MEATiCode: A comprehensive proteomic LC-MS/MS method for simultaneous species identification in meat authentication.

Area of Science:

• Food science and technology
• Analytical chemistry
• Proteomics

Background:

• Food fraud, particularly in the meat industry, poses significant risks to consumer trust, market integrity, and public health.
• Conventional meat authentication methods, such as DNA barcoding, face limitations with processed or cooked products due to DNA degradation.

Purpose of the Study:

• To introduce and validate MEATiCode, a novel proteomic workflow for accurate meat species identification.
• To assess the efficacy of MEATiCode in detecting adulteration in various meat products, including processed and cooked samples.

Main Methods:

• Development of a comprehensive proteomic liquid chromatography tandem mass spectrometry (LC-MS/MS) workflow.
• Utilizing a novel database search approach (MEATiCode) for peptide analysis.
• Simple sample preparation followed by LC-MS/MS analysis of extracted meat peptides.

Main Results:

• MEATiCode successfully differentiated between multiple meat species (beef, pork, chicken, lamb) in both raw and cooked products.
• The method demonstrated high sensitivity, with a Limit of Detection (LoD) as low as 0.5%.
• Reliable detection of adulteration was achieved, even in highly processed or cooked meat products.

Conclusions:

• MEATiCode provides a robust and sensitive proteomic solution for meat authentication.
• This workflow overcomes limitations of DNA-based methods for processed meat products.
• MEATiCode enhances the ability to combat food fraud and ensure meat product integrity.