A Review on Food Safety Analysis Based on Spectroscopic Techniques and Machine Learning

Area of Science:

• Analytical Chemistry
• Computational Science
• Food Science

Background:

• Spectroscopic techniques and machine learning (ML) are increasingly integrated for advanced analysis.
• This synergy shows significant promise across food safety, pharmaceuticals, and agriculture.
• Food safety analysis is a critical area benefiting from these combined methodologies.

Purpose of the Study:

• To review the application of spectroscopic techniques coupled with ML in food safety analysis.
• To provide an overview of the capabilities of this combined approach.
• To identify challenges and suggest pathways for industrial adoption.

Main Methods:

• Outlining fundamental principles of common spectroscopic methods (e.g., spectroscopy).
• Explaining theoretical foundations of relevant machine learning (ML) models (e.g., classification, regression).
• Systematically examining applications in food adulteration identification, hazard classification, and contaminant quantification.

Main Results:

• The combined approach is effective for identifying food adulteration.
• It enables qualitative classification of unsafe food components.
• Quantitative detection of contaminants and residues is achievable.

Conclusions:

• The integration of spectroscopy and ML presents a robust paradigm for food safety monitoring.
• Key technical and practical challenges currently hinder widespread industrial adoption.
• Further research is needed to translate laboratory findings into real-world solutions for enhanced food safety.