamhossein Shahgholi, Mohammad Shirzad-Iraj, Juan Ignacio Arribas
Institutions: University of Mohaghegh Ardabili, Universidad de Valladolid
Language: English
Subfield: Analytical Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,450
Awards: G3721756624, G4659357
OpenAlex ID: W7116095081