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

Ali Zia1,2, Muhammad Husnain1,3, Sally Buck1

  • 1Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia.

Current Research in Food Science
|April 15, 2025
PubMed
Summary

Deep learning models combined with near-infrared (NIR) spectroscopy enhance chickpea flour quality assessment. Convolutional Neural Networks (CNNs) significantly improved predictions for protein, starch, and fiber content over traditional methods.

Keywords:
AIChickpea flour datasetDeep learningFlour qualityNIRPLSRSpectroscopy

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Area of Science:

  • Agricultural Science
  • Food Science
  • Data Science

Background:

  • Chickpea flour is crucial for plant-based diets, but its quality varies due to genetics, environment, and processing.
  • Standardizing chickpea flour quality is challenging, impacting its use in food production.
  • Accurate and efficient quality assessment methods are needed for consistent plant-based food products.

Purpose of the Study:

  • To integrate deep learning (DL) models with near-infrared (NIR) spectroscopy for improved chickpea flour quality assessment.
  • To compare the performance of various DL models (CNNs, ViTs, GCNs) against traditional methods.
  • To evaluate the accuracy of DL models in predicting key nutritional and compositional attributes of chickpea flour.

Main Methods:

  • Utilized a dataset of 136 chickpea varieties for analysis.
  • Applied deep learning models including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Graph Convolutional Networks (GCNs).
  • Compared the performance of the most effective DL model (CNN) against Partial Least Squares Regression (PLSR).

Main Results:

  • CNN-based deep learning models outperformed the traditional PLSR method in accuracy.
  • Accurate predictions were achieved for protein content, starch, soluble sugars, insoluble fibers, total lipids, and moisture levels.
  • AI-enhanced NIR spectroscopy demonstrated potential for non-destructive, rapid, and reliable chickpea flour analysis.

Conclusions:

  • Deep learning models integrated with NIR spectroscopy offer a promising approach for revolutionizing chickpea flour quality control.
  • AI-driven methods can lead to more consistent and higher-quality plant-based food products.
  • Further advancements in DL models could enable industrial applicability for enhanced food quality assessment.