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TRUVACE RECORD VERSION
record: TRV-2026-0260
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-18T14:16:23.311485Z
status: published
lens: trace
sector: lifestyle
headline: Explainable artificial intelligence techniques for interpretation of food models: a review
dek: 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…
gain_title: 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.
problem_title: Opaque AI decision-making in food quality models hinders adoption by inspectors and limits reliability, with explainable AI still underutilized in Food Engineering.
trace_subject: use of explainable AI techniques to interpret food quality control models for contaminant detection and freshness assessment
gain_reading: 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.
gain_evidence: 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
problem_reading: Opaque AI decision-making in food quality models hinders adoption by inspectors and limits reliability, with explainable AI still underutilized in Food Engineering.
problem_evidence: their opaque decision-making process hinders adoption | XAI remains underutilized in Food Engineering, limiting model reliability
quick_read: Published April 24, 2026, this peer-reviewed review examines the use of explainable AI in Food Engineering. It describes how AI models using spectral imaging are used to detect contaminants and assess freshness to meet food quality standards, but their complexity creates opacity that hinders adoption by quality control inspectors.

The work matters because transparency is needed for reliable food safety decisions that affect public health. It shows XAI can make model reasoning inspectable by highlighting influential wavelengths and image regions, while also documenting that XAI remains underutilized, leaving questions about best practices, scalability across food types, and validation in operational inspection workflows.
limitation: XAI remains underutilized in Food Engineering, which limits model reliability and adoption despite available explanation methods.
tag: Automated dual reading
key_points: AI is being applied in Food Engineering to meet stringent food quality standards but increasingly complex models raise concerns about opacity. | Survey proposes taxonomy for food quality research using XAI organized by data types and explanation methods to guide method selection. | Example application is spectral imaging models for detecting contaminants and assessing freshness where XAI highlights influential wavelengths and image regions.
rundown: The review focuses on Food Engineering applications where AI analyzes complex data such as spectral imaging to predict quality attributes. It notes demand for accurate predictions to meet stringent standards has driven more complex models.

It surveys XAI approaches including SHAP (Shapley Additive Explanations) and Grad-CAM (Gradient-weighted Class Activation Mapping) and organizes existing work by data types and explanation methods, highlighting trends, challenges and opportunities for adoption.
sources:
- peer_reviewed | Artificial Intelligence Review | https://doi.org/10.1007/s10462-026-11553-2 | 2026-04-24
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