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Explainable AI-Guided Hyperspectral Feature Selection in Fruit Quality Assessment and Spatial Visualization.

Most Mira Khatun1, Md Zohurul Islam2, Md Niaz Imtiaz2

  • 1Department of Statistics, Pabna University of Science and Technology, Pabna, Bangladesh.

Journal of Food Science
|March 20, 2026
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Summary
This summary is machine-generated.

This study introduces a novel method using a genetic algorithm (GA) and explainable artificial intelligence (XAI) for selecting key wavelengths to predict apple dry matter content (DMC). This approach enhances non-destructive food quality assessment and visualization.

Keywords:
explainable artificial intelligencefeature selectiongenetic algorithmhyperspectral imagingmachine learningspatial visualization

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Area of Science:

  • Agricultural Science
  • Food Science
  • Data Science

Background:

  • Hyperspectral imaging (HSI) coupled with machine learning offers non-destructive food quality assessment.
  • Spectral data often suffers from multicollinearity and redundancy, hindering model accuracy and efficiency.
  • Effective key wavelength selection is crucial for optimizing HSI-based quality prediction models.

Purpose of the Study:

  • To develop and validate an innovative method for selecting key wavelengths for predicting apple dry matter content (DMC).
  • To integrate explainable artificial intelligence (XAI) with a genetic algorithm (GA) for feature selection in HSI.
  • To compare the performance of the proposed method against existing feature selection techniques.

Main Methods:

  • A hybrid approach combining a genetic algorithm (GA) with explainable artificial intelligence (XAI) was employed for key wavelength selection.
  • Partial least squares regression (PLSR) was used to build a predictive model based on the selected wavelengths.
  • The performance was evaluated against Recursive Feature Elimination (RFE) and Competitive Adaptive Reweighted Sampling (CARS).
  • The selected features were applied to hyperspectral images for pixelwise DMC visualization.

Main Results:

  • The GA-XAI selected feature set resulted in a superior PLSR model compared to RFE and CARS.
  • The developed model achieved a high coefficient of determination (R2) of 0.46 and a low root mean squared error (RMSE) of 0.70%.
  • Pixelwise visualization of apple DMC distribution was successfully achieved, offering spatial insights into fruit composition.

Conclusions:

  • The integration of XAI with evolutionary feature selection provides a transparent and efficient strategy for non-destructive fruit quality assessment.
  • This method effectively addresses multicollinearity and redundancy in spectral data, improving prediction accuracy.
  • The developed approach enables both accurate prediction and spatial visualization of fruit quality parameters like DMC.