Authentication of pork in meat mixtures using PRM mass spectrometry of myosin peptides

Quantification of 37 glucocorticoids in chicken muscle by UHPLC-Q-Orbitrap-MS with parallel reaction monitoring

The quantification capability of high-resolution mass spectrometry (HRMS) has received increasing interest from analysts. In this study, we present a method for analyzing 37 glucocorticoids in chicken muscle using UHPLC-Q-Orbitrap MS with parallel reaction monitoring (PRM). The analytes were extracted using acetonitrile (ACN) containing 0.1% formic acid and subjected to commercial PRiME HLB solid-phase extraction (SPE) cartridge clean-up. Under optimized conditions, the analytes were separated on an analytical column and subsequently detected using a high-resolution hybrid quadrupole/Orbitrap mass spectrometer coupled with PRM scan mode. The Q-Orbitrap with PRM exhibited remarkable sensitivity, with limits of quantification (LOQs) ranging from 0.08 μg kg-1 to 7.59 μg kg-1. To validate the method, we conducted intra- and inter-day tests using a blank matrix sample at different spiking levels. The achieved results demonstrated satisfactory recovery values (74.1-97.5%) and precise results (RSDs < 15%) for all the studied analytes. In application, we found dexamethasone with 6.5 μg kg-1 and fluorometholone with 3.9 μg kg-1 in two chicken samples. These findings suggest that the UHPLC-Q-Orbitrap system, in conjunction with the SPE sample preparation method, has great potential as a routine quantification approach for multiple glucocorticoid residues in chicken samples.

Proteomic Analysis of Honey: Peptide Profiling as a Novel Approach for New Zealand Mānuka (Leptospermum scoparium) Honey Authentication

New Zealand mānuka (Leptospermum scoparium) honey is a premium food product. Unfortunately, its high demand has led to "not true to label" marketed mānuka honey. Robust methods are therefore required to determine authenticity. We previously identified three unique nectar-derived proteins in mānuka honey, detected as twelve tryptic peptide markers, and hypothesized these could be used to determine authenticity. We invoked a targeted proteomic approach based on parallel reaction-monitoring (PRM) to selectively monitor relative abundance of these peptides in sixteen mānuka and twenty six non-mānuka honey samples of various floral origin. We included six tryptic peptide markers derived from three bee-derived major royal jelly proteins as potential internal standards. The twelve mānuka-specific tryptic peptide markers were present in all mānuka honeys with minor regional variation. By comparison, they had negligible presence in non-mānuka honeys. Bee-derived peptides were detected in all honeys with similar relative abundance but with sufficient variation precluding their utility as internal standards. Mānuka honeys displayed an inverse relationship between total protein content and the ratio between nectar- to bee-derived peptide abundance. This trend reveals an association between protein content on possible nectar processing time by bees. Overall, these findings demonstrate the first successful application of peptide profiling as an alternative and potentially more robust approach for mānuka honey authentication.

Isoelectric point barcode and similarity analysis with the earth mover's distance for identification of species origin of raw meat

In this work, by combining the microcolumn isoelectric focusing (mIEF) and similarity analysis with the earth mover's distance (EMD) metric, we proposed the concept of isoelectric point (pI) barcode for the identification of species origin of raw meat. At first, we used the mIEF to analyze 14 meat species, including 8 species of livestock and 6 species of poultry, to generate 140 electropherograms of myoglobin/hemoglobin (Mb/Hb) markers. Secondly, we binarized the electropherograms and converted them into the pI barcodes that only showed the major Mb/Hb bands for the EMD analysis. Thirdly, we efficiently developed the barcode database of 14 meat species and successfully used the EMD method to identify 9 meat products thanks to the high throughput of mIEF and the simplified format of the barcode for similarity analysis. The developed method had the merits of facility, rapidity and low cost. The developed concept and method had evident potential to the facile identification of meat species.

Molecular insights into quality and authentication of sheep meat from proteomics and metabolomics

Sheep meat (encompassing lamb, hogget and mutton) is an important source of animal protein in many countries, with a unique flavour and sensory profile compared to other red meats. Flavour, colour and texture are the key quality attributes contributing to consumer liking of sheep meat. Over the last decades, various factors from 'farm to fork', including production system (e.g., age, breed, feeding regimes, sex, pre-slaughter stress, and carcass suspension), post-mortem manipulation and processing (e.g., electrical stimulation, ageing, packaging types, and chilled and frozen storage) have been identified as influencing different aspects of sheep meat quality. However conventional meat-quality assessment tools are not able to elucidate the underlying mechanisms and pathways for quality variations. Advances in broad-based analytical techniques have offered opportunities to obtain deeper insights into the molecular changes of sheep meat which may become biomarkers for specific variations in quality traits and meat authenticity. This review provides an overview on how omics techniques, especially proteomics (including peptidomics) and metabolomics (including lipidomics and volatilomics) are applied to elucidate the variations in sheep meat quality, mainly in loin muscles, focusing on colour, texture and flavour, and as tools for authentication. SIGNIFICANCE: From this review, we observed that attempts have been made to utilise proteomics and metabolomics techniques on sheep meat products for elucidating pathways of quality variations due to various factors. For instance, the improvement of colour stability and tenderness could be associated with the changes to glycolysis, energy metabolism and endogenous antioxidant capacity. Several studies identify proteolysis as being important, but potentially conflicting for quality as the enhanced proteolysis improves tenderness and flavour, while reducing colour stability. The use of multiple analytical methods e.g., lipidomics, metabolomics, and volatilomics, detects a wider range of flavour precursors (including both water and lipid soluble compounds) that underlie the possible pathways for sheep meat flavour evolution. The technological advancement in omics (e.g., direct analysis-mass spectrometry) could make analysis of the proteins, lipids and metabolites in sheep meat routine, as well as enhance the confidence in quality determination and molecular-based assurance of meat authenticity.

Highly sensitive pork meat detection using copper(ii) tetraaza complex by electrochemical biosensor

Three copper(ii) tetraaza complexes [Cu(ii)LBr]Br (1a), [Cu(ii)L(CIO4)](CIO4) (2a) and [Cu(ii)L](CIO4)2 (2b), where L = 5,5,7,12,12,14-hexamethyl-1,4,8,11-tetraazacyclotetradeca-7,14-diene were prepared and confirmed by FTIR, 1HNMR and 13CNMR. The binding interaction of complex (1a, 2a, 2b) with calf thymus DNA (CT-DNA) was investigated using UV-vis absorption, luminescence titrations, viscosity measurements and molecular docking. The findings suggested that complex 1a, 2a and 2b bind to DNA by electrostatic interaction, and the strengths of the interaction were arranged according to 2b > 1a > 2a. The differences in binding strengths were certainly caused by the complexes' dissimilar charges and counter anions. Complex 2b, with the biggest binding strength towards the DNA, was further applied in developing the porcine sensor. The developed sensor exhibits a broad linear dynamic range, low detection limit, good selectivity, and reproducibility. Analysis of real samples showed that the biosensor had excellent selectivity towards the pork meat compared to chicken and beef meat.

Feasibility of identifying the authenticity of fresh and cooked mutton kebabs using visible and near-infrared hyperspectral imaging

Mutton kebab is an attractive type of meat product with high nutritional value, and is favored by consumers worldwide. However, mutton kebab is often subjected to adulteration due to its high price. Chicken, duck, and pork are frequently used as adulterated substitutes. The purpose of current study aims at developing a methodology based on hyperspectral imaging (HSI, 400-1000 nm) for identifying the authenticity of fresh and cooked mutton kebabs. Kebab samples were individually scanned using HSI system in their fresh and cooked states. Spectra of chicken, duck, pork, and mutton kebabs were first extracted from representative regions of interest (ROIs) identified in their calibrated hyperspectral images. After that, principal component analysis (PCA) was carried out, and results showed that the first three or two PCs were effective for identifying fresh or cooked samples of different meat species. Different effective modeling algorithms including k-nearest neighbor (KNN), partial least squares discriminant analysis (PLS-DA), and support vector machine (SVM) algorithms combined with different preprocessing methods were employed to develop classification models. Performances exhibited that PLS-DA models using raw spectra outperformed the KNN and SVM models, and the accuracies reached both 100 % in prediction sets for fresh and cooked meat kebabs, respectively. Moreover, compared to iteratively variable subset optimization (IVSO), random frog (RF), and successive projections algorithm (SPA) algorithms, the PC loadings successfully screened 14 and 8 effective wavelengths for fresh and cooked meat kebabs, respectively, from the complex original full-band wavelengths. The PC-PLS-DA models showed the optimal predicted performances with overall classification accuracies of 97.5 % and 100 %, sensitivity values of 1.00 and 1.00, specificity values of 0.97 and 1.00, precisions of 0.91 and 1.00, for fresh and cooked mutton kebabs, respectively. Furthermore, the visualization of classification maps confirmed the experimental results intuitively. Overall, it was evident that HSI showed immense potential to identify the authenticity of fresh and cooked mutton kebabs when substituted by different meats including chicken, duck, and pork.

Relative quantification of pork and beef in meat products using global and species-specific peptide markers for the authentication of meat composition

We used global and species-specific peptide markers for a relative quantitative determination of pork and beef in raw and processed meat products made of the two meat species. Four groups of products were prepared (i.e., minced raw meats, sausages, raw and fried burgers) in order to represent products with different extents of food processing. In each group, the products varied in the pork/beef proportions. All products were analysed by multiple reaction monitoring mass spectrometry (MRM-MS) for the presence/concentration of pork- and beef-specific peptide markers, as well as global markers - peptides widely distributed in muscle tissue. The combined MRM-MS analysis of pork-specific peptide HPGDFGADAQGAMSK, beef-specific peptide VLGFHG and global marker LFDLR offered the most reliable validation of declared pork/beef compositions across the whole range of meat products. Our work suggests that a simultaneous analysis of global and species-specific peptide markers can be used for composition authentication in commercial pork/beef products.

Untargeted metabolomics and proteomics approach using liquid chromatography-Orbitrap high resolution mass spectrometry to detect pork adulteration in Pangasius hypopthalmus meat

Pangasius hypopthalmus is well known as a good source of protein. However, Pangasius hypopthalmus meat (PHM) can be adulterated with pork for economic concern, thus, analytical methods for authentication are required. Untargeted metabolomics and proteomics using liquid chromatography-high resolution mass spectrometry (LC-HRMS) and chemometrics of principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) was successfully used to differentiate authentic and adulterated PHM with the good of fitness (R > 0.95) and good of predictivity (Q > 0.5). Metabolites of PC(o-18:0/18:2(9Z,12Z)) was found to be a potential marker for pork whereas DMPC (dimyristoylphosphatidylcholine) was a potential marker for PHM. Meanwhile, pork peptide marker of myoglobin (HPGDFGADAQGAMSK) and β-hemoglobin (FFESFGDLSNADAVMGNPK) could be identified. Both metabolomics and proteomics using LC-HRMS could detect pork at the lowest concentration level (0.5% w/w). In conclusion, untargeted metabolomics and proteomics using LC-HRMS in combination with chemometrics could be used as powerful methods to detect pork adulteration in fish meat.

Screening of specific quantitative peptides of beef by LC-MS/MS coupled with OPLS-DA

A rapid, simple, and efficient analysis methodology for screening specific quantitative peptides of beef was established based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) coupled with orthogonal partial least squares-discriminant analysis (OPLS-DA). The OPLS-DA model was built to select species-specific peptides that make a significant contribution to classification. Peptides with statistical significance were selected based on the variable importance in the projection (VIP) values and univariate P values. After the workflow of the statistical process, three specific quantitative peptides were identified by using homemade products with different beef contents. A quantification method for selected specific quantitative peptides was established by using LC-MS/MS. The quantitative results were applied to commercialized beef products. The developed method has high sensitivity, specificity, and repeatability. The results of this study proved that the integration of LC-MS/MS coupled with OPLS-DA is an efficient method for screening specific quantitative peptides and identification of the authenticity of meat products.

Rapid LC-MS/MS method for the detection of seven animal species in meat products

The adulteration of meat products has been reported worldwide, and detection of specific peptides through mass spectrometry (MS) is a reliable method for meat species identification. However, the practical application of this method is limited by complicated steps and long reaction time of the traditional sample preparation. Therefore, this paper introduced a convenient and time-saving sample preparation by optimizing the steps of reduction, alkylation, digestion, and purification. With the rapid sample preparation, 35 species-specific peptides for seven species (pig, cattle, sheep, deer, chicken, duck, and turkey) were screened using high-resolution MS, and a rapid LC-MS/MS method was established. The method only takes 3 h from sample receipt to results. The meat species of 20 processed meat products were detected, and three samples were found potentially adulterated. The method is proved to have high sensitivity, specificity, practicability with respect to rapid identification of meat species in meat products.

Development of a two-level control system for the analysis of the composition of meat products

Because of the increased demand for processed meat, there is an urgent need to introduce specific identification methods. Strategies such as molecular genetics and the physical condition of meat are used to quickly explore multi-component products. However, a single methodology does not always unambiguously classify a product as counterfeit. In laboratory practice, as a rule, screening techniques are rarely used in the first stage, followed by arbitration. This work aimed to study individual methodologies using artificially falsified meat samples as examples and to identify their composition based on muscle tissue. For the experiments, the three most common types of raw meat were selected: pork, beef, and chicken. The calculation of the content of muscle tissue was carried out according to the BEFFE method. The study of muscle protein was carried out by ICA, ELISA, PCR, microstructural analysis, and mass spectrometric identification. In this connection, we proposed a multilevel control system for multicomponent meat products. Both classical methodologies, such as calculation by prescription bookmarks (BEFFE) and microstructural analysis, and approaches of highly sensitive methodologies, such as identification of muscle tissue by marker peptides (LC/MS-MRM) and semi-quantitative PCR analysis, were evaluated.

Chemometrics in Tandem with Hyperspectral Imaging for Detecting Authentication of Raw and Cooked Mutton Rolls

Authentication assurance of meat or meat products is critical in the meat industry. Various methods including DNA- or protein-based techniques are accurate for assessing meat authenticity, however, they are destructive, expensive, or laborious. This study explores the feasibility of chemometrics in tandem with hyperspectral imaging (HSI) for identifying raw and cooked mutton rolls substitution by pork and duck rolls. Raw or cooked samples (n = 180) of three meat species were prepared to collect hyperspectral images in range of 400-1000 nm. Spectra were extracted from representative regions of interest (ROIs), and spectral principal component analysis (PCA) revealed that PC1 and PC2 were effective for the identification. Different methods including standard normal variable (SNV), first and second derivatives, and normalization were individually employed for spectral preprocessing, and modeling methods of partial least squares-discriminant analysis (PLS-DA) and support vector machines (SVM) were also individually applied to develop classification models for both the raw and the cooked. Results showed that PLS-DA model developed by raw spectra presented the highest 100% correct classification rate (CCR) of success in all sets. After that, effective wavelengths selected by successive projections algorithm (SPA) built optimal simplified models which didn't influence the modeling results compared with full spectra regardless of the meat roll states. Therefore, SPA-PLS-DA models were subsequently used to visualize the raw and cooked meat rolls classification. As a consequence, the general meat species of both raw and cooked meat rolls were readily discernible in pixel-wise manner by generating classification maps. The results showed that HSI combined with chemometrics can be used to identify the authentication of raw and cooked mutton rolls substituted by pork and duck rolls accurately. This promising methodology provides a reference which can be extended to the classification or grading of other meat rolls.

A quantitative method for detecting meat contamination based on specific polypeptides

OBJECTIVE

This study was aimed to establish a quantitative detection method for meat contamination based on specific polypeptides.

METHODS

Thermally stable peptides with good responses were screened by high resolution liquid chromatography tandem mass spectrometry. Standard curves of specific polypeptide were established by triple quadrupole mass spectrometry. Finally, the adulteration of commercial samples was detected according to the standard curve.

RESULTS

Fifteen thermally stable peptides with good responses were screened. The selected specific peptides can be detected stably in raw meat and deep processed meat with the detection limit up to 1% and have a good linear relationship with the corresponding muscle composition.

CONCLUSION

This method can be effectively used for quantitative analysis of commercial samples.

Comparative database search engine analysis on massive tandem mass spectra of pork-based food products for halal proteomics

Mass spectrometry-based proteomics relies on dedicated software for peptide and protein identification. These software include open-source or commercial-based search engines; wherein, they employ different algorithms to establish their scoring and identified proteins. Although previous comparative studies have differentiated the proteomics results from different software, there are still yet studies specifically been conducted to compare and evaluate the search engine in the field of halal analysis. This is important because the halal analysis is often using commercial meat samples that have been subjected to various processing, further complicating its analysis. Thus, this study aimed to assess three open-source search engines (Comet, X! Tandem, and ProteinProspector) and a commercial-based search engine (ProteinPilot™) against 135 raw tandem mass spectrometry data files from 15 types of pork-based food products for halal analysis. Each database search engine contained high false-discovery rate (FDR); however, a post-searching algorithm called PeptideProphet managed to reduce the FDR, except for ProteinProspector and ProteinPilot™. From this study, the combined database search engine (executed by iProphet) reveals a thorough protein list for pork-based food products; wherein the most abundant proteins are myofibrillar proteins. Thus, this proteomics study will aid the identification of potential peptide and protein biomarkers for future precision halal analysis. SIGNIFICANCE: A critical challenge of halal proteomics is the availability of a database to confirm the inferential peptides as well as proteins. Currently, the established database such as UniProtKB is related to animal proteome; however, the halal proteomics is related to the highly processed meat-based food products. This study highlights the use of different database search engines (Comet, X! Tandem, ProteinProspector, and ProteinPilot™) and their respective algorithms to analyse 135 raw tandem mass spectrometry data files from 15 types of pork-based food products. This is the first attempt that has compared different database search engines in the context of halal proteomics to ensure the effectiveness of controlling the FDR. Previous studies were just focused on the advantages of a certain algorithm over another. Moreover, other previous studies also have mainly reported the use of mass spectrometry-based shotgun proteomics for meat authentication (the most similar field to halal analysis), but none of the studies were reported on halal aspects that used samples originated from highly processed food products. Hence, a systematic comparative study is duly needed for a more comprehensive and thorough proteomics analysis for such samples. In this study, our combinatorial approach for halal proteomics results from the different search engines used (Comet, X! Tandem, and ProteinProspector) has successfully generated a comprehensive spectral library for the pork-based meat products. This combined spectral library is freely available at https://data.mendeley.com/datasets/6dmm8659rm/3. Thus far, this is the first and new attempt at establishing a spectral library for halal proteomics. We also believe this study is a pioneer for halal proteomics that aimed at non-conventional and non-model organism proteomics, protein analytics, protein bioinformatics, and potential biomarker discovery.

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.

Peptide markers for distinguishing guinea fowl meat from that of other species using liquid chromatography-mass spectrometry

The inability to easily identify the animal species in highly processed meat products makes them highly susceptible to adulterations. Reliable methods for detecting the species origin of meat used in processed food are required to ensure adequate labelling and minimize food fraud and allergenic potential. Liquid chromatography high resolution mass spectrometry was employed to identify new heat-stable guinea-fowl-specific peptide markers that can differentiate guinea fowl meat from other commonly consumed animal species, including closely related poultry species, in highly processed food products. We identified 26 unique guinea-fowl-specific markers. The high stability of guinea-fowl-specific peptides was confirmed by analysing food products with guinea fowl meat content ranging from 4% to 100%. The findings indicate that sensitive and reliable LC-MS/MS methods can be developed for the targeted detection and quantification of guinea fowl meat in highly processed meat products.

Application of Proteomic Technologies to Assess the Quality of Raw Pork and Pork Products: An Overview from Farm-To-Fork

The quality assurance of pork meat and products includes the study of factors prior to slaughter such as handling practices, diet and castration, and others during the post-mortem period such as aging, storage, and cooking. The development over the last two decades of high-throughput techniques such as proteomics offer great opportunities to examine the molecular mechanisms and study a priori the proteins in the living pigs and main post-mortem changes and post-translational modifications during the conversion of the muscle into the meat. When the most traditional crossbreeding and rearing strategies to improve pork quality were assessed, the main findings indicate that metabolic pathways early post-mortem were affected. Among the factors, it is well documented that pre-slaughter stress provokes substantial changes in the pork proteome that led to defective meat, and consequently, novel protein biomarkers should be identified and validated. Additionally, modifications in pork proteins had a strong effect on the sensory attributes due to the impact of processing, either physical or chemical. Maillard compounds and protein oxidation should be monitored in order to control proteolysis and volatile compounds. Beyond this, the search of bioactive peptides is becoming a paramount goal of the food and nutraceutical industry. In this regard, peptidomics is a major tool to identify and quantify these peptides with beneficial effects for human health.

LC-QTOF-MS identification of rabbit-specific peptides for authenticating the species composition of meat products

Rabbit is a healthy meat, with low allergenicity and excellent nutritional properties. The global popularity of rabbit meat makes it a target for food fraud. We present a LC-QTOF-MS/MS approach for detecting and identifying rabbit-specific peptide-markers from thermally processed meat products to differentiate rabbit from other commonly-consumed animal species. We identified 49 heat-stable specific peptides. We selected the most stable markers for testing complex meat matrices by analysing pâtés-type products with a rabbit meat content ranging from 5% to 85%. Of the 49 heat-stable peptides detected in pure cooked rabbit meat, three were consistently detected in all investigated pâté samples i.e., SSVFVADPK, AFFGHYLYEVAR and PHSHPALTPEQK. Monitoring meat species other than rabbit in the examined pâtés using pork-, lamb- and chicken-specific peptides identified the presence of undeclared chicken in two samples. The results confirm that LC-QTOF-MS/MS is a suitable tool for multi-species detection in processed meat products, particularly for authentication purposes.

Current analytical methods for porcine identification in meat and meat products

Authentication of meat products is critical in the food industry. Meat adulteration may lead to religious apprehensions, financial gain and food-toxicities such as meat allergies. Thus, empirical validation of the quality and constituents of meat is paramount. Various analytical methods often based on protein or DNA measurements are utilized to identify meat species. Protein-based methods, including electrophoretic and immunological techniques, are at times unsuitable for discriminating closely related species. Most of these methods have been replaced by more accurate and sensitive detection methods, such as DNA-based techniques. Emerging technologies like DNA barcoding and mass spectrometry are still in their infancy when it comes to their utilization in meat detection. Gold nanobiosensors have shown some promise in this regard. However, its applicability in small scale industries is distant. This article comprehensively reviews the recent developments in the field of analytical methods used for porcine identification.