Detection Of Meat Adulteration Research Articles

Development and application of a low-cost and portable multi-channel spectral detection system for mutton adulteration

It is important to develop low-cost, fast and portable meat adulteration detection systems to ensure the meat authenticity and safety in complex market environments. A multi-channel spectral detection system for meat adulteration was developed in this study. The core hardware of the system mainly includes a designed spectral module and a Raspberry pi controller. The spectral module consists of three multi-channel spectral sensors and LED lamps with specific wavelengths, containing 18 channels covering a range of 410–940 nm. The software was developed based on PyQt5. After completing the construction of the system, the detection distance was discussed and determined to be 4 mm. Based on the spectral data collected by the developed system, the models for classifying pure mutton, pure pork, mutton flavour essence adulteration, colourant adulteration and adulterated mutton with pork were established and compared. Four intelligent optimisation algorithms were further used to improve the model performance. The results of the test set showed that the support vector classification (SVC) model optimised by a sparrow search algorithm (SSA) obtained the best classification performance, with an accuracy of 97.59% and a Kappa coefficient of 0.9696. After the SSA-SVC was incorporated into the sensor software, the system performance was evaluated using external validation samples. The overall accuracy of the system was 94.29%. The system took about 5.31 s to detect a sample, and the total weight of the system was 1.55 kg. Overall, the developed portable spectral system has considerable potential to rapidly and accurately discriminate adulterated mutton in the field.

LAMP assay coupled CRISPR/LbCas12a system in a single-tube method for visual detection of meat adulteration

Meat adulteration frequently infringes upon consumers' rights and interests, disrupts the market, and violates religious beliefs. However, implementing on-site detection of meat samples remains technically challenging for widespread applications. Here, we developed a point-of-care testing (POCT) platform for fast, highly sensitive, and on-site detection of meat adulteration, based on loop-mediated isothermal amplification (LAMP) coupled the clustered regularly interspaced short palindromic repeats (CRISPR) system in a single-tube. This platform integrates the efficient exponential amplification capacity of LAMP and the high specificity recognition capability of CRISPR, reducing the false positive results and minimizing LAMP aerosol contamination risk. Pork was selected as the target for adulteration detection, qualitative detection by the naked eye can be achieved within 1 h from sample collection to results with a sensitivity as high as 0.05 % to detect binary mixtures of pork in beef and the limit of DNA detection was at 50 pg/μL which 10 times higher than the individual LAMP assay. This work is expected to offer a powerful universal POCT platform for addressing a range of meat adulteration issues, suitable for enhancing food safety measures.

Possibility of using fatty acid profiles for the authentication of beef adulterated with pork, donkey, and dog meat

Detection of meat adulteration is a critical issue in food labeling procedures and a serious concern related to food fraud, authenticity, and religious beliefs. The current work detected and quantified adulteration of raw ground beef with pork, donkey, and dog meat based on fatty acid profiles using GC-MS/MS. The study design incorporated pork, donkey, or dog meat with beef meat; negative and positive controls were used for the different meat species. Results demonstrated several significant differences (p < 0.05) in fatty acid contents between mixed/adulterated meat and pure beef. In addition, higher total saturated fatty acid levels in ground beef (57.91%) compared to dog fat (46.44%), donkey (38.71%), and pork fat (lard) (40.23%). High total unsaturated fatty acids content was observed in donkey (61.92%), pork (59.77%), and dog (53.56%) fats compared to beef fat (42.09%). On the other hand, total unsaturated and monounsaturated fatty acids in beef meat were lower than in pork, donkey, and dog meat. Moreover, the highest trans-fatty acid content was found in pork compared to the other meat types. All incorporated samples correlated positively with pure ground beef concerning the fatty acid profiles. Therefore, alteration of the mixed beef fatty acid profiles was a potential indicator for adulteration since a marked decrease in total saturated fatty acid content and an increase in unsaturated fatty acids was observed in the substituted samples. We concluded that GC-MS/MS-based fatty acid profiling is a promising technique that can be used to detect meat adulteration.

Identification and quantification of adulteration in frozen-thawed meat of different breeds by NIR spectroscopy and chemometrics

This work aims to compare the characteristic near-infrared absorption bands of different breeds of meats and to develop a qualitative and semi-quantitative method for the detection of adulteration in various types of frozen meat based on near-infrared spectroscopy and chemometrics methods. The adulterated samples were in various combinations of pork, beef and mutton adulterated with different proportions of pork, beef, lamb, duck and chicken. The results showed that the accuracy of the PLS-DA model that identified all adulteration types together was the lowest (AUCP= 0.8265), while the accuracy of the PLS-DA model of lamb-chicken and beef-chicken was the highest (AUCP= 1). AUCP of other adulteration types was higher than 0.92. And the PLSR model constructed by all the adulteration types together had the worst result (Rp2= 0.5348, RMSECV=0.1895, RPD=1.48). The results of PLSR prediction models constructed by the adulteration types separately all reached the expectation (Rp2= 0.8707–0.9703，RMSECV= 0.0637–0.1089，RPD= 2.78–5.80). The results of the PLS-DA models and PLSR models using characteristic band spectral information are basically the same or slightly improved compared with those results of the models using full-band spectral information. Therefore, NIR spectroscopy and chemometrics methods can directly qualitatively identified all adulteration types, but it is more accurate to semi-quantitatively predict the proportion of adulteration according to different adulteration types. The near-infrared absorption of moisture and fat contributed more to the detection of meat adulteration.

Research Progress of Electronic Nose and Near-Infrared Spectroscopy in Meat Adulteration Detection

China is a large consumer of meat and meat products. People’s daily diets include a variety of meat, but meat food adulteration problems are common. This paper discusses the research progress of the electronic nose and near-infrared spectroscopy in the field of meat adulteration detection. Through the study of dozens of related papers in recent years, it has been found that the use of the electronic nose and near-infrared spectroscopy for meat detection has the advantages of speed, a nondestructive nature, high sensitivity, strong quantitative analysis, high automation, a wide applicability, an improved product quality, and cost reduction over the traditional detection, but it may be limited in detecting the adulteration of a specific meat, and there are issues with the life and stability of the sensors of the electronic nose in the process of detection, along with the problems of the high requirements for the modeling of the data of near-infrared spectroscopy. This paper takes adulterated meat as the research object and briefly summarizes the detection principles of the electronic nose and near-infrared spectroscopy, as well as the types of sensors applied in the electronic nose. The research progress of the electronic nose and near-infrared detection technology in meat adulteration assessment is reviewed, the advantages and disadvantages of the two in practical application are analyzed, the classification of pattern recognition methods and their applications in meat identification are described, and the feasibility and practical significance of the joint application of the two in meat adulteration detection are envisioned. Meanwhile, the challenges faced by the two in meat detection are pointed out.

High-throughput identification of meat ingredients in adulterated foods based on centrifugal integrated purification-CRISPR array

Rapid, accurate, and sensitive analytical methods for the detection of food fraud are now an urgent requirement in the global food industry to ensure food quality. In response to this demand, a centrifugal integrated purification-CRISPR array for meat adulteration (CIPAM) was established. In detail, CIPAM system combines microneedles for DNA extraction and RAA-CRISPR/Cas12a integrated into a centrifugal microfluidic chip for the detection of meat adulteration. The RAA-CRISPR/Cas12a reaction reagents were pre-embedded into the different reaction chambers on the microfluidic chip to achieve the streamline of operations, markedly simplifying the detection process. The whole reaction was completed within 30 min with a detection limit of 0.1 % (w/w) in pig, chicken, duck, and lamb products. Referring to the results of the standard method, CIPAM system achieved 100 % accuracy. The automatic multiplex detection process implemented in the developed CIPAM system met the needs of food regulatory authorities.

Development of a high-throughput DNA isolation method for livestock and poultry meat based on mesoporous metal-organic framework-coated solid-phase microextraction device

A rapid, portable, simple, low-cost, and high-throughput DNA isolation method was developed for the quantitative identification of meat adulteration based on mesoporous UiO-66 coated solid phase microextraction (SPME) devices. The high-throughput DNA extraction system consists of 96 UiO-66 SPME devices and a polyformaldehyde plate, which can extract DNA from 96 livestock and poultry meat samples simultaneously. At the same time, several parameters of the high throughput DNA isolation method were optimized, including lysis buffer volume, elution buffer volume, lysis time and elution time, etc. Compared with the single DNA isolation device, the 96-SPME high-throughput extraction system maintains the original good universality, reproducibility, chemical stability, and reusability. On this basis, it obtained higher DNA concentration (80 ± 34 ng μL−1), satisfactory DNA purity (A260/A280=2.4) and shorter extraction time (less than 7.2 s per sample), which are much more convenient and efficient than the conventional kit method and the single SPME DNA isolation method. In addition, the conventional multiplex polymerase chain reaction and real-time polymerase chain reaction techniques were used for the qualitative and quantitative detection of meat adulteration. The DNA obtained from this extraction method is highly reproducible and sensitive for the identification of duck ingredients in beef, with a detection range of 1–100 % and a minimum detection limit of 1 % (w/w).

CRISPR/Cas12a triggered SERS and naked eye dual-mode biosensor for ultrasensitive and on-site detection of nucleic acid via cascade signal amplification

Highly sensitive and on-site detection of nucleic acids has always been a critical issue in the field of analytical chemistry. Surface-enhanced Raman scattering (SERS)-based biosensing exhibits huge potential in nucleic acid detection, while the applicability is restricted in trace nucleic acid screening due to the lack of appropriate signal recognition, transducing and amplification technologies. Inspired by the specific recognition of CRISPR/Cas12a and the improved sensitivity through cascade signal amplification, we innovatively proposed a CRISPR/Cas12a triggered SERS and naked eye dual-mode biosensor for ultrasensitive and on-site detection of nucleic acid via cascade signal amplification. Upon the target DNA recognition, the activated CRISPR/Cas12a indiscriminately cleaved substrate ssDNA, leading to the failure of toehold-mediated DNA-strand displacement reaction (TSDR), and triggering hybridization chain reaction (HCR) to assemble numerous G-quadruplex/hemin DNAzyme (GQH DNAzyme) for cascade signal amplification. The generated GQH DNAzyme catalyzed the oxidation of l-cysteine to cystine, perturbing the aggregation of 4-NTP@AuNPs, resulting in significant Raman signal change. On the other hand, GQH DNAzyme catalyzed the oxidation of 2,2′-azino-di-(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS), leading to obvious color change to realize portable naked-eye detection. Through this strategy, target nucleic acid concentration was tactfully transformed into sensitive Raman and portable visualization signals, and the limit of detection were as low as 34.9 aM and 1 pM, respectively. Then, this biosensor was successfully applied to meat adulteration detection, which showed superb selectivity, sensitivity and applicability for on-site detection of trace nucleic acid in complicated food matrix.

Simple Thermal Analysis as a Green Method for the Detection of Meat Adulteration

Differential scanning calorimetry (DSC) is one of the most widely developed thermal analysis methods for meat samples for halal authentication of food or processed products. Research on adulteration detection for various types of meat and its derivatives has been developed before and still requires organic solvents. Therefore, the concept of the "green method" is being tried to develop in this research. DSC analyses are performed in the same experimental conditions for all sample powder: sample mass 2 mg, temperature range 30–400 °C, and heating rate 20 °C min−1. The results showed there is a characteristic minor endothermic peak for each meat. Chemometric analysis was carried out using the principal component analysis (PCA) method to ensure that the thermal characteristics of each meat were utterly different in both pure and mixed meat. The results of this analysis indicate that each pure meat has a different score plot. Therefore, the developed thermal analysis method is quite reliable in determining the different types of meat based on the characteristic minor endothermic peak in the thermogram and the score plot from PCA analysis.

Current progress on meat food authenticity detection methods

Meat adulteration could be intentional or unintentional, which may infringe consumers’ rights and interests, endanger public health, disrupt the market, and hinder the development of the meat industry. Determining fast and efficient meat adulteration detection methods is therefore critical for the development of the food industry and the safety of consumers. Based on the classification of target substances, adulteration detection technologies are classified in protein-, metabolite-, or nucleic acids-based. Although these detection techniques have yielded good identification results when applied to meat adulteration detection, certain shortcomings are still observed. This review aims to summarize the latest progress on meat adulteration detection technologies, thus providing a reference basis for food safety and surveillance departments and researchers within this field.

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

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

Integrating microneedle DNA extraction to hand-held microfluidic colorimetric LAMP chip system for meat adulteration detection

Most microfluidic-based “sample-in-result-out” systems suffer sophisticated microfluidic production processes, high-cost chips, and expensive instruments. They cannot be used in the meat market as well as farmer's markets in rural areas. Here, we developed a hand-held microfluidic chip system for on-site meat species qualitative authentication detection which integrated a simple microneedle DNA extraction and a visual loop-mediated isothermal amplification (LAMP). The chip can be used by easily pricking meat samples, simply hand-shaking the chip, and readily available isothermal heating instead of a complicated DNA extraction process and microfluidic control device. The system demonstrates high specificity and sensitivity for selected six species of meat samples and low to 1% simulated adulteration could be detected within 60 min. Besides, the whole cost was less than 1 dollar. The integrated hand-held microfluidic detection system offers a simple, fast, low-cost “sample-in-result-out” point-of-care device which could be extended to medical diagnosis and animal/plant disease identification.

CRISPR/Cas Precisely Regulated DNA-Templated Silver Nanocluster Fluorescence Sensor for Meat Adulteration Detection.

Meat adulteration can cause consumer fraud, food allergies, and religious issues. Rapid and sensitive detection methods are urgently demanded to supervise meat authenticity. Herein, a clustered regularly interspaced short palindromic repeats (CRISPR)/Cas precisely regulated DNA-templated silver nanocluster (DNA-AgNC) sensor was ingeniously designed to detect meat adulteration. Specific sequence recognition of CRISPR/Cas12a allowed accurate identification of target DNA. The emerging label-free fluorescent probes, DNA-AgNCs, a class of promising fluorophores in biochemical analysis with attractive photostability and remarkably enhanced fluorescence properties, were first introduced as the substrates of CRISPR/Cas12a system, allowing a sensitive output of amplified signals through the precise regulation of the unique target DNA-activated trans-cleavage activity of Cas12a. Based on this specific recognition, efficient signal transduction of CRISPR/Cas12a, and the outstanding fluorescence properties of DNA-AgNCs, the proposed strategy achieved a satisfactory linear range from 10 pM to 1 μM with a limit of detection (LOD) as low as 1.9 pM, which can achieve sensitive detection of meat adulteration.

Multiple crRNAs-assisted CRISPR/Cas12a assay targeting cytochrome b gene for amplification-free detection of meat adulteration

Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas systems have been widely applied in nucleic acid analysis for the high specificity. Coupled with pre-amplification steps, the sensitivity of CRISPR-based detection is greatly improved. However, an extra pre-amplification step not only complicates the detection procedures but may also cause aerosol contaminations in the process of transferring amplified solution into CRISPR system. In this study, we demonstrate that combination of multiple crRNAs in CRISPR/Cas12a system can enhance the detection sensitivity. Based on it, we establish a multiple crRNAs enhanced CRISPR (meCRISPR) method and apply it to meat adulteration identification. Take cytochrome b (Cyt b) gene as a target, meCRISPR method can directly detect as low as 1.13 ng/μL extracted pork DNA and 5% (w/w) pork contamination in pork and beef meat mixtures. There is no cross-reaction with extracted chicken, beef, duck and fish DNA. meCRISPR reaction is incubated at an isothermal temperature, and the detection process can be completed in a designed portable apparatus with a heat block, a light emitting diode and filters. For the simplicity, specificity and sufficient sensitivity of meCRISPR method, it will have great prospects in species identification, food adulteration, and genetically modified food detection.

Detection of Meat Adulteration by PCR-RFLP: An Update

Detection of Meat Adulteration by PCR-RFLP: An Update

Development and Application of a Visual Duck Meat Detection Strategy for Molecular Diagnosis of Duck-Derived Components

To make meat adulteration detection systems faster, simpler and more efficient, we established a duck-derived meat rapid detection Recombinase Polymerase Amplification (dRPA) method by using interleukin 2 (IL-2) from nuclear genomic DNA as the target gene to design specific primers. We tested the dRPA detection system by comparing its sensitivity and specificity using real-time fluorescent PCR technology. By adjusting the ratio of reagents, this method shortens the time of DNA extraction and visualizes results in combination with colloidal gold immunoassay strips. Our results demonstrate that this dRPA method could specifically detect duck-derived components with a sensitivity of up to 23 copies/μL and the accuracy of the results is consistent with real-time fluorescent PCR. Additionally, dRPA can detect at least 1% of the duck meat content by mixing beef and mutton with duck meat in different proportions, which was verified by spot-check market samples. These results can be visualized with colloidal gold immunoassay strips with the same accuracy as real-time fluorescent RPA. dRPA can complete detection within 30 min, which shortens existing detection time and quickly visualizes the detection results on-site. This lays the groundwork for future large-scale standardized duck origin detection.

Application and progress in high-throughput sequencing technology for meat adulteration detection

Adulteration in meat products is a widespread issue that could lead to serious threats to public health and religious violations. Technology that offers rapid, sensitive, accurate and reliable detection of meat species is the key to an effectual monitoring and control against meat adulteration. In recent years, high-throughput sequencing-based DNA metabarcoding technology has developed rapidly. With the characteristics of being high-throughput, highly precise and high-speed, this technology can simultaneously identify multiple species in complex samples, thus offering pronounced advantages in the surveillance of adulteration in meat and meat products. Starting with an introduction of the major developments in the high-throughput sequencing technology in the past two decades, this review provides an overview of the technical characteristics and research methods of DNA metabarcoding, summarizes the application of DNA metabarcoding technology in meat adulteration detection over the last few years, discusses the challenges of using DNA metabarcoding technology in the detection of meat adulteration, and provides future prospects on the development of this technology.

A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose.

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.

Label-free and reagentless electrochemical genosensor based on graphene acid for meat adulteration detection

With the increased demand for beef in emerging markets, the development of quality-control diagnostics that are fast, cheap and easy to handle is essential. Especially where beef must be free from pork residues, due to religious, cultural or allergic reasons, the availability of such diagnostic tools is crucial. In this work, we report a label-free impedimetric genosensor for the sensitive detection of pork residues in meat, by leveraging the biosensing capabilities of graphene acid - a densely and selectively functionalized graphene derivative. A single stranded DNA probe, specific for the pork mitochondrial genome, was immobilized onto carbon screen-printed electrodes modified with graphene acid. It was demonstrated that graphene acid improved the charge transport properties of the electrode, following a simple and rapid electrode modification and detection protocol. Using non-faradaic electrochemical impedance spectroscopy, which does not require any electrochemical indicators or redox pairs, the detection of pork residues in beef was achieved in less than 45 min (including sample preparation), with a limit of detection of 9% w/w pork content in beef samples. Importantly, the sample did not need to be purified or amplified, and the biosensor retained its performance properties unchanged for at least 4 weeks. This set of features places the present pork DNA sensor among the most attractive for further development and commercialization. Furthermore, it paves the way for the development of sensitive and selective point-of-need sensing devices for label-free, fast, simple and reliable monitoring of meat purity.

Development of a Duck Genomic Reference Material by Digital PCR Platforms for the Detection of Meat Adulteration.

Low-cost meat, such as duck, is frequently used to adulterate more expensive foods like lamb or beef in many countries. However, the lack of DNA-based reference materials has limited the quality control and detection of adulterants. Here, we report the development and validation of duck genomic DNA certified reference materials (CRMs) through the detection of the duck interleukin 2 (IL2) gene by digital PCR (dPCR) for the identification of duck meat in food products. The certified value of IL2 in CRMs was 5.78 ± 0.51 × 103 copies/μL with extended uncertainty (coverage factor k = 2) based on IL2 quantification by eight independent collaborating laboratories. Quantification of the mitochondrial gene cytb revealed a concentration of 2.0 × 106 copies/μL, as an information value. The CRMs were also used to determine the limit of detection (LOD) for six commercial testing kits, which confirmed that these kits meet or exceed their claimed sensitivity and are reliable for duck detection.