Current analytical methods for porcine identification in meat and meat products

Rapid detection of murine-derived ingredients in meat products using real-time enzymatic recombinase amplification

BACKGROUND

Accurate identification of meat species is critical to prevent economic fraud and safeguard public health. The use of inappropriate meat sources, such as murine, poses significant health risks because of potential contamination with pathogens and allergens, leading to foodborne illnesses. The present study aimed to develop a novel real-time enzymatic recombinase amplification (ERA) method for the rapid and specific detection of murine DNA in meat products.

RESULTS

A novel ERA primer and probe set was designed, targeting a murine-specific single-copy nuclear gene identified through bioinformatics analysis. The assay demonstrates high specificity, showing no amplification in commonly consumed meats, other animals or major crops. Additionally, it exhibits remarkable sensitivity, detecting as few as five copies of murine genomic DNA. For practical application, the ERA method could effectively identify mouse DNA in laboratory-prepared samples at concentrations as low as 0.5% and also quantify samples with mouse DNA content as low as 5%. It also accurately detects the presence of murine-derived ingredients in commercially available meat products. The detection process is straightforward, utilizing a simple isothermal device for incubation, blue light excitation and a smartphone camera for result interpretation. This rapid analysis can be completed within 20 min.

CONCLUSION

The newly developed real-time ERA method provides a valuable tool for standardizing meat trade practices, promoting food safety and enhancing consumer confidence in the authenticity of meat products. © 2024 Society of Chemical Industry.

Integrating different detection techniques and data analysis methods for comprehensive food authenticity verification

Traditional food testing methods, primarily confined to laboratory settings, are increasingly inadequate to detect covert food adulteration techniques. Hence, a crucial review of recent technological strides to combat food fraud is essential. This comprehensive analysis explores state-of-the-art technologies in food analysis, accentuating the pivotal role of sophisticated data processing methods and the amalgamation of diverse technologies in enhancing food authenticity testing. The paper assesses the merits and drawbacks of distinct data processing techniques and explores their potential synergies. The future of food authentication hinges on the integration of portable smart detection devices with mobile applications for real-time food analysis, including miniaturized spectrometers and portable sensors. This integration, coupled with advanced machine learning and deep learning for robust model construction, promises to achieve real-time, on-site food detection. Moreover, effective data processing, encompassing preprocessing, chemometrics, and regression analysis, remains indispensable for precise food authentication.

Recent advancements in meat traceability, authenticity verification, and voluntary certification systems

The growing demand for transparency in the food industry has led to significant advancements in meat traceability. Ensuring the authenticity and origin of meat products is critical for consumer trust, public health, and compliance with regulations. This paper reviews recent innovations in meat traceability, with a focus on blockchain technology as a novel approach to ensuring traceability. Additionally, advanced methods for verifying meat authenticity and origin, such as isotope fingerprinting, DNA analysis, and spectroscopic methods, are discussed. The role of voluntary certification schemes in enhancing traceability and authenticity verification in the meat industry is also explored. The findings highlight the importance of integrating cutting-edge technologies and certification schemes to build a robust and transparent meat supply chain.

Nanopore-Based Biomimetic ssDNA Reporters for Multiplexed Detection of Meat Adulteration

Meat adulteration detection is crucial for ensuring food safety, protecting consumer rights, and maintaining market integrity. However, the current methods face challenges in achieving multiplexed detection through efficient signal conversion and output. This study introduces a nanopore-based approach for the simultaneous detection of multiple meat adulteration. Leveraging the interaction between DADA dipeptide and vancomycin, we designed a series of biomimetic ssDNA reporters with DADA-Van tags for multiplexed signal output. These ssDNA reporters can generate highly distinctive current blockage signals, which can be discriminated simultaneously by the current waveform. Combined with PCR-based strand displacement, ssDNA reporters can be released from magnetic beads in the presence of the target gene and are analyzed using α-hemolysin (α-HL) nanopores for multiplexed signal output. The signal frequency for each target gene has a linear relationship with the specific concentration range (for duck: 1 × 10-3 ∼ 10 nmol/L, for pork: 1 × 10-2 ∼ 10 nmol/L, and for chicken: 1 × 10-3 ∼ 10 nmol/L). The limits of detection are as follows: 0.418 pmol/L for duck, 4.473 pmol/L for pork, and 0.531 pmol/L for chicken. This method effectively enables the simultaneous detection of adulterated chicken, pork, and duck meat in lamb samples and holds potential for broad applications in ensuring the authenticity and safety of meat products.

Critical review and recent advances of emerging real-time and non-destructive strategies for meat spoilage monitoring

Monitoring and evaluating food quality, especially meat quality, has received a growing interest to ensure human health and decrease waste of raw materials. Standard analytical approaches used for meat spoilage assessment suffer from time consumption, being labor-intensive, operation complexity, and destructiveness. To overcome shortfalls of these traditional methods and monitor spoilage microorganisms or related metabolites of meat products across the supply chain, emerging analysis devices/systems with higher sensitivity, better portability, on-line/in-line, non-destructive and cost-effective property are urgently needed. Herein, we first overview the basic concepts, causes, and critical monitoring indicators associated with meat spoilage. Then, the conventional detection methods for meat spoilage are outlined objectively in their strengths and weaknesses. In addition, we place the focus on the recent research advances of emerging non-destructive devices and systems for assessing meat spoilage. These novel strategies demonstrate their powerful potential in the real-time evaluation of meat spoilage.

Double lateral flow immunosensing of undeclared pork and chicken components of meat products

Adulteration of meat products is a serious problem in the modern society. Consumption of falsified meat products can be hazardous to health and/or lead to violating religious dietary principles. To identify such products, rapid and simple test systems for point-of-need detection are in demand along with complex laboratory methods. This study presents the first double lateral flow (immunochromatographic) test system, which allows simultaneous revealing two prevalent types of falsifications-undeclared addition of pork and chicken components to meat products. In the proposed test system, porcine myoglobin (MG) and chicken immunoglobulin Y (IgY) were used as specific biomarkers recognizable by antibodies. Within the optimization of the analysis, the concentrations of the immune reagents and regimes of their application on the working membrane were selected, which provided minimal limits of detection (LODs) for both analytes. The developed test system enables the detection of MG and IgY with the LODs of 10 and 12 ng/mL, respectively, which accords to addition of 0.1% of the undeclared meat compounds. The applicability of the test system to control the composition of raw meat mixtures and cooked food products was confirmed. The developed approach can be considered as a promising tool for monitoring composition of meat products.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13197-024-05944-y.

Advances and challenges in conventional and modern techniques for halal food authentication: A review

Food is one of the most necessary needs since human civilization. For Muslims, it is mandatory to consume halal food. From a halal authentication perspective, adulteration of food products is an emerging challenge worldwide. The demand for halal food consumption has resulted in an ever-increasing need for halal product validity. In the market, there are several food products in which actual ingredients and their source are not mentioned on the label and cannot be observed by the naked eye. Commonly nonhalal items include pig derivatives like lard, pork, and gelatin derivatives, dead meats, alcohol, blood, and prohibited animals. Purposely, various conventional and modern methods offer precise approaches to ensure the halalness and wholesomeness of food products. Conventional methods are physiochemical (dielectric) and electrophoresis. At the same time, modern techniques include high-pressure liquid chromatography (HPLC), gas chromatography (GC), electronic nose (E-Nose), polymerase chain reaction (PCR), enzyme-linked immunosorbent assay (ELISA), differential scanning calorimetry (DSC), nuclear magnetic resonance (NMR), near-infrared (NIR) spectroscopy, and Fourier transform infrared (FTIR) spectroscopy. This review intends to give an extensive and updated overview of conventional and modern analytical methods for ensuring food halal authenticity.

Enhancing the authenticity of animal by-products: harmonization of DNA extraction methods from novel ingredients

Introduction: The increasing global pressure to explore alternative protein sources derived from animal by-products has opened-up opportunities, but it has also created the need to assess their compliance with labelling statements, to ensure consumer's trust in the composition of both feed and food products. Assessing the authenticity of highly processed animal by-products, particularly within the rapidly expanding Halal food market, presents a significant challenge due to the lack of robust and standardized methodologies. However, the success of DNA based authenticity system is highly dependent on the extracted DNA quantity, quality, and purity ratios from heterogeneous matrices. Material and methods: In this work, nine DNA extraction methods were tested on selected processed animal by-products with high-value and interest for the feed industry: meals from poultry meat, blood and feather, and hydrolysates from swine meat and bone, fish, and black soldier fly. The proposed DNA extraction methods are developed to specifically target swine-specific mitochondrial region, as a case study. Results and discussion: Both the conventional CTAB method and the commercial kits, specifically Invisorb® Spin Tissue Mini and NucleoSpin™ Food, demonstrated superior extraction efficiency and quality ratios. Nevertheless, commercial kits enabled faster detection in comparison to the conventional methods. The absence of swine DNA was successfully validated and confirmed in all animal meals and hydrolysates that did not contain swine in their composition beforehand, demonstrating their compliance with the Halal market requirements.

Accurate molecular identification of different meat adulterations without carryover contaminations on a microarray chip PCR-directed microfluidic lateral flow strip device

Meat adulteration-based food fraud has recently become one of the global major economical, illegal, religious, and public health concerns. In this work, we developed a microarray chip polymerase chain reaction (PCR)-directed microfluidic lateral flow strip (LFS) device that facilitates the accurate and simultaneous identification of beef adulterated with chicken, duck, and pork, especially in processed beef products. To realize this goal, four pairs of amplification primers were designed and applied for specifically amplifying genomic DNA extracted from mixed meat powders in microarray chip. With the prominent advantage of this device lies in the flexible combination and integration of sample loading, detection, and reporting in microstructures, all the DNA amplicons can be individually visualized on the LFS unit, leading to the appearance of test lines (TC line, TD line, TP line, or TB line) as well as the control line (C line) for the species identification and quantification in beef products. Based on this new method, the adulterants were successfully distinguished and identified in mixtures down to 0.01% (wt.%) while the carryover aerogel contamination in routine molecular diagnostic laboratories was effectively avoided. The practicability, accuracy, and reliability of the device were further confirmed by using real-time PCR as a gold standard control on the successful identification of 50 processed ground meat samples sourced from local markets. The method and device proposed herein could be a useful tool for on-site identification of food authentication.

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.

Highly Sensitive Immunochromatographic Detection of Porcine Myoglobin as Biomarker for Meat Authentication Using Prussian Blue Nanozyme

This study was aimed at the sensitive immunodetection of porcine myoglobin (MG) as a species-specific biomarker in meat products. The enhanced lateral flow immunoassay (LFIA) was created in the sandwich format using monoclonal antibodies (Mab) with specificity to porcine MG and labeled by Prussian blue nanoparticles (PBNPs) as peroxidase-mimicking nanozymes. Signal amplification was provided by the colored product of oxidation catalyzed by the PBNPs. Several Mab-PBNP conjugates with different antibody loads were synthesized; the one that provided the best analytical characteristics of the LFIA was selected. Advanced optimization of the test system was carried out. As a result, the visual limit of detection (LOD) of MG was 1.5 ng/mL. Involvement of the catalytic nanozyme properties allowed the LOD to be decreased by ~9 times in comparison to the LFIA based on gold nanomarkers, and by ~27 times compared to the LFIA based on PBNP coloration. The assay time was 30 min, including catalytic enhancement. A simple technique of meat sample pre-treatment aimed at effective MG extraction and matrix disposal was proposed. The specificity of the LFIA towards the pork meat was demonstrated. The applicability of the created test system was shown by testing extracts obtained from finished meat products.

Amplification-Based CRISPR/Cas12a Biosensor Targeting the COX1 Gene for Specific Detection of Porcine DNA

We propose a CRISPR/Cas12a-mediated recombinase polymerase amplification (RPA) detection method that combines RPA with Cas12a cleavage for the detection of halal food adulteration, which is of global concern, particularly for Muslim consumers. We optimized the reagent concentrations for the Cas12a cleavage steps and designed and screened gRNA targeting a conserved area of the mitochondrial cytochrome C oxidase subunit I (COX1) gene. This procedure successfully detected the presence of porcine components as low as 5 pg/μL in the linear range of 5-1000 pg/μL. The assay's detection limit was 500 times lower than CRISPR-based approaches that exclude a preamplification step, allowing the detection of trace porcine DNA in food samples. The assay additionally showed no cross-reaction with nontarget species. Therefore, this detection platform shows tremendous potential as a method for the quick, sensitive, and specific detection of porcine-derived components.

Improving the quality and processing efficiency of beef jerky via drying in confined conditions of pre-stretching

Inspired by the mechanical enhancement of hydrogel via drying in confined conditions, we applied this strategy to beef jerky manufacture for improving the quality and processing efficiency. In our study, beef strips were pre-stretched and then dried in a tensile state, and the confined conditions were achieved by controlling the stretched strains from 20% to 120%. Compared with the sample dried freely, beef jerky dried in confined conditions of different pre-stretching strains exhibited improved quality based on texture and sensory analysis. Additionally, this method also enhanced processing efficiency by reducing approximately 50% drying time. The excellent sensory quality and good texture of beef jerky were obtained as the pre-stretching strain was 80%. Drying beef strips in confined conditions made muscle fibers tense and enhanced hydrophobicity of myofibrillar proteins, leading to a compact structure with high shear force and anisotropy, and rapid water loss in beef jerky. This facile and green method provides a promising route to enrich the existing technologies of jerky processing.

Detection of a Thermal Stable-Soluble Protein (TSSP) as a Marker of Peanut Adulteration Using a Highly Sensitive Indirect Enzyme-Linked Immunosorbent Assay based on Monoclonal Antibodies

Food allergy represents a severe problem for many societies, including sensitive populations, academies, health authorities, and the food industry. Peanut allergy occupies a special place in the food allergy spectrum. To prevent consumption by consumers suffering from a peanut allergy, a rapid and sensitive detection method is essential to identify unintended peanut adulteration in processed foods. In this study, we produced four monoclonal antibodies (MAbs; RO 3A1-12, PB 4C12-10, PB 5F9-23, and PB 6G4-30) specific to thermo-stable and soluble proteins (TSSPs) of peanut and developed an enzyme-linked immunosorbent assay (ELISA) based on the MAbs. Among them, PB 5F9-23 MAb was firmly bound to Ara h 1, and other MAbs strongly reacted to Ara h 3 in the Western blot analysis. An antibody cocktail solution of the MAbs was used to enhance the sensitivity of an indirect ELISA, and the limit of detection of the indirect ELISA based on the antibody cocktail solution was 1 ng/ml and improved compared to the indirect ELISA based on the single MAb (11 ng/ml). The cross-reaction analysis revealed the high specificity of developed MAbs to peanut TSSPs without cross-reaction to other food allergens, including nuts. Subsequently, analyzing processed foods by indirect ELISA, all foods labeled as containing peanuts in the product description were confirmed to be positive. The results indicate that the developed antibodies exhibit high specificity and sensitivity to peanuts and can be used as bio-receptors in immunoassays or biosensors to detect intentional or unintentional adulteration of peanuts in processed foods, particularly heat-processed foods.

Quantification of Pork, Chicken, Beef, and Sheep Contents in Meat Products Using Duplex Real-Time PCR

Accurate methods for meat speciation and quantification are essential for ensuring the supply of safe and wholesome meat and composite products with animal origins to negate the potential associated hazards, aid classification of consignments at the import control system, and thwart food fraud committed for financial gain. To better enhance meat safety control and combat food fraud, this study developed two duplex real-time polymerase chain reaction (real-time PCR) systems specifically designed for chicken, pork, sheep, and beef, using single-copy, chromosomally encoded, species-specific gene sequences to accurately measure the content of each meat type in meat products. DNA extracted from the raw and boiled reference materials prepared in varying proportions (ranging from 1% to 75%) were used in the development of the duplex assay to derive calibration factors to determine the meat content in different meat products. The method was further validated using proficiency test samples and market monitoring samples. Our findings showed that this method exhibits high specificity and sensitivity, with a significant accuracy range of 0.14% to 24.07% in quantifying the four meat types in both raw and processed meat products. Validation results further confirmed the effectiveness of our method in accurately quantifying meat content. Thus, we have demonstrated the duplex qPCR assays as promising approaches for implementation in routine analysis to strengthen meat safety control systems and combat meat fraud, thereby safeguarding consumer health and trust in the meat industry.

The Optimization of a Label-Free Electrochemical DNA Biosensor for Detection of Sus scrofa mtDNA as Food Adulterations

Fast, sensitive, and easy-to-use methods for detecting DNA related to food adulteration, health, religious, and commercial purposes are evolving. In this research, a label-free electrochemical DNA biosensor method was developed for the detection of pork in processed meat samples. Gold electrodeposited screen-printed carbon electrodes (SPCEs) were used and characterized using SEM and cyclic voltammetry. A biotinylated probe DNA sequence of the Cyt b S. scrofa gene mtDNA used as a sensing element containing guanine substituted by inosine bases. The detection of probe-target DNA hybridization on the streptavidin-modified gold SPCE surface was carried out by the peak guanine oxidation of the target using differential pulse voltammetry (DPV). The optimum experimental conditions of data processing using the Box-Behnken design were obtained after 90 min of streptavidin incubation time, at the DNA probe concentration of 1.0 µg/mL, and after 5 min of probe-target DNA hybridization. The detection limit was 0.135 µg/mL, with a linearity range of 0.5-1.5 µg/mL. The resulting current response indicated that this detection method was selective against 5% pork DNA in a mixture of meat samples. This electrochemical biosensor method can be developed into a portable point-of-care detection method for the presence of pork or food adulterations.

A voltammetric immunosensor based on a nanoporous alumina millirod for detection of porcine serum albumin

A voltammetric immunosensor was developed for detection of porcine serum albumin (PSA) to identify raw meat products adulterated with pork. A novel strategy to fabricate multiple individual nanoporous alumina (NPA) millirods (length, 5.0 mm; diameter, 1.0 mm) as the biorecognition platform is described. Each NPA millirod was covalently bioconjugated with anti-PSA capturing antibodies (α-PSAC). Following immunocapture, the PSA bound to the α-PSAC/NPA millirod bioconjugate were tagged with gold nanoparticles (AuNPs) functionalized with anti-PSA detection antibodies as the signaling probe. Subsequently, the AuNPs were voltammetrically analyzed to quantify the target PSA. The immunosensor exhibited 100 % specificity and high sensitivity to PSA with a limit of detection (LoD) of 50 (range, 0-1000) pg/mL (R² = 0.9907). Real-world applicability was successfully validated using pork/beef adulterated mixtures with a LoD of 0.05 % (w/w). Overall, the detection performance of the proposed immunosensor was excellent and, thus, is suitable for surveillance of food safety and quality.

Development of novel isothermal-based DNA amplification assay for detection of pig tissues in adulterated meat

For the first time, we describe an innovative polymerase spiral reaction (PSR) assay for the rapid, simple, and accurate detection of pig tissues or pork in adulterated meat including heat-treated and processed ones. The PSR assay specifically targeting the mitochondrial cytochrome b (cyt-b) gene of the pig was successfully optimized permitting assay results in 65 min time. The developed detection method was 100% specific amplifying only the cyt-b gene and displaying negative results with all the tested non-pork meats. The sensitivity of the developed PSR (760 fg porcine DNA) was tenfold better than the end-point PCR and able to detect heat-treated (121 °C) and adulterated (0.5% pork in beef) meat and processed pork products such as sausages, salami, meatball, soup, curry, etc. The developed PSR-based method can be used for point-of-care detection with minimum instrumentation and technical expertise to guarantee instant clearance of exported and imported meat products. This is the first time that PSR has been adapted for food authenticity purposes.

DNA-based detection of pork content in food

Determination of halal food is essential in ensuring the tranquillity of consumers, especially Muslims. Halal products mean they are free from prohibited ingredients according to Islamic law. One ingredient that is prohibited is food products containing pork and its derivatives. An accurate verification method with a fast result is necessary to meet this requirement for halal food. DNA quantification of pork is now believed to be able to make accurate and quick decisions, as DNA acts as a reservoir or biological characterization of all living things, including pigs, according to specific characteristics of molecular and connection settings. Various DNA-based methods developed include PCR, biosensor and CRISPR methods. This review discussed various DNA-based Keywords: biosensor, CRISPR, detection, DNA, pork, PCR methods, including PCR, biosensor and CRISPR, to detect pork content in food. Among these methods, CRISPR is considered the easiest, fastest and most accurate. Therefore, it is important to develop this method further in the future. In this article, we provide a short review on DNA-based methods for detection of pork content in food products.

Integration of Non-targeted Proteomics Mass Spectrometry with Machine Learning for Screening Cooked Beef Adulterated Samples

The degradation of ingredients in heat-processed meat products makes their authentication challenging. In this study, protein profiles of raw beef, chicken, duck, pork, and binary simulated adulterated beef samples (chicken-beef, duck-beef, and pork-beef) and their heat-processed samples were obtained by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Heat-stable characteristic proteins were found by screening the overlapping characteristic protein ion peaks of the raw and corresponding heat-processed samples, which were discovered by partial least-squares discriminant analysis. Based on the 36 heat-stable characteristic proteins, qualitative classification for the raw and heat-processed meats was achieved by extreme gradient boosting. Moreover, quantitative analysis via partial least squares regression was applied to determine the adulteration ratio of the simulated adulterated beef samples. The validity of the approach was confirmed by a blind test with the mean accuracy of 97.4%. The limit of detection and limit of quantification of this method were determined to be 5 and 8%, respectively, showing its practical aspect for the beef authentication.

Review of CRISPR/Cas Systems on Detection of Nucleotide Sequences

Nowadays, with the rapid development of biotechnology, the CRISPR/Cas technology in particular has produced many new traits and products. Therefore, rapid and high-resolution detection methods for biotechnology products are urgently needed, which is extremely important for safety regulation. Recently, in addition to being gene editing tools, CRISPR/Cas systems have also been used in detection of various targets. CRISPR/Cas systems can be successfully used to detect nucleic acids, proteins, metal ions and others in combination with a variety of technologies, with great application prospects in the future. However, there are still some challenges need to be addressed. In this review, we will list some detection methods of genetically modified (GM) crops, gene-edited crops and single-nucleotide polymorphisms (SNPs) based on CRISPR/Cas systems, hoping to bring some inspiration or ideas to readers.

Rapid detection of duck ingredient in adulterated foods by isothermal recombinase polymerase amplification assays

Duck is often used in meat fraud as a substitute for more expensive meats. Rapid detection of duck ingredient in meat products is of great significance for combating meat fraud and safeguarding the interests of consumers. Therefore, we aim to develop duck-specific recombinase polymerase amplification (RPA)-based assays for the rapid detection of duck ingredient in animal-derived foods. Using Cytb gene as target, the real-time RPA and RPA combined with lateral flow strips (LFS RPA) were developed successfully for the rapid detection of ducks in 20 min at 39 °C and 40 °C, respectively. The assays did not show cross-reactions with 6 other livestock and poultry. The developed RPA assays could detect 10 pg duck genomic DNA per reaction and 0.1 % (w/w) duck ingredient in duck and mutton mixed powder within 30 min, including a rapid nucleic acid extraction. Furthermore, duck ingredient could be detected in 30 different actual foods including heat-processed meats and blood products. Therefore, duck-specific real-time RPA and LFS RPA assays were successfully developed with good specificity and sensitivity, which could enable rapid detection of duck ingredient in the field and provide technical support for combating the meat fraud.

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.

Bibliometric Review on the Volatile Organic Compounds in Meat

Meat flavor is an important aspect of meat quality that also influences consumer demand, and is therefore very important for the meat industry. Volatile organic compounds (VOCs) contribute in large part to the flavor of meat, and while increasing numbers of articles are published on this topic, reviews of these articles are very scarce. Therefore, our aim was to perform a bibliometric analysis of the scientific publications on VOCs in meat over the period 2000-2020. We selected 611 scientific sources from the Scopus database related to VOCs in meat (seafood excluded). The bibliometric information retrieved included journals, authors, countries, institutions, keywords, and citations. From this analysis, we drew up a list of the most important journals, authors, countries, and institutions, and the trends in VOC research on meat. We conducted a social network analysis (SNA) to identify the collaborations among the many authors and countries, and a keyword analysis to generate a network map of the authors' keywords. We also determined which meat species were most frequently chosen as research subjects, traced the evolution of the various methods/instruments used, and explored the research tendencies. Finally, we point out the need for further research in defining meat quality, improving meat flavor, identifying adulterants, and certifying the authenticity of meat.

Relative quantification of pork and beef in meat products using global and species-specific peptide markers for the authentication of meat composition

We used global and species-specific peptide markers for a relative quantitative determination of pork and beef in raw and processed meat products made of the two meat species. Four groups of products were prepared (i.e., minced raw meats, sausages, raw and fried burgers) in order to represent products with different extents of food processing. In each group, the products varied in the pork/beef proportions. All products were analysed by multiple reaction monitoring mass spectrometry (MRM-MS) for the presence/concentration of pork- and beef-specific peptide markers, as well as global markers - peptides widely distributed in muscle tissue. The combined MRM-MS analysis of pork-specific peptide HPGDFGADAQGAMSK, beef-specific peptide VLGFHG and global marker LFDLR offered the most reliable validation of declared pork/beef compositions across the whole range of meat products. Our work suggests that a simultaneous analysis of global and species-specific peptide markers can be used for composition authentication in commercial pork/beef products.

Cleavable molecular beacon-based loop-mediated isothermal amplification assay for the detection of adulterated chicken in meat

A simple, sensitive, specific and fast method based on the loop-mediated isothermal amplification (LAMP) technique and cleavable molecular beacon (CMB) was developed for chicken authentication detection. LAMP and CMB were used for DNA amplification and amplicon analysis, respectively. Targeting the mitochondrial cytochrome b gene of chickens, five primers and one CMB probe were designed, and their specificity was validated against nine other animal species. The structure of CMB and concentrations of dNTPs, MgSO₄, betaine, RNase H2, primers and CMB were optimized. The CMB-LAMP assay was completed within 17 min, and its limit of detection for chicken DNA was 1.5 pg μL^-1. Chicken adulteration as low as 0.5% was detected in beef, and no cross-reactivity was observed. Finally, this assay was successfully applied to 20 commercial meat products. When combined with our developed DNA extraction method (the extraction time was 1 min: lysis for 10 s, washing for 20 s and elution for 30 s), the entire process (from DNA extraction to results analysis) was able to be completed within 20 min, which is at least 10 min shorter than other LAMP-based methods. Our method showed great potential for the on-site detection of chicken adulteration in meat.

Identification of Guiboutia species by NIR-HSI spectroscopy

Near infrared hyperspectral imaging (NIR-HSI) spectroscopy can be a rapid, precise, low-cost and non-destructive way for wood identification. In this study, samples of five Guiboutia species were analyzed by means of NIR-HSI. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used after different data treatment in order to improve the performance of models. Transverse, radial, and tangential section were analyzed separately to select the best sample section for wood identification. The results obtained demonstrated that NIR-HSI combined with successive projections algorithm (SPA) and SVM can achieve high prediction accuracy and low computing cost. Pre-processing methods of SNV and Normalize can increase the prediction accuracy slightly, however, high modelling accuracy can still be achieved by raw pre-processing. Both models for the classification of G. conjugate, G. ehie and G. demeusei perform nearly 100% accuracy. Prediction for G. coleosperma and G. tessmannii were more difficult when using PLS-DA model. It is evidently clear from the findings that the transverse section of wood is more suitable for wood identification. NIR-HSI spectroscopy technique has great potential for Guiboutia species analysis.

CRISPR-Cas-based detection for food safety problems: Current status, challenges, and opportunities

Food safety is one of the biggest public issues occurring around the world. Microbiological, chemical, and physical hazards can lead to food safety issues, which may occur at all stages of the supply chain. In order to tackle food safety issues and safeguard consumer health, rapid, accurate, specific, and field-deployable detection methods meeting diverse requirements are one of the imperative measures for food safety assurance. CRISPR-Cas system, a newly emerging technology, has been successfully repurposed in biosensing and has demonstrated huge potential to establish conceptually novel detection methods with high sensitivity and specificity. This review focuses on CRISPR-Cas-based detection and its current status and huge potential specifically for food safety inspection. We firstly illustrate the pending problems in food safety and summarize the popular detection methods. We then describe the potential applications of CRISPR-Cas-based detection in food safety inspection. Finally, the challenges and futuristic opportunities are proposed and discussed. Generally speaking, the current food safety detection methods are still unsatisfactory in some ways such as being time-consuming, displaying unmet sensitivity and specificity standards, and there is a comparative paucity of multiplexed testing and POCT. Recent studies have shown that CRISPR-Cas-based biosensing is an innovative and fast-expanding technology, which could make up for the shortcomings of the existing methods or even replace them. To sum up, the implementation of CRISPR-Cas and the integration of CRISPR-Cas with other techniques is promising and desirable, which is expected to provide "customized" and "smart" detection methods for food safety inspection in the coming future.

Hyperspectral imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends

Meat products are particularly plagued by safety problems because of their complicated structure, various production processes and complex supply chains. Rapid and non-invasive analytical methods to evaluate meat quality have become a priority for the industry over the conventional chemical methods. To achieve rapid analysis of safety and quality parameters of meat products, hyperspectral imaging (HSI) is now widely applied in research studies for detecting the various components of different meat products, but its application in meat production and supply chain integrity as a quality control (QC) solution is still ambiguous. This review presents the fresh look at the current states of HSI research as both the scope and the applicability of the HSI in the meat quality evaluation expanded. The future application scenarios of HSI in the supply chain and the future development of HSI hardware and software are also discussed, by which HSI technology has the potential to enable large scale meat product testing. With a fully adapted for factory setting HSI, the inspection coverage can reliably identify the chemical properties of meat products. With the introduction of Food Industry 4.0, HSI advances can change the meat industry to become from reactive to predictive when facing meat safety issues.

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HSI has shown promising early signs in the non-destructive analysis of meat quality and safety.

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Hyperspectral imaging (HSI) is now widely applied in research studies for different meat products with the help of machine learning methods.

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With a fully adapted factory setting and robust machine learning of HSI, the inspection coverage can reach 100% of the target meat.

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HSI can change the meat industry to become from reactive to predictive when facing issues, this will be translated into fewer recalls, less meat fraud, and less waste.

Next Generation Sequencing-based DNA metabarcoding for animal species profiling in fish feed

The expansion of worldwide aquaculture has been accompanied by extensive growth in the fish feed industry. However, improper labelling of many commercially available fish feeds has raised security and safety concerns over the species' origin of the ingredients. The inclusion of ruminants-derived ingredients in fish feed is prohibited according to EU legislation while porcine inclusion in fish feed has been a great concern among Muslim farmers. In contrast to the limited species that could be simultaneously determined using multiplex PCR, this study utilised Next Generation Sequencing-based DNA metabarcoding assay to determine the compositional profiles of animal species in fish feed samples in a more holistic manner. In relation to the religious issue associated with porcine-derived ingredients in fish feed, this study firstly aimed to determine the sensitivity of the methods in profiling fish feed adulterated with porcine blood and muscle tissues. Next, 10 commercially available fish feed samples were analysed. As a result, a detection limit of as low as 3% (w/w) porcine muscle and blood in the laboratory-prepared fish feed was obtained. The analysis of 10 commercial fish feeds shows surprising findings: 50% of the feeds contain Sus scrofa and 80% contain Bos taurus, a ruminant. Only one commercial fish feed was found to be solely composed of marine species. This study shows that commercial fish feeds sold in Malaysia contain undesirable animal species, and emphasises the need for accurate and legally enforced labelling of mammalian species in fish feed products.

Fusion of a low-cost electronic nose and Fourier transform near-infrared spectroscopy for qualitative and quantitative detection of beef adulterated with duck

A low-cost electronic nose (E-nose) based on colorimetric sensors fused with Fourier transform-near-infrared (FT-NIR) spectroscopy was proposed as a rapid and convenient technique for detecting beef adulterated with duck. The total volatile basic nitrogen, protein, fat, total sugar and ash contents were measured to investigate the differences of basic properties between raw beef and duck; GC-MS was employed to analyze the difference of the volatile organic compounds emitted from these two types of meat. For variable selection and spectra denoising, the simple T-test (p < 0.05) separately intergraded with first derivative, second derivative, centralization, standard normal variate transform, and multivariate scattering correction were performed and the results compared. Extreme learning machine models were built to identify the adulterated beef and predict the adulteration levels. Results showed that for recognizing the independent samples of raw beef, beef-duck mixtures, and raw duck, FT-NIR offered a 100% identification rate, which was superior to the E-nose (83.33%) created herein. In terms of predicting adulteration levels, the root means square error (RMSE) and the correlation coefficient (r) for independent meat samples using FT-NIR were 0.511% and 0.913, respectively. At the same time, for E-nose, these two indicators were 1.28% and 0.841, respectively. When the E-nose and FT-NIR data were fused, the RMSE decreased to 0.166%, and the r improved to 0.972. All the results indicated that fusion of the low-cost E-nose and FT-NIR could be employed for rapid and convenient testing of beef adulterated with duck.

The Feasibility of Two Handheld Spectrometers for Meat Speciation Combined with Chemometric Methods and Its Application for Halal Certification

Handheld visible-near-infrared (Vis-NIR) and near-infrared (NIR) spectroscopy can be cost-effective, rapid, non-destructive and transportable techniques for identifying meat species and may be valuable for enforcement authorities, retail and consumers. In this study, a handheld Vis-NIR (400-1000 nm) and a handheld NIR (900-1700 nm) spectrometer were applied to discriminate halal meat species from pork (halal certification), as well as speciation of intact and ground lamb, beef, chicken and pork (160 meat samples). Several types of class modeling multivariate approaches were applied. The presented one-class classification (OCC) approach, especially with the Vis-NIR sensor (95-100% correct classification rate), was found to be suitable for the application of halal from non-halal meat-species discrimination. In a discriminant approach, using the Vis-NIR data and support vector machine (SVM) classification, the four meat species tested could be classified with accuracies of 93.4% and 94.7% for ground and intact meat, respectively, while with partial least-squares discriminant analysis (PLS-DA), classification accuracies were 87.4% (ground) and 88.6% (intact). Using the NIR sensor, total accuracies of the SVM models were 88.2% and 81.5% for ground and intact meats, respectively, and PLS-DA classification accuracies were 88.3% (ground) and 80% (intact). We conclude that the Vis-NIR sensor was most successful in the halal certification (OCC approaches) and speciation (discriminant approaches) for both intact and ground meat using SVM.

Recent advances in halal food authentication: Challenges and strategies

Increasing public awareness of food quality and safety has prompted a rapid increase in food authentication of halal food, which covers the production method, technical processing, identification of undeclared components, and species substitution in halal food products. This urges for extensive research into analytical methods to obtain accurate and reliable results for monitoring and controlling the authenticity of halal food. Nonetheless, authentication of halal food is often challenging because of the complex nature of food and the increasing number of food adulterants that cause detection difficulties. This review provides a comprehensive and impartial overview of recent studies on the analytical techniques used in the analysis of halal food authenticity (from 1980 to the present, but there has been no significant trend in the choice of techniques for authentication of halal food during this period). Additionally, this review highlights the classification of different methodologies based on validity measures that provide valuable information for future developments in advanced technology. In addition, methodological developments, and novel emerging techniques as well as their implementations have been explored in the evaluation of halal food authentication. This includes food categories that require halal authentication, illustrating the advantages and disadvantages as well as shortcomings during the use of all approaches in the halal food industry.

Rapid Full-Cycle Technique to Control Adulteration of Meat Products: Integration of Accelerated Sample Preparation, Recombinase Polymerase Amplification, and Test-Strip Detection

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

Advances in Nanomaterials-Based Electrochemical Biosensors for Foodborne Pathogen Detection

Electrochemical biosensors utilizing nanomaterials have received widespread attention in pathogen detection and monitoring. Here, the potential of different nanomaterials and electrochemical technologies is reviewed for the development of novel diagnostic devices for the detection of foodborne pathogens and their biomarkers. The overview covers basic electrochemical methods and means for electrode functionalization, utilization of nanomaterials that include quantum dots, gold, silver and magnetic nanoparticles, carbon nanomaterials (carbon and graphene quantum dots, carbon nanotubes, graphene and reduced graphene oxide, graphene nanoplatelets, laser-induced graphene), metal oxides (nanoparticles, 2D and 3D nanostructures) and other 2D nanomaterials. Moreover, the current and future landscape of synergic effects of nanocomposites combining different nanomaterials is provided to illustrate how the limitations of traditional technologies can be overcome to design rapid, ultrasensitive, specific and affordable biosensors.

Chemometrics in Tandem with Hyperspectral Imaging for Detecting Authentication of Raw and Cooked Mutton Rolls

Authentication assurance of meat or meat products is critical in the meat industry. Various methods including DNA- or protein-based techniques are accurate for assessing meat authenticity, however, they are destructive, expensive, or laborious. This study explores the feasibility of chemometrics in tandem with hyperspectral imaging (HSI) for identifying raw and cooked mutton rolls substitution by pork and duck rolls. Raw or cooked samples (n = 180) of three meat species were prepared to collect hyperspectral images in range of 400-1000 nm. Spectra were extracted from representative regions of interest (ROIs), and spectral principal component analysis (PCA) revealed that PC1 and PC2 were effective for the identification. Different methods including standard normal variable (SNV), first and second derivatives, and normalization were individually employed for spectral preprocessing, and modeling methods of partial least squares-discriminant analysis (PLS-DA) and support vector machines (SVM) were also individually applied to develop classification models for both the raw and the cooked. Results showed that PLS-DA model developed by raw spectra presented the highest 100% correct classification rate (CCR) of success in all sets. After that, effective wavelengths selected by successive projections algorithm (SPA) built optimal simplified models which didn't influence the modeling results compared with full spectra regardless of the meat roll states. Therefore, SPA-PLS-DA models were subsequently used to visualize the raw and cooked meat rolls classification. As a consequence, the general meat species of both raw and cooked meat rolls were readily discernible in pixel-wise manner by generating classification maps. The results showed that HSI combined with chemometrics can be used to identify the authentication of raw and cooked mutton rolls substituted by pork and duck rolls accurately. This promising methodology provides a reference which can be extended to the classification or grading of other meat rolls.

Artificial bionic taste sensors coupled with chemometrics for rapid detection of beef adulteration

The purpose of this study was to investigate the potential of taste sensors coupled with chemometrics for rapid determination of beef adulteration. A total of 228 minced meat samples were prepared and analyzed via raw ground beef mixed separately with chicken, duck, and pork in the range of 0 ~ 50% by weight at 10% intervals. Total sugars, protein, fat, and ash contents were also measured to validate the differences between raw meats. For sensing the water-soluble chemicals in the meats, an electronic tongue based on multifrequency large-amplitude pulses and six metal electrodes (platinum, gold, palladium, tungsten, titanium, and silver) was employed. Fisher linear discriminant analysis (Fisher LDA) and extreme learning machine (ELM) were used to model the identification of raw and the adulterated meats. While an adulterant was detected, the level of adulteration was predicted using partial least squares (PLS) and ELM and the results compared. The results showed that superior recognition models derived from ELM were obtained, as the recognition rates for the independent samples in different meat groups were all over 90%; ELM models were more precisely than PLS models for prediction of the adulteration levels of beef mixed with chicken, duck, and pork, with root mean squares error for the independent samples of 0.33, 0.18, and 0.38% and coefficients of variance of 0.914, 0.956, and 0.928, respectively. The results suggested that taste sensors combined with ELM could be useful in the rapid detection of beef adulterated with other meats.