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 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.

翻译：人工智能已成为分析复杂数据和解决高难度任务的关键技术。其应用范围已超越计算机科学，延伸至食品工程等多个学科领域，该领域对满足严苛食品质量标准的准确可靠预测需求日益增长。然而，这需要日益复杂的AI模型，从而引发担忧。为此，可解释性人工智能应运而生，旨在提供AI决策过程的洞察，帮助开发者和用户理解模型。尽管如此，XAI在食品工程中的应用仍显不足，限制了模型的可靠性。例如，在食品质量控制中，采用光谱成像的AI模型可检测污染物或评估新鲜度，但其不透明的决策过程阻碍了应用。诸如SHAP和Grad-CAM等XAI技术可精准定位对预测贡献最大的光谱波长或图像区域，从而提升透明度，并帮助质量控制检验员验证AI生成的结果。本综述构建了一个用于分类基于XAI技术的食品质量研究的分类体系，按数据类型和解释方法组织，以指导研究人员选择合适的方法。我们还重点介绍了趋势、挑战和机遇，以促进XAI在食品工程领域的应用。