Non-targeted 1H NMR metabolomic fingerprinting and chemometric approach in differentiating meat species for potential halal authentication

Food fraud, driven by unethical practices for quick financial gain, has become a global concern. Meat and meat products are particularly vulnerable to fraudulent activities due to their ability to blend without noticeable changes in appearance or texture. However, the consequences of meat fraud extend beyond financial losses, affecting consumers who adhere to dietary restrictions, such as Muslims and Jews avoiding pork or Hindus abstaining from beef, leading to significant emotional distress. Halal food fraud has gained significant attention, encompassing practices such as the misuse of halal labels, falsification of halal certificates, product mislabeling, and the introduction of non-halal ingredients into halal products. These fraudulent activities often include the substitution or dilution of one meat species for another, including the use of different body parts of the same species. Furthermore, non-meat ingredients, such as plant or dairy fillers in processed meat products, can inadvertently introduce prohibited substances into the supply chain, raising concerns among Muslim consumers about contamination with prohibited (haram) substances during food processing and logistics. This highlights the urgent need for improved meat authentication and traceability measures. Meat authentication faces challenges due to the similarity in characteristics and appearances among different meat types, and the complexity of processed foods makes visually detecting pork adulteration nearly impossible. To overcome these obstacles, a combination of targeted and untargeted analytical methods has been developed. Conventional targeted approaches like protein and DNA analysis are effective for known markers but may miss unexpected ones. Non -targeted metabolomics, a cutting-edge technique, measures a wide range of metabolites without altering the sample, providing a comprehensive analysis of meat composition. When coupled with proton nuclear magnetic resonance (1H NMR), it generates distinct fingerprint patterns for each sample, enabling accurate and efficient food authenticity verification. This study aimed to qualitatively differentiate between five types of meat samples—beef, buffalo, chicken, mutton, and pork—based on their metabolites. Metabolites were extracted using perchloric acid and bi-phase methanol-chloroform, for polar metabolites and chloroform for non-polar metabolites. Analysis of spectral data acquired through 1H NMR revealed 23 important metabolites in the polar fraction and 26 compounds in the lipophilic fraction that significantly contributed to the differentiation of meat types. Chemometric analysis, employing the Principal Component Analysis (PCA) as an unsupervised method and Partial Least Squares Discriminant Analysis (PLS DA) as a supervised method, demonstrated complete differentiation between meat types. Notable metabolites, including lactate, betaine, glutathione, myo inositol, IMP, carnosine, and acetate from the polar fraction, played a crucial role in pork detection. Additionally, linoleic acid from the lipophilic fraction significantly discriminated pork from other meat types. The metabolic fingerprinting approach successfully distinguished between pure and adulterated meat samples, even at a 10% adulterant level (pork), and effectively differentiated pure meat from meat-based products. In conclusion, the integration of NMR-based metabolomics with chemometric analysis offers a significant and dependable method for authenticating meat species. The specificity and selectivity of this approach enable the identification of essential metabolite markers, which could be used in rapid test kits to boost food safety and consumer trust. This scientific advancement plays a crucial role in addressing the growing issue of meat fraud and ensuring the authenticity of meat and meat-based products.

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