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Proteomics is a key analytical method in meat research thanks to its potential in investigating the proteins at interplay in post-mortem muscles. This study aimed to characterize for the first time the differences in early post-mortem muscle proteomes of chickens raised under two farming systems: organic versus antibiotic-free. Forty post-mortem Pectoralis major muscle samples from two chicken strains (Ross 308 versus Ranger Classic) reared under organic versus antibiotic-free farming systems were characterized and compared using two-dimensional electrophoresis and LC-MS/MS mass spectrometry. Within antibiotic-free and organic farming systems, 14 and 16 proteins were differentially abundant between Ross 308 and Ranger Classic, respectively. Within Ross 308 and Ranger Classic chicken strains, 12 and 18 proteins were differentially abundant between organic and antibiotic-free, respectively. Bioinformatics was applied to investigate the molecular pathways at interplay, which highlighted the key role of muscle structure and energy metabolism. Antibiotic-free and organic farming systems were found to significantly impact the muscle proteome of chicken breast meat. This paper further proposes a primary list of putative protein biomarkers that can be used for chicken meat or farming system authenticity.

This article discusses the potential of game meat as a diversifying factor in the meat market and the need for standardized parameters to ensure consumer satisfaction. It emphasizes the importance of new technologies in enhancing consistency and accuracy in game meat quality evaluation. The article also explores the application of High-Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HR-MAS NMR) spectroscopy in characterizing game meats based on their species of origin. The study demonstrates that HR-MAS NMR spectroscopy, combined with multivariate analysis, is a powerful tool for differentiating game meats. However, further research is needed to explore specific applications and the relationship between metabolites and taste, sensory, and texture properties. [Extracted from the article]

In recent years, consumer interest in meat authenticity has increased. Fraudulent claims are most likely to be regarding meat origin, meat substitution, meat processing treatment, and non-meat ingredient additions. This study focuses on the substitution of meat species in processed kebab-like food sales in Poland. The growing popularity of kebab-like foods and the limited number of official inspections of this type of food make this topic interesting. In this study, the results reveal that 60% of the foods analyzed contain an undeclared ingredient or the substitution of an expensive ingredient with a cheaper option. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The performance of visible-near infrared hyperspectral imaging (Vis-NIR-HSI) (400–1000 nm) and shortwave infrared hyperspectral imaging (SWIR-HSI) (1116–1670 nm) combined with different classification and regression (linear and non-linear) multivariate methods were assessed for meat authentication. In Vis-NIR-HSI, total accuracies in the prediction set for SVM and ANN-BPN (the best classification models) were 96 and 94 % surpassing the performance of SWIR-HSI with 88 and 89 % accuracy, respectively. In Vis-NIR-HSI, the best-obtained coefficient of determinations for the prediction set (R2p) were 0.99, 0.88, and 0.99 with root mean square error in prediction (RMSEP) of 9, 24 and 4 (%w/w) for pork in beef, pork in lamb and pork in chicken, respectively. In SWIR-HSI, the best-obtained R2p were 0.86, 0.77, and 0.89 with RMSEP of 16, 23 and 15 (%w/w) for pork in beef, pork in lamb and pork in chicken, respectively. The results ascertain that Vis-NIR-HSI coupled with multivariate data analysis has better performance rather than SWIR-HIS.

The development of novel detection technology for meat species authenticity is imperative. Here, we developed a machine learning-supported, dual-channel biosensor-in-microdroplet platform for meat species authenticity detection named CC-drop ( C RISPR/Cas12a digital single-molecule microdroplet biosensor). This strategy allowed us to quickly identify and analyze animal-derived components in foods. This biosensor was enabled by CRISPR/Cas12a-based fluorescence lighting-up detection, and the nucleic acid signals can be recognized by a Cas12a-crRNA binary complex to trigger the trans -cleavage of any by-stander reporter single-stranded (ss) DNA, in which nucleic acid signals can be converted and amplified to fluorescent readouts. The ultralocalized microdroplet reactor was constructed by reducing the reaction volume from up to picoliter to accommodate the aforementioned reaction to further enhance the sensitivity to even render an amplification-free nucleic acid detection. Moreover, we established a smartphone App coupled with a random forest machine learning model based on parameters such as area, fluorescent intensity, and counting number to ensure the accuracy of image recording and processing. The sample-to-result time was within 80 min. Importantly, the proposed biosensor was able to accurately detect the ND1 (pork-specific) and IL-2 (duck-specific) genes in deep processed meat-derived foods that usually had truncated DNA, and the results were more robust and practical than conventional real-time polymerase chain reaction after a side-by-side comparison. All in all, the proposed biosensor can be expected to be used for rapid food authenticity and other nucleic acid detections in the future.

In recent years, consumer interest in meat authenticity has increased. Fraudulent claims are most likely to be regarding meat origin, meat substitution, meat processing treatment, and non-meat ingredient additions. This study focuses on the substitution of meat species in processed kebab-like food sales in Poland. The growing popularity of kebab-like foods and the limited number of official inspections of this type of food make this topic interesting. In this study, the results reveal that 60% of the foods analyzed contain an undeclared ingredient or the substitution of an expensive ingredient with a cheaper option.

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Many factors, such as the farming systems and preslaughter rearing practices, can influence the physiological and metabolic functions of poultry with consequent effects on poultry meat quality. In this trial, label-free shotgun proteomics was used to analyze the early post-mortem Pectoralis major muscle proteomes of Ross 308 and Ranger Classic chicken strains raised under two divergent farming systems these being organic and antibiotic-free. The combination of chemometrics using partial-least-square discriminant analysis (PLS-DA) and shotgun proteomics allowed clear discrimination between the different groups. Chicken strains were discriminated by differences in the abundance of 73 and 62 proteins within the antibiotic-free and organic farming systems, respectively. The abundances of 71 and 52 proteins were impacted by the farming system within the Ross 308 and Ranger Classic chicken strains, respectively. The analyses allowed for the proposal of several putative biomarkers of meat authenticity, which were found to be related to muscle structure and energy metabolism pathways. This study is a significant step forward in elucidating the potential of proteomics profiling and chemometrics in chicken meat, which may provide opportunities for the efficient assessment of chicken authenticity.

Major issues related to meat in our modern society are its authenticity and traceability. Meat consumers are facing problems related to addition of low value meat to high quality meat. The current study was designed to explore the potential of analytical method for the rapid detection and identification of meat. Meat of four species (beef, pork, chicken and turkey) were collected for the chemical (moisture content, ash content, crude fat and crude protein), quality (pH, color and drip loss) and spectral analysis through Fourier transform infrared spectroscopy (FT-IR). Pork meat showed highest crude protein (22.20%) and crude fat contents (5.85%). Highest pH (6.14) and drip loss (1.84%) was seen in turkey meat and pork meat, respectively. Spectral data collected from FT-IR spectroscopy was analyzed through principle component analysis and partial least square regression model. The FT-IR spectra obtained after absorption in infrared region explained that all the meat samples were different based on their OH, CH and CH2 stretching in bonds. The near infrared region around 2300-1400 cm-1 had maximum spectral information required for the discriminant investigations based on the pigments contained in different species of meat to the physicochemical characteristics (moisture, intramuscular fats and fatty acids). Coefficients of determination for calibration (R²C) and validation (R²V), root mean square errors of calibration (RMSEC) and root mean square error of prediction (RMSEP) for moisture, crude protein, crude fat, ash, pH, drip loss and color L*, a*, b* of different meat samples were measured. The color value b* developed strong prediction with R²C = 0.941 and R²V= 0.872. [ABSTRACT FROM AUTHOR]

True verification of meat species is critical for religious, economical, legal, and/or public health concerns. Current methods available for verification of meat species are very time-consuming and/or costly. So considering the necessity, a novel polymerase chain reaction assay (PCR)–based ARMS (amplification refractory mutation system) type identification assay for detection of 6 most common mammalian species—Equus asinus (donkey), Equus caballus (horse), Bos Taurus (cow), Bubalus bubalis (buffalo), Capra hircus (goat), and Canis lupus familiaris (dog)—was described in present study by designing species-specific forward primers against variable regions and a single universal reverse primer against a very conserved region of nuclear beta actin (ACTB) gene by investigation of areas of homology and variation. As compared with conventional designing of primers, ARMS type primer designing is a better alternative as it allows more room for choice and offers differentiation of closely related species by exploiting just a single nucleotide base difference. PCR bands of 128, 229, 273, 362, 710, and 796 bp were generated on electrophoretic gel for buffalo, donkey, cow, horse, dog, and goat meat species respectively. Besides singleplexing, duplex (multiplex) PCR for donkey and horse, donkey and goat, and donkey and buffalo were also performed which successfully generated corresponding bands. The method is a simple and straightforward setup; results can be interpreted easily in a short time and do not need validation by sequencing. Results of the present study clearly demonstrate that the method can be used as an identification tool for differentiation between cow, buffalo, goat, donkey, horse, and dog species. [ABSTRACT FROM AUTHOR]

Abstract Food frauds are a critical issue in the field of food safety and quality. Given the high added value, and the complexity of the matrix, processed meat products are among those most susceptible of adulteration. Despite all the efforts made by the official control authorities and by the food industry to counteract these frauds, the undeclared replacement of meat species with cheaper ones is still widespread. The meat species allowed for food consumption are many, and their specific and accurate detection in highly processed food products requires very sensitive and selective analytical methods. In this work, a LC-MS method was developed to identify and quantify eight different meat species (duck, rabbit, chicken, turkey, buffalo, equine, deer and sheep) in a complex food matrix, such as Bolognese sauce. After protein extraction and trypsin digestion, a species-specific peptide marker for each species was chosen for qualification and quantification. The method was validated on real Bolognese sauce samples prepared in an industrial environment, showing a good sensitivity (LOD 0.2–0.8% on whole finished product) and the possibility, using specifically defined calibration lines, to quantify the amount of meat present coming from different species. Highlights • Species-specific marker peptides were identified for eight meat species. • A LC-MS based method was developed for the detection of the marker peptides. • Bolognese sauces were prepared with increasing amount of each species. • Using proper calibration curves the method can quantify the meat species present. • A validation was performed analyzing blind Bolognese sauce samples. [ABSTRACT FROM AUTHOR]

This study evaluated the application of label-free, relative quantification of highly processed meat proteins to authenticate processed meat products. Quantitation was based on proteins for which heat-stable and species-specific peptide markers had been detected. Using nano-LC-QTOF-MS/MS, 20 new, heat-stable peptide markers unique to chicken, duck and goose were identified, with significant scores. The method enabled detection of 1% ( w/w ) of chicken and 1% ( w/w ) pork in a mixture of the meat of three species, as well as 0.8% ( w/w ) beef proteins in commercial poultry frankfurters. The proteins’ abundance was computed using a spectral matching technique, termed the absolute protein expression (APEX) proteomics tool. This method includes a correction factor for each protein, based on the peptide MS detection probabilities, which are influenced by the physicochemical properties of the peptide. Considerable differences in abundance of myofibrillar and sarcoplasmic proteins were observed between samples and illegal proportions of ingredients were discovered. [ABSTRACT FROM AUTHOR]

Meat species specification is important for consumer protection and increases concern in food labelling regulations enforcement. Although regulations exist for processed meat products, information on the prevalence of meat products mislabelling and regulatory compliance in Malaysia is lacking. In this study, 143 prepacked (beef and poultry) meat products (sausages, cold cut meats, cooked whole muscle meats, breaded products, meatballs and ground meats) were purchased from several national and international supermarket chains in Malaysia. These samples were analysed for the presence of common meat species (buffalo, cattle, chicken, goat, sheep, duck and goose) and meats prohibited by Islamic laws (“Haram”) (cat, dog, monkey, pig and rat) using species-specific primers. The results showed that 112 (78.3%) samples were mislabelled, attributed by the false declaration of species and/or presence of undeclared meat species. The mislabelled products consisted of 17/28, 3/4, 6/8, 19/25, 48/56, and 19/22 of sausage, cold cuts, cooked whole muscle meat, breaded product, ground meat, and meatball samples, respectively. Buffalo DNA was detected in 40 out of the 58 samples labelled as beef. The presence of undeclared chicken and buffalo DNA were detected in 33/58 and 62/84 of beef and chicken products, respectively. The five “Haram” meat sources, however, were not detected in all meat products tested. The presence of chicken or buffalo DNA in these products could be attributed to unintentional cross contamination from food processing equipment, especially meat grinder, and lack of proper cleaning or inadequate hygiene. In conclusion, this study shows that majority of the samples are not legally compliant, signifying that substitution and mislabelling of meat products are commonplace in Malaysia. Strict implementation of the Malaysia Food Regulations 1985 alongside with regular surveillance and monitoring programmes are compulsory to alleviate and deter mislabelling issues. [ABSTRACT FROM AUTHOR]

• Adulteration of meat with offal has been highlighted as a risk to food security. • No single method has been validated to determine offal adulteration of meat. • Low cost, high throughput technologies must support legislative requirements. • Ratios of elemental composition demonstrate potential to detect adulteration. • Peptide markers offer a tool to determine offal tissues. Offal tissues carry a lower market value compared to skeletal meats in some global markets. The inclusion of offal in any meat product in the EU and UK must be declared on the label. While many technologies have been applied to the challenge of determining adulteration with offal in meat products, no single method has been recognised and validated as a reliable test to support legislative requirements. This literature review investigated appropriate methods to determine the adulteration of meat with offal. The aim was to identify technologies suitable for future validation to underpin a high throughput, low-cost method suitable for application by enforcement laboratories. Considering all of the methods, technologies which determine elemental composition and peptide markers were particularly highlighted as demonstrating potential for future development to determine a wide range of offal tissues to support the safety and integrity of the food chain. [ABSTRACT FROM AUTHOR]

There is a need for robust analytical methods to support enforcement of food labelling legislation. Proteomics is emerging as a complementary methodology to existing tools such as DNA and antibody-based techniques. Here we describe the development of a proteomics strategy for the determination of meat species in highly processed foods. A database of specific peptides for nine relevant animal species was used to enable semi-targeted species determination. This principle was tested for horse meat speciation, and a range of horse-specific peptides were identified as heat stable marker peptides for the detection of low levels of horse meat in mixtures with other species. [ABSTRACT FROM PUBLISHER]

To allow detection of meat from the most popular game species in Poland, we developed a PCR-based method for identification of roe deer ( Capreolus capreolus), red deer ( Cervus elaphus), and hare ( Lepus europaeus). The designed primers were based on the noncoding, mitochondrial D-loop region. Amplicon sizes ranged from 116 to 255 bp. The primers exhibited no cross-reactivity with the DNA from common slaughter and other game species. The detection limit of the assay was established to be below 0.001 % in raw red deer ( C. elaphus) and hare ( L. europaeus) meat, and below 0.01 % in raw roe deer ( C. capreolus) meat, whereas <0.5 % of hare and red deer meat in processed samples could be detected. The PCR-based assay was used for authentication of 17 samples of raw game meat and 32 samples of game meat-containing products available in Polish markets. Analysis of all tested raw meat and processed products revealed the presence of DNA of investigated species in concordance with producers' declarations. [ABSTRACT FROM AUTHOR]

Abstract: More than ever, people today demand clear and reliable information about the food they consume. This has a great impact on the economy since the consumer's choice is greatly influenced by the food composition detailed in labelling. In the case of processed meat products this is going to be especially important because a simple visual inspection would not allow us to discriminate between the different components so easily as in the case of fresh meat. In order to assure fair trade, food safety and freedom of choice, honest and accurate food label is a requisite that must be assured by legal authorities. To do that, robust and reliable methodologies of analysis must be implemented in control laboratories. In relation to this, the present review intends to give an overview of the different analytical strategies that have been traditionally used by control laboratories to assess meat authenticity or that could be interesting alternatives for fraud detection in near future. [Copyright &y& Elsevier]

Adulterated food can be defined as food incompatible with the declaration of the seller. In the case of meat and meat articles, adulterations refer not only to the replacement of ingredients but also to inappropriate information concerning the origin of raw materials. Methods aiming at investigating meat and meat product authenticity may be based either on the analysis of protein composition or on the analysis of nucleic acids. At the present time, meat and meat product authenticity investigations based on protein analysis employ electrophoretic, enzymic, and chromatographic methods, sometimes supported by the mass spectrometry technique. On the other hand, species identification is often based on polymerase chain reaction (PCR). Biochips present a promising technology. [ABSTRACT FROM AUTHOR]

Abstract: Fourier transform mid-infrared (FT-IR) spectroscopy was evaluated as a tool to discriminate between carcasses of suckling lambs according to the rearing system. Fat samples (39 perirenal and 67 omental) were collected from carcasses of lambs from up to three sheep dairy farms, reared on either ewes milk (EM) or milk replacer (MR). Fatty acid composition of the samples from each fat deposit was first analyzed and, when discriminant-partial least squares regression (PLS) was applied, a perfect discrimination between rearing systems could be established. Additionally, FT-IR spectra of fat samples were obtained and discriminant-PLS and artificial neural network (ANN) based analysis were applied to data sets, the latter using principal component analysis (PCA) or support vector machines (SVM) as processing procedure. Perirenal fat samples were perfectly discriminated from their FT-IR spectra. However, analysis of omental fat showed misclassification rates of 9–13%, with the ANN approach showing a higher discrimination power. [Copyright &y& Elsevier]

In the meat industry, ensuring product authenticity is important due to its impact on human diet, processing chain as well as for ensuring fair trade practices. This study aims at investigating the potential of snapshot hyperspectral imaging (HSI) and deep learning approach for red-meat classification by combining the spectral and spatial features of HSI data of red-meat products. Moreover, this study provides a comprehensive comparison between snapshot HSI and a standard line-scanning HSI system. A novel deep 3D convolution neural network (3D-CNN) model is proposed for extracting the combined features and then classifying the meat in the HSI image. An innovative graph-based post-processing method is also proposed for enhancing the prediction of the 3D-CNN approach. Results show that the 3D-CNN approach significantly enhanced the overall accuracy of state-of-the-art models. Despite limitations in the spectral information from snapshot HSI, the 3D-CNN model shows robustness in classifying red-meat with an overall accuracy of 96.9% and 97.1% for Near-Infrared (NIR) and visible (VIS) snapshot HSI, respectively. A comparison between the HSI systems revealed that state-of-the-art models are insufficient for achieving accurate classification with snapshot HSI data while the 3D-CNN achieves excellent classification accuracy on all systems by utilizing the whole image information. The current study opens the door for further research towards real-time red-meat authenticity and mobile HSI systems using snapshot HSI and deep learning models. • Snapshot HSI systems collect images at video rate and completely portable systems. • Deep learning provides a deep understanding of spectral-spatial features of meat. • 3D-CNN networks capture the interaction between the chemical and textural of meat. • Deep 3D-CNN provides a stable accuracy on line-scanning and snapshot HSI images. • Machine learning models were highly affected by the spectral range and resolution. [ABSTRACT FROM AUTHOR]

Abstract: Extensive substitution and undeclared species have been recently detected in meat products in South Africa, Europe and Asia. Here we review the methodologies utilized in the identification of species in red meat products and highlight the advantages and drawbacks of these methods. The problem is of a different nature in countries with easily accessible game meat and poor or nonexistent monitoring systems in place. Recommendations are drawn for meat DNA testing in these two scenarios. [Copyright &y& Elsevier]