Recent advances in halal food authentication: Challenges and strategies

Highly selective ethanol vapour sensing materials for a new generation of gas sensors based on molecularly imprinted polymers

A simple gas sensor consisting of a molecularly imprinted polymer-carbon nanotube composite cast onto a screen-printed electrode has been developed with extremely high selectivity for ethanol vapour over methanol vapour. Ethanol gas sensors typically display selectivity for ethanol over methanol in the range 2-4 times, while the mean ratio of ethanol to methanol response observed with the described device was 672. This selectivity was achieved under ambient conditions. Additionally, the molecularly imprinted polymer was produced using reagents previously applied in the development of a device selective for methanol, with only the template being changed. This demonstrates the versatility of molecular imprinting and provides a foundation for their greater integration into future gas sensors.

Development of a real-time PCR protocol for the detection of chicken DNA in meat products

Food falsification is a pressing issue in today's food industry, with fraudulent substitution of costly ingredients with cheaper alternatives occurring globally. Consequently, developing straightforward and efficient diagnostic systems to detect such fraud is a top priority in scientific research. The aim of the work was to develop a test system and protocol for polymerase chain reaction (PCR) to detect in food products of animal origin the substitution of expensive meat raw materials for by-products of poultry processing. For this, real-time polymerase chain reaction (RT-PCR) was used, which allows determining the qualitative and quantitative substitution in raw and technologically prepared products. Other methods for detecting falsification - enzyme immunoassay (ELISA/ELISA) or express methods in the form of a lateral flow immunoassay are less informative. The extraction of nucleic acids for real-time polymerase chain reaction depends on the source matrix, with higher concentrations obtained from germ cells and parenchymal organs. Extraction from muscle and plant tissues is more challenging, but thorough grinding of these samples improves nucleic acid concentration by 1.5 times using DNA extraction kits. The selection of primers and fluorescent probes through GenBank and PCR Primer Design/DNASTAR software enables efficient amplification and identification of target chicken DNA fragments in various matrices.

Application of machine learning approach on halal meat authentication principle, challenges, and prospects: A review

Meat is a source of essential amino acids that are necessary for human growth and development, meat can come from dead, alive, Halal, or non-Halal animal species which are intentionally or economically (adulteration) sold to consumers. Sharia has prohibited the consumption of pork by Muslims. Because of the activities of adulterators in recent times, consumers are aware of what they eat. In the past, several methods were employed for the authentication of Halal meat, but numerous drawbacks are attached to this method such as lack of flexibility, limited application, time,consumption and low level of accuracy and sensitivity. Machine Learning (ML) is the concept of learning through the development and application of algorithms from given data and making predictions or decisions without being explicitly programmed. The techniques compared with traditional methods in Halal meat authentication are fast, flexible, scaled, automated, less expensive, high accuracy and sensitivity. Some of the ML approaches used in Halal meat authentication have proven a high percentage of accuracy in meat authenticity while other approaches show no evidence of Halal meat authentication for now. The paper critically highlighted some of the principles, challenges, successes, and prospects of ML approaches in the authentication of Halal meat.

Analysis of dog meat adulteration in beef meatballs using non-targeted UHPLC-Orbitrap HRMS metabolomics and chemometrics for halal authentication study

Due to the different price and high quality, halal meat such as beef can be adulterated with non-halal meat with low price to get an economical price. The objective of this research was to develop an analytical method for halal authentication testing of beef meatballs (BM) from dog meat (DM) using a non-targeted metabolomics approach employing liquid chromatography-high-resolution mass spectrometry (LC-HRMS) and chemometrics. The differentiation of authentic BM from that adulterated with DM was successfully performed using partial least square-discriminant analysis (PLS-DA) with high accuracy (R2X = 0.980, and R2Y = 0.980) and good predictivity (Q2 = 0.517). In addition, partial least square (PLS) and orthogonal PLS (OPLS) were successfully used to predict the DM added (% w/w) in BM with high accuracy (R2 > 0.990). A number of metabolites, potential for biomarker candidates, were identified to differentiate BM and that adulterated with DM. It showed that the combination of a non-targeted LC-HRMS Orbitrap metabolomics and chemometrics could detect up to 0.1% w/w of DM adulteration. The developed method was successfully applied for analysis of commercial meatball samples (n = 28). Moreover, pathway analysis revealed that beta-alanine, histidine, and ether lipid metabolism were significantly affected by dog meat adulteration. In summary, this developed method has great potential to be developed and used as an alternative method for analysis of non-halal meats in halal meat products.

Towards halal pharmaceutical: Exploring alternatives to animal-based ingredients

Halal is a crucial concept for Muslim consumers regarding consumed products, including pharmaceutical ingredients, which are essential in modern medicine. To address the issue of using porcine-sourced ingredients in pharmaceuticals, it is essential to search for halal alternatives derived from poultry, animal by-products from meat processing, marine sources, and plants. However, the complexity of this problem is further compounded by the rapid advances in innovation and technology, which can lead to adulteration of ingredients derived from pigs. Other challenges include the sustainability of alternative materials, management of waste or by-products practice, halal awareness, certification, government policies, religious adherence of consumers, food suppliers, marketers, and purchasing of products. The importance of halal and non-halal problems, specifically in the context of pharmaceutical materials, is still rarely discussed, including alternatives derived from poultry, animal by-products, marine sources, and plants. Due to the increasing global population, there is a growing need to increase awareness and concern among Muslim consumers for halal products, including pharmaceuticals. Therefore, this research aimed to investigate the importance of halal and non-halal issues in pharmaceutical ingredients, the potential impact on the Muslim community, as well as opportunities and challenges in the search for alternative ingredients.

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.

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

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

Advances and key considerations of liquid chromatography–mass spectrometry for porcine authentication in halal analysis

The halal food industries are rapidly expanding to fulfill global halal demands. Non-halal substances such as porcine proteins are often added intentionally or unintentionally to products. The development of highly selective and sensitive analytical tools is necessary, and liquid chromatography–mass spectrometry is a powerful tool that can cope with the challenge. The LC–MS method has great potential for halal authentication, because it has high sensitivity and low detection limit and detects several species markers and different tissue origins at once within one species. This article provides an understanding of recent advances in the application of LC–MS for the improvement of porcine authentication. Sample preparation, marker selection, separation and mass spectrometry conditions, quantitative assessment, and data processing for protein identification were all covered in detail to choose the most suitable method for the analytical needs.

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.