Pork Adulteration Research Articles

A portable multifunctional biosensor based on a newly screened endogenous reference gene for pork adulteration detection

The adulteration of animal-derived food is a widespread problem with meat adulteration emerging as a global concern. To address the need for on-site rapid detection and visual analysis, a portable multifunction biosensor (PMB) was developed. This device integrates Recombinase Polymerase Amplification (RPA), CRISPR/Cas12a assay, and a custom-designed 3D-printed multifunction device, specifically for the visual and rapid detection of pork in food. Initially, a screening model was constructed using Perl bioinformatics based on chromosomal gene information, and the LOC110262058 gene was selected as a reliable endogenous reference gene for pigs. Subsequently, a one-tube RPA-CRISPR/Cas12a system was established to simplify the procedures, converting pork-specific signals into fluorescence while preventing cross-contamination. In addition, an independently designed 3D-printed multifunction device was developed to support the RPA-CRISPR/Cas12a system for one-tube detection, incorporating functions such as heating, centrifugation, and imaging. The reaction conditions of the PMB were optimized to reduce the reaction time to 20–30 min with a sensitivity of 0.01 g/100 g. The analysis of actual samples indicated that the PMB has high selectivity and practical effectiveness for pork detection. The proposed biosensor offers several detection advantages, including output, ease of use, stability, and independence from complex equipment, making it a powerful method for rapid pork detection in food.

Accurate and simultaneous detection of pork and horse meat adulteration by double tailed recombinase polymerase amplification integrated with SERS based two-color lateral flow nucleic acid hybridization strip

Meat adulteration is a growing concern for consumers and researchers, necessitating the development of a simple and robust method for identifying animal sources in meat products. In this study, a double-tailed recombinase polymerase amplification (RPA) integrated with surface-enhanced Raman scattering (SERS) based two-color lateral flow nucleic acid hybridization strip (LFNAHS) was established for simultaneous detection of pork and horse meat. Magenta and cyan gold nanoparticles functionalized with specific probes and Raman dyestuff were used to color the LFNAHS based on RPA amplification products. The assay successfully detected colored test lines for horse and pork adulteration, with quantitative analysis achieved through Raman intensity measurements. The method showed a high specificity with no cross-reactivity and a detection limit of 0.01 % (wt%). Authenticity testing of 40 donkey products yielded results consistent with PCR-agarose gel electrophoresis. This assay can be extended to identify other meat sources by replacing the specific primer sets. This versatile method can be adapted for identifying other meat sources, making it a valuable tool for accurate, sensitive, and quantitative meat authentication in resource-limited settings.

Shortwave infrared hyperspectral imaging for the determination of pork adulteration in minced beef and lamb

Meat adulteration raises religious concerns and economic, quality, and safety issues. Therefore, ensuring that no unnecessary meat is added to the labeled primary meat is important. This study used shortwave hyperspectral imaging (SWIR HSI) working at 895–2504 nm to predict the level of pork adulteration in minced beef and lamb samples. Minced beef and lamb meat samples were adulterated with 2%–50% (w/w) minced pork. Unsupervised principal component analysis (PCA) was used to analyze and visualize the spectral data to determine their similarities. Spectral data were used to develop a partial least squares regression (PLSR) model to predict pork adulteration in minced beef and lamb. PLSR, with several spectral preprocessing methods, resulted in R2 0.97 and RMSE 2.47–2.55 for mixed pork and beef (Pork + Beef) model and R2 0.84–0.91 and RMSE 5.07–6.10 for mixed pork and lamb (Pork + Lamb) model. The developed PLSR model was then applied to each pixel in an image to obtain chemical maps to visualize the presence of pork in minced beef and lamb. This study demonstrated the potency of SWIR HSI to be applied in the sorting machine in meat industries to detect adulteration in meat.

Real-time PCR method based on single-copy nuclear DNA sequences for the quantitative detection of pork adulteration in processed beef products

Meat adulteration, which adversely impacts food safety, economy, and religious beliefs, has come to be considered as a matter of global concern. The lack of a standard method that can be employed to quantitatively detect the pork content in processed meat products has led to demands for an accurate and sensitive method capable of detecting the proportion of pork in processed beef products. In this study, we aimed to develop a real-time PCR-based method to accurately and quantitatively detect pork adulteration in processed beef. We used bovine and porcine species-specific sequences to design primers and probes that specifically amplified bovine and porcine genes. Our real-time PCR method was evaluated via quantitative detection of reference samples and found to be highly accurate. Sensitivity analyses indicated that the limit of detection for beef as well as for pork was as low as five copies, while the limit of quantification for beef and for pork was 0.1%. To compare qualitative and quantitative results, beef samples cross-contaminated with pork were analyzed using both quantitative and standard qualitative methods. No undeclared meat components were found among the 25 processed meat products that were analyzed to evaluate the applicability of the quantitative method. However, the beef content in some processed beef products was very low, even approaching zero. This real-time PCR method may be reliably applied for quantitatively detect pork adulteration in processed beef products. Thus, this method of ours may help promote healthy and sustainable development of meat products.

Development of FTIR Spectroscopy and multivariate calibration for authentication beef meatballs from pork meat

In several countries, especially Indonesia, meatballs are one of the favorite foods made from meat. The high price of beef is a reason for adulterated beef meat with pork to gain a significant profit. All food products must not contain pig derivatives such as pork in Muslim societies. This study aimed to develop Fourier Transform Infrared (FTIR) spectroscopy in combination with linear discriminant analysis (LDA) for classifying pork in beef meatballs and multivariate calibration for qualitative and quantitative analysis methods using partial least square regression (PLSR) and principal component regression (PCR). The lipid component from meatballs containing pork and beef in different concentrations was obtained by employing three lipid extraction methods, namely BlighDyer, Folch, and Soxhlet methods, and subjected to attenuated total reflectance (ATR) spectral measured at wavenumbers of 4000-650 cm-1 . LDA at 3800-800 cm-1 was able to differentiate between beef meatballs and meatballs containing pork meat (PM). Additionally, PLSR using second derivative FTIR spectra at wavenumbers of 3100-900 cm-1 delivered great region for predicting pork levels in beef meatballs extracted using Folch methods with coefficient determination (R2 ) values was > 0.99 and 0.02% for root mean square error of calibration (RSMEC) and root mean square error of prediction (RSMEP) values, respectively. PCR using absorbance values of 1st derivative FTIR spectra at wavenumbers 1400-800 cm-1 for Bligh-Dyer method and 3800-800 cm-1 for Soxhlet was offered the R2 value was> 0.99 with low RSMEC and RSMEP values for prediction of pork fat in beef meatballs, respectively. It can be concluded that FTIR spectroscopy on the Savitzy-Golay derivatization method in combination with chemometrics was an accurate and quick approach for authenticating pork adulteration in beef meatballs.

Rapid and non-destructive detection of ponceau 4R red colored pork

The characteristic colour of pork desired by consumers is a widespread phenomenon on the Ghanaian market that has led to some suspected adulteration practices. Currently available methods for monitoring pork quality are time consuming but above all, destructive (destroys the integrity of meat). This study aimed to develop rapid models that can be used to detect, classify and predict the presence of ponceau 4R in fresh pork in the Kumasi metropolis of Ghana using near-infrared spectroscopy together with chemometrics. Fresh pork samples, 120 obtained from the markets and 120 adulterated artificially in the laboratory, were subjected to near-infrared measurements. The spectra obtained were evaluated using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Partial Least Square Regression (PLSR). PCA and LDA showed that scanning the skin of the pork and pretreating the spectra with Savitzky-Golay smoothing sufficed for further chemometric analysis. The classification models built using LDA showed similarities between samples obtained from the markets and the artificially adulterated samples, indicating the presence of colour adulterant. The models also revealed the importance of processing time in making the adulterated meat more appealing to consumers. PLSR, however, yielded poor results for predicting colour and adulterant concentration. In effect, PCA and LDA methods proved to be better alternatives for the detection of colored pork adulteration and can be adopted for quality control applications together with near infrared spectroscopy.

Analysis of Pork in Beef Sausages Using LC-Orbitrap HRMS Untargeted Metabolomics Combined with Chemometrics for Halal Authentication Study.

Beef sausage (BS) is one of the most favored meat products due to its nutrition and good taste. However, for economic purposes, BS is often adulterated with pork by unethical players. Pork consumption is strictly prohibited for religions including Islam and Judaism. Therefore, advanced detection methods are highly required to warrant the halal authenticity of BS. This research aimed to develop a liquid chromatography-high-resolution mass spectrometry (LC-HRMS) method to determine the halal authenticity of BS using an untargeted metabolomics approach. LC-HRMS was capable of detecting various metabolites in BS and BS containing pork. The presence of pork in BS could be differentiated using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) with high accuracy. PLS-DA perfectly classified authentic BS and BS containing pork in all concentration levels of pork with R2X = (0.821), R2Y(= 0.984), and Q2 = (0.795). The level of pork in BS was successfully predicted through partial least squares (PLS) and orthogonal PLS (OPLS) chemometrics. Both models gave high R2 (>0.99) actual and predicted values as well as few errors, indicating good accuracy and precision. Identification of discriminating metabolites' potential as biomarker candidates through variable importance for projections (VIP) value revealed metabolites of 2-arachidonyl-sn-glycero-3-phosphoethanolamine, 3-hydroxyoctanoylcarnitine, 8Z,11Z,14Z-eicosatrienoic acid, D-(+)-galactose, oleamide, 3-hydroxyhexadecanoylcarnitine, arachidonic acid, and α-eleostearic acid as good indicators to detect pork. It can be concluded that LC-HRMS metabolomics combined with PCA, PLS-DA, PLS, and OPLS was successfully used to detect pork adulteration in beef sausages. The results imply that LC-HRMS untargeted metabolomics in combination with chemometrics is a promising alternative as an analytical technique to detect pork in sausage products. Further analysis of larger samples is required to warrant the reproducibility.

A rapid abiotic/biotic hybrid sandwich detection for trace pork adulteration in halal meat extract.

In this study, we prepared molecularly imprinted polymer nanogels with good affinity for the Fc domain of immunoglobulin G (IgG) using 4-(2-methacrylamidoethylaminomethyl) phenylboronic acid as a modifiable functional monomer for post-imprinting in-cavity modification of a fluorescent dye (F-Fc-MIP-NGs). A novel nanogel-based biotic/abiotic hybrid sandwich detection system for porcine serum albumin (PSA) was developed using F-Fc-MIP-NGs as an alternative to a secondary antibody for fluorescence detection and another molecularly imprinted polymer nanogel capable of recognizing PSA (PSA-MIP-NGs) as a capturing artificial antibody, along with a natural antibody toward PSA (Anti-PSA) that was used as a primary antibody. After incubation of PSA and Anti-PSA with F-Fc-MIP-NGs, the PSA/Anti-PSA/F-Fc-MIP-NGs complex was captured by immobilized PSA-MIP-NGs for fluorescence measurements. The analysis time was less than 30 min for detecting pork adulteration of 0.01 wt% in halal beef and lamb meats. The detection limit was comparable to that of frequently used immunoassays. Therefore, we believe that this method is a promising, sensitive, and rapid detection method for impurities in real samples and could be a simple, inexpensive, and rapid alternative to conventional methods that have cumbersome procedures of 4 hours or more.

Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-Nose.

Food adulteration is the most serious problem found in the food industry as it harms people’s healths and undermines their beliefs. The present study is focused on designing and developing a smart electronic nose (SE-Nose) for the qualitative and quantitative fast-track detection of food adulteration. The SE-Nose methodology is comprised of a dataset, sample slicing window protocol, normalization, pattern recognition, and output blocks. The dataset pork adulteration in beef is used to validate the SE-Nose methodology. The sample slicing window protocol extracts the early part of the signal. The sample slicing window protocol and pattern recognition models (classification and regression models) together achieved the high-performance and fast-track detection of pork adulteration in beef. With classification models, the qualitative analysis of adulteration is measured, and with regression models, the quantitative analysis of adulteration is measured. An accuracy of 99.996% and an RMSE of 0.02864 were achieved with the SVM classification and regression model. The recognition time in detecting pork adulteration in beef with SVM models is 40 s. With the proposed SE-Nose methodology, the recognition time is reduced by one-third. To validate the classification and regression models, a 10-fold cross-validation method was used.

Detection of pork adulteration in beef with ladder-shape melting temperature isothermal amplification (LMTIA) assay

ABSTRACT The ladder-shape melting temperature isothermal amplification (LMTIA) is a newly developed technology, and the objective of this study is to establish the LMTIA assay for detection of pork in beef. The LMTIA primers were designed with the prolactin receptor gene of Sus scrofa as the target. The LMTIA reaction system was optimized, and the performance of the LMTIA assay in specificity, sensitivity and detection limit (LOD) was determined. Our results showed that the LMTIA assay was able to specifically detect 10 ng genomic DNA (gDNAs) of Sus scrofa, the sensitivity of the LMTIA assay was 1 ng genomic DNA of Sus scrofa, and the LMTIA assay was able to detect the pork adulterated in beef with 0.1% LOD, which was the same as that of reported PCR assay or LAMP assay. This study holds a promise for facilitating the surveillance of the commercial adulteration of pork in beef.

Comparison of ELISA and PCR Assays for Detection of Pork Adulteration in Halal-Labelled Beef Products

Background: Food adulteration with pork in processed beef products is one of the most serious issues in a food sector in a Muslim-majority country since it is related to religious food ethics regarding the halal products. The goal of this research is to test the suitability of ingredients in beef floss and its Halal by knowing the presence of porkDNA and protein in those products. Methods: Meat products were prepared from two famous marketplaces in Indonesia labeled contain beef meat. In this study, a qualitative Enzyme-Linked Immunosorbent Assay (ELISA) test was compared to a conventional Polymerase Chain Reaction (PCR) assay to determine pork adulteration in beef floss. Results: The results of the ELISA test showed that two products labeling Halal and containing beef ingredients were positive for pork. Those two samples continued testing using conventional PCR assay. The result of the conventional PCR assay was negative for those two samples. Conclusion: It may be helpful to utilize both traditional PCR and ELISA for species detection due to the possibly inhibiting compounds contained in some processed meat products. The results of this research suggest that ELISA is better than conventional PCR method for product samples that have received an intensive heating process.

An Improved Classification of Pork Adulteration in Beef Based on Electronic Nose Using Modified Deep Extreme Learning with Principal Component Analysis as Feature Learning

An Improved Classification of Pork Adulteration in Beef Based on Electronic Nose Using Modified Deep Extreme Learning with Principal Component Analysis as Feature Learning

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.

Rational incorporating of loop-mediated isothermal amplification with fluorescence anisotropy for rapid, sensitive and on-site identification of pork adulteration

Meat and meat products are one of the valuable foods that provide a variety of essential nutrients for human beings, and their authentication is of great significance for guaranteeing food quality and safety and also the human health. In this research, we present a proof-of-concept strategy of loop-mediated isothermal amplification (LAMP) coupled fluorescence-anisotropy (FP) for rapid, sensitive and accurate judgement of adulteration. Relationship between LAMP and FP is easily constructed by using FITC-labeled primer, which shows limited FP signal at its free state because of fast rotation. With pork DNA, the short primer can be efficiently amplified into macromolecules by LAMP, undergoing significant increase of molecular weight restricting the rotation of FITC. This change leads to a dramatic enhancement of FP signal for adulteration screening. Results show that LAMP-FP well integrates the high efficiency of LAMP with the simplicity of FP, achieving detection limit of 0.01% (wt. %). The performance of LAMP-FP strategy is comparable to those of qPCR. Importantly, this unique method is also available for the commercial processed products. We expect this LAMP-FP will provide a new direction to build versatile analytical tools in food safety monitoring.

Graphene Decorated Gold Nanoparticles Modified Screen-Printed Carbon Electrode for Detection of Pork Adulteration

Graphene Decorated Gold Nanoparticles Modified Screen-Printed Carbon Electrode for Detection of Pork Adulteration

Investigation of pork meat in chicken- and beef-based commercial products by ELISA and real-time PCR sold at retail in Kosovo

Food adulteration and fraudulent practices are widely observed in the food industry worldwide and are of great concern for Balkan countries. This study aims at investigating the level of undeclared pork meat in commercial beef and chicken meat products sold in Kosovo by implying one commercial enzyme-linked immunosorbent assay (ELISA) and two confirmatory real-time polymerase chain reaction (PCR) approaches [ready-to-use real-time PCR and real-time PCR with primers specific for pork mitochondrial deoxyribonucleic acid (DNA)]. In supermarkets in the capital city, Prishtina, 62 meat products were randomly sampled, and the three methods were applied. Additionally, these three approaches were evaluated for their practicability, reproducibility, and cost. The results showed that pork was present in 32% of beef- and 8% chicken-based products. ELISA and real-time PCR with pork specific primers showed 100% of reproducibility for beef- and chicken-based products. In contrast, the ready-to-use real-time PCR kit showed 100% reproducibility in chicken-, but only 75% in beef-based samples. ELISA was more rapid than both real-time PCR approaches, but it was more challenging when large numbers of samples were processed. The real-time PCR approach with pork specific primers was the cheapest, while the ready-to-use real-time PCR was the most practical method. Commercial ELISA, in combination with real-time PCR with pork specific primers, provides a reliable and affordable testing methodology that can be implemented for rapid detection and monitoring of pork adulteration in diverse commercial foods.

Identification of meat species in processed meat products by using protein based laser induced breakdown spectroscopy assay

The detection of meat fraud and mislabeling in processed meat products is a raising concern for consumers. The aim of this study was to develop and demonstrate the potential of protein-based laser induced breakdown spectroscopy (LIBS) method to be used for the identification of beef, chicken, and pork in fermented sausage and salami products. In this respect, bulk protein and protein fractions rich in sarcoplasmic and myofibrillar protein of sausage and salami products were obtained and subjected to LIBS analysis. LIBS spectrum was evaluated with chemometric methods to classify meat species and determine adulteration ratio by using principal component analysis and partial least square analysis, respectively. Limit of detection values for chicken and pork adulteration in beef sausage were found as 3.68 and 3.83% for myofibrillar fraction, while those values in beef salami were found as 3.80 and 3.47% for sarcoplasmic fraction, respectively.

Determination of Pork Adulteration in Roasted Beef Meatballs using Fourier Transform Infrared Spectroscopy in Combination with Chemometrics

The pork adulteration in food is a matter of concern for Muslims worldwide. This study aimed to quantitatively analyze and classify pork and beef in roasted meatballs using the Fourier Transform Infrared (FTIR) spectroscopy method combined with chemometrics. To obtain the FTIR spectral data, pure beef meatballs and beef meatballs added with pork at various concentrations were analyzed in the mid-infrared region corresponding to wavenumbers ranging from 4000 to 400 cm-1. Samples of beef meatballs randomly selected from the market were analyzed in the optimum condition in terms of wavenumbers capable of providing best modeling. Qualitative and quantitative analyses were performed on the optimum wavenumbers of 1100-900 cm-1 using chemometrics of Partial Least Square (PLS) and Principal Component Analysis (PCA), respectively. The PLS calibration model for the relationship between actual values of pork (x-axis) and predicted FTIR values (y-axis) of roasted meatballs resulted in the equation of y = 0.99965x + 0.0221, with a coefficient of determination (R2-value) of 0.99965 and root mean square error of calibration (RMSEC) of 0.7137%. The PCA indicated differences in quadrants between pure beef and pork roasted meatballs. Most samples of marketed beef meatballs lied in the beef meatball quadrant. However, one sample of marketed beef meatballs was in the pork meatball quadrant; thus, it contained pork. To conclude, the FTIR spectroscopy method combined with chemometrics could qualitatively and quantitatively identify the ingredient of pork in roasted beef meatballs.

Ensemble Learning for Optimizing Classification of Pork Adulteration in Beef Based on Electronic Nose Dataset

Ensemble Learning for Optimizing Classification of Pork Adulteration in Beef Based on Electronic Nose Dataset

PCR based method for authentication of pork in raw and processed products as well as in binary meat mixtures

In this study, amplification of species-specific marker of mitochondrial DNA origin was carried out to detect pork in raw, processed and meat mixtures containing varying concentrations of pork, viz. 1, 10, 50, 75 and 100%. The species-specific marker for pork was tested in raw pork from different breeds/varieties of pig such as Hampshire, Yorkshire, Ghungroo, Duroc, Rani, and Asha. The size of the amplified product was 290 bp in all the breeds/ varieties. The results were consistent in processed pork products, viz. frankfurter sausage, salami, cocktail sausage, pork slice, ham, and pork curry which were subjected to different cooking temperatures ranging from 75 to 121°C. In case of all the mixtures with different concentrations of pork, similar results were observed. Subsequently, this marker was tested for cross-amplification by checking them with beef, carabeef, mutton, chevon, chicken, and duck meat and no amplification was observed. The results suggested that the DNA marker used in this study is highly species-specific and reliable to detect pork adulteration, unambiguously, in raw, processed as well as in meat mixtures containing pork. This technique is rapid, simple and economical as compared to other methods of pork adulteration detection.