Explainable Artificial Intelligence techniques for interpretation of food datasets: a review
It tackles the problem of opaque AI decision-making in Food Engineering, which limits model trust and application, by providing a taxonomy and guidance for researchers, though it is incremental as a review.
The paper reviews eXplainable AI (XAI) techniques for interpreting food datasets, addressing the need for transparency in AI models used in food quality control to enhance reliability and adoption.
Artificial Intelligence (AI) has become essential for analyzing complex data and solving highly-challenging tasks. It is being applied across numerous disciplines beyond computer science, including Food Engineering, where there is a growing demand for accurate and trustworthy predictions to meet stringent food quality standards. However, this requires increasingly complex AI models, raising reliability concerns. In response, eXplainable AI (XAI) has emerged to provide insights into AI decision-making, aiding model interpretation by developers and users. Nevertheless, XAI remains underutilized in Food Engineering, limiting model reliability. For instance, in food quality control, AI models using spectral imaging can detect contaminants or assess freshness levels, but their opaque decision-making process hinders adoption. XAI techniques such as SHAP (Shapley Additive Explanations) and Grad-CAM (Gradient-weighted Class Activation Mapping) can pinpoint which spectral wavelengths or image regions contribute most to a prediction, enhancing transparency and aiding quality control inspectors in verifying AI-generated assessments. This survey presents a taxonomy for classifying food quality research using XAI techniques, organized by data types and explanation methods, to guide researchers in choosing suitable approaches. We also highlight trends, challenges, and opportunities to encourage the adoption of XAI in Food Engineering.