Explainable artificial intelligence techniques for interpretation of food models: a review

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…

• FD
  Fundação de Amparo à Pesquisa do Estado de São Paulo

  Awards: 2019/27354-3, 2019/, 2019/03812-2, 2019/27354, 2023/07385-7, 307094/2021-9, Code 001
• CD
  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

  Awards: 2019/27354-3, 307094/2021-9
• CN
  Conselho Nacional de Desenvolvimento Científico e Tecnológico

  Awards: 307094/2021, 2019/27354-3, 140914/2021-8, 2019/03812-2, 307094/2021-9
• UD
  Università degli Studi di Trieste