Enhanced food authenticity control using machine learning-assisted elemental analysis.

Area of Science:

• Food Science
• Analytical Chemistry
• Data Science

Background:

• Growing public concern necessitates reliable food authenticity verification.
• Current methods for food traceability and quality control face limitations.
• Ensuring the integrity of food supply chains is paramount for consumer trust.

Purpose of the Study:

• To review methods for food authenticity, focusing on traceability and quality control.
• To highlight limitations of traditional detection techniques (morphology, organic compounds).
• To present elemental analysis combined with machine learning as a superior solution.

Main Methods:

• Review of existing literature on food authenticity detection.
• Analysis of elemental composition as a stable detection indicator.
• Application of machine learning algorithms for data analysis and pattern recognition.
• Comparison of different machine learning algorithms for accuracy and efficiency.

Main Results:

• Elemental analysis offers inherent stability and reliability for food authenticity.
• Machine learning effectively processes large datasets for accurate detection.
• Combining elemental data with machine learning enhances both accuracy and efficiency.
• Algorithm comparison identifies optimal models for specific food authenticity challenges.

Conclusions:

• Elemental analysis coupled with machine learning presents a robust strategy for food authenticity.
• This integrated approach overcomes limitations of conventional methods.
• It significantly improves public trust through enhanced food traceability and quality control.