Meat Adulteration Research Articles

Thermography and deep learning for detection of ground beef adulteration

The aim of this study was to evaluate the use of thermal imaging as a rapid and non-destructive method for the detection of adulteration in minced beef. The adulterant materials investigated included avian offal (chicken gizzard) and ovine offal (sheep lung), which have been observed to be used as adulterants in the market. The weight ratios (w/w) of these impurities ranged from 0 to 30 % at 5 % intervals along with the pure samples. Since induction of a temperature gradient helps in thermographic diagnosis, heating and cooling pretreatments were utilized and compared. Convolutional neural network models were developed to identify the adulteration levels in ground beef. The models trained using data of both thermal processes (heating and cooling) were able to detect 8 levels of sheep lung adulteration in ground beef with 99 % accuracy. It was also able to detect the adulteration level of chicken gizzards in the warming method with 99 % accuracy and in the cooling method with 84 % accuracy. Since the method was capable of detecting impurities as low as 5 %, it points to a promising technique with high speed and accuracy for the detection of various types of adulteration in minced meat.

A novel rapid detection method for chicken adulteration based on recombinant polymerase amplification and multicomponent nuclease (MNAzyme)

The identification of meat adulteration is of great significance to food safety. This work reports a novel rapid detection platform for chicken adulteration based on recombinase polymerase amplification (RPA) and multicomponent nuclease (MNAzyme). The conserved sequence target of the Cytb gene was used to design RPA primers and MNAzyme. First, the RPA amplification products were introduced into the detection system, which included MNAzyme and DNA-RNA composite probes. When the chicken-derived RPA amplification product was present in the sample, it generated an “input” signal that triggered MNAzyme's catalytic core to cleave the DNA-RNA complex probe, leading to a fluorescence signal. The entire procedure, from nucleic acid extraction to detection, took just 60 min, and the results could be easily interpreted using a portable fluorescent thermostatic amplifier. The RPA-MNAzyme assay detected 8 copies/µL of chicken DNA in 60 min, which is consistent with the results of the RPA assay, demonstrating that the method is highly accurate and specific (does not cross-react with nucleic acids from other animal sources). And it could detect as low as 0.5% chicken in beef and lamb. By harnessing MNAzyme to specifically identify target genes in RPA amplification products, the platform eliminates the need for RPA primer screening and RPA probe design, reducing experimental costs.

Detection of adulteration of cattle and buffalo meat through NIRS and chemometric analysis

The objective of this study was to evaluate the efficacy of near-infrared (NIR) reflectance spectroscopy in detecting adulteration in cattle and buffalo meat. A total of 16 samples were tested, 2 of which were pure and 14 were adulterated. The beef samples were adulterated by mixing buffalo meat in the range of 0-28% (w/w) at approximately 2% increments according to weight. To detect adulteration, DLP® NIRscan™ Nano Software was used to gather spectra. The Unscrambler X program was used to develop calibration and validation models utilizing principal component regression and partial least squares. Root mean square error of calibration (RMSEc), root mean square error of cross-validation (RMSEcv), coefficient of calibration (R2 c ), and coefficient of cross-validation (R2 cv) were used to assess the accuracy of the calibration models. The R2 value of 0.90 or above indicates that the regression model is excellent. For the PCR model, the predicted R2 cv value was 0.73 and for the PLSR model, the predicted R2 cv value was 0.98 through leverage correction. In cross-validation, the R2 cv value was 0.65 for both the PCR and PLSR models. According to the findings, it is suggested that NIR spectroscopy is a reasonably efficient method for detecting adulteration in cattle meat with buffalo meat.

DNA-BASED IDENTIFICATION OF PROCESSED MEAT ADULTERATION WITHIN KADUNA METROPOLIS

Processed meat adulteration poses significant risks to consumer’s health and trust, with fraudulent practices like meat substitution, filler addition, and false labeling compromising product quality and authenticity. Insufficient regulatory oversight exacerbates these issues, necessitating stricter regulations, improved inspection procedures, and increased consumer awareness to ensure the safety and transparency of processed meat products. This study employed a DNA-based approach to detect adulteration in processed meats using primers specific to cattle cytochrome C oxidase, pig cytochrome C oxidase, and chicken 12S rRNA. Forty-four processed meat samples, including beef meat pie (8), chicken meat pie (8), beef minced meat (9), chicken minced meat (9), pork steak (5), and pork balangu (5), were randomly collected from stores in four Kaduna Metropolis markets: Central Market, Sabon Tasha, Barnawa, and Kawo. Results showed that 25.0% of beef meat pies and 77.8% of beef minced meat were adulterated, with an overall adulteration level of 52.9% for processed beef. For processed chicken, 37.5% of chicken meat pies and 44.4% of chicken minced meat were adulterated, yielding an overall 41.2% adulteration rate. All ten processed pork samples were authentic. Central Market samples had a 40% adulteration rate, with 50% involving beef and 50% chicken. Sabon Tasha showed 9.1% beef adulteration. Barnawa had 30.8% adulteration, with 25.0% beef and 75.0% chicken. Kawo recorded a 70.0% adulteration rate, with 42.9% beef and 57.1% chicken. These findings demonstrate the effectiveness of DNA-based methods in detecting processed meat adulteration.

Establishment and Validation of a Multiplex PCR Detection System for the Identification of Six Common Edible Meat Components.

To establish a rapid, accurate, and sensitive multiplex PCR detection method for the simultaneous identification of the six common edible meats (beef, lamp, chicken, pork, goose, duck), and to evaluate its application value in meat adulteration identification. Based on complete mitochondrial genomic sequences of six species in the GenBank database, DNA sequences (cattle：16S rRNA; sheep：COX-1; chickens：Cytb; pig：COX-1; goose：NADH2; duck：16S rRNA) with intra-species conservation and inter-species specificity were screened, and species-specific primers were designed to construct a multiplex PCR detection system that can simultaneously detect the meat of six common species. The species specificity, sensitivity and reproducibility of the system were studied, and the simulated mixture sample detection was performed. This study successfully constructed a multiplex PCR detection system that can detect the meats of six common species simultaneously. The system was not effective in DNA amplification of non-target species. When the DNA template sizes were 0.062 5-2 ng/μL, the amplified products of all six species could be detected. The duck component was still detected when the mixing ratio of duck and beef was as low as 0.5%. This study constructs and establishes a multiplex PCR detection system with strong specificity, high sensitivity, and good reproducibility. It can accurately identify the components of animal origin in common edible meats and provide a simple and practical method for identifying adulteration of common edible meats and meat products in China.

Real-time quantitative detection of hydrocolloid adulteration in meat based on Swin Transformer and smartphone.

Hydrocolloids are widely used in meat products as common food additives. However, research has indicated that excessive consumption of these hydrocolloids may have potential health implications. Currently, consumers mainly rely on sensory evaluation to identify hydrocolloid adulteration in meat products. Although many studies on quantitative detection of hydrocolloids have been conducted by biochemical methods in laboratory environments, there is currently a lack of effective tools for consumers and regulators to obtain real-time and reliable information on hydrocolloid adulteration. To address this challenge, a smartphone-based computer vision method was developed to quantitatively detect carrageenan adulteration in beef in this work. Specifically, Swin Transformer models, along with pre-training and fine-tuning techniques, were used to successfully automate the classification of beef into nine different levels of carrageenan adulteration, ranging from 0% to 20%. Among the tested models, Swin-Tiny (Swin-T) achieved the highest trade-off performance, with a Top-1 accuracy of 0.997, a detection speed of 3.2ms, and a model size of 103.45 Mb. Compared to computer vision, the electrochemical impedance spectroscopy achieved a lower accuracy of 0.792 and required a constant temperature environment and a waiting time of around 30 min for data stabilization. In addition, Swin-T model was also capable of distinguishing between different types of hydrocolloids with a Top-1 accuracy of 0.975. This study provides consumers and regulators with a valuable tool to obtain real-time quantitative information about meat adulteration anytime, anywhere. PRACTICAL APPLICATION: This research provides a practical solution for regulators and consumers to non-destructively and quantitatively detect the content and type of hydrocolloids in beef in real-time using smartphones. This innovation has the potential to significantly reduce the costs associated with meat quality testing, such as the use of chemical reagents and expensive instruments.

Research on meat adulteration based on one-dimensional convolutional neural network (1DCNN) combined with electrochemical technology

Research on meat adulteration based on one-dimensional convolutional neural network (1DCNN) combined with electrochemical technology

Untargeted metabolomics using liquid chromatography-high resolution mass spectrometry and chemometrics for analysis of non-halal meats adulteration in beef meat.

The adulteration of raw beef (BMr) with dog meat (DMr) and pork (PMr) becomes a serious problem because it is associated with halal status, quality, and safety of meats. This research aimed to develop an effective authentication method to detect non-halal meats (dog meat and pork) in beef using metabolomics approach. Liquid chromatography-high resolution mass spectrometry (LC-HRMS) using untargeted approach combined with chemometrics was applied for analysis non-halal meats in BMr. The untargeted metabolomics approach successfully identified various metabolites in BMr DMr, PMr, and their mixtures. The discrimination and classification between authentic BMr and those adulterated with DMr and PMr were successfully determined using partial least square-discriminant analysis (PLS-DA) with high accuracy. All BMr samples containing non-halal meats could be differentiated from authentic BMr. A number of discriminating metabolites with potential as biomarkers to discriminate BMr in the mixtures with DMr and PMr could be identified from the analysis of variable importance for projection value. Partial least square (PLS) and orthogonal PLS (OPLS) regression using discriminating metabolites showed high accuracy (R2>0.990) and high precision (both RMSEC and RMSEE <5%) in predicting the concentration of DMr and PMr present in beef indicating that the discriminating metabolites were good predictors. The developed untargeted LC-HRMS metabolomics and chemometrics successfully identified non-halal meats adulteration (DMr and PMr) in beef with high sensitivity up to 0.1% (w/w). A combination of LC-HRMS untargeted metabolomic and chemometrics promises to be an effective analytical technique for halal authenticity testing of meats. This method could be further standardized and proposed as a method for halal authentication of meats.

Development of a duplex recombinase polymerase amplification-lateral flow strip assay with 1 min of DNA extraction for simultaneous identification of pork and chicken ingredients

Meat adulteration is a challenge faced by the global food industry. However, existing protein-based methods or DNA-based polymerase chain reactions are time-consuming and require specialist devices. Therefore, a rapid and on-site assay is urgently needed for identifying meat species. In this study, we developed a duplex recombinase polymerase amplification-lateral flow strip (dRPA-LFS) assay coupled with a rapid DNA extraction method (utilizing the UiO-66 coated stick as the DNA isolation device, and the isolation can be completed in 1 minute, including lysis, washing, and desorption in three steps) for the identification of pork and chicken ingredients. Targeted genes, the pork (Porcine) mitochondrial ND2 gene and chicken (Gallus gallus) cytochrome B gene, were designed with primers and probes. The whole dRPA-LFS detection procedure can be finished within 18.5 minutes (including 1 minute of rapid DNA extraction, 15 minutes of dRPA amplification, and 2.5 minutes of LFS semi-quantitative detection by the naked eye), and the result can also be quantified by ImageJ software within 15 minutes. No positive amplification was observed in beef, lamb, duck, rabbit, goose, ostrich, and horse meat DNA. This assay is sensitive and can detect as low as 0.1 % (wt %) of pork and chicken in simulated adulterated meat mixtures. This assay was successfully applied to the authentication identification of 30 commercial beef and lamb products.

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.

Volatile Compounds for Discrimination between Beef, Pork, and Their Admixture Using Solid-Phase-Microextraction-Gas Chromatography-Mass Spectrometry (SPME-GC-MS) and Chemometrics Analysis.

This study addresses the prevalent issue of meat species authentication and adulteration through a chemometrics-based approach, crucial for upholding public health and ensuring a fair marketplace. Volatile compounds were extracted and analyzed using headspace-solid-phase-microextraction-gas chromatography-mass spectrometry. Adulterated meat samples were effectively identified through principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). Through variable importance in projection scores and a Random Forest test, 11 key compounds, including nonanal, octanal, hexadecanal, benzaldehyde, 1-octanol, hexanoic acid, heptanoic acid, octanoic acid, and 2-acetylpyrrole for beef, and hexanal and 1-octen-3-ol for pork, were robustly identified as biomarkers. These compounds exhibited a discernible trend in adulterated samples based on adulteration ratios, evident in a heatmap. Notably, lipid degradation compounds strongly influenced meat discrimination. PCA and PLS-DA yielded significant sample separation, with the first two components capturing 80% and 72.1% of total variance, respectively. This technique could be a reliable method for detecting meat adulteration in cooked meat.

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 &lt; 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.

A multiplex DNA probe-based method for simultaneous identification of adulteration in meat samples

Meat adulteration and admixing are prevalent malpractices observed in processed and raw meat samples, where the consumption of adulterated meat has been associated with food allergies, financial losses, and consumer distrust. Meat authentication is pivotal to address these concerns. The meat authenticity can be determined through genetic, protein, and immunological markers and advanced detection methods. However, these methods often target a single species and lack the specificity to distinguish closely related species. Here, in the present study, we have developed a multiplex detection method based on the species-specific primers and probes, that can target four meat species in one reaction. The developed method amplifies the mitochondrial genomic regions of chicken, pork, sheep and goat using TaqMan multiplex probe-based RT-qPCR assay. Unique pairs of species-specific primers and probes that target specific mitochondrial DNA (mtDNA) regions of each species were designed and screened for specificity and sensitivity. The detection limit for species identification using the designed primers in real-time qPCR assays was 0.1 picogram per microliter (pg/μL) DNA detected in singleplex reaction and facilitates the simultaneous detection of closely related species, such as goat and sheep. Further, DNA-based probes were utilized in a multiplex real-time qPCR assay to identify chicken, pork, sheep and goat DNA in a single tube reaction. The multiplex assay was validated for raw and processed meat products, demonstrating its applications in ensuring the quality of meat products and safeguarding consumer interests.

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.

CRISPR/Cas12a integrated electrochemiluminescence biosensor for pufferfish authenticity detection based on NiCo2O4 NCs@Au as a coreaction accelerator

Meat adulteration has brought economic losses, health risks, and religious concerns, making it a pressing global issue. Herein, combining the high amplification efficiency of polymerase chain reaction (PCR) and the accurate recognition of CRISPR/Cas12, a sensitive and reliable electrochemiluminescence (ECL) biosensor was developed for the detection of pufferfish authenticity using NiCo2O4 NCs@Au-ABEI as nanoemitters. In the presence of target DNA, the trans-cleavage activity of CRISPR/Cas12a is activated upon specific recognition by crRNA, and then it cleaves dopamine-modified single stranded DNA (ssDNA-DA), triggering the ECL signal from the “off” to “on” state. However, without target DNA, the trans-cleavage activity of CRISPR/Cas12a is silenced. By rationally designing corresponding primers and crRNA, the biosensor was applied to specific identification of four species of pufferfish. Furthermore, as low as 0.1 % (w/w) adulterate pufferfish in mixture samples could be detected. Overall, this work provides a simple, low-cost and sensitive approach to trace pufferfish adulteration.

RAPID DETECTION OF RAT MEAT ADULTERATION IN BEEF SAUSAGES USING FTIR‑ATR SPECTROSCOPY AND CHEMOMETRICS FOR HALAL AUTHENTICATION

Objective: The objective of this study was to employ Fourier Transform Infrared-Attenuated Total Reflectance (FTIR‑ATR) spectroscopy in combination with chemometrics for the analysis of rat meat adulteration in beef sausages.&#x0D; Methods: Lipid components in sausages were extracted using three extraction methods, namely Bligh and Dyer, Folch, and Soxhlet methods. The lipid components extracted were then analysed using FTIR‑ATR spectroscopy, and their spectra obtained were used as variables during chemometrics modeling. Samples were prepared by mixing beef with adulterant of rat meat in the concentration range of 0-100% of rat meat. Each sample was scanned using FTIR-Attenuated Total Reflectance (ATR) spectroscopy in three replicates at 4000-650 cm-1 wavenumber region.&#x0D; Results: The absorbance values at wavenumbers regions of 3100-700 cm-1 were used to discriminate lipid components extracted by the Bligh Dyer, Folch, and Soxhlet Method with an accuracy level of 100%. The prediction of rat sausages was successfully determined using multivariate calibrations of Partial Least Square (PLS) and Principle Component Regression (PCR) using optimised conditions.&#x0D; Conclusion: FTIR-ATR spectroscopy coupled with chemometrics is a rapid and accurate method for detecting and quantifying rat meat in beef sausages for halal authentication.

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).

Comparison of four isothermal amplification techniques: LAMP, SEA, CPA, and RPA for the identification of chicken adulteration

In recent years, with growing concern worldwide about meat and meat product adulteration, dozens of DNA-based isothermal amplification techniques have been developed and applied to detect meat adulteration. Thus, we evaluated four mainstream isothermal amplification techniques, including loop-mediated isothermal amplification (LAMP), denaturation bubble-mediated strand exchange amplification (SEA), cross-priming amplification (CPA), and recombinase polymerase amplification (RPA) focusing on the limit of detection, simplicity, amplification time and cost to confirm the most effective and suitable method for point-of-care testing (POCT) of chicken and chicken product authenticity. The LAMP, CPA, and RPA primers all targeted the chicken mitochondrial cytochrome b gene. The SEA primers were provided by the SEA kit. All methods showed good specificity to chicken. Among them, LAMP, CPA, and RPA have the lowest detection limit, with 1.2 pg μL−1. As low as 0.1% chicken in mutton can be detected in LAMP and RPA methods. Although RPA costs 10 times more than LAMP, the system and primers of LAMP are too complex. Therefore, it must be admitted that RPA is the most suitable method in multiplex detection, and LAMP is much better than the other three methods in single-plex detection. Our research provides an excellent reference value for the rapid on-site detection of meat and meat product adulteration.