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Research Article

Exploring Untargeted metabolomics for halal authentication of Triceps brachii, Longissimus Dorsi, and Biceps femoris of meat muscles

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Pages 3148-3159 | Received 11 Sep 2023, Accepted 15 Oct 2023, Published online: 01 Nov 2023

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.