Highly Sensitive Immunochromatographic Detection of Porcine Myoglobin as Biomarker for Meat Authentication Using Prussian Blue Nanozyme

This study was aimed at the sensitive immunodetection of porcine myoglobin (MG) as a species-specific biomarker in meat products. The enhanced lateral flow immunoassay (LFIA) was created in the sandwich format using monoclonal antibodies (Mab) with specificity to porcine MG and labeled by Prussian blue nanoparticles (PBNPs) as peroxidase-mimicking nanozymes. Signal amplification was provided by the colored product of oxidation catalyzed by the PBNPs. Several Mab-PBNP conjugates with different antibody loads were synthesized; the one that provided the best analytical characteristics of the LFIA was selected. Advanced optimization of the test system was carried out. As a result, the visual limit of detection (LOD) of MG was 1.5 ng/mL. Involvement of the catalytic nanozyme properties allowed the LOD to be decreased by ~9 times in comparison to the LFIA based on gold nanomarkers, and by ~27 times compared to the LFIA based on PBNP coloration. The assay time was 30 min, including catalytic enhancement. A simple technique of meat sample pre-treatment aimed at effective MG extraction and matrix disposal was proposed. The specificity of the LFIA towards the pork meat was demonstrated. The applicability of the created test system was shown by testing extracts obtained from finished meat products.

An integrated digital polymerase chain reaction chip for multiplexed meat adulteration detection

Meat adulteration detection is a common concern of consumers. Here, we proposed a multiplex digital polymerase chain reaction method and a low-cost device for meat adulteration detection. Using a polydimethylsiloxane microfluidic device, polymerase chain reaction reagents could be pump-free loaded into microchambers (40 × 40 chambers) automatically. Due to the independence of multiplex fluorescence channels, deoxyribonucleic acid templates extracted from different animal species could be distinguished by one test. In this paper, we designed primers and probes for four types of meat (beef, chicken, pork, and duck) and labeled each of the four fluorescent markers (hexachlorocyclohexane [HEX], 6-carboxyfluorescein [FAM], X-rhodamine [ROX], and cyanine dyes 5 [CY5]) on the probes. Specific detection and mixed detection experiments were performed on four types of meat, realizing a limit of detection of 3 copies/µL. A mixture of four different species can be detected by four independent fluorescence channels. The quantitative capability of this method is found to meet the requirements of meat adulteration detections. This method has great potential for point-of-care testing together with portable microscopy equipment.

Integration of Non-targeted Proteomics Mass Spectrometry with Machine Learning for Screening Cooked Beef Adulterated Samples

The degradation of ingredients in heat-processed meat products makes their authentication challenging. In this study, protein profiles of raw beef, chicken, duck, pork, and binary simulated adulterated beef samples (chicken-beef, duck-beef, and pork-beef) and their heat-processed samples were obtained by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Heat-stable characteristic proteins were found by screening the overlapping characteristic protein ion peaks of the raw and corresponding heat-processed samples, which were discovered by partial least-squares discriminant analysis. Based on the 36 heat-stable characteristic proteins, qualitative classification for the raw and heat-processed meats was achieved by extreme gradient boosting. Moreover, quantitative analysis via partial least squares regression was applied to determine the adulteration ratio of the simulated adulterated beef samples. The validity of the approach was confirmed by a blind test with the mean accuracy of 97.4%. The limit of detection and limit of quantification of this method were determined to be 5 and 8%, respectively, showing its practical aspect for the beef authentication.

Current State of Metabolomics Research in Meat Quality Analysis and Authentication

In the past decades, as an emerging omic, metabolomics has been widely used in meat science research, showing promise in meat quality analysis and meat authentication. This review first provides a brief overview of the concept, analytical techniques, and analysis workflow of metabolomics. Additionally, the metabolomics research in quality analysis and authentication of meat is comprehensively described. Finally, the limitations, challenges, and future trends of metabolomics application in meat quality analysis and meat authentication are critically discussed. We hope to provide valuable insights for further research in meat quality.

Pre-Degassed Microfluidic Chamber-Based Digital PCR Device for Meat Authentication Applications

Droplet digital polymerase chain reaction (ddPCR) suffers from the need for specific equipment and skilled personnel; thus, we here present a chamber-based digital PCR microfluidic device that is compatible with fluorescence image read-out systems and removes bubbles by a pre-degassed microfluidic device that consists of a pilot channel and micro chamber arrays. Digitalized PCR reagents are introduced into micro chambers, and thermocycles are taken to perform a DNA amplification process. Then, fluorescence images of a micro chamber array are read out and analyzed to obtain the total number of positive chambers. Thereby, the copy numbers of target DNA are calculated for quantitative detections. As a validation, this device is evaluated by the application of meat authentication. We performed dPCR tests using DNA templates extracted from a pure mutton DNA template with different dilutions. Then, the dPCR chip was used to identify the meat authentication using mutton–chicken mixtures with different mass ratios, showing its performance in real biotechnical applications.

LIQUID CHROMATOGRAPHY-MASS SPECTROMETRY BOTTOM-UP PROTEOMIC METHODS IN ANIMAL SPECIES ANALYSIS OF PROCESSED MEAT FOR FOOD AUTHENTICATION AND THE DETECTION OF ADULTERATIONS

This review offers an overview of the current status and the most recent advances in liquid chromatography-mass spectrometry (LC-MS) techniques with both high-resolution and low-resolution tandem mass analyzers applied to the identification and detection of heat-stable species-specific peptide markers of meat in highly processed food products. We present sets of myofibrillar and sarcoplasmic proteins, which turned out to be the source of 105 heat-stable peptides, detectable in processed meat using LC-MS/MS. A list of heat-stable species-specific peptides was compiled for eleven types of white and red meat including chicken, duck, goose, turkey, pork, beef, lamb, rabbit, buffalo, deer, and horse meat, which can be used as markers for meat authentication. Among the 105 peptides, 57 were verified by multiple reaction monitoring, enabling identification of each species with high specificity and selectivity. The most described and monitored species by LC-MS/MS so far are chicken and pork with 26 confirmed heat-stable peptide markers for each meat. In thermally processed samples, myosin, myoglobin, hemoglobin, l-lactase dehydrogenase A and β-enolase are the main protein sources of heat-stable markers. © 2019 John Wiley & Sons Ltd. Mass Spec Rev.

Meat authentication: a new HPLC-MS/MS based method for the fast and sensitive detection of horse and pork in highly processed food

Fraudulent blending of food products with meat from undeclared species is a problem on a global scale, as exemplified by the European horse meat scandal in 2013. Routinely used methods such as ELISA and PCR can suffer from limited sensitivity or specificity when processed food samples are analyzed. In this study, we have developed an optimized method for the detection of horse and pork in different processed food matrices using MRM and MRM(3) detection of species-specific tryptic marker peptides. Identified marker peptides were sufficiently stable to resist thermal processing of different meat products and thus allow the sensitive and specific detection of pork or horse in processed food down to 0.24% in a beef matrix system. In addition, we were able to establish a rapid 2-min extraction protocol for the efficient protein extraction from processed food using high molar urea and thiourea buffers. Together, we present here the specific and sensitive detection of horse and pork meat in different processed food matrices using MRM-based detection of marker peptides. Notably, prefractionation of proteins using 2D-PAGE or off-gel fractionation is not necessary. The presented method is therefore easily applicable in analytical routine laboratories without dedicated proteomics background.