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Artificial intelligence (AI) and machine learning (ML) enhance food science through advanced sensing technologies. However, successful implementation requires addressing challenges in data collection, model development, and validation for reliable food safety applications.

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Area of Science:

  • Food Science
  • Analytical Chemistry
  • Data Science

Background:

  • Modern food science increasingly relies on digital technologies, including sensors and spectroscopy, for applications from farm to fork.
  • Data analytics, encompassing chemometrics, machine learning (ML), and artificial intelligence (AI), are crucial for interpreting data from these sensing technologies.

Purpose of the Study:

  • To explore the advantages and opportunities of AI applications in food science.
  • To critically discuss the challenges and issues hindering the effective implementation and utilization of AI methods with sensing technologies.

Main Methods:

  • Review of scientific literature on AI and ML applications in food science.
  • Analysis of challenges associated with integrating AI/ML with sensing and instrumental methods.

Main Results:

  • AI and ML have enabled the development of sophisticated models for understanding food composition and systems.
  • Numerous studies demonstrate the growing trend of AI/ML adoption in food science applications.

Conclusions:

  • While AI and ML offer significant potential in food science, critical issues such as sample selection, understanding signal effects, limited sample sizes, model overfitting, and inadequate validation must be addressed for robust application.