| 1 | Discrimination of halal (chicken, beef) and non-halal meats (wild boar meat) | Principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), orthogonal projection to latent structures-discriminant analysis (OPLS-DA), and heatmap analysis | 1. Metabolites were effectively detected in beef meat (BM), canine meat (CM), and wild boar meat (WBM) by liquid chromatography-high-resolution mass spectrometry (LC-HRMS).2. The three varieties of meat were successfully distinguished using chemometrics.3. Important differentiating metabolites for BM-WBM and CM-WBM were found.4. A potential technique for meat verification that guarantees quality, safety, and halal certification is the combination of LC-HRMS and chemometrics. | Windarsih et al. (2024b) |
| 2 | Authentication of chicken meat from different slaughtering procedures | PCA, cluster analysis, correlation network analysis, and PLS-DA | 1. Using chemometrics and ultra high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS), chicken flesh was effectively verified using slaughtering techniques.2. A total of 28 distinct metabolite profiles were found, with 3-methylhistidine, creatine, and carnosine serving as the main differentiators.3. Halal, non-halal, and shubha-halal chicken flesh were successfully categorized and distinguished using chemometric approaches.4. Thirteen possible biomarkers that can be utilized for trustworthy halal verification were found using PLS-DA.5. In order to safeguard consumers against non-halal and shubha-halal items, the study validates untargeted metabolomics as a precise and selective technique for confirming the halal quality of chicken flesh. | Maritha et al. (2024) |
| 3 | Authentication of porcine, bovine, and goat bone gelatins | PCA, cluster analysis, and PLS-DA | 1. By effectively differentiating gelatin origins using UHPLC-HRMS and chemometrics, gelatin’s halal certification was guaranteed.2. Gelatin generated from goats, cows, and pigs showed distinct metabolic variations, as demonstrated by PCA and cluster analysis.3. By identifying 15 important metabolites, PLS-DA made it possible to accurately authenticate the provenance of gelatin.4. The study validated metabolomics as a very successful and selective method for confirming gelatin’s halal certification in culinary and cosmetic applications. | Harlina et al. (2024) |
| 4 | Chemometrics-identification of potential peptide markers of pork, beef and chicken | PCA and OPLS-DA | 1. Species-specific peptide indicators for identifying pork adulteration in halal foods were effectively discovered using chemometrics-assisted shotgun proteomics.2. Reliable classification was ensured using PCA and OPLS-DA, which successfully differentiated peptide profiles between chicken, beef, and pork.3. The fundamental structure of the selected peptide markers was confirmed by the consistent and complementary findings obtained from the combination of peptide mass fingerprinting (PMF) and targeted tandem liquid chromatography-mass spectrometry (LC-MS).4. This work provided a scientifically proven method for identifying pork contamination in beef and poultry, establishing a reliable and repeatable strategy for halal meat certification. | Yuswan et al. (2018) |
| 5 | Halal authentication of triceps brachii (TB), longissimus dorsi (LD), and biceps femoris (BF) of meat muscles | PCA, cluster analysis, and PLS-DA | 1. Different metabolic patterns in the TB, LD, and BF muscle regions of beef and pork were effectively discovered by untargeted metabolomics.2. Meat samples were correctly categorized by chemometric analysis according to their metabolite makeup.3. Important metabolite indicators that accurately identify halal meat were found using PLS-DA.4. The work strengthens halal certification techniques by offering a data-driven strategy for differentiating between beef and pork. | Maritha et al. (2023b) |
| 6 | Analysis of dog meat (DM) adulteration in beef meatballs (BM) using non-targeted | PLS-DA, PLS, and orthogonal PLS | 1. Halal BM were successfully validated using chemometrics and non-targeted LC-HRMS metabolomics.2. The technique ensured great sensitivity by detecting DM adulteration down to 0.1% (w/w).3. Biomarker metabolites that differentiated samples containing DM from BM were found.4. Pathway analysis showed that DM adulteration considerably changed the metabolism of ether lipids, histidine, and beta-alanine.5. The method’s practical usefulness was demonstrated by its effective application to commercial meatball samples (n=28).6. This work supports halal authentication efforts by highlighting a practical and dependable method for identifying non-halal adulteration in halal beef products. | Windarsih et al. (2024a) |
| 7 | Analysis of pork in beef sausages | PCA, PLS-DA, PLS, orthogonal PLS, and variable importance for projection analysis | 1. The metabolite variations between real and fake BS were successfully identified by LC-HRMS untargeted metabolomics.2. Reliable classification was ensured by the precise separation of BS from pork-adulterated samples by PCA and PLS-DA.1. PLS and OPLS showed excellent accuracy and low error in their predictions of the degree of pork adulteration with r2>0.99 and root mean square error of calibrations (RMSEC) 1.32%.2. Eight important metabolites were shown to be biomarkers for the identification of pork, including oleamide, α-eleostearic acid, and arachidonic acid.3. A reliable technique for identifying pork adulteration in beef sausages was LC-HRMS in conjunction with chemometrics. | Windarsih et al. (2023) |
| 8 | Detection of pork in tuna meat for halal authentication | PCA and PLS-DA | 1. Pork-adulterated TM was successfully separated from genuine samples using LC-HRMS untargeted metabolomics in conjunction with PCA and PLS-DA.2. 21 metabolites were shown to be promising indicators for the identification of pork.3. Pork-specific peptide markers, such as FFESFGDLSNADAVMGNPK (beta-hemoglobin), HDPSLLPWTASYDPGSAK (carbonic anhydrase 3), and HPGDFGADAQGAMSK (myoglobin), were identified by proteomics analysis.4. Pork adulteration was effectively identified by both proteomics and metabolomics with a sensitivity of 0.5%.5. This work suggests using LC-HRMS-based proteomics and metabolomics as a standard operating procedure for identifying non-halal adulteration in meat products and validates it as an efficient and trustworthy technique for halal authentication. | Suratno et al. (2023) |