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Minced pork jowl meat, also called the sticking-piece, is commonly used to be adulterated in minced pork, which influences the overall product quality and safety. In this study, hyperspectral imaging (HSI) methodology was proposed to identify and visualize this kind of meat adulteration. A total of 176 hyperspectral images were acquired from adulterated meat samples in the range of 0%–100% (w/w) at 10% increments using a visible and near-infrared (400–1000 nm) HSI system in reflectance mode. Mean spectra were extracted from the regions of interests (ROIs) and represented each sample accordingly. The performance comparison of established partial least square regression (PLSR) models showed that spectra pretreated by standard normal variate (SNV) performed best with Rp2 = 0.9549 and residual predictive deviation (RPD) = 4.54. Furthermore, functional wavelengths related to adulteration identification were individually selected using methods of principal component (PC) loadings, two-dimensional correlation spectroscopy (2D-COS), and regression coefficients (RC). After that, the multispectral RC-PLSR model exhibited the most satisfactory results in prediction set that Rp2 was 0.9063, RPD was 2.30, and the limit of detection (LOD) was 6.50%. Spatial distribution was visualized based on the preferred model, and adulteration levels were clearly discernible. Lastly, the visualization was further verified that prediction results well matched the known distribution in samples. Overall, HSI was tested to be a promising methodology for detecting and visualizing minced jowl meat in pork.

Customers are very concerned about high-quality products whose provenance is healthy. The identification of meat authenticity is a subject of growing concern for a variety of reasons, including religious, economic, legal, and public health. Between March and April of 2023, 150 distinct marketable beef product samples from various retailers in El-Fayoum, Egypt, were gathered. There were 30 samples of each of the following: luncheon, kofta, sausage, burger, and minced meat. Every sample underwent a histological investigation as well as subjected to a standard polymerase chain reaction (PCR) analysis to identify meat types that had not been stated by Egyptian regulations. According to the obtained data, the meat products under scrutiny contained a variety of unauthorized tissues which do not match Egyptian regulations. Furthermore, the PCR results indicated that the chicken, camels, donkeys, and pigs derivatives were detected in 60%, 30%, 16%, and 8% of examined samples, respectively. In conclusion, besides displaying a variety of illegal tissues, the majority of the meat items under examination were tainted with flesh from many species. As a result, it is crucial to regularly inspect these products before they are put on the market to ensure that they comply with the law and don’t mislead customers Furthermore, it is advisable for authorities to implement rigorous oversight of food manufacturing facilities to ensure the production of safe and wholesome meat.

To combat poultry meat adulteration, we developed a poultry universal primer-based fluorescent PCR (PUP-fPCR). Through comprehensive genomic alignment analysis, a poultry-specific nuclear DNA sequence containing phylogenetically conserved regions and hypervariable segments with interspecies nucleotide polymorphisms was employed to develop universal primers targeting conserved flanking sequences and TaqMan probes for hypervariable segments. Then, a multiplex quantitative PCR method incorporating universal primers with four TaqMan probes was developed with high specificity and sensitivity (limit of detection: 0.005 ng). Analytical performance evaluation using prepared DNA mixtures revealed robust accuracy (relative deviation: 0.80–5.05%) and precision (relative standard deviation: 0.94–13.84%). This single-tube multiplex system leverages the spectral discrimination of TaqMan probes to simultaneously detect four poultry species, overcoming primer competition issues inherent in conventional multiplex PCR designs. This integrated approach reduces system complexity while maintaining detection efficiency, providing regulatory agencies with a robust tool for combating meat adulteration and ensuring food quality supervision.

Beef is one of the most widely consumed meats worldwide, and its products are often adulterated with other meats. To protect consumers’ rights and facilitate regulation by relevant government departments, it is important to develop a rapid and accurate method to detect adulteration in meat and meat products. In this study, we employed bioinformatics methods to identify specific sequences of cattle, pig, chicken, and duck, and designed primers and probes accordingly. A method based on recombinase polymerase amplification (RPA) combined with lateral flow dipstick (LFD) was developed for rapid visual detection of the authenticity of beef and beef products. The RPA reaction was conducted at 37℃ for 20 min. The amplification products were then diluted and applied to the sample pad of the LFD. Results were visible to the naked eye within 5 min. The results demonstrated that the method could specifically differentiate components of bovine, porcine, chicken, and duck origin, with a limit of detection (LOD) of approximately 20 copies for each species. The method was applied to 10 commercially available beef products. Of which, five samples were detected with porcine-derived components. The results of the RPA–LFD method were verified using PCR and observed to be consistent between the methods. Compared with other methods, this method is easy to use, requires no specialized equipment, and delivers results in about 30 min from amplification to detection, making it suitable for rapid visual detection on-site. Therefore, it can serve as a technical reference for detecting adulteration in meat and meat products.

The present study was aimed at developing a low-cost but rapid technique for qualitative and quantitative detection of beef adulterated with pork. An electronic nose based on colorimetric sensors was proposed. The fresh beef rib steaks and streaky pork were purchased and used from the local agricultural market in Suzhou, China. The minced beef was mixed with pork ranging at levels from 0%~100% by weight at increments of 20%. Protein, fat, and ash content were measured for validation of the differences between the pure beef and pork used in basic chemical compositions. Fisher linear discriminant analysis (Fisher LDA) and extreme learning machine (ELM) were utilized comparatively for identification of the ground pure beef, beef–pork mixtures, and pure pork. Back propagation-artificial neural network (BP-ANN) models were built for prediction of the adulteration levels. Results revealed that the ELM model built was superior to the Fisher LDA model with higher identification rates of 91.27% and 87.5% in the training and prediction sets respectively. Regarding the adulteration level prediction, the correlation coefficient and the root mean square error were 0.85 and 0.147 respectively in the prediction set of the BP-ANN model built. This suggests, from all the results, that the low-cost electronic nose based on colorimetric sensors coupled with chemometrics has a great potential in rapid detection of beef adulterated with pork.

Meat adulteration is a global problem which undermines market fairness and harms people with allergies or certain religious beliefs. In this study, a novel framework in which a one-dimensional convolutional neural network (1DCNN) serves as a backbone and a random forest regressor (RFR) serves as a regressor, named 1DCNN-RFR, is proposed for the quantitative detection of beef adulterated with pork using electronic nose (E-nose) data. The 1DCNN backbone extracted a sufficient number of features from a multichannel input matrix converted from the raw E-nose data. The RFR improved the regression performance due to its strong prediction ability. The effectiveness of the 1DCNN-RFR framework was verified by comparing it with four other models (support vector regression model (SVR), RFR, backpropagation neural network (BPNN), and 1DCNN). The proposed 1DCNN-RFR framework performed best in the quantitative detection of beef adulterated with pork. This study indicated that the proposed 1DCNN-RFR framework could be used as an effective tool for the quantitative detection of meat adulteration.

Meat adulteration, particularly the substitution of high-value beef with cheaper poultry (chicken, duck) or pork for illicit economic gain, poses significant threats to consumer rights, market integrity, and public health. Accurate identification of components in mixed meat samples is crucial for combating such fraud. Traditional species detection methods have limitations as qPCR with species-specific probes can only target a subset of known species, while Sanger sequencing is inadequate for mixed samples and rapid on-site detection. Building on the previously developed ClassIdent pipeline targeting the mitochondrial 12S rRNA gene and QNome nanopore sequencing, this study focused on verifying its applicability in mixed meat identification. We prepared 48 simulated samples (18 single-source and 30 mixed samples) to mimic common meat fraud scenarios (beef adulterated with chicken, duck, or pork), including 9 binary combinations (weight ratios 1:1, 1:2, 1:4) and 1 quaternary combination (1:1:1:1), with each mixed sample sequenced in triplicate. All single-source samples were accurately identified by ClassIdent, with an average sequence identity of 99.60 %. For mixed samples, ClassIdent successfully distinguished all component species, and the read proportion of each species showed a positive correlation with the fresh weight mixing ratio. The mean absolute difference between the read proportion and the actual fresh weight ratio ranged from 1.11 % to 21.32 % across all mixed combinations. This discrepancy is primarily attributed to biological variables (e.g., interspecific differences in cell size and mitochondrial DNA copy number) and technical biases (including variations in DNA extraction efficiency and species-specific PCR amplification preferences). This study validates the potential of ClassIdent for rapid and reliable detection of meat adulteration, supporting its application in food safety supervision and forensic investigation. •Validates ClassIdent (12S rRNA nanopore sequencing) for meat adulteration detection.•Accurately identifies single-source (18) and mixed (30) samples, sequence identity ≥ 99.32 %.•Read proportion correlates with fresh weight ratio in mixtures, affected by biological/technical factors.•Offers a rapid, reliable tool for food safety supervision and forensic investigation, quantifiable for optimization.

Purpose Consumption of game meat is growing when compared to other meats. It is susceptible to adulteration because of its cost and availability. Spectroscopy may lead to rapid methodologies for detecting adulteration. The purpose of this study is to detect the adulteration of wild fallow deer (Dama dama) meat with domestic goat (G) (Capra aegagrus hircus) meat, for samples stored for different periods of time using Fourier transform infrared (FTIR) spectroscopy coupled with chemometric. Design/methodology/approach Meat was cut and mixed in different percentages, transformed into mini-burgers and stored at 3°C from 12 to 432 h and periodically examined for FTIR, pH and microbial analysis. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were applied to detect adulteration. Findings The PCA model, applied to the spectral region from 1,138 to 1,180, 1,314 to 1,477, 1,535 to 1,556 and from 1,728 to 1,759 cm−1, describes the adulteration using four principal components which explained 95 per cent of variance. For the levels of Adulteration A1 (pure meat), A2 (25 and 50 %w/wG) and A3 (75 and 100 %w/wG) for an external set of samples, the correlation coefficients for prediction were 0.979, 0.941 and 0.971, and the room mean square error were 8.58, 12.46 and 9.47 per cent, respectively. Originality/value The PLS-DA model predicted the adulteration for an external set of samples with high accuracy. The proposed method has the advantage of allowing rapid results, despite the storage time of the adulterated meat. It was shown that FTIR combined with chemometrics can be used to establish a methodology for the identification of adulteration of game meat, not only for fresh meat but also for meat stored for different periods of time.

Verifying the authenticity of food products is essential due to the recent increase in counterfeit meat-containing food products. The existing methods of detection have a number of disadvantages. Therefore, simple, cheap, and sensitive methods for detecting various types of meat are required. In this study, we propose a rapid full-cycle technique to control the chicken or pig adulteration of meat products, including 3 min of crude DNA extraction, 20 min of recombinase polymerase amplification (RPA) at 39 °C, and 10 min of lateral flow assay (LFA) detection. The cytochrome B gene was used in the developed RPA-based test for chicken and pig identification. The selected primers provided specific RPA without DNA nuclease and an additional oligonucleotide probe. As a result, RPA–LFA, based on designed fluorescein- and biotin-labeled primers, detected up to 0.2 pg total DNA per μL, which provided up to 0.001% w/w identification of the target meat component in the composite meat. The RPA–LFA of the chicken and pig meat identification was successfully applied to processed meat products and to meat after heating. The results were confirmed by real-time PCR. Ultimately, the developed analysis is specific and enables the detection of pork and chicken impurities with high accuracy in raw and processed meat mixtures. The proposed rapid full-cycle technique could be adopted for the authentication of other meat products.

Meat adulteration have become a global issue, which has increasingly raised concerns due to not only economic losses and religious issues, but also public safety and its negative effects on human health. Using optimal primers for seven target species, a multiplex PCR method was developed for the molecular authentication of camel, cattle, dog, pig, chicken, sheep and duck in one tube reaction. Species-specific amplification from the premixed total DNA of seven species was corroborated by DNA sequencing. The limit of detection (LOD) is as low as 0.025 ng DNA for the simultaneous identification of seven species in both raw and heat-processed meat or target meat: as little as 0.1% (w/w) of the total meat weight. This method is strongly reproducible even while exposed to intensively heat-processed meat and meat mixtures, which renders it able to trace meat origins in real-world foodstuffs based on the authenticity assessment of commercial meat samples. Therefore, this method is a powerful tool for the inspection of meat adulterants and has broad application prospects.