The relevance of different near infrared technologies and sample treatments for predicting meat quality traits in commercial beef cuts.

Visible and near infrared reflectance (Vis-NIR, 350 to 1800 nm), and near infrared transmittance (NIT, 850 to 1050 nm) spectroscopy were used to predict beef quality traits of intact and ground meat samples. Calibration equations were developed from reference data (n = 312) of pH, color traits (L*, a*, and b*), ageing loss (%), cooking loss (%), and Warner-Bratzler shear force (WBSF, N) using partial least squares regressions. Predictive ability of the models was assessed by coefficient of determination of cross-validation (R(2)(CV)) and root mean square error of cross-validation. Quality traits were better predicted on intact than on ground samples, and the best results were obtained using Vis-NIR spectroscopy. Predictions were good (R(2)(CV) = 0.62 to 0.73) for pH, L*, and a*, hardly sufficient (R(2)(CV) = 0.34 to 0.60) for b*, cooking loss, and WBSF, and unsatisfactory for ageing loss (R(2)(CV) = 0.15). Vis-NIR spectroscopy might be used to predict some physical beef quality traits on intact meat samples.

[1]  A. Fisher,et al.  Effects of fatty acids on meat quality: a review. , 2004, Meat science.

[2]  Wim Verbeke,et al.  Beliefs, attitude and behaviour towards fresh meat consumption in Belgium: empirical evidence from a consumer survey , 1999 .

[3]  L. Istasse,et al.  Prediction of technological and organoleptic properties of beef Longissimus thoracis from near-infrared reflectance and transmission spectra. , 2004, Meat science.

[4]  G. Bittante,et al.  Heritability of performance test traits in Chianina, Marchigiana and Romagnola breeds , 2009 .

[5]  D. Alomar,et al.  Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS). , 2003, Meat science.

[6]  G. Bittante,et al.  Near-infrared reflectance spectroscopy predictions as indicator traits in breeding programs for enhanced beef quality. , 2011, Journal of animal science.

[7]  N. Prieto,et al.  Potential use of near infrared reflectance spectroscopy (NIRS) for the estimation of chemical composition of oxen meat samples. , 2006, Meat science.

[8]  Daniel Cozzolino,et al.  Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy , 2006 .

[9]  Yongliang Liu,et al.  Prediction of color, texture, and sensory characteristics of beef steaks by visible and near infrared reflectance spectroscopy. A feasibility study. , 2003, Meat science.

[10]  R. Roehe,et al.  Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review. , 2009, Meat science.

[11]  N. Prieto,et al.  Ability of near infrared reflectance spectroscopy (NIRS) to estimate physical parameters of adult steers (oxen) and young cattle meat samples. , 2008, Meat science.

[12]  W. R. Windham,et al.  Prediction of physical, color, and sensory characteristics of broiler breasts by visible/near infrared reflectance spectroscopy. , 2004, Poultry science.

[13]  M. Dikeman,et al.  The effect of low-intensity ultrasound treatment on shear properties, color stability and shelf-life of vacuum-packaged beef semitendinosus and biceps femoris muscles. , 1997, Meat science.

[14]  C. Russo,et al.  Meat quality traits of longissimus thoracis, semitendinosus and triceps brachii muscles from Chianina beef cattle slaughtered at two different ages , 2004 .

[15]  C Borggaard,et al.  Optical measurements of pH in meat. , 1999, Meat science.

[16]  M De Marchi,et al.  Feasibility of the direct application of near-infrared reflectance spectroscopy on intact chicken breasts to predict meat color and physical traits. , 2011, Poultry science.

[17]  M De Marchi,et al.  At-line prediction of fatty acid profile in chicken breast using near infrared reflectance spectroscopy. , 2012, Meat science.

[18]  M. Marchi,et al.  Genetic traceability of meat using microsatellite markers , 2008 .

[19]  M De Marchi,et al.  Use of near infrared transmittance spectroscopy to predict fatty acid composition of chicken meat. , 2012, Food chemistry.

[20]  M. Edney,et al.  Analysis of Feed Barley by near Infrared Reflectance Technology , 1994 .

[21]  Mitsuru Mitsumoto,et al.  Near‐Infrared Spectroscopy Determination of Physical and Chemical Characteristics in Beef Cuts , 1991 .

[22]  Daniel Cozzolino,et al.  The use of visible and near-infrared reflectance spectroscopy to predict colour on both intact and homogenised pork muscle , 2003 .

[23]  E A Navajas,et al.  Online prediction of fatty acid profiles in crossbred Limousin and Aberdeen Angus beef cattle using near infrared reflectance spectroscopy. , 2011, Animal : an international journal of animal bioscience.

[24]  J B Morgan,et al.  Predicting beef tenderness using near-infrared spectroscopy. , 2008, Journal of animal science.

[25]  Daniel Cozzolino,et al.  Identification of animal meat muscles by visible and near infrared reflectance spectroscopy , 2004 .

[26]  P. Williams,et al.  Chemical principles of near-infrared technology , 1987 .

[27]  P. Dardenne,et al.  The Use of near Infrared Spectroscopy for the Analysis of Fresh Grass Silage , 1994 .

[28]  Fang Cheng,et al.  On-line prediction of fresh pork quality using visible/near-infrared reflectance spectroscopy. , 2010, Meat science.

[29]  Karen Brunsø,et al.  Consumer perception of meat quality and implications for product development in the meat sector-a review. , 2004, Meat science.

[30]  M. F. Trombetta,et al.  Meat quality traits of Marchigiana beef cattle , 2007 .

[31]  D. V. Byrne,et al.  Warmed-over flavour in porcine meat - a combined spectroscopic, sensory and chemometric study. , 2000, Meat science.

[32]  S. Calabrò,et al.  Effects of two protein sources and energy level of diet on the performance of young Marchigiana bulls. 2. Meat quality , 2008 .

[33]  L. Istasse,et al.  Prediction of organoleptic and technological characteristics of pork meat by near infrared spectroscopy , 2003 .

[34]  P. Williams,et al.  Comparison of Commercial near Infrared Transmittance and Reflectance Instruments for Analysis of Whole Grains and Seeds , 1993 .

[35]  G. Hemke,et al.  Prediction of pork quality using visible/near-infrared reflectance spectroscopy. , 2006, Meat science.

[36]  G. Bittante,et al.  Performance testing of bulls in AI: Report of a working group of the commission on cattle production , 1981 .

[37]  J. Hocquette,et al.  Relationship between collagen characteristics, lipid content and raw and cooked texture of meat from young bulls of fifteen European breeds. , 2011, Meat science.

[38]  R. L. Joseph Recommended Method for Assessment of Tenderness , 1979 .

[39]  M. De Marchi,et al.  Breed assignment test in four Italian beef cattle breeds. , 2008, Meat science.

[40]  G. Ripoll,et al.  Near-infrared reflectance spectroscopy for predicting chemical, instrumental and sensory quality of beef. , 2008, Meat science.

[41]  I. Murray,et al.  The use of visible and near infrared reflectance spectroscopy to predict beef M. longissimus thoracis et lumborum quality attributes. , 2008, Meat science.

[42]  Daniel Cozzolino,et al.  Visible and near Infrared Reflectance Spectroscopy for the Determination of Moisture, Fat and Protein in Chicken Breast and Thigh Muscle , 1996 .

[43]  P. Williams,et al.  Near-Infrared Technology in the Agricultural and Food Industries , 1987 .

[44]  G. Bittante Italian animal genetic resources in the Domestic Animal Diversity Information System of FAO , 2011 .

[45]  M. Hubert,et al.  Robust methods for partial least squares regression , 2003 .

[46]  Daniel Cozzolino,et al.  Effect of Sample Presentation and Animal Muscle Species on the Analysis of Meat by near Infrared Reflectance Spectroscopy , 2002 .

[47]  J. Shenk,et al.  Application of NIR Spectroscopy to Agricultural Products , 1992 .