Explainable AI-driven wavelength selection for hyperspectral imaging of honey products

Abstract

Hyperspectral imaging (HSI) offers a rapid and non-destructive method for inspecting various food and agricultural products. Though HSI delivers rich data with its continuous spectral bands, it simultaneously presents challenges due to its high dimensionality, potential redundancy, and noise interference. To tackle these challenges, dimension reduction becomes essential in HSI data processing. Our study integrated 11 explainable artificial intelligence (XAI) algorithms with convolutional neural networks (CNNs) to craft an advanced wavelength selection method tailored for honey product analysis. We developed two CNN models and applied five normalization techniques to differentiate honey derived from 23 distinct botanical sources. Through the XAI algorithms, we determined the significance of each spectral wavelength, pinpointing the most characteristic bands. We then used the top eight spectral bands—selected from the averaged significance scores across the XAI algorithms—to optimize the combination of CNN classifiers and normalization techniques. This approach not only streamlines HSI data analysis but also validates the robustness of our XAI-based feature selection methodology. Experimental results demonstrated that the CNN achieved a macro average F1 score of ≥0.99 using just 8 spectral bands selected by averaging the feature importance scores from different XAI algorithms. The generalization ability of our proposed band selection method was further demonstrated by experiments on three HSI remote sensing databases. Overall, our research highlights the potential of XAI algorithms in feature selection, significantly reducing the dimensionality of HSI data without compromising performance. This approach establishes a methodological foundation for designing a cost-effective multispectral imaging system capable of distinguishing the botanical origin of honey.