This research aimed at developing a rapid and inexpensive method for identifying the species origin of meat, as a way to detect adulterated meats sold in markets or restaurants. Recently, meat adulteration become a concern, most recently in the 2013 horse meat scandal in Europe. Meat authentication was usually achieved by nucleic acid-based methods such as polymerase chain reaction (PCR), which rely on species-specific sequence of nucleobases as markers. Besides, protein-based methods have been developed recently, which employs liquid chromatography – mass spectrometry (LC-MS) to detect species-specific peptides markers. These methods, however, are expensive or time-consuming and thus not likely suitable for surveillance. We thus aimed to develop a rapid and inexpensive method f...[ Read more ]

This research aimed at developing a rapid and inexpensive method for identifying the species origin of meat, as a way to detect adulterated meats sold in markets or restaurants. Recently, meat adulteration become a concern, most recently in the 2013 horse meat scandal in Europe. Meat authentication was usually achieved by nucleic acid-based methods such as polymerase chain reaction (PCR), which rely on species-specific sequence of nucleobases as markers. Besides, protein-based methods have been developed recently, which employs liquid chromatography – mass spectrometry (LC-MS) to detect species-specific peptides markers. These methods, however, are expensive or time-consuming and thus not likely suitable for surveillance. We thus aimed to develop a rapid and inexpensive method for meat authentication based on protein profiling on the easily parallelized MALDI-TOF mass spectrometer platform. In this method, proteins from meat are directly extracted by homogenisation of the meat in 6M urea/ 1M thiourea/50mM Tris-HCl solution, desalted and subject to MALDI-TOF mass spectrometer. On average, each test can be finished within 15-20min. Spectra from known samples were processed and used to construct a consensus spectral library. Identification was achieved by matching the spectrum of an unknown sample to those in the library employing a weighted cosine similarity with various enhancements to emphasize unique features of each species. Pork (S. scrofa), beef (B. taurus) and goat (C. aegagrus hircus) meat samples from different individuals and different parts of the animal, including raw meat and meat cooked in different manners, totally 72 samples, were correctly identified. The method was also applied to meat mix samples, and a method for estimating the composition of meat mix was proposed. The results showed that the proposed method showed great promise for identification of meat species.