An Updated Review of Meat Authenticity Methods and Applications.

Area of Science:

• Food Science
• Analytical Chemistry
• Food Safety

Background:

• Food adulteration, particularly in high-value meat products, poses a significant economic challenge and compromises product quality.
• Consumers require assurance of meat authenticity and accurate labeling regarding content and origin.
• Detecting adulteration, including species substitution and mislabeling, is analytically challenging.

Purpose of the Study:

• To review current analytical and immunochemical methods for assessing meat authenticity.
• To focus on methods verifying geographic origin and sensory characteristics of meat products.
• To provide an overview of statistical analyses, including multivariate analysis (MAV), for meat authenticity assessment.

Main Methods:

• Review of scientific literature on analytical techniques for meat authenticity.
• Exploration of immunochemical assays for species identification and origin determination.
• Analysis of multivariate statistical methods (e.g., principal component analysis, discriminant analysis, cluster analysis) applied to meat authenticity data.

Main Results:

• Analytical and immunochemical methods offer robust approaches to detect meat adulteration.
• Multivariate analysis (MAV) techniques are effective in classifying meat based on origin and detecting compositional deviations.
• The combination of analytical data with statistical modeling enhances the reliability of authenticity assessments.

Conclusions:

• Accurate meat authenticity verification is crucial for consumer protection and market integrity.
• Advanced analytical techniques coupled with statistical analysis provide powerful tools to combat food fraud.
• Further research into integrated approaches can improve the detection of complex adulteration scenarios.