Use of chromatographic-based techniques and chemometrics for halal authentication of food products: A review

Figures & data

Figure 1. Three different scenarios (a, b, and c) of chemometrics applications employing the chromatograms as variable for obtaining the analytical purposes (classification of halal and non-halal products as well as prediction the levels of non-halal components in the products). Adapted from Ref.^[24].

Table 1. The application of liquid chromatography (HPLC and LC-MS/MS) for analysis of halal components in the food and pharmaceutical products

Methods	Issues	Results	References
HPLC-UV detection	Identification of pork in meatball products	HPLC-UV in combination with PCA could classify meatballs containing pork and beef in the products using variable of hydrolysis of Triacylglycerols (TAGs). However, the authors did not mention which TAG markers contribute to this classification	^[Citation30]
HPLC-Fluorescence detector	Identification of pork through amino acid composition	HPLC using fluorescence detector has been successfully applied for differentiation of pork and other animal meats based on analysis of derivatized amino acids with ortho-phthalaldehyde. The amino acid VAL can be identified as marker for differentiating pork from the other meats studied (beef, chicken mutton, and chevon)	^[Citation31]
HPLC-Fluorescence detector	Detection of pig collagen using D,L-amino acids	Pre column derivatization using R(-)-4-(3-isothiocyanato pyrrolidin-1-yl)-7-(N,N-dimethylamino sulfonyl)-2,1,3-benzoxadiazole [R(-)-DBD-PyNCS] could be used to determine D,L-amino acids in pork collagen. Three amino acids of D-Asp, D-Pro, and D-Hyp were first detected in pork collagen samples	^[Citation32]
LC-MS/MS with multiple reaction monitoring (MRM)	Detection of Horse and Pork in Halal Beef	Biomarker peptides were successfully identified by a shotgun proteomic approach using tryptic digests of protein extracts. Pork was identified by peptide markers: TLAFLFAER (from myosin-4) and SALAHAVQSSR (from myosin-1 and myosin-4). The detection limit is 0.55% horse or pork in a beef matrix	^[Citation33]
HPLC−MS/MS with MRM	Detection of pork in highly processed food by analysis of specific tryptic marker peptides	HPLC-MS/MS using MRM has been successfully applied for analysis of pork in some processed food products (cooking, frying and baking) based on peptide markers which are specific for pork. The peptide markers of pork identified based on MRM experiment were: marker 1 (YDIINLR) markers 2 (TLAFLFAER) and 3 (SALAHAVQSSR)	^[Citation34]
LC-MS/MS	Differentiation of porcine gelatine and bovine gelatine	LC-MS/MS in combination with exploratory data analysis of PCA could discriminate porcine and bovine gelatines. Based on loading plot PCA, peptides appearing in retention time (tR) 32 min could be identified as peptide markers	^[Citation35]
Nano UPLC-Q-TOF-MS	Differentiation of porcine and bovine gelatin in food products	Marker peptide of bovine and porcine gelatin could be detected using nano UPLC-Q-TOF-MS based data dependent technique in yogurt, cheese, and ice cream. The method could be used to detect bovine and porcine gelatin in the mixtures	^[Citation36]
Nano UPLC-Q-TOF-MS	Differentiation of porcine and bovine gelatin in food products	Marker peptide of bovine and porcine gelatin could be detected using nano UPLC-Q-TOF-MS based data dependent technique in yogurt, cheese, and ice cream. The method could be used to detect bovine and porcine gelatin in the mixtures	^[Citation36]
LC-MS QTRAP	Gelatin speciation (bovine, porcine, and fish)	LC-MS in combination with PCA could differentiate bovine, porcine, and fish gelatin. PLS-DA could be used for classification of pure gelatin and adulterated gelatin (fish and bovine) with porcine gelatin using several concentration levels of porcine gelatin	^[Citation37]
LC-MS/MS	Discrimination of raw beef, pork, poultry and their mixtures	Protein of troponin I (TnI), enolase 3, L-lactate dehydrogenase (LDH), triose-phosphate isomerase (TPI), Tropomyosin 1, and carbonic anhydrase 3 could be used as potential markers to distinguish mammals and poultry	^[Citation38]
LC-Q-TOF-MS	Differentiation between dead-on arrival and normally slaughtered of poultry meat	LC-Q-TOF-MS could be used to differentiate between normally slaughtered and dead-on arrival poultry meat based on metabolic profiles analyzed using multivariate analysis. Using METLIN and analysis of chemical standards, metabolite of sphingosine was found to be potential marker for dead-on arrival poultry meat	^[Citation39]
UPLC-TOF-MS	Metabolite’s differentiation of broiler chicken slaughtered using different techniques	UPLC-TOF-MS could be used to distinguish between halal slaughtering method and non-halal slaughtering method of broiler chicken based on their metabolite profiles. Non-halal slaughtered method demonstrated high amino acid and high glucose breakdown	^[Citation40]
LC-HRMS	Analysis of pork meat in meat mixtures using PRM	Five peptides of myosin were screened and used for PRM analysis using LC-Orbitrap HRMS. Peptide of KLETDISQIQGEMEDIVQEAR was found to be the most sensitive peptide with LOD value of 0.5% in meat mixtures	^[Citation41]
UPLC-MS	Detection of pork adulteration in beef using metabolomics approach	PLS-DA using metabolomics data obtained from untargeted measurement could classify pure and adulterated beef samples with pork. There was a significant difference in the metabolism of inositol, glutathione, and sphingolipid between beef and pork	^[Citation42]
LC-MS/MS	Detection of pork adulteration in meat samples using carbonic anhydrase 3 as a marker	Three peptides from carbonic anhydrase 3 were found as marker of pork (EPITVSSDQMAK, GGPLTAAYR, HDPSLLPWTASYDPGSAK). Quantification analysis could be performed using those three peptides with perfect quantitative ability and provided good correlation and recovery results	^[Citation43]

Table 2. The application of gas chromatography (GC-FID and GC-MS) for analysis of halal components in the food and pharmaceutical products

Methods	Issues	Results	References
GC-FID for analysis of alcohol	Determination of ethanol contents in vinegar	The maximum contents of ethanol in vinegar is 1.0%. GC-FID could determine the levels of ethanol (alcohol) in the marketed vinegar samples. The detection level of ethanol was about 0.4 mg%	^[Citation51]
GC-FID for analysis of ethanol in foods	Determination of ethanol in different processed foods and beverages	Extraction technique using aqueous extraction assisted magnetic-stirring could be used to extract ethanol from different foods and beverages. GC-FID successfully used to determine ethanol with good validity. The validated method was successfully used to determine ethanol in 108 food and beverage products	^[Citation52]
GC-MS for analysis of alcohol	Determination of alcohol in fermented black tape ketan using GC-MS	GC-MS could be used for quantitative analysis of alcohol content in fermented black tape ketan with good recovery (89%). The alcohol concentrations determined at 3, 10, 17, 24, and 31 days were 4.295, 4.23, 5.005, 4.747, and 5.344% v/v, respectively	^[Citation53]
GC-FID for analysis of lard	Differentiation of lard from other edible fats using GC-FID and chemometrics	Lard contains high amount of C18: 2cis and low amount of C16:0. Chemometrics of PCA and K-mean cluster analysis could differentiate lard adulteration on chicken fat and beef tallow at low concentrations (0.5%-10%)	^[Citation54]
GC-MS for analysis of pork	Analysis of fatty acids a fatty acid methyl esters of pork (non-halal meats) in sausages compared with beef sausages (halal meat)	The dominant fatty acids in pork sausage are palmitic, myristic, oleic acid, and lauric acids. While fatty acids dominating in beef sausage are palmitic, oleic, stearic and myristic acids. The chemometrics of PCA could classify sausages according to meat sources (beef and pork)	^[Citation55]
GC-MS for analysis of rat meat	Analysis of rat meat (non-halal meat) and its classification with other meats using chemometrics of PCA	Six fatty acids, i.e. myristic, palmitoleic, palmitic, linoleic, oleic and stearic acids combined with PCA could classify rat meat and other meats	^[Citation56]
Headspace GC-MS for analysis of pork	Differentiation of pork (non-halal meat) and pork sausages from beef, mutton and chicken meats	The samples were introduced into GC instrument using headspace, and volatile compounds present in the evaluated samples were separated using GC and detected by MS. The chemometrics of PCA provided good separation between pork-based sausages and halal meat-based sausages	^[Citation57]
GC-MS for analysis of lard	Analysis of lard (non-halal fat derived from adipose tissue of pig) in chocolate products	The fatty acid of 11,14-eicosadienoic acid is used as fatty acid marker for identification of lard	^[Citation58]
GC-MS-SPME for analysis of wild boar	Volatilomics analysis of non-halal (wild boar) meat ball using GC-MS-SPME and chemometrics	PLS-DA could be used to differentiate volatile compounds of halal meatball and non-halal meatball. Compounds of β-cymene, 3-methyl-butanal, and 2-pentanol were found to be potential markers for chicken meatball. Compounds of 5-ethyl-m-xylene, benzaldehyde, and 3-ethyl-2-methyl-1,3-hexadiene were associated to the potential markers of beef meatball. Compounds of pentanal, 2,6-dimethylcyclohexanone, 1-undecanol, cyclobutanol, 2,4,5-trimethyl-thiazole, and 5-ethyl-3-(3-methyl-5-phenyl pyrazol-1-yl)-1,2,4-triazol-4-amine could be used as potential markers as wild boar meatball	^[Citation59]
HS-SPME-GC-MS for analysis of minced beef and pork meat	Volatilomics analysis using HS-SPME-GC-MS combined with multivariate analysis to differentiate minced beef and pork meat	GC-MS based on volatilomics analysis and chemometrics of PCA and PLS-DA could be used to differentiate minced beef and pork meat. Heptanal, octanal, butanol, pentanol, hexanol, 1-penten-3-ol, 2-octen-1-ol, 3-hydroxy-2-butanone were associated to the potential markers of beef whereas pentanal, hexanal, decanal, nonanal, benzaldehyde, trans-2-hexenal, trans-2-heptenal could be used as potential volatile compound markers of pork meat	^[Citation60]
GC-MS for analysis of pork	Detection of pork in beef meatball using GC-MS and chemometrics	PCA using fatty acid compositions of pure beef meatball and adulterated beef meatball using pork as the variables successfully differentiate pure and adulterated beef meatball. The ratio of SFA:MUFA of pork meatball was 1.0	^[Citation61]
GC-MS for analysis of house rat	Detection of rat house in beef meatball by analysis of fat using G-CMS	The fatty acids composition of house rats were myristate (0.19 ± 0.03)%, palmitoleate (2.40 ± 0.29)%, methyl palmitate (27.65 ± 0.32)%, oleate (45.81 ± 3.25)%, and stearate (4.65 ± 0.28)%. Analysis using PCA could differentiate beef meatball and beef meatball containing rat house meat. Further analysis using PCA demonstrated that fatty acids of house rats have high similarity to chicken fatty acids	^[Citation62]
GC-MS for analysis of lard	Detection of lard in wheat biscuits using GC-MS and chemometrics	PCA using fatty acids composition could differentiate lard, wheat biscuits, and adulterated wheat biscuits with lard. PLS-DA could be used to find potential marker for differentiation. Fatty acid of C18:3n6 is suggested as potential marker to distinguish pure wheat biscuits and adulterated wheat biscuits with lard	^[Citation63]
GC-MS for analysis of dog fat	Detection of dog fat from other animal fats using GC-MS and chemometrics	Nine types of fatty acids in dog fat were found such as lauric, myristate, pentadecanoate, palmitoleate, palmitate, margarate, oleat, stearic, and arachidonic. Analysis PCA showed that dog fat is close to lard	^[Citation64]
GC-MS for analysis of rat fat	Detection of Sprague Dawley rat fat in meatball using GC-MS and chemometrics	PCA could differentiate meatball and adulterated meatball with Sprague Dawley rat meats. Further analysis revealed that the Sprague Dawley rat fat is close to beef fat	^[Citation65]

Bosque-Sendra, J. M.; Cuadros-Rodríguez, L.; Ruiz-Samblás, C.; de la Mata, A. P. Combining Chromatography and Chemometrics for the Characterization and Authentication of Fats and Oils from Triacylglycerol Compositional data-A Review. Anal. Chim. Acta. 2012, 724, 1–11. DOI: 10.1016/j.aca.2012.02.041.

 PubMed Web of Science ®Google Scholar

Ahda, M.; Guntarti, A.; Kusbandari, A.; Guntarti, A.; Kusbandari, A.; Kusbandari, A. Application of high-pressure Liquid Chromatography for Analysis of Lard in the Meatball Product Combined with Principal Component Analysis. Asian J. Pharm. Clin. Res. 2016, 9(6), 120–123. DOI: 10.22159/ajpcr.2016.v9i6.13831.

 Google Scholar

Jorfi, R; Shuhaimi, M; Che Man, Y. B; Mat Hashim, D.; Sazili, A. Q; Ebrahimi, M. Amino Acid Composition Analysis of Beef, Mutton, Chevon, Chicken and Pork by HPLC Method. 57th International Congress of Meat Science and Technology. 2011;1–4.

 Google Scholar

Huang, Y.; Zhang, W.; Shi, Q.; Toyo’Oka, T.; Min, J. Z. Determination of d,l-Amino Acids in Collagen from Pig and Cod Skins by UPLC Using Pre-column Fluorescent Derivatization. Food Anal. Methods. 2018, 11(11), 3130–3137. DOI: 10.1007/s12161-018-1288-9.

 Web of Science ®Google Scholar

Von Bargen, C.; Dojahn, J.; Waidelich, D.; Humpf, H. U.; Brockmeyer, J. New Sensitive high-performance Liquid chromatography-tandem Mass Spectrometry Method for the Detection of Horse and Pork in Halal Beef. J. Agric. Food Chem. 2013, 61(49), 11986–11994. DOI: 10.1021/jf404121b.

 PubMed Web of Science ®Google Scholar

Von Bargen, C.; Brockmeyer, J.; Humpf, H. U. Meat Authentication: A New HPLC-MS/MS Based Method for the Fast and Sensitive Detection of Horse and Pork in Highly Processed Food. J. Agric. Food Chem. 2014, 62(39), 9428–9435. DOI: 10.1021/jf503468t.

 PubMed Web of Science ®Google Scholar

Salamah, N.; Erwanto, Y.; Martono, S.; Maulana, I.; Rohman, A. Differentiation of Bovine and Porcine Gelatines Using LC-MS/MS and Chemometrics. Int. J. Appl. Pharm. 2019, 11(4), 2–6. DOI: 10.22159/ijap.2019v11i4.30248.

 Google Scholar

Yilmaz, M. T.; Kesmen, Z.; Baykal, B.; Sagdic, O.; Kacar, O.; Yetim, H.; Yetim, H., and Baykal, A. T. A Novel Method to Differentiate Bovine and Porcine Gelatins in Food Products: NanoUPLC-ESI-Q-TOF-MSE Based Data Independent Acquisition Technique to Detect Marker Peptides in Gelatin. Food Chem. 2013, 141(3), 2450–2458. DOI: 10.1016/j.foodchem.2013.05.096.

 PubMed Web of Science ®Google Scholar

Jannat, B.; Ghorbani, K.; Shafieyan, H.; Kouchaki, S.; Behfar, A.; Sadeghi, N.; Beyramysoltan, S.; Rabbani, F.; Dashtifard, S.; Sadeghi, M., et al. Gelatin Speciation Using real-time PCR and Analysis of Mass spectrometry-based Proteomics Datasets. Food Control. 2018, 87, 79–87. DOI: 10.1016/j.foodcont.2017.12.006.

 Web of Science ®Google Scholar

Kim, G. D.; Seo, J. K.; Yum, H. W.; Jeong, J. Y.; Yang, H. S. Protein Markers for Discrimination of Meat Species in Raw Beef, Pork and Poultry and Their Mixtures. Food Chem. 2017, 217, 163–170. DOI: 10.1016/j.foodchem.2016.08.100.

 PubMed Web of Science ®Google Scholar

Sidwick, K. L.; Johnson, A. E.; Adam, C. D.; Pereira, L.; Thompson, D. F. Use of Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry and Metabonomic Profiling to Differentiate between Normally Slaughtered and Dead on Arrival Poultry Meat. Anal. Chem. 2017, 89(22), 12131–12136. DOI: 10.1021/acs.analchem.7b02749.

 PubMed Web of Science ®Google Scholar

Ali, N. S. M.; Zabidi, A. R.; Manap, M. N. A.; Smsns, Z.; Yahaya, N. Effect of Different Slaughtering Methods on Metabolites of Broiler Chickens Using Ultra high-performance Liquid chromatography-time of flight-mass Spectrometry (UHPLC-TOF-MS). Food Res. 2020, 4(S1), 33–138. DOI: 10.26656/fr.2017.4(s1).s06.

 Google Scholar

Pan XD, Chen J, Chen Q, Huang BF, Han JL. Authentication of Pork in Meat Mixtures Using PRM Mass Spectrometry of Myosin Peptides. RSC Adv. 2018;8:11157–11162.

 PubMed Web of Science ®Google Scholar

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.

 PubMed Web of Science ®Google Scholar

Li, Y.; Zhang, Y.; Kang, C.; Zhao, W.; Li, S.; Wang, S. Assessment of Carbonic Anhydrase 3 as a Marker for Meat Authenticity and Performance of LC-MS/MS for Pork Content. Food Chem. 2021, 342, 128240. DOI: 10.1016/j.foodchem.2020.128240.

 PubMed Web of Science ®Google Scholar

Pulungan, I. N. R.; Kartosentono, S.; Prawita, A. Validation Gas Chromatography-Fid Method for Analysis of Ethanol Content in Vinegar. J Halal Prod Res. 2018, 1(2), 22. DOI:10.20473/jhpr.vol.1-issue.2.22-31.

 Google Scholar

Mansur, A. R.; Oh, J.; Lee, H. S.; Oh, S. Y. Determination of Ethanol in Foods and Beverages by Magnetic stirring-assisted Aqueous Extraction Coupled with GC-FID: A Validated Method for Halal Verification. Food Chem. 2022, 366, 130526. DOI: 10.1016/j.foodchem.2021.130526.

 PubMed Web of Science ®Google Scholar

Muchtaridi, M.; Musfiroh, I.; Hambali, N. N.; Indrayati, W. Determination of Alcohol Contents of Fermentated Black Tape Ketan Based on Different Fermentation Time Using Specific Gravity, Refractive Index and GC-MS Methods. J. Microbiol. Biotechnol. Food Sci. 2012, 2(3), 933–946.

 Google Scholar

Dahimi, O.; Hassan, M. S.; Rahim, A. A.; Abdulkarim, S. M.; A, S. M. Differentiation of Lard from Other Edible Fats by Gas chromatography-flame Ionisation Detector (GC-FID) and Chemometrics. J. Food Pharm. Sci. 2014, 2, 27–31.

 Google Scholar

Guntarti, A.; Ahda, M.; Kusbandari, A. Determining Fatty Acids and Halal Authentication of Sausage. Food Res. 2020, 4(2), 495–499. DOI: 10.26656/fr.2017.4(2).261.

 Google Scholar

Guntarti, A.; Gandjar, I. G.; Jannah, N. M. Authentication of Wistar Rat Fats with Gas Chromatography Mass Spectrometry Combined by Chemometrics. Potravin Slovak J Food Sci. 2020, 14, 52–57. DOI: 10.5219/1229.

 Google Scholar

Nurjuliana, M.; Che Man, Y. B.; Mat Hashim, D.; Mohamed, A. K. S. Rapid Identification of Pork for Halal Authentication Using the Electronic Nose and Gas Chromatography Mass Spectrometer with Headspace Analyzer. Meat Sci. 2011, 88(4), 638–644. DOI: 10.1016/j.meatsci.2011.02.022.

 PubMed Web of Science ®Google Scholar

Rahayu, W. S.; Sundhani, E.; Saputri, S. D. The Use of Fourier Transform Infrared Spectroscopy (FTIR) and Gas Chromatography Mass Spectroscopy (GCMS) for Halal Authentication in Imported Chocolate with Various Variants. J. Food Pharm. Sci. 2014, 3, 6–11.

 Google Scholar

Pranata, A. W.; Yuliana, N. D.; Amalia, L.; Darmawan, N. Volatilomics for Halal and non-halal Meatball Authentication Using solid-phase microextraction–gas chromatography–mass Spectrometry. Arab. J. Chem. 2021, 14(5), 103146. DOI: 10.1016/j.arabjc.2021.103146.

 Web of Science ®Google Scholar

Pavlidis, D. E.; Mallouchos, A.; Ercolini, D.; Panagou, E. Z.; Nychas, G. J. E. A Volatilomics Approach for off-line Discrimination of Minced Beef and Pork Meat and Their Admixture Using HS-SPME GC/MS in Tandem with Multivariate Data Analysis. Meat Sci. 2019, 151, 43–53. DOI: 10.1016/j.meatsci.2019.01.003.

 PubMed Web of Science ®Google Scholar

Ahda, M.; Guntarti, A.; Kusbandari, A.; Melianto, Y. Halal Food Analysis Using GC-MS Combined with Principal Component Analysis (Pca) Based on Saturated and Unsaturated Fatty Acid Composition. Songklanakarin J. Sci. Technol. 2021, 43(2), 352–355.

 Google Scholar

Salamah, N.; Guntarti, A.; Ayu Lestari, P.; Gholib Gandjar, I. Fat Analysis of House Rat (Rattus Tanezumi) in Meatball Using Gas chromatography-mass Spectrometry (GC-MS) Combined with Principal Component Analysis. 2022, Indones J Pharm. DOI: 10.22146/ijp.1781.

 Google Scholar

Azizan, N. I.; Mokhtar, N. F. K.; Arshad, S.; Sharin, S. N.; Mohamad, N.; Mustafa, S., and Hashim, A. M. Detection of Lard Adulteration in Wheat Biscuits Using Chemometrics-Assisted GCMS and Random Forest. Food Anal. Methods.2021, 14(11), 2276–2287. DOI: 10.1007/s12161-021-02046-9.

 Web of Science ®Google Scholar

Guntarti, A. Authentication of Dog Fat with Gas Chromatography-Mass Spectroscopy Combined with Chemometrics. Int. J. Chem. 2018, 10(4), 124 DOI: 10.5539/ijc.v10n4p124.

 Google Scholar

Guntarti, A.; Ningrum, K. P.; Gandjar, I. G.; Salamah, N. Authentication of Sprague Dawley Rats (Rattus Norvegicus) Fat with GC-MS (Gas Chromatography-Mass Spectrometry) Combined with Chemometrics. Int. J. Appl. Pharm. 2021, 13(2), 1–6. DOI: 10.22159/jap.2021v13i2.40130.

 Google Scholar