Unlocking chickpea flour potential: AI-powered prediction for quality assessment and compositional characterisation

Abstract

The growing demand for sustainable, nutritious, and environmentally friendly food sources has placed chickpea flour as a vital component in the global shift to plant-based diets. However, the inherent variability in the composition of chickpea flour, influenced by genetic diversity, environmental conditions, and processing techniques, poses significant challenges to standardisation and quality control. This study explores the integration of deep learning models with near-infrared (NIR) spectroscopy to improve the accuracy and efficiency of chickpea flour quality assessment. Using a dataset comprising 136 chickpea varieties, the research compares the performance of several state-of-the-art deep learning models, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Graph Convolutional Networks (GCNs), and compares the most effective model, CNN, against the traditional Partial Least Squares Regression (PLSR) method. The results demonstrate that CNN-based models outperform PLSR, providing more accurate predictions for key quality attributes such as protein content, starch, soluble sugars, insoluble fibres, total lipids, and moisture levels. The study highlights the potential of AI-enhanced NIR spectroscopy to revolutionise quality assessment in the food industry by offering a non-destructive, rapid, and reliable method for analysing chickpea flour. Despite the challenges posed by the limited dataset, deep learning models exhibit capabilities that suggest that further advancements would allow their industrial applicability. This research paves the way for broader applications of AI-driven quality control in food production, contributing to the development of more consistent and high-quality plant-based food products.