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http://dx.doi.org/10.5851/kosfa.2022.e29

Determination of Adulteration of Chicken Meat into Minced Beef Mixtures using Front Face Fluorescence Spectroscopy Coupled with Chemometric  

Saleem, Asima (National Institute of Food Science and Technology (NIFSAT), Faculty of Food, Nutrition and Home Sciences (FFNHS), University of Agriculture)
Sahar, Amna (National Institute of Food Science and Technology (NIFSAT), Faculty of Food, Nutrition and Home Sciences (FFNHS), University of Agriculture)
Pasha, Imran (National Institute of Food Science and Technology (NIFSAT), Faculty of Food, Nutrition and Home Sciences (FFNHS), University of Agriculture)
Shahid, Muhammad (Department of Biochemistry, Faculty of Sciences, University of Agriculture)
Publication Information
Food Science of Animal Resources / v.42, no.4, 2022 , pp. 672-688 More about this Journal
Abstract
The objective of this study was to explore the potential of front face fluorescence spectroscopy (FFFS) as rapid, non-destructive and inclusive technique along with multi-variate analysis for predicting meat adulteration. For this purpose (FFFS) was used to discriminate pure minced beef meat and adulterated minced beef meat containing (1%, 2%, 3%, 4%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%) of chicken meat as an adulterant in uncooked beef meat samples. Fixed excitation (290 nm, 322 nm, and 340 nm) and fixed emission (410 nm) wavelengths were used for performing analysis. Fluorescence spectra were acquired from pure and adulterated meat samples to differentiate pure and binary mixtures of meat samples. Principle component analysis, partial least square regression and hierarchical cluster analysis were used as chemometric tools to find out the information from spectral data. These chemometric tools predict adulteration in minced beef meat up to 10% chicken meat but are not good in distinguishing adulteration level from 1% to 5%. The results of this research provide baseline for future work for generating spectral libraries using larger datasets for on-line detection of meat authenticity by using fluorescence spectroscopy.
Keywords
front face fluorescence spectroscopy; adulteration; chemometric analysis; meat;
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1 Deniz E, Gunes Altuntas E, Ayhan B, Igci N, Ozel Demiralp D, Candogan K. 2018. Differentiation of beef mixtures adulterated with chicken or turkey meat using FTIR spectroscopy. J Food Process Preserv 42:e13767.   DOI
2 Sahar A, Boubellouta T, Lepetit J, Dufour E. 2009. Front-face fluorescence spectroscopy as a tool to classify seven bovine muscles according to their chemical and rheological characteristics. Meat Sci 83:672-677.   DOI
3 Dufour E, Frencia JP. 2001. Les spectres de fluorescence frontale: Une empreinte digitale de la viande. Viandes Prod Carnes 22:9-14.
4 Egelandsdal B, Dingstad G, Togersen G, Lundby F, Langsrud O. 2005. Autofluorescence quantifies collagen in sausage batters with a large variation in myoglobin content. Meat Sci 69:35-46.   DOI
5 Egelandsdal B, Wold JP, Sponnich A, Neegard S, Hildrum KI. 2002. On attempts to measure the tenderness of longissimus dorsi muscles using fluorescence emission spectra. Meat Sci 60:187-202.   DOI
6 Ha J, Kim S, Lee J, Lee S, Lee H, Choi Y, Oh H, Yoon Y. 2017. Identification of pork adulteration in processed meat products using the developed mitochondrial DNA-based primers. Korean J Food Sci Anim Resour 37:464-468.   DOI
7 Hoffmann B, Munch S, Schwagele F, Neususs C, Jira W. 2017. A sensitive HPLC-MS/MS screening method for the simultaneous detection of lupine, pea, and soy proteins in meat products. Food Control 71:200-209.   DOI
8 Jira W, Munch S. 2019. A sensitive HPLC-MS/MS screening method for the simultaneous detection of barley, maize, oats, rice, rye and wheat proteins in meat products. Food Chem 275:214-223.   DOI
9 Rahman UU, Sahar A, Pasha I, Rahman SU, Sohaib M, Ishaq A, Chughtai MFJ, Zafar H. 2017. Augmenting quality and microbial safety of broiler meat at refrigeration storage by applying chemical interventions. J Food Process Preserv 41:e13030.   DOI
10 Alexandrakis D, Downey G, Scannell AGM. 2008. Detection and identification of bacteria in an isolated system with nearinfrared spectroscopy and multivariate analysis. J Agric Food Chem 56:3431-3437.   DOI
11 Danezis GP, Tsagkaris AS, Camin F, Brusic V, Georgiou CA. 2016. Food authentication: Techniques, trends & emerging approaches. TrAC Analyt Chem 85:123-132.   DOI
12 Premanandh J. 2013. Horse meat scandal-a wake-up call for regulatory authorities. Food Control 34:568-569.   DOI
13 Lopez MCG, Alegre MLM. 2009. Detection of adulterations: Addition of foreign proteins. In Handbook of processed meats and poultry analysis. Nollet LML, Toldra F (ed). CRC Press, Boca Raton, FL, USA. pp 571-600.
14 Troy DJ, Ojha KS, Kerry JP, Tiwari BK. 2016. Sustainable and consumer-friendly emerging technologies for application within the meat industry: An overview. Meat Sci 120:2-9.   DOI
15 Vasconcelos H, Saraiva C, de Almeida JMMM. 2014. Evaluation of the spoilage of raw chicken breast fillets using Fourier transform infrared spectroscopy in tandem with chemometrics. Food Bioprocess Technol 7:2330-2341.
16 Vigneau E, Qannari EM, Jaillais B, Mazerolles G, Bertrand D. 2006. Methodes predictives. In La spectroscopie infrarouge et ses applications analytiques. Bertrand D (ed). Tec & Doc, Paris, France. pp 347-397.
17 Xiong Z, Sun DW, Pu H, Gao W, Dai Q. 2017. Applications of emerging imaging techniques for meat quality and safety detection and evaluation: A review. Crit Rev Food Sci Nutr 57:755-768.   DOI
18 Yang L, Wu T, Liu Y, Zou J, Huang Y, Babu VS, Lin L. 2018. Rapid identification of pork adulterated in the beef and mutton by infrared spectroscopy. J Spectrosc 2018:2413874.
19 Spink J, Ortega DL, Chen C, Wu F. 2017. Food fraud prevention shifts the food risk focus to vulnerability. Trends Food Sci Technol 62:215-220.   DOI
20 Ait-Kaddour A, Thomas A, Mardon J, Jacquot S, Ferlay A, Gruffat D. 2016. Potential of fluorescence spectroscopy to predict fatty acid composition of beef. Meat Sci 113:124-131.   DOI
21 Ait-Kaddour A, Loudiyi M, Ferlay A, Gruffat D. 2018. Performance of fluorescence spectroscopy for beef meat authentication: Effect of excitation mode and discriminant algorithms. Meat Sci 137:58-66.   DOI
22 Karoui R, Dufour E, De Baerdemaeker J. 2006a. Common components and specific weights analysis: A tool for monitoring the molecular structure of semi-hard cheese throughout ripening. Anal Chim Acta 572:125-133.   DOI
23 Jolliffe IT, Cadima J. 2016. Principal component analysis: A review and recent developments. Philos Trans Royal Soc Math Phys Eng Sci 374:20150202.
24 Kartheek M, Smith AA, Muthu AK, Manavalan R. 2011. Determination of adulterants in food: A review. J Chem Pharm Res 3:629-636.
25 Rady A, Adedeji A. 2018. Assessing different processed meats for adulterants using visible-near-infrared spectroscopy. Meat Sci 136:59-67.   DOI
26 Abbas O, Zadravec M, Baeten V, Mikus T, Lesic T, Vulic A, Prpic J, Jemersic L, Pleadin J. 2018. Analytical methods used for the authentication of food of animal origin. Food Chem 246:6-17.   DOI
27 Ait-Kaddour A, Boubellouta T, Chevallier I. 2011. Development of a portable spectrofluorimeter for measuring the microbial spoilage of minced beef. Meat Sci 88:675-681.   DOI
28 Alamprese C, Amigo JM, Casiraghi E, Engelsen SB. 2016. Identification and quantification of turkey meat adulteration in fresh, frozen-thawed and cooked minced beef by FT-NIR spectroscopy and chemometrics. Meat Sci 121:175-181.   DOI
29 Boughattas F, Le Fur B, Karoui R. 2019. Identification and quantification of tuna species in canned tunas with sunflower medium by means of a technique based on front face fluorescence spectroscopy (FFFS). Food Control 101:17-23.   DOI
30 Karoui R, Hassoun A, Ethuin P. 2017. Front face fluorescence spectroscopy enables rapid differentiation of fresh and frozenthawed sea bass (Dicentrarchus labrax) fillets. J Food Eng 202:89-98.   DOI
31 Karoui R, Thomas E, Dufour E. 2006b. Utilisation of a rapid technique based on front-face fluorescence spectroscopy for differentiating between fresh and frozen-thawed fish fillets. Food Res Int 39:349-355.   DOI
32 Al-Kahtani HA, Ismail EA, Asif Ahmed M. 2017. Pork detection in binary meat mixtures and some commercial food products using conventional and real-time PCR techniques. Food Chem 219:54-60.   DOI
33 Hassoun A, Karoui R. 2015. Front-face fluorescence spectroscopy coupled with chemometric tools for monitoring fish freshness stored under different refrigerated conditions. Food Control 54:240-249.   DOI
34 Boyaci IH, Temiz HT, Uysal RS, Velioglu HM, Yadegari RJ, Rishkan MM. 2014. A novel method for discrimination of beef and horsemeat using Raman spectroscopy. Food Chem 148:37-41.   DOI
35 Bro R. 1996. Multiway calibration. Multilinear PLS. J Chemometr 10:47-61.   DOI
36 Cawthorn DM, Steinman HA, Hoffman LC. 2013. A high incidence of species substitution and mislabelling detected in meat products sold in South Africa. Food Control 32:440-449.   DOI
37 Dufour E, Riaublanc A. 1997. Potentiality of spectroscopic methods for the characterisation of dairy products. I. Front-face fluorescence study of raw, heated and homogenised milks. Le Lait 77:657-670.   DOI
38 Gatellier P, Sante-Lhoutellier V, Portanguen S, Kondjoyan A. 2009. Use of meat fluorescence emission as a marker of oxidation promoted by cooking. Meat Sci 83:651-656.   DOI
39 Hassoun A, Sahar A, Lakhal L, Ait-Kaddour A. 2019. Fluorescence spectroscopy as a rapid and non-destructive method for monitoring quality and authenticity of fish and meat products: Impact of different preservation conditions. LWT-Food Sci Technol 103:279-292.   DOI
40 Kamruzzaman M, Makino Y, Oshita S. 2015. Non-invasive analytical technology for the detection of contamination, adulteration, and authenticity of meat, poultry, and fish: A review. Anal Chim Acta 853:19-29.   DOI
41 Lawrie RA, Ledward DA. 2006. Lawrie's meat science. Woodhead Publishing, Sawston, UK.
42 Meza-Marquez OG, Gallardo-Velazquez T, Osorio-Revilla G. 2010. Application of mid-infrared spectroscopy with multivariate analysis and soft independent modeling of class analogies (SIMCA) for the detection of adulterants in minced beef. Meat Sci 86:511-519.   DOI
43 Nunes KM, Andrade MVO, Santos Filho AMP, Lasmar MC, Sena MM. 2016. Detection and characterisation of frauds in bovine meat in natura by non-meat ingredient additions using data fusion of chemical parameters and ATR-FTIR spectroscopy. Food Chem 205:14-22.   DOI
44 Sahar A, Boubellouta T, Dufour E. 2011. Synchronous front-face fluorescence spectroscopy as a promising tool for the rapid determination of spoilage bacteria on chicken breast fillet. Food Res Int 44:471-480.   DOI
45 Sahar A, Dufour E. 2015. Classification and characterization of beef muscles using front-face fluorescence spectroscopy. Meat Sci 100:69-72.   DOI
46 Sahar A, ur Rahman U, Kondjoyan A, Portanguen S, Dufour E. 2016. Monitoring of thermal changes in meat by synchronous fluorescence spectroscopy. J Food Eng 168:160-165.   DOI
47 Skjervold PO, Taylor RG, Wold JP, Berge P, Abouelkaram S, Culioli J, Dufour E. 2003. Development of intrinsic fluorescent multispectral imagery specific for fat, connective tissue, and myofibers in meat. J Food Sci 68:1161-1168.   DOI
48 Zhao X, Lin CW, Wang J, Oh DH. 2014. Advances in rapid detection methods for foodborne pathogens. J Microbiol Biotechnol 24:297-312.   DOI