medigraphic.com

2012, Number 2

<< Back Next >>

Vet Mex 2012; 43 (2)

Potential use of near infrared reflectance spectroscopy (NIRS) for the identification of beef, llama and horse jerky

Mamani-Linares W, Dlomar D, Gallo C

Language: English/Spanish
References: 40
Page: 133-141
PDF size: 358.70 Kb.


ABSTRACT

Visible and near infrared reflectance spectroscopy (VIS/NIRS) was evaluated as a tool to discriminate jerky from different species. Spectra were taken by reflectance in a NIRSystems 6500 monochromator and the software NIRS 3.0 and WinIsi II Version 1.02A were used. Twenty samples of jerky corresponding to beef, llama and horses, respectively, were ground, homogenized and analyzed spectrally. The regression equations (PLS) were developed testing different mathematical treatments. The results for jerky show that NIRS can successfully discriminate 100% of llama, 95% of horses and 80% of beef samples, probably as a consequence of differences in intramuscular fat, protein and water contents of the different species. Thus, NIRS is a fast, inexpensive and non-destructive method that can be used to discriminate jerky from these species.


REFERENCES

  1. MONIN G. Recent methods for predicting quality of whole meat. Meat Sci 1998; 49: S231-S243.

  2. CORDELLA CH, MOUSSA I, MARTEL AC, SBIRRAZZUOLI N, LIZZANI-CUVELIER L. Recent developments in food characterization and adulteration detection: Technique-oriented perspectives. J Agric Food Chem 2002; 50: 1751-1764.

  3. HARGIN KD. Authenticity issues in meat and meat products. Meat Sci 1996; 43: 277.

  4. AL JOWDER O, KEMSLEY K, WILSON RH. Detection of adulteration in cooked meat products by mid-infrared spectroscopy. J Agric Food Chem 2002; 50: 1325-1329.

  5. MEZA-MARQUEZ OG, GALLARDO-VELAZQUEZ T, OSORIO-REVILLA G. 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 2010; 86: 511-519.

  6. AL JOWDER O, DEFERNEZ M, KEMSLEY K, WILSON RH. Mid-infrared spectroscopy and chemometrics for the authentication of meat products. J Agric Food Chem 1999; 47: 3210-3218.

  7. MURRAY I, AUCOTT L, PIKE IH. Use of discriminant analysis on visible and near infrared reflectance spectra to detect adulteration of fishmeal with meat and bone meal. J Near Infrared Spectrosc 2001; 9: 297-311.

  8. PATTERSON RLS, JONES SJ. Review of current techniques for the verification of the species origin of meat. Analyst 1990; 115: 501-505.

  9. SMITH CJ. Applications of immunoassay to the detection of food adulteration. An overview. In: MORGAN MRA, SMITH CJ, WILLIAMS PA, editors. Food safety and quality assurance-applications of immunoassay Systems. London: Elsevier Applied Science, 1991: 13-32.

  10. SHARMA NK, SRIVATAVA VA, GILL JPS, JOSHI DV. Differentiation of meat from food animals by enzyme assay. Food Control 1994; 5: 219-221.

  11. SIEBERTE S, BENEKE B, BENTLER W. Beef, pork, and sheep meat-detecting previous frozen treatment by isoelectric-focusing in polyamide acryl gel (PAGIF) during routine diagnosis. Fleischwirtschaft 1994; 74: 417-420.

  12. FAJARDO V, GONZALEZ I, LOPEZ-CALLEJA I, MARTIN M, ROJAS T, GARCIA PE et al. PCR identification of meats from chamois (Rupicapra rupicapra), pyrenean ibex (Capra pyrenaica), and mouflon (Ovis ammon) targeting specific sequences from the mitochondrial D-loop region. Meat Sci 2007; 76: 644-652.

  13. KESMEN Z, GULLUCE A, SAHIN F, YETIM H. Identification of meat species by TaqMan-based real-time PCR assay. Meat Sci 2009; 82: 444-449.

  14. RAAMSDONK LWD, HOLST C, BAETEN V, BERBEN G, BOIX A, JONG J. New developments in the detection and identification of processed animal proteins in feeds. Anim Feed Sci Tech 2007; 133: 63-83.

  15. AHMED FE. Detection of genetically modified orga-nisms in foods, Trends Biotechnol 2002; 20: 215-223.

  16. LIU Y, LYON BG, WINDHAM WR, LYON CE, SAVAGE EM. Prediction of physical, color, and sensory characteristics of broiler breasts by visible/near infrared reflectance spectroscopy. Poult Sci 2004; 83: 1467-1474.

  17. PRIETO N, ROSS DW, NAVAJAS EA, NUTE GR, RICHARDSON RI, HYSLOP JJ et al. On-line application of visible and near infrared reflectance spectroscopy to predict chemical-physical and sensory characteristics of beef quality. Meat Sci 2009; 83: 96-103.

  18. ALISHAHI A, FARAHMAND H, PRIETO N, COZZOLINO D. Identification of transgenic foods using NIR spectroscopy: A review. Spectrochim Acta A Mol Biomol Spectrosc 2010; 75: 1-7.

  19. ALOMAR D, GALLO C, CASTAÑEDA M, FUCHSLOCHER R. Chemical and discriminant analysis of beef meat by near infrared reflectance spectroscopy (NIRS). Meat Sci 2003; 63: 441-450.

  20. VILJOEN M, HOFFMANB LC, BRAND TS. Prediction of the chemical composition of mutton with near infrared reflectance spectroscopy. Small Rum Res 2007; 69: 88-94.

  21. RIPOLL G, ALBERTÍ P, PANEA B, OLLETA JL, SAÑUDO C. Near-infrared reflectance spectroscopy for predicting chemical, instrumental. and sensory quality of beef. Meat Sci 2008; 80: 697-702.

  22. ELLEKJAER MR, NAES T, ISAKSSON T, SOLHEIM R. Identification of sausages with fat-substitutes using near infrared spectroscopy. In: HILDRUM KI, ISAKSSON T, NAES T, TANDBERG A, editors. Near infrared spectroscopy: Bridging the gap between data analysis and NIR applications. London: Ellis Horwood, 1992: 320-326.

  23. PRIETO N, ANDRES S, GIRALDEZ FJ, MANTECON AR, LAVIN P. Discrimination of adult steers (oxen) and young cattle ground meat samples by near infrared reflectance spectroscopy (NIRS). Meat Sci 2008; 79: 198-201.

  24. RIPOLL G, ALCALDE MJ, HORCADA A, PANEA B. Suckling kid breed and slaughter weight discrimination using muscle colour and visible reflectance. Meat Sci 2011; 87: 151-156.

  25. DOWNEY G, BEAUCHENE D. Discrimination between fresh and frozen then thawed beef M longissimus dorsi by combined visible and near infrared reflectance spec-troscopy: A feasibility study. Meat Sci 1997; 45: 353-363.

  26. THYOLDT K, ISAKSSON T. Differentiation of frozen and unfrozen beef using near infrared spectroscopy. J Sci Food Agric 1997; 73: 525-532.

  27. OSORIO MT, ZUMALACARREGUI JM, PRIETO N, GIRALDEZ FJ, ANDRES S, MATEO J. Differentiation between carcasses from suckling lambs reared with ewe milk or milk replacers by fatty acid composition and near infrared reflectance spectroscopy of perirenal fat. Small Rum Res 2007; 72: 221-226.

  28. PLA M, HERNANDEZ P, ARIÑO B, RAMIREZ JA, DIAZ I. Prediction of fatty acid content in rabbit meat and discrimination between conventional and organic production systems by NIRS methodology. Food Chem 2007; 100: 165-170.

  29. DIAN PHM, ANDUEZA D, JESTIN M, PRADO IN, PRACHE S. Comparison of visible and near infrared reflectance spectroscopy to discriminate between pasture-fed and concentrate-fed lamb carcasses. Meat Sci 2008; 80: 1157-1164.

  30. TEJERINA D, LOPEZ-PARRA MM, GARCIA-TORRES S. Potential used of near infrared reflectance spectroscopy to predict meat physico-chemical composition of guinea fowl (Numida meleagris) reared under different production systems. Food Chem 2009; 113: 1290-1296.

  31. DING HB, XU RJ. Differentiation of beef and kangaroo meat by visible and near infrared reflectance spectroscopy. J Food Sci 1999; 64: 814-817.

  32. MCELHINNEY J, DOWNEY G. Chemometric processing of visible and near infrared reflectance spectra for species identification in selected raw homogenized meat. J Near Infrared Spectrosc 1999; 7: 145-154.

  33. DOWNEY G. Discrimination strategies in food authentication: What is the correct question to ask? NIR News 2000; 11: 8-9.

  34. COZZOLINO D, MURRAY I. Identification of animal meat muscles by visible and near infrared reflectance spectroscopy. Lebensm Wissenschaft Technol 2004; 37: 447-452.

  35. GAYO J, SCOTT AH. Detection and Quantification of Species Authenticity and Adulteration in Crabmeat Using Visible and Near-Infrared Spectroscopy. J Agric Food Chem 2007; 55: 585-592.

  36. ORTIZ-SOMOVILLA V, ESPAÑA-ESPAÑA F, DE PEDRO-SANZ EJ, GAITAN-JURADO AJ. Meat mixture detection in Iberian pork sausages. Meat Sci 2005; 71; 490-497.

  37. MAMANI-LINARES LW, GALLOC, ALOMAR D. Identification of cattle, llama and horse meat by near infrared reflectance o transflectance spectroscopy. Meat Sci 2012, 90, 378-385

  38. ISI (INFRASOFT INTERNATIONAL, LLC). ISI windows near infrared software, WinISI II, version 1.02A, Foss NIRSystems, Silver Spring, MD, 1999: 236.

  39. STRYER L. Biochemistry. 4th ed. New York: Stanford University, WH Freeman and Co., 1995.

  40. CEN H, HE Y. Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends Food Sci Technol 2007; 18: 72-83.




2020     |     www.medigraphic.com