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Machine Learning-Based Analytical Systems: Food Forensics.

Ranbir1, Manish Kumar1, Gagandeep Singh2

  • 1Department of Chemistry, Indian Institute of Technology Ropar, Rupnagar 140001, Punjab, India.

ACS Omega
|January 2, 2023
PubMed
Summary
This summary is machine-generated.

Array-based sensors combined with machine learning offer a powerful approach to enhance food safety and quality monitoring. These advanced methods enable rapid, accurate detection of contaminants and biogenic amines, crucial for food forensics.

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Area of Science:

  • Food Science and Technology
  • Analytical Chemistry
  • Biotechnology

Background:

  • Food safety is a critical global concern due to various contaminants like pathogens, toxins, and adulterants.
  • Biogenic amines are key indicators of protein-rich food spoilage and freshness.
  • Conventional detection methods face limitations in speed, accuracy, and complexity.

Purpose of the Study:

  • To review recent advancements in array-based sensor systems for food safety and quality monitoring.
  • To highlight the role of machine learning and multivariate analytics in interpreting sensor data.
  • To discuss the application of these technologies in food forensics.

Main Methods:

  • Review of recently reported array-based sensor systems.
  • Application of multivariate analytical techniques for data interpretation.
  • Utilization of machine learning-based neural networks for analyte detection.

Main Results:

  • Array-based sensing strategies are emerging as accurate and precise analytical methods.
  • Machine learning significantly enhances the interpretation of complex sensor response patterns.
  • Electrical and chemical sensor arrays show promise for commercial food safety applications.

Conclusions:

  • Array-based sensor systems, supported by machine learning and multivariate analytics, offer a robust solution for food safety monitoring.
  • These integrated approaches facilitate fast, reliable detection of food contaminants and spoilage indicators.
  • The focus is shifting towards developing practical analytical methods for food forensics and quality control.