Aflatoxin B1 contamination level detection in almond kernels through short wave infrared hyperspectral image analysis

Aflatoxin B1 (AFB1) is a toxic fungal metabolite that contaminates almonds from cultivation to harvesting. It leads to chronic health problems and significant economic loss to the producers. Therefore, a fast and non-invasive detection technique is crucial for safeguarding food safety by swiftly identifying and eliminating contaminated almonds from the supply chain. Hyperspectral imaging has been explored as a potential non-destructive technology for detecting AFB1. However, the diverse geometries of almonds present a significant challenge on acquired images, thereby impacting the accuracy of the developed prediction and classification models. This study investigates the effectiveness of short-wave infrared (SWIR) hyperspectral imaging combined with deep learning for detecting AFB1 in almonds of varying geometries. Initially, partial least squares regression (PLSR) and support vector machine (SVM) regression models were evaluated for quantification, while SVM and quadratic discriminant analysis (QDA) classifiers were applied for classification. The results indicated that spectral responses varied with almond thickness, making quantification models unreliable for industrial applications. The Competitive Adaptive Reweighted Sampling (CARS) algorithm was employed to identify key spectral features for developing multi-spectral AFB1 classification models to evaluate the feasibility of high-speed, accurate in-line detection. The deep learning approach significantly outperformed traditional machine learning models, with the pre-trained Inception V3 network achieving a cross-validation accuracy of 84.82 %, an F1-score of 0.8522, and an area under curve of 0.893. These findings highlight the superiority of deep learning-based hyperspectral imaging for accurate and reliable AFB1 detection in almonds with diverse shapes and thicknesses . • Almonds Are susceptible to aflatoxin B1 in warm and humidity temperatures. • Uses SWIR hyperspectral imaging for non-destructive aflatoxin B1 detection. • Almond thickness variation impacts the hyperspectral image response. • Aflatoxin B1 classification effective on thickness varies almonds. • Binary classification as more feasible for inline aflatoxin B1 detection.