aceExplainable artificial intelligence techniques for interpretation of food models: a review
Abstract 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 reliable predictions to meet stringent food quality standards. However, this requires increasingly complex AI models, raising concerns. In response, eXplainable AI (XAI) has emerged to provide insights into AI decision-making, aiding model int…
Explainable AI methods such as SHAP and Grad-CAM improve food quality control by identifying which spectral wavelengths or image regions drive predictions, increasing transparency for inspectors verifying contaminant detection and freshness assessments.
Opaque AI decision-making in food quality models hinders adoption by inspectors and limits reliability, with explainable AI still underutilized in Food Engineering.
XAI remains underutilized in Food Engineering, which limits model reliability and adoption despite available explanation methods.
Evidence
- Peer-reviewedArtificial Intelligence Review2026-04-24
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Truvace Impact Record TRV-2026-0260, v1: “Explainable artificial intelligence techniques for interpretation of food models: a review.” Truvace, 2026-07-18. /record/TRV-2026-0260 (accessed at citation time). sha256 dc64dbb3365076f0…
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