Protein markers for discrimination of meat species in raw beef, pork and poultry and their mixtures

Food authentication, current issues, analytical techniques, and future challenges: A comprehensive review

Food authentication and contamination are significant concerns, especially for consumers with unique nutritional, cultural, lifestyle, and religious needs. Food authenticity involves identifying food contamination for many purposes, such as adherence to religious beliefs, safeguarding health, and consuming sanitary and organic food products. This review article examines the issues related to food authentication and food fraud in recent periods. Furthermore, the development and innovations in analytical techniques employed to authenticate various food products are comprehensively focused. Food products derived from animals are susceptible to deceptive practices, which can undermine customer confidence and pose potential health hazards due to the transmission of diseases from animals to humans. Therefore, it is necessary to employ suitable and robust analytical techniques for complex and high-risk animal-derived goods, in which molecular biomarker-based (genomics, proteomics, and metabolomics) techniques are covered. Various analytical methods have been employed to ascertain the geographical provenance of food items that exhibit rapid response times, low cost, nondestructiveness, and condensability.

Identification of animal species of origin in meat based on glycopeptide analysis by UPLC-QTOF-MS

Adulteration of meat and meat products causes a concerning threat for consumers. It is necessary to develop novel robust and sensitive methods which can authenticate the origin of meat species to compensate for the drawbacks of existing methods. In the present study, the sarcoplasmic proteins of six meat species, namely, pork, beef, mutton, chicken, duck and turkey, were analyzed by one-dimensional gel electrophoresis. It was found that enolase could be used as a potential biomarker protein to distinguish between livestock and poultry meats. The glycosylation sites and glycans of enolase were analyzed by UPLC-QTOF-MS and a total of 41 glycopeptides were identified, indicating that the enolase N-glycopeptide profiles of different meats were species-specific. The identification models of livestock meat, poultry and mixed animal were established based on the glycopeptide contents, and the explanation degree of the three models was higher than 90%. The model prediction performance and feasibility results showed that the average prediction accuracy of the three models was 75.43%, with the animal-derived meat identification model showing superiority in identifying more closely related species. The obtained results indicated that the developed strategy was promising for application in animal-derived meat species monitoring and the quality supervision of animal-derived food.

Developments of Plant-Based Emulsion-Type Sausage by Using Grey Oyster Mushrooms and Chickpeas

Plant-based (PB) meat alternatives are developing due to the consumer's demand, especially those who are mainly health-concerned. Soy proteins (SP) are commonly used as the main ingredients for PB meat analogues; however, SP may have adverse effects on the cognitive function and mood of humans. This study aimed to use grey oyster mushroom (GOM) and chickpea flour (CF) as an alternative source of SP to prepare emulsion-type sausages (ES). The effect of different hydrocolloids and oil on the quality of sausage was also investigated. The sausage was prepared using different concentrations of GOM and CF (20:20, 25:15, and 30:10 w/w). The GOM to CF ratio 25:15 was selected for the ES based on protein content, textural properties, and sensory attributes. The result indicated that sausage containing konjac powder (KP) and rice bran oil (RBO) provided a better texture and consumer acceptability. The final product showed higher protein (36%, dry basis), less cooking loss (4.08%), purge loss (3.45%), higher emulsion stability, and better consumer acceptability than the commercial sausage. The best recipe for mushroom-based ES is 25% GOM, 15% CF, 5% KP, and 5% RBO. In addition, GOM and CF could be an alternative option to replace SP in PB meat products.

Rapid and sensitive detection of box turtles using an electrochemical DNA biosensor based on a gold/graphene nanocomposite

The Southeast Asian box turtle, Cuora amboinensis, is an ecologically important endangered species which needs an onsite monitoring device to protect it from extinction. An electrochemical DNA biosensor was developed to detect the C. amboinensis mitochondrial cytochrome b gene based on an in silico designed probe using bioinformatics tools, and it was also validated in wet-lab experiments. As a detection platform, a screen-printed carbon electrode (SPCE) enhanced with a nanocomposite containing gold nanoparticles and graphene was used. The morphology of the nanoparticles was analysed by field-emission scanning electron microscopy and structural characteristics were analysed by using energy-dispersive X-ray, UV-vis, and Fourier-transform infrared spectroscopy. The electrochemical characteristics of the modified electrodes were studied by cyclic voltammetry, differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy. The thiol-modified synthetic DNA probe was immobilised on modified SPCEs to facilitate hybridisation with the reverse complementary DNA. The turtle DNA was distinguished based on hybridisation-induced electrochemical change in the presence of methylene blue compared to their mismatches, noncomplementary, and nontarget species DNA measured by DPV. The developed biosensor exhibited a selective response towards reverse complementary DNAs and was able to discriminate turtles from other species. The modified electrode displayed good linearity for reverse complementary DNAs in the range of 1 × 10-11-5 × 10-6 M with a limit of detection of 0.85 × 10-12 M. This indicates that the proposed biosensor has the potential to be applied for the detection of real turtle species.

Adoption of analytical technologies for verification of authenticity of halal foods - a review

Halal authentication has become essential in the food industry to ensure food is free from any prohibited ingredients according to Islamic law. Diversification of food origin and adulteration issues have raised concerns among Muslim consumers. Therefore, verification of food constituents and their quality is paramount. From conventional methods based on physical and chemical properties, various diagnostic methods have emerged relying on protein or DNA measurements. Protein-based methods that have been used in halal detection including electrophoresis, chromatographic-based methods, molecular spectroscopy and immunoassays. Polymerase chain reaction (PCR) and loop-mediated isothermal amplification (LAMP) are DNA-based techniques that possess better accuracy and sensitivity. Biosensors are miniatured devices that operate by converting biochemical signals into a measurable quantity. CRISPR-Cas is one of the latest novel emerging nucleic acid detection tools in halal food analysis as well as quantification of stable isotopes method for identification of animal species. Within this context, this review provides an overview of the various techniques in halal detection along with their advantages and limitations. The future trend and growth of detection technologies are also discussed in this review.

Omics-Based Analytical Approaches for Assessing Chicken Species and Breeds in Food Authentication

Chicken is known to be the most common meat type involved in food mislabeling and adulteration. Establishing a method to authenticate chicken content precisely and identifying chicken breeds as declared in processed food is crucial for protecting consumers' rights. Categorizing the authentication method into their respective omics disciplines, such as genomics, transcriptomics, proteomics, lipidomics, metabolomics, and glycomics, and the implementation of bioinformatics or chemometrics in data analysis can assist the researcher in improving the currently available techniques. Designing a vast range of instruments and analytical methods at the molecular level is vital for overcoming the technical drawback in discriminating chicken from other species and even within its breed. This review aims to provide insight and highlight previous and current approaches suitable for countering different circumstances in chicken authentication.

DNA/Nano based advanced genetic detection tools for authentication of species: Strategies, prospects and limitations

Authentication, detection and quantification of ingredients, and adulterants in food, meat, and meat products are of high importance these days. The conventional techniques for the detection of meat species based on lipid, protein and DNA biomarkers are facing challenges due to the poor selectivity, sensitivity and unsuitability for processed food products or complex food matrices. On the other hand, DNA based molecular techniques and nanoparticle based DNA biosensing strategies are gathering huge attention from the scientific communities, researchers and are considered as one of the best alternatives to the conventional strategies. Though nucleic acid based molecular techniques such as PCR and DNA sequencing are getting greater successes in species detection, they are still facing problems from its point-of-care applications. In this context, nanoparticle based DNA biosensors have gathered successes in some extent but not to a satisfactory stage to mark with. In recent years, many articles have been published in the area of progressive nucleic acid-based technologies, however there are very few review articles on DNA nanobiosensors in food science and technology. In this review, we present the fundamentals of DNA based molecular techniques such as PCR, DNA sequencing and their applications in food science. Moreover, the in-depth discussions of different DNA biosensing strategies or more specifically electrochemical and optical DNA nanobiosensors are presented. In addition, the significance of DNA nanobiosensors over other advanced detection technologies is discussed, focusing on the deficiencies, advantages as well as current challenges to ameliorate with the direction for future development.

Comparative database search engine analysis on massive tandem mass spectra of pork-based food products for halal proteomics

Mass spectrometry-based proteomics relies on dedicated software for peptide and protein identification. These software include open-source or commercial-based search engines; wherein, they employ different algorithms to establish their scoring and identified proteins. Although previous comparative studies have differentiated the proteomics results from different software, there are still yet studies specifically been conducted to compare and evaluate the search engine in the field of halal analysis. This is important because the halal analysis is often using commercial meat samples that have been subjected to various processing, further complicating its analysis. Thus, this study aimed to assess three open-source search engines (Comet, X! Tandem, and ProteinProspector) and a commercial-based search engine (ProteinPilot™) against 135 raw tandem mass spectrometry data files from 15 types of pork-based food products for halal analysis. Each database search engine contained high false-discovery rate (FDR); however, a post-searching algorithm called PeptideProphet managed to reduce the FDR, except for ProteinProspector and ProteinPilot™. From this study, the combined database search engine (executed by iProphet) reveals a thorough protein list for pork-based food products; wherein the most abundant proteins are myofibrillar proteins. Thus, this proteomics study will aid the identification of potential peptide and protein biomarkers for future precision halal analysis. SIGNIFICANCE: A critical challenge of halal proteomics is the availability of a database to confirm the inferential peptides as well as proteins. Currently, the established database such as UniProtKB is related to animal proteome; however, the halal proteomics is related to the highly processed meat-based food products. This study highlights the use of different database search engines (Comet, X! Tandem, ProteinProspector, and ProteinPilot™) and their respective algorithms to analyse 135 raw tandem mass spectrometry data files from 15 types of pork-based food products. This is the first attempt that has compared different database search engines in the context of halal proteomics to ensure the effectiveness of controlling the FDR. Previous studies were just focused on the advantages of a certain algorithm over another. Moreover, other previous studies also have mainly reported the use of mass spectrometry-based shotgun proteomics for meat authentication (the most similar field to halal analysis), but none of the studies were reported on halal aspects that used samples originated from highly processed food products. Hence, a systematic comparative study is duly needed for a more comprehensive and thorough proteomics analysis for such samples. In this study, our combinatorial approach for halal proteomics results from the different search engines used (Comet, X! Tandem, and ProteinProspector) has successfully generated a comprehensive spectral library for the pork-based meat products. This combined spectral library is freely available at https://data.mendeley.com/datasets/6dmm8659rm/3. Thus far, this is the first and new attempt at establishing a spectral library for halal proteomics. We also believe this study is a pioneer for halal proteomics that aimed at non-conventional and non-model organism proteomics, protein analytics, protein bioinformatics, and potential biomarker discovery.

Comparative study of weaning pigs' muscle proteins using two-dimensional electrophoresis

The proteostasis system of animals, including various types of protein modification during the growth stage, leads to an almost incomprehensible number of possible forms of protein, and each can regulate numerous functions. In the presented work, the composition of muscle tissue protein from different portions of piglets was studied to understand the main muscle protein formation. Comparative analysis of weaned piglets' main muscle protein from l. dorsi, biceps femoris, and brachiocephalicus were analyzed using two-dimensional electrophoresis. Changes in the staining intensity of protein fractions inherent in different muscles were revealed. As part of this work, candidate groups of pig muscle proteins have been selected. Eleven protein spots were revealed for the longest muscle of the back, and seven for the biceps; the muscles of the neck are characterized by indicators of low protein fraction volume. Among the proteins found, myosin light chains, phosphoglycerate mutase, troponins, and adenylate kinase is most likely present. The obtained results of protein identification in muscle tissues, obtained during the intensive growth period, will allow a more detailed understanding of protein regulation, function, and interactions in complex biological systems, which will subsequently be significantly important for biomonitoring health and predicting farm animals productivity.

LIQUID CHROMATOGRAPHY-MASS SPECTROMETRY BOTTOM-UP PROTEOMIC METHODS IN ANIMAL SPECIES ANALYSIS OF PROCESSED MEAT FOR FOOD AUTHENTICATION AND THE DETECTION OF ADULTERATIONS

This review offers an overview of the current status and the most recent advances in liquid chromatography-mass spectrometry (LC-MS) techniques with both high-resolution and low-resolution tandem mass analyzers applied to the identification and detection of heat-stable species-specific peptide markers of meat in highly processed food products. We present sets of myofibrillar and sarcoplasmic proteins, which turned out to be the source of 105 heat-stable peptides, detectable in processed meat using LC-MS/MS. A list of heat-stable species-specific peptides was compiled for eleven types of white and red meat including chicken, duck, goose, turkey, pork, beef, lamb, rabbit, buffalo, deer, and horse meat, which can be used as markers for meat authentication. Among the 105 peptides, 57 were verified by multiple reaction monitoring, enabling identification of each species with high specificity and selectivity. The most described and monitored species by LC-MS/MS so far are chicken and pork with 26 confirmed heat-stable peptide markers for each meat. In thermally processed samples, myosin, myoglobin, hemoglobin, l-lactase dehydrogenase A and β-enolase are the main protein sources of heat-stable markers. © 2019 John Wiley & Sons Ltd. Mass Spec Rev.

Discrimination of meat from fur-producing and food-providing animals using mass spectrometry-based proteomics

Non-edible meat from fur-producing animals entering into meat consumption chain could pose a serious threat to public health. For the purpose of risk prevention and control of meat safety, in this study, marker peptides for discriminating non-edible meat of fur-producing animals (including fox, silver fox, blue fox, raccoon dog, ussuri raccoon dog, mink and American mink) from meat of food-providing animals (including pig, cattle, sheep and donkey) were explored by shot-gun proteomics and verified by target approach. Two mass spectrometry platforms combined with bioinformatic and chemometric tools were integratedly emloyed for method development. Meat samples were first subjected to in-solution protein digestion and the subsequently tryptic peptides were profiled and quantitated by ultra-high pressure liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q/TOF MS) with sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) mode. Candidate marker peptides screened by chemometric tools were further filtered for their biological specificity and detectability through bioinformatics analysis as well as multiple reaction monitoring (MRM) verification with UHPLC-triple quadrupole mass spectrometry (UHPLC-QQQ MS). As a result, 9 peptides, out of 104 candidates, were selected as markers for discriminating analysis, of which DQTLQEELAR was validated as primary indicator of non-edible meat from the concerned fur-producing animals. An MRM method based on the developed marker peptides for routine use was finally proposed for risk alarming of non-edible meat from fur-producing animals in food safety control.

Development and Validation of a Novel Five-Dye Short Tandem Repeat Panel for Forensic Identification of 11 Species

Species identification of unknown biological samples is of fundamental importance for forensic applications, especially in crime detection, poaching, and illegal trade of endangered animals as well as meat fraud. In this study, a novel panel was developed to simultaneously identify 10 different animal species (Gallus domesticus, Anas platyrhynchos domesticus, Ovis aries, Sus scrofa domesticus, Bos taurus, Equus caballus, Columba livia domestica, Rattus norvegicus, Mus musculus, and Canis lupus familiaris) and human beings by amplifying 22 short tandem repeat (STR) loci in a multiplex PCR using a set of five fluorescently labeled dyes. This novel 22-STR panel was validated by optimization of PCR conditions as well as species specificity, sensitivity, reproducibility, precision, DNA mixture, and tissue/organ consistency. The results of developmental validation showed that the 22-STR loci achieved high species specificity among 10 animal species and human beings, and the sensitivity of this panel was 0.09 ng. This 22-STR panel identified different meats in mixed samples, and the minimum detected mixture ratio in the current test was 10% (0.1 ng/1 ng). This sensitive, accurate, and specific 22-STR panel can be used for forensic species identification and the detection of meat fraud and adulteration.

Protein based evaluation of meat species by using laser induced breakdown spectroscopy

Meat adulteration through partial substitution with cheaper species or mislabeling causes significant problems in terms of health, religious beliefs, economy, and product quality. Therefore, identification of meat species is crucial for monitoring and prevention of meat fraud. In the present study, protein based laser induced breakdown spectroscopy method was developed for the first time to identify three meat species (beef, chicken and pork) by using bulk proteins and protein fractions, namely actin and myosin. LIBS spectra were evaluated with principal component analysis for clustering pattern of meat species, and partial least square analysis was performed to determine adulteration ratio. In PLS analysis, limit of detection (LOD) values for beef adulteration with chicken and pork meat were calculated as 2.84% and 3.89% by using bulk proteins, respectively.

Rapid discrimination of intact beef, venison and lamb meat using Raman spectroscopy

With increasing demand for fast and reliable techniques for intact meat discrimination, we explore the potential of Raman spectroscopy in combination with three chemometric techniques to discriminate beef, lamb and venison meat samples. Ninety (90) intact red meat samples were measured using Raman spectroscopy, with the acquired spectral data preprocessed using a combination of rubber-band baseline correction, Savitzky-Golay smoothing and standard normal variate transformation. PLSDA and SVM classification were utilized in building classification models for the meat discrimination, whereas PCA was used for exploratory studies. Results obtained using linear and non-linear kernel SVM models yielded sensitivities of over 87 and 90 % respectively, with the corresponding specificities above 88 % on validation against a test set. The PLSDA model yielded over 80 % accuracy in classifying each of the meat specie. PLSDA and SVM classification models in combination with Raman spectroscopy posit an effective technique for red meat discrimination.

Assessment of carbonic anhydrase 3 as a marker for meat authenticity and performance of LC-MS/MS for pork content

In recent years, food fraud is a global issue that has raised wide public concern. Mass spectrometry techniques have a significant advantage of qualitatively and quantitatively analyzing food authenticity, especially for highly processed meat products. In this work, a simple and specific, rapid resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed and validated for the determination of pork content in processed meat products according to internal standard (ISTD) method. To improve the efficiency of sample preparation, simplified bead-beating and enzymolysis process were investigated. In contrast with different heat-stable and specific porcine-peptides, EPITVSSDQMAK, GGPLTAAYR, HDPSLLPWTASYDPGSAK from Carbonic anhydrase 3 proved to have an excellent quantitative ability, thus obtaining good linear relationship and satisfactory recovery. This method was successfully applied to different types of meat products, thus demonstrating that complex mixtures of pork content can be accurately quantified.

Wójciak K. Peptides from Different Carcass Elements of Organic and Conventional Pork-Potential Source of Antioxidant Activity

The growing consumer interest in organic foods, as well as, in many cases, the inconclusiveness of the research comparing organic and conventional foods, indicates a need to study this issue further. The aim of the study was to compare the effects of meat origin (conventional vs. organic) and selected elements of the pork carcass (ham, loin, and shoulder) on the meat proteome and the antioxidant potential of its peptides. The peptidomic approach was used, while the ability of antioxidants to scavenge 2,2'-azino-bis-3-ethylbenzthiazoline-6-sulfonic acid (ABTS), to chelate Fe(II) ions, and to reduce Fe(III) was determined. Most peptides were derived from myofibrillary proteins. The meat origin and the element of the pork carcass did not have a significant effect on the proteome. On the other hand, the pork origin and the carcass element significantly affected the iron ion-chelating capacity (Fe(II)) and the reducing power of peptides. In particular, pork ham from conventional rearing systems had the best antioxidant properties in relation to potential antioxidant peptides. This could be a factor for human health, as well as for stabilized meat products (e.g., toward lipid oxidation).

Current analytical methods for porcine identification in meat and meat products

Authentication of meat products is critical in the food industry. Meat adulteration may lead to religious apprehensions, financial gain and food-toxicities such as meat allergies. Thus, empirical validation of the quality and constituents of meat is paramount. Various analytical methods often based on protein or DNA measurements are utilized to identify meat species. Protein-based methods, including electrophoretic and immunological techniques, are at times unsuitable for discriminating closely related species. Most of these methods have been replaced by more accurate and sensitive detection methods, such as DNA-based techniques. Emerging technologies like DNA barcoding and mass spectrometry are still in their infancy when it comes to their utilization in meat detection. Gold nanobiosensors have shown some promise in this regard. However, its applicability in small scale industries is distant. This article comprehensively reviews the recent developments in the field of analytical methods used for porcine identification.

Rapid Identification of Rainbow Trout Adulteration in Atlantic Salmon by Raman Spectroscopy Combined with Machine Learning

This study intends to evaluate the utilization potential of the combined Raman spectroscopy and machine learning approach to quickly identify the rainbow trout adulteration in Atlantic salmon. The adulterated samples contained various concentrations (0-100% w/w at 10% intervals) of rainbow trout mixed into Atlantic salmon. Spectral preprocessing methods, such as first derivative, second derivative, multiple scattering correction (MSC), and standard normal variate, were employed. Unsupervised algorithms, such as recursive feature elimination, genetic algorithm (GA), and simulated annealing, and supervised K-means clustering (KM) algorithm were used for selecting important spectral bands to reduce the spectral complexity and improve the model stability. Finally, the performances of various machine learning models, including linear regression, nonlinear regression, regression tree, and rule-based models, were verified and compared. The results denoted that the developed GA-KM-Cubist machine learning model achieved satisfactory results based on MSC preprocessing. The determination coefficient (R2) and root mean square error of prediction sets (RMSEP) in the test sets were 0.87 and 10.93, respectively. These results indicate that Raman spectroscopy can be used as an effective Atlantic salmon adulteration identification method; further, the developed model can be used for quantitatively analyzing the rainbow trout adulteration in Atlantic salmon.

Species-specific peptide-based liquid chromatography-mass spectrometry monitoring of three poultry species in processed meat products

The detection of adulteration and mislabeling of food products, including intensively processed meat, is a challenge which needs urgent solutions to protect consumers' rights. The aim of the study was to demonstrate the feasibility of species-specific peptide-based LC-MS methods for monitoring duck, goose and chicken in processed meat products. Food commodities of various compositions, subjected to various treatments, including homogenization, cooking, roasting, drying, and sterilization during production, were examined to ensure that MS-based methods are resistant to matrix composition changes. A qualitative LC-QQQ multiple reaction monitoring (MRM) method was developed which allows high-confidence monitoring of duck, goose and chicken meat (ten specific peptides), simultaneously with beef and pork (seven peptides), in the presence of turkey meat, in highly processed food. The developed LC-MS methods can be used for food authentication, monitoring of the food composition conformity with label statements and detection of adulteration of poultry-containing food products.

Analysis of myofibrillar and sarcoplasmic proteins in pork meat by capillary gel electrophoresis

Myofibrillar and sarcoplasmic proteins were extracted from pork meat (M. Longissimus dorsi) and then separated by capillary gel electrophoresis (CGE). Migration time and peak areas of individual protein molecules in the electropherogram were analysed. The electropherograms obtained after the separation of myofibrillar proteins contained 53 well-separated peaks, of which the following were identified: thymosin, myosin light chain-3 (MLC-3), myosin light chain-2 (MLC-2), troponin C, troponin I, myosin light chain-1 (MLC-1), tropomyosin 1, tropomyosin 2, troponin T, actin, desmin, troponin, C protein, and myosin heavy chain (MHC). The relative concentration of the identified myofibrillar proteins was 74.5%. Of the 56 separated sarcoplasmic proteins the following were identified: myoglobin, myokinase, triosephosphate isomerase, phosphoglycerate mutase, lactate dehydrogenase, glyceraldehyde phosphate dehydrogenase, aldolase, creatine kinase, enolase, phosphoglucose isomerase, pyruvate kinase, phosphoglucomutase, and phosphorylase b. The relative concentration of the identified sarcoplasmic proteins was 83.6% of all sarcoplasmic proteins extracted from the pork meat.

Classification of Chicken Parts Using a Portable Near-Infrared (NIR) Spectrophotometer and Machine Learning

Identification of different chicken parts using portable equipment could provide useful information for the processing industry and also for authentication purposes. Traditionally, physical-chemical analysis could deal with this task, but some disadvantages arise such as time constraints and requirements of chemicals. Recently, near-infrared (NIR) spectroscopy and machine learning (ML) techniques have been widely used to obtain a rapid, noninvasive, and precise characterization of biological samples. This study aims at classifying chicken parts (breasts, thighs, and drumstick) using portable NIR equipment combined with ML algorithms. Physical and chemical attributes (pH and L*a*b* color features) and chemical composition (protein, fat, moisture, and ash) were determined for each sample. Spectral information was acquired using a portable NIR spectrophotometer within the range 900-1700 nm and principal component analysis was used as screening approach. Support vector machine and random forest algorithms were compared for chicken meat classification. Results confirmed the possibility of differentiating breast samples from thighs and drumstick with 98.8% accuracy. The results showed the potential of using a NIR portable spectrophotometer combined with a ML approach for differentiation of chicken parts in the processing industry.

Simultaneous determination of heat stable peptides for eight animal and plant species in meat products using UPLC-MS/MS method

Food adulteration and fraud is driven by economic interests; it is thus necessary to establish a high-through method that allows quantitative identification of familiar animal and plant proteins for global use. In this study, a sensitive mass spectrometric approach for the detection of eight species, including pork, beef, lamb, chicken, duck, soy, peanut, and pea, is presented and the heat stability and specificity of screened peptides are verified. To improve screening efficiency of specific peptides, several key data searching parameters, including peptides, sequence lengths, sequence coverage, and unique peptides, are investigated. Using this approach, it is possible to detect a 0.5% contamination of any of the eight species. The method is proven to have high sensitivity, specificity, repeatability, and a low quantitative detection limit with respect to adulteration of diverse types of meat products.