ABSTRACT
The demand for authentic meat has increased due to the growing consumption of halal meat. In this study, a combined metabolomics and chemometrics approach was employed to authenticate beef and pork samples from specific muscle groups. By using an untargeted metabolomics analysis, distinct metabolite profiles were observed in TB, LD, and BF muscles, differentiating pork from beef. Subsequently, Principal Component Analysis (PCA) results confirmed the distinguishable metabolite profiles between beef and pork. Cluster analysis further revealed that the metabolites in each muscle of beef and pork have different characteristics. Additionally, Partial Least Squares-Discriminant Analysis identified 15 potential metabolites could be used to authenticate the halal status of meat. Creatine, L-carnitine, carnosine, nicotinamide, L-phenylalanine, DL-lactic acid, acetyl L-carnitine, hypoxanthine, inosine, DL-malic acid, N-methyl-2-pyrrolidone, L-glutathione, Inosine-5-monophosphate (IMP), L-tyrosine, and palmitoylcarnitine is a potential metabolite to differentiate beef and pork. This study offers valuable insights into determining the halal status of meat based on metabolite profiles.
Introduction
Halal meat has become an increasing concern, particularly in Indonesia, where the majority of the population is Muslim[Citation1]. To address this issue, the Indonesian government has made it mandatory for all food products to obtain a halal product certificate by October 2024 (Law No. 33/2014). Halal meat, which includes beef, is a dietary requirement for Muslims. However, a significant concern arises when halal meat is mixed with non-halal meat, such as combining beef with pork. Economic factors often drive this mixing, but it poses a serious issue for the integrity of halal meat products.[Citation2] When beef and pork are combined, the halal status of the meat is compromised. It is important to use a method of halal authentication for meat. One of the frequently used methods for halal meat authentication is Polymerase Chain Reaction (PCR).[Citation3–5] This method is selective for analyzing beef and porcine DNA, but it requires complex sample preparation procedures. However, due to the addition of a small quantity of pork to beef mixture, more target DNA extraction is required. As a result, PCR is unable to detect the target DNA, leading to the possibility of false-negative results.[Citation6] There is a need for an alternative method, which is needed for halal authentication in meat through a metabolomics approach using UHPLC-HRMS (Ultra High-Performance Liquid Chromatography-High Resolution Mass Spectrometry).[Citation7]
Metabolomics is a qualitative and quantitative analysis method that focuses on metabolites with a modest molecular weight of 100 to 1,000 in cells, tissues, and biological fluids. These metabolites are the end products resulting from gene expressions influenced by interactions between the genome system and the environment.[Citation8] Metabolites are made up of an intermediate component as well as products, and by using metabolomics, distinct features that can serve as a fingerprint for metabolites are found in samples, including meat.[Citation9] This approach can assess the components of substances globally with minimum sample preparation. By analyzing metabolites present in both beef and pork, metabolomics offers insights into their respective profiles. Subsequently, the metabolite profiles of pork are likely to differ from those of beef, making the method a valuable guideline for determining the halal status of the meat.[Citation10]
To assess halal authentication, it is crucial to analyze the metabolite profiles of the Triceps Brachii (TB), Longissimus Dorsi (LD), and Biceps Femoris (BF) muscles of beef and pork, as they are most frequently consumed.[Citation11–13] To determine the halal status of meat, it is crucial to authenticate all three muscles. In this situation, the metabolomics technique utilizes UHPLC-HRMS equipment, known for its good selectivity and sensitivity for halal authentication.[Citation14] The spectral data generated from the analysis using UHPLC-HRMS alone is insufficient for validating halal authentication on TB, LD, and BF of beef and pork. Additional analysis in the form of chemometric analysis is required.[Citation15] A chemometric analysis is needed to determine whether beef and pork metabolite profiles are different using PCA (Principal Component Analysis).[Citation16] PCA describes the metabolite distribution in each beef and pork muscle. Partial Least Squares Discriminant Analysis (PLS-DA) is then employed to identify the metabolite profiles that can differentiate between beef and pork. The results of the PLS-DA analysis provide the most influential metabolite profiles to determine the halal status of beef and pork. Metabolite profiles for halal meat verification can be generated by chemometric analyses using PCA and PLS-DA.[Citation17] By examining the TB, LD, and BF muscles of beef and pork, these analyses contribute to the authentication of the halal status of meat, ensuring consumer protection against non-halal products.
Material and methods
Materials
Methanol, water, formic acid, and acetonitrile for LC-MS grade were all pro-analysis quality chemical reagents (Merck, Germany). In this study, TB, LD, and BF muscles of beef and pork used were obtained from the local market in Bandung (Pasar Bandung), Indonesia. Beef TB, LD, and BF are segregated from pork to avoid cross-contamination. Separation of packaging containers begins at the point of meat purchase and continues to the laboratory prior to metabolite extraction.
Extraction of metabolites from meat
The meat sample (50 mg) was placed in a microcentrifuge tube to which 1 ml of methanol was added. The mixture was vortexed for 30 seconds and then sonicated for 30 minutes at room temperature. The sample was centrifuged at 1,400 × g for 5 minutes to separate the supernatant. The supernatant was taken and filtered using a 0.22 µm PTFE filter. The resulting metabolites were stored in vials at −20°C before further analysis. This process was conducted for the TB, LD, and BF muscles of beef and pork.
Metabolomics analysis using UHPLC-HRMS
The instruments used in the metabolomics approach were UHPLC and Q Exactive (Thermo Scientific, USA) from the Advance Research Laboratory of IPB University. The UHPLC conditions involved using a C18 Accucore (Thermo Scientific, USA) 100 × 2.1 mm x 1.5 μm stationary phase. The mobile phase consisted of two components: mobile phase A, which was 0.1% formic acid in water, and mobile phase B, which was 0.1% formic acid in methanol. The mobile phase in the metabolomics analysis was a gradient where minutes 0–16: 5% B up to 90%, 10% A; minutes 17–20: 90% B, 10% A; and minutes 20–25: 5% B, 95% A, with a flow rate of 0.3 μL/min. The temperature was 40°C, and the injection volume was 3 µL. The data were read at m/z mass intervals of 66.7–1,000 m/z, and the spectral fragments were obtained at a resolution of 17,500–75,000 Hz. The metabolite spectra obtained were further matched with Discover Compound data, and the process was replicated three times. The detailed parameters of the LC gradient and MS instrument are shown in Supplementary F1.
Statistical and Chemometric Analysis
The statistical analysis of the metabolite compounds was conducted using one-way variance (ANOVA) in SPSS 16.0 software (SPSS, Chicago, IL). If the results were significant (P < .05), then, chemometrics analysis was performed, including PCA, PLS-DA, and heatmap analysis for classification using Metaboanalyst 5.0 (https://www.metaboanalyst.ca/). Each sample was replicated three times.
Results and discussion
Selectivity mobile phase of metabolomics
The metabolomics analysis of beef and pork on TB, LD, and BF muscles for halal authentication proved that UHPLC-HRMS is a selective method. The number of metabolites discovered shows the method’s selectivity. Among the different mobile phase combinations tested, the gradient mixture of water and methanol exhibited the highest level of selectivity, surpassing water and acetonitrile. Specifically, the analysis using the water and acetonitrile mobile phase provided 31 metabolites, while the analysis using the water and methanol mobile phase generated 177 metabolites. As a result, selecting the water and methanol mobile phase is the best UHPLC-HRMS mobile phase condition for halal authentication using metabolomics techniques, as it enables the detection of a greater number of metabolites (Supplemental F1). Water and methanol mobile phases can extract metabolites from beef and pork more than water and acetonitrile mobile phases.[Citation18]
Untargeted metabolomics
The untargeted metabolomics analysis conducted on TB, LD, and BF muscles of beef and pork yielded a total of 177 compounds. The further examination focused on the 177 metabolites with a mass-to-charge ratio (m/z) percentage value exceeding 70%, resulting in the identification of 61 metabolites. These metabolites exhibited an MZ value of over 70% in both beef and pork samples, indicating their consistency and reliability. The error mass used in this analysis was 5 ppm to ensure the spectral data were compatible with the library-based data (discover compound).[Citation19–21] This indicated that the results of the untargeted metabolomics study are reliable. The metabolites discovered in pork were the same as those discovered in beef, although at different levels. This is consistent with Kim et al.,[Citation22] in which NMR examination of beef and pork metabolites revealed the same metabolites but at different amounts. Subsequently, this study found that leucine, isoleucine, glycine, and phenylalanine were present in both beef and pork but in different amounts, indicating that pork metabolite profiles differ from beef. Distinct characteristics were observed in the metabolite compositions across different muscles of beef and pork. In addition, the results of the one-way ANNOVA statistical analysis test showed that TB, LD and BF of beef and pork had a p value of less than 0.05. This indicates that each part of beef and pork has significant metabolite differences. shows the metabolite content profiles in each beef and pork muscle.
Table 1. Putative identification metabolites in each muscle of beef and pork meats.
Distinct metabolite profiles were observed in the TB, LD, and BF muscles of pork. The three most prevalent metabolites in pork TB are creatine, L-phenylalanine, and DL-malic acid. In pork LD, the most prominent metabolites are creatine, carnosine, and nicotinamide. Creatine, L-phenylalanine, and carnosine were pork BF’s three most common metabolites. The composition of the metabolites differed between pork TB, LD, and BF. According to Jang et al.,[Citation23] the metabolites in each area of pork differed, such as xanthine in the pancreas and acylcarnitine in the leg (muscle).
The profiles of the metabolites in the TB, LD, and BF muscles of beef varied. The three most prevalent metabolites in beef TB are carnosine, L(-)-carnitine, and creatinine. The most abundant metabolites in beef LD are carnosine, L(-)-carnitine, and creatinine. Although the quantities differed, the primary metabolites in beef TB and LD were consistent. The three primary metabolites in beef BF are L(-)-carnitine, myristyl sulfate, and dodecyl sulfate. The primary metabolites in the various beef muscles varied. The metabolite content in beef Longissimus and Psoas Muscles exhibited the same metabolites, but their quantities are different, including L-carnitine, D-malic acid, and hypoxanthine.[Citation24]
The main metabolites identified in pork TB are creatine, L-phenylalanine, and DL-malic acid, while creatinine, L(-)-carnitine and carnosine are the key metabolites in beef TB. The primary metabolite profiles of beef and pork TB are different. These distinct metabolite profiles can contribute to the differentiation between beef and pork TB. In pork LD, the three primary metabolites are creatine, carnosine, and nicotinamide, while creatinine, L(-)-carnitine and carnosine are the three main metabolites in beef LD. The metabolite profiles of beef and pork LD meat were found to be different, indicating that pork LD differs from beef LD. In pork BF, the main metabolites are creatine, L-Phenylalanine, and carnosine, while L(-)-carnitine, myristyl sulfate, and dodecyl sulfate are the key metabolites in beef BF. This further elucidates the differentiation between beef and pork BF. Trivedi et al.[Citation39] discovered that the distinct metabolites in beef and pork included creatinine, citric acid, and glycine. Chemometrics analysis can be used to clarify the variations in the profiles of metabolites in beef and pork, as well as the metabolites that indicate halal status.[Citation26]
Metabolomics and Chemometrics Analysis
In this study on metabolic, a total of 18 samples were selected and divided into six groups, each compromising of three biological replicates (). A thorough examination of the data revealed that the R2 values ranged from 0.85 to 1.00, indicating a high level of repeatability for each sample. The Pearson correlation coefficient is employed to assess the degree of correlation between variables. The objective is to ensure that the variables exhibit no correlation by the process of grouping and reducing them into new principal components that are mutually uncorrelated. This is done to facilitate further principal component analysis (PCA). The samples within each group showed clear differentiation, as evident from the PCA analysis of the metabolite content, and exhibited satisfactory reproducibility (). Previous studies utilized chemometrics for the authentication of halal meat.[Citation27–29] Based on their metabolites, chemometric analysis can be used to describe the distinctions between beef and pork. Beef and pork TB, LD, and BF profiles are examined using PCA.[Citation30–32] The halal-determining metabolites in beef and pork are predicted using PLS-DA.[Citation33–35] Meanwhile, objects are grouped based on shared characters using cluster analysis. According to the cluster analysis, each muscle of beef and pork has the same properties.[Citation25,Citation36]
Figure 1. Quality control and analysis of metabolome data of meat muscles. A: Pearson correlation analysis between metabolome samples. B: PCA analysis between metabolome samples. TB: Triceps brachii; LD: Longissimus dorsi; and BF: Biceps femoris.
PCA provided a clearer picture of the metabolite profiles in each beef and pork muscle in . The PCA results showed distinct metabolite profiles for the TB, LD, and BF muscles of both beef and pork. showed that the metabolite profiles of pork TB, LD, and BF form three distinct groups, signifying noticeable differences in the metabolites present in these muscles. The metabolites in beef TB, LD, and BF formed separate groupings, are indicating that these three muscles had distinct metabolite profiles.
Figure 2. Score Plot of Principal Component Analysis in Beef and Pork. TB: Triceps brachii; LD: Longissimus dorsi; and BF: Biceps femoris.
Figure 3. Score Plot of Principal Component Analysis in Each Muscle and TB, LD, BF of the Mixed Meats (Beef and Pork) of Beef and Pork Meat. TB: Triceps brachii; LD: Longissimus dorsi; and BF: Biceps femoris.
The results obtained from PCA analysis, as shown in , present the distinct metabolite profiles when the muscles of beef and pork are combined. The PCA results highlight that the combined TB, LD, and BF components of beef and pork exhibit unique metabolite profiles. This implies that halal authentication can be performed on all three components of beef and pork using metabolomics and PCA. To identify the specific metabolites that contribute to halal authenticity, a PLS-DA analysis is necessary. A chemometric technique called PLS-DA is used to estimate possible metabolites that could be utilized to authenticate halal products. Subsequently, PLS-DA can predict probable substances that meat may include as halal indicators. In the context of this study, PLS-DA identified 15 candidate metabolites that could serve as markers for halal meat, as shown in .
Figure 4. Heatmap, cluster analysis and VIP Score for TB, LD, and BF muscles in both meats (beef and pork) TB: Triceps brachii; LD: Longissimus dorsi; and BF: Biceps femoris.
According to , a total of 15 metabolite chemicals serve as halal authentication indicators in the TB, LD, and BF muscles of beef and pork. Windarsih et al. 2022 reported that there were 15 possible metabolites based on the VIP score for detecting pork in Pangasius hypophthalmus meat, one of which was xanthine.[Citation6] According to Ali et al.[Citation34] there was an increase in amino acids in chicken meat that was slaughtered in a non-halal manner. Furthermore, Trivedi et al.[Citation39] discovered an increase in sphingolipids and fatty acids in a study they conducted. According to a previous study by Jia et al.,[Citation40] the radiation process on meat increased L-phenylalanine, L-isoleucine, L-histidine, guanosine, guanine, creatinine, glutathione, and nicotinic acid while decreasing inosine 5′-monophosphate (IMP) and guanine 5′-monophosphate.
To further explore the characteristics of beef and pork, a cluster analysis was performed. displays the results of a heatmap-based cluster analysis. The analysis clearly shows that beef TB, LD, BF, pork TB, LD, and BF exhibit distinct clustering patterns, indicating inherent differences in traits and attributes between beef and pork. Li et al.[Citation41] used cluster analysis to describe pork metabolism during storage. Windarsih et al.[Citation42] reported that cluster analysis was utilized to detect pork in meatballs using a metabolomics technique. Therefore, cluster analysis, which elucidates the variations in TB, LD, and BF between beef and pork, can serve as a means to verify the halal status of meat.
Conclusion
In conclusion, selective UHPLC-HRMS proved to be a successful metabolomics approach for the authentication of beef and pork in TB, LD, and BF muscles. By analyzing the metabolomics data sets of these three muscles, comprehensive information could be obtained to authenticate the halal status of meat. It was observed that the metabolite profiles of pork muscles differed from those of beef. The chemometric investigation revealed that the metabolites profiles in each beef and pork muscle differed. The PCA results visually explained the differences between beef and pork in TB, LD, and BF. Cluster analysis indicated that each muscle of beef and pork had distinct properties enabling their classification based on their metabolites. For halal authentication, PLS-DA analysis provided information on possible metabolites for beef and pork. Furthermore, 15 metabolites were identified as potential markers for halal authentication of beef and pork, including creatine, L-carnitine, carnosine, nicotinamide, L-phenylalanine, DL-lactic acid, acetyl L-carnitine, hypoxanthine, inosine, DL-malic acid, N-methyl-2-pyrrolidone, L-glutathione, Inosine-5-monophosphate (IMP), L-tyrosine, and palmitoylcarnitine.
Credit authorship contribution statement
Vevi Maritha and Putri Widyanti Harlina: Investigation, Data curation, Writing – original draft. Mohamad Rafi and Fang Geng: Data curation, Methodology. Putri Widyanti Harlina: Methodology, Funding acquisition, Conceptualization. Ida Musfiroh, Muchtaridi Muchtaridi, Putri Widyanti Harlina: Supervision, Writing review & editing.
Supplemental Material
Download ()Acknowledgments
The corresponding author extends thank to the Universitas Padjadjaran, Indonesia for the funding support.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The data presented in this study are available on request from the corresponding author.
Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/10942912.2023.2272568.
Additional information
Funding
References
- Purwanto, H.; Fauzi, M.; Wijayanti, R.; Al Awwaly, K. U.; Jayanto, I.; Mahyuddin; Purwanto, A.; Fahlevi, M.; Adinugraha, H. H.; Syamsudin, R. A., et al. Developing Model of Halal Food Purchase Intention Among Indonesian Non-Muslim Consumers: An Explanatory Sequential Mixed Methods Research. Syst. Rev. Pharm. 2020, 11, 396–407. DOI: 10.31838/SRP.2020.10.63.
- Vanany, I.; Soon, J. M.; Maryani, A.; Wibawa, B. M. Determinants of Halal-Food Consumption in Indonesia. J. Islam. Mark. 2020, 11(2), 507–521. DOI: 10.1108/JIMA-09-2018-0177/FULL/XML.
- Alikord, M.; Keramat, J.; Kadivar, M.; Momtaz, H.; N Eshtiaghi, M.; Homayouni-Rad, A. Multiplex-PCR as a Rapid and Sensitive Method for Identification of Meat Species in Halal-Meat Products. Recent Pat Food Nutr. Agric. 2017, 8(3). DOI: 10.2174/2212798409666170113151213.
- Amaral, J. S.; Santos, G.; Oliveira, M. B. P. P.; Mafra, I. Quantitative Detection of Pork Meat by EvaGreen Real-Time PCR to Assess the Authenticity of Processed Meat Products. Food. Cont. 2017, 72, 53–61. DOI: 10.1016/J.FOODCONT.2016.07.029.
- Denyingyhot, A.; Srinulgray, T.; Mahamad, P.; Ruangprach, A.; Sa-I, S.; Saerae, T.; Vesaratchavest, M.; Dahlan, W.; Keeratipibul, S. Modern On-Site Tool for Monitoring Contamination of Halal Meat with Products from Five Non-Halal Animals Using Multiplex Polymerase Chain Reaction Coupled with DNA Strip. Food. Cont. 2022, 132, 108540. DOI: 10.1016/J.FOODCONT.2021.108540.
- Windarsih, A.; Suratno; Warmiko, H. D.; Indrianingsih, A. W.; Rohman, A.; Ulumuddin, Y. I. Untargeted Metabolomics and Proteomics Approach Using Liquid Chromatography-Orbitrap High Resolution Mass Spectrometry to Detect Pork Adulteration in Pangasius Hypopthalmus Meat. Food Chem. 2022, 386, 132856. DOI: 10.1016/J.FOODCHEM.2022.132856.
- Windarsih, A.; Rohman, A.; Riswanto, F. D. O.; Dachriyanus; Yuliana, N. D.; Bakar, N. K. A. The Metabolomics Approaches Based on LC-MS/MS for Analysis of Non-Halal Meats in Food Products: A Review. Agric. 2022, 12(7), 984. DOI: 10.3390/AGRICULTURE12070984.
- Crestani, E.; Harb, H.; Charbonnier, L. M.; Leirer, J.; Motsinger-Reif, A.; Rachid, R.; Phipatanakul, W.; Kaddurah-Daouk, R.; Chatila, T. A. Untargeted Metabolomic Profiling Identifies Disease-Specific Signatures in Food Allergy and Asthma. J. Allergy Clin. Immunol. 2020, 145, 897–906. DOI: 10.1016/J.JACI.2019.10.014.
- Erban, A.; Fehrle, I.; Martinez-Seidel, F.; Brigante, F.; Más, A. L.; Baroni, V.; Wunderlin, D.; Kopka, J. Discovery of Food Identity Markers by Metabolomics and Machine Learning Technology. Sci. Rep. 2019, 9(1), 1–19. DOI: 10.1038/s41598-019-46113-y.
- Yang, Y.; Pan, D.; Sun, Y.; Wang, Y.; Xu, F.; Cao, J. 1H NMR-Based Metabolomics Profiling and Taste of Stewed Pork-Hock in Soy Sauce. Food. Res. Int. 2019, 121, 658–665. DOI: 10.1016/J.FOODRES.2018.12.035.
- Junior, M. A. W.; dos Santos, C. L.; Lôbo, I. P.; Junqueira, R. S.; Lima, L. P.; Farias, T. J.; dos Santos, J. L.; da Silva, A. M. Fatty Acid Composition in Muscles from Lambs Fed Diets Containing Agroindustrial Co-Products. Rev. Bras. Zootec. 2018, 47, e20170333. DOI: 10.1590/RBZ4720170333.
- Apata, E.; Omojola, A.; Eniolorunda, O.; Apata, O.; Okubanjo, A. Effects of Breeds and Spices on Water Holding Capacity and Consumers Acceptability of Goat Meat (Chevon). Niger. J. Anim. Sci. 2016, 18, 275–282. DOI: 10.4314/TJAS.V18I1.
- Lorenzo, J. M.; Pateiro, M. Influence of Type of Muscles on Nutritional Value of Foal Meat. Meat. Sci. 2013, 93, 630–638. DOI: 10.1016/J.MEATSCI.2012.11.007.
- Harlina, P. W.; Maritha, V.; Musfiroh, I.; Huda, S.; Sukri, N.; Muchtaridi, M. Possibilities of Liquid Chromatography Mass Spectrometry (LC-MS)-Based Metabolomics and Lipidomics in the Authentication of Meat Products: A Mini Review. Food. Sci. Anim. Resour. 2022, 42, 744–761. DOI: 10.5851/KOSFA.2022.E37.
- Sabir, A.; Rafi, M.; Darusman, L. K. Discrimination of Red and White Rice Bran from Indonesia Using HPLC Fingerprint Analysis Combined with Chemometrics. Food. Chem. 2017, 221, 1717–1722. DOI: 10.1016/J.FOODCHEM.2016.10.114.
- Castejón, D.; García-Segura, J. M.; Escudero, R.; Herrera, A.; Cambero, M. I. Metabolomics of Meat Exudate: Its Potential to Evaluate Beef Meat Conservation and Aging. Anal. Chimica. Acta. 2015, 901, 1–11. DOI: 10.1016/J.ACA.2015.08.032.
- Maritha, V.; Harlina, P. W.; Musfiroh, I.; Gazzali, A. M.; Muchtaridi, M. The Application of Chemometrics in Metabolomic and Lipidomic Analysis Data Presentation for Halal Authentication of Meat Products. Mol. 2022, 27(21), 7571. DOI: 10.3390/MOLECULES27217571.
- Man, K. Y.; Chan, C. O.; Tang, H. H.; Dong, N. P.; Capozzi, F.; Wong, K. H.; Kwok, K. W. H.; Chan, H. M.; Mok, D. K. W. Mass Spectrometry-Based Untargeted Metabolomics Approach for Differentiation of Beef of Different Geographic Origins. Food. Chem. 2021, 338, 127847. DOI: 10.1016/J.FOODCHEM.2020.127847.
- Zhou, Z.; Shen, X.; Tu, J.; Zhu, Z. J. Large-Scale Prediction of Collision Cross-Section Values for Metabolites in Ion Mobility-Mass Spectrometry. Anal. Chem. 2016, 88(22), 11084–11091. DOI: 10.1021/acs.analchem.6b03091.
- Xing, S.; Yu, H.; Liu, M.; Jia, Q.; Sun, Z.; Fang, M.; Huan, T. Recognizing Contamination Fragment Ions in Liquid Chromatography–Tandem Mass Spectrometry Data. J. Am. Soc. Mass Spectrom. 2021, 32(9), 2296–2305. DOI: 10.1021/jasms.0c00478.
- Soltow, Q. A.; Strobel, F. H.; Mansfield, K. G.; Wachtman, L.; Park, Y.; Jones, D. P. High-Performance Metabolic Profiling with Dual Chromatography-Fourier-Transform Mass Spectrometry (DC-FTMS) for Study of the Exposome. Metabolomics. 2013, 9(S1), 132–143. DOI: 10.1007/s11306-011-0332-1.
- Kim, H. C.; Ko, Y. J.; Kim, M.; Choe, J.; Yong, H. I.; Jo, C. Optimization of 1D 1H Quantitative NMR (Nuclear Magnetic Resonance) Conditions for Polar Metabolites in Meat. Food. Sci. Anim. Resour. 2019, 39(1), 1. DOI: 10.5851/KOSFA.2018.E54.
- Jang, C.; Hui, S.; Zeng, X.; Cowan, A. J.; Wang, L.; Chen, L.; Morscher, R. J.; Reyes, J.; Frezza, C.; Hwang, H. Y., et al. Metabolite Exchange Between Mammalian Organs Quantified in Pigs. Cell. Metab. 2019, 30(3), 594–606.e3.
- Abraham, A.; Dillwith, J. W.; Mafi, G. G.; VanOverbeke, D. L.; Ramanathan, R. Metabolite Profile Differences Between Beef Longissimus and Psoas Muscles During Display. Meat. Musc. Biol. 2017, 1(1). DOI: 10.22175/mmb2016.12.0007.
- Marzuki, S. Z. S.; Hall, C. M.; Ballantine, P. W. Measurement of Restaurant Manager Expectations Toward Halal Certification Using Factor and Cluster Analysis. Procedia - Soc. Behav. Sci. 2014, 121, 291–303. DOI: 10.1016/J.SBSPRO.2014.01.1130.
- Rohman, A.; Fadzillah, N. A. Application of Spectroscopic and Chromatographic Methods for the Analysis of Non-Halal Meats in Food Products. Multifact. Protoc. Biotechnol. 2021, 2, 75–92. DOI: 10.1007/978-3-030-75579-9_5/COVER.
- Dashti, A.; Müller-Maatsch, J.; Weesepoel, Y.; Parastar, H.; Kobarfard, F.; Daraei, B.; Aliabadi, M. H. S.; Yazdanpanah, H. The Feasibility of Two Handheld Spectrometers for Meat Speciation Combined with Chemometric Methods and Its Application for Halal Certification. Foods. 2022, 11(1), 71. DOI: 10.3390/foods11010071.
- Yuswan, M. H.; Aizat, W. M.; Desa, M. N. M.; Hashim, A. M.; Rahim, N. A.; Mustafa, S.; Mohamed, R.; Lamasudin, D. U. Improved Gel-Enhanced Liquid Chromatography-Mass Spectrometry by Chemometrics for Halal Proteomics. Chemom. Intell. Lab. Syst. 2019, 192, 103825. DOI: 10.1016/J.CHEMOLAB.2019.103825.
- Lestari, D.; Rohman, A.; Syofyan, S.; Yuliana, N. D.; Abu Bakar, N. K. B.; Hamidi, D. Analysis of Beef Meatballs with Rat Meat Adulteration Using Fourier Transform Infrared (FTIR) Spectroscopy in Combination with Chemometrics. Int. J. Food Prop. 2022, 25(1), 1446–1457. DOI: 10.1080/10942912.2022.2083637.
- Tazi, I.; Isnaini, N. L.; Mutmainnah, M.; Ainur, A. Principal Component Analysis (PCA) Method for Classification of Beef and Pork Aroma Based on Electronic Nose. Indones. J. Halal Res. 2019, 1(1), 5–8. DOI: 10.15575/IJHAR.V1I1.4155.
- Sarno, R.; Triyana, K.; Sabilla, S. I.; Wijaya, D. R.; Sunaryono, D.; Fatichah, C. Detecting Pork Adulteration in Beef for Halal Authentication Using an Optimized Electronic Nose System. IEEE. Access. 2020, 2020, 221700–221711. DOI: 10.1109/ACCESS.2020.3043394.
- Avian, C.; Leu, J. S.; Prakosa, S. W.; Faisal, M. An Improved Classification of Pork Adulteration in Beef Based on Electronic Nose Using Modified Deep Extreme Learning with Principal Component Analysis as Feature Learning. Food Anal. Methods. 2022, 15(11), 3020–3031. DOI: 10.1007/s12161-022-02361-9.
- Irnawati, I.; Windarsih, A.; Indrianingsih, A. W.; Apriyana, W.; Ratnawati, Y. A.; Hazairin Nadia, L. O. M.; Rohman, A. Rapid Detection of Tuna Fish Oil Adulteration Using FTIR-ATR Spectroscopy and Chemometrics for Halal Authentication. J. Appl. Pharm. Sci. 2023, 13, 231–239. DOI: 10.7324/japs.2023.120270.
- Ali, N. S. M.; Zabidi, A. R.; Manap, M. N. A.; Zahari, S. M. S. N. S.; Yahaya, N. Identification of Metabolite Profile in Halal and Non-Halal Broiler Chickens Using Fourier-Transform Infrared Spectroscopy (Ftir) and Ultra High Performance Liquid Chromatography- Time of Flight- Mass Spectrometry (UHPLC-TOF-MS). Malaysian Appl. Biol. 2020, 49(3), 87–93. DOI: 10.55230/MABJOURNAL.V49I3.1548.
- Maritha, V.; Kusumawati, D.; SantosoWahito Nugroho, H. 107-114 Section A-Research Paper Possibilities Volatilomics Approach Combination Chemometrics for Halal Authentication in Pharmaceutical Products Eur. Chem. Bull 107–114, doi:10.31838/ecb/2023.12.s3.014.
- Rohman, A.; Windarsih, A. The Application of Molecular Spectroscopy in Combination with Chemometrics for Halal Authentication Analysis: A Review. Int. J. Mol. Sci. 2020, 21(14), 5155. DOI: 10.3390/IJMS21145155.
- Saputra, I.; Jaswir, I.; Akmeliawati, R. Identification of Pig Adulterant in Mixture of Fat Samples and Selected Foods Based on FTIR-PCA Wavelength Biomarker Profile. Int. J. Advan. Sci. Eng. & Info. Tech. 2018, 8(6), 8. DOI: 10.18517/ijaseit.8.6.7689.
- Rahayu, W. S.; Martono, S.; Sudjadi; Rohman, A.; Sudjadi, S. The Potential Use of Infrared Spectroscopy and Multivariate Analysis for Differentiation of Beef Meatball from Dog Meat for Halal Authentication Analysis. J. Adv. Vet. Anim. Res. 2018, 5(3), 307–314. DOI: 10.5455/JAVAR.2018.E281.
- Trivedi, D. K.; Hollywood, K. A.; Rattray, N. J. W.; Ward, H.; Trivedi, D. K.; Greenwood, J.; Ellis, D. I.; Goodacre, R. Meat, the Metabolites: An Integrated Metabolite Profiling and Lipidomics Approach for the Detection of the Adulteration of Beef with Pork. Analyst. 2016, 141(7), 2155–2164. DOI: 10.1039/C6AN00108D.
- Jia, W.; Fan, Z.; Shi, Q.; Zhang, R.; Wang, X.; Shi, L. LC-MS-Based Metabolomics Reveals Metabolite Dynamic Changes During Irradiation of Goat Meat. Food. Res. Int. 2021, 150, 110721. DOI: 10.1016/J.FOODRES.2021.110721.
- Li, H.; Geng, W.; Haruna, S. A.; Zhou, C.; Wang, Y.; Ouyang, Q.; Chen, Q. Identification of Characteristic Volatiles and Metabolomic Pathway During Pork Storage Using HS-SPME-GC/MS Coupled with Multivariate Analysis. Food. Chem. 2022, 373, 131431. DOI: 10.1016/J.FOODCHEM.2021.131431.
- Windarsih, A.; Riswanto, F. D. O.; Bakar, N. K. A.; Yuliana, N. D.; Dachriyanus; Rohman, A. Detection of Pork in Beef Meatballs Using LC-HRMS Based Untargeted Metabolomics and Chemometrics for Halal Authentication. Molecules. 2022, 27(23), 8325. DOI: 10.3390/MOLECULES27238325/S1.