Assessment of tenderness of aged bovine gluteus medius muscles using Raman spectroscopy.

A portable 671 nm Raman system was evaluated as a rapid and non-destructive device for the assessment of beef tenderness using 175 gluteus medius muscles (99 for calibration, 76 for validation) aged at -1 °C and 7 °C for fourteen days. Raman and shear force (SF) measurements were performed with the aged beef. The samples stored at -1 °C showed on average only slightly increased SF values. The correlation of Raman spectra with SF using partial least squares regression yielded cross-validated predictions of SF for both storage temperatures with coefficients of determination R(2)cv=0.33-0.79. Validation with independent samples resulted in predictions with R(2)val=0.33. Using thresholds between 30 and 49N, tough and tender samples could be discriminated with partial least squares discriminant analysis with 70-88% and 59-80% accuracy during cross-validation and validation, respectively. These results demonstrate the principle feasibility to predict the SF and thus toughness of raw, aged gluteus beef cuts with a portable Raman device showing potential for grading beef cuts.

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

[2]  M. Miller,et al.  Consumer assessment of beef palatability from four beef muscles from USDA Choice and Select graded carcasses. , 2014, Meat science.

[3]  Ashok Samal,et al.  Optical scattering in beef steak to predict tenderness using hyperspectral imaging in the VIS-NIR region , 2008 .

[4]  Heinar Schmidt,et al.  Preliminary investigation on the relationship of Raman spectra of sheep meat with shear force and cooking loss. , 2013, Meat science.

[5]  Gerard Downey,et al.  Prediction of Tenderness and other Quality Attributes of Beef by near Infrared Reflectance Spectroscopy between 750 and 1100 nm; Further Studies , 2001 .

[6]  Technical note: validation of a model for online classification of US Select beef carcasses for longissimus tenderness using visible and near-infrared reflectance spectroscopy. , 2012, Journal of animal science.

[7]  M. J. Kerr,et al.  Measuring the shear force of lamb meat cooked from frozen samples: comparison of two laboratories , 2010 .

[8]  S. Lonergan,et al.  Biochemistry of postmortem muscle - lessons on mechanisms of meat tenderization. , 2010, Meat science.

[9]  W. Verbeke,et al.  Relationships between sensory evaluations of beef tenderness, shear force measurements and consumer characteristics. , 2014, Meat science.

[10]  Achim Kohler,et al.  Extended multiplicative signal correction in vibrational spectroscopy, a tutorial , 2012 .

[11]  S. Shackelford,et al.  Evaluation of slice shear force as an objective method of assessing beef longissimus tenderness. , 1999, Journal of animal science.

[12]  J. Köhler,et al.  Identification of the early postmortem metabolic state of porcine M. semimembranosus using Raman spectroscopy , 2014 .

[13]  L. Duizer,et al.  Relationships between sensory and objective measures of meat tenderness of beef m. longissimus thoracis from bulls and steers. , 2002, Meat science.

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

[15]  S. Shackelford,et al.  On-line classification of US Select beef carcasses for longissimus tenderness using visible and near-infrared reflectance spectroscopy. , 2005, Meat science.

[16]  R. Cassens Postmortem Changes in Muscle , 1990 .

[17]  Y. R. Chen,et al.  Near-infrared reflectance analysis for predicting beef longissimus tenderness. , 1998, Journal of animal science.

[18]  Field testing of a system for online classification of beef carcasses for longissimus tenderness using visible and near-infrared reflectance spectroscopy. , 2012, Journal of animal science.

[19]  Kay Sowoidnich,et al.  A Prototype Hand-Held Raman Sensor for the in situ Characterization of Meat Quality , 2010, Applied spectroscopy.

[20]  Da-Wen Sun,et al.  Recent developments of hyperspectral imaging systems and their applications in detecting quality attributes of red meats: A review , 2014 .

[21]  D. Hopkins,et al.  A comparison of technical replicate (cuts) effect on lamb Warner-Bratzler shear force measurement precision. , 2015, Meat science.

[22]  S. Shackelford,et al.  Development of optimal protocol for visible and near-infrared reflectance spectroscopic evaluation of meat quality. , 2004, Meat science.

[23]  A. Brugiapaglia,et al.  Relationship between beef consumer tenderness perception and Warner-Bratzler shear force. , 2008, Meat science.

[24]  Claus Borggaard,et al.  Preliminary investigations on the effects of ageing and cooking on the Raman spectra of porcine longissimus dorsi. , 2008, Meat science.

[25]  R. Rødbotten,et al.  Prediction of beef quality attributes from early post mortem near infrared reflectance spectra , 2000 .

[26]  F. Sigona,et al.  Preliminary Investigations , 2018, Statistics with Applications in Biology and Geology.

[27]  Heinar Schmidt,et al.  Predicting tenderness of fresh ovine semimembranosus using Raman spectroscopy. , 2014, Meat science.

[28]  Kay Sowoidnich,et al.  Multispektrale, Diodenlaser-basierte Raman-Untersuchungen zur In-situ-Analytik ausgewählter Fleischsorten , 2012 .

[29]  Heinar Schmidt,et al.  Early Postmortem Prediction of Meat Quality Traits of Porcine Semimembranosus Muscles Using a Portable Raman System , 2014, Food and Bioprocess Technology.

[30]  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.

[31]  M. Miller,et al.  Consumer thresholds for establishing the value of beef tenderness. , 2001, Journal of animal science.

[32]  D. S. Hale,et al.  National Beef Tenderness Survey-2010: Warner-Bratzler shear force values and sensory panel ratings for beef steaks from United States retail and food service establishments. , 2013, Journal of animal science.

[33]  J. Savell,et al.  Warner-Bratzler shear evaluations of 40 bovine muscles. , 2003, Meat science.

[34]  Glenn A. Kranzler,et al.  Predicting beef tenderness using near-infrared spectroscopy , 2004, SPIE Optics East.

[35]  Heinar Schmidt,et al.  Raman spectroscopy compared against traditional predictors of shear force in lamb m. longissimus lumborum. , 2014, Meat science.

[36]  Jean-Louis Damez,et al.  Meat quality assessment using biophysical methods related to meat structure. , 2008, Meat science.

[37]  A. Reicks,et al.  Demographics and beef preferences affect consumer motivation for purchasing fresh beef steaks and roasts. , 2011, Meat science.

[38]  Bjørn-Helge Mevik,et al.  Prediction and Classification of Tenderness in Beef from Non-Invasive Diode Array Detected NIR Spectra , 2001 .

[39]  L. Farmer,et al.  Preliminary investigation of the application of Raman spectroscopy to the prediction of the sensory quality of beef silverside. , 2004, Meat science.

[40]  D. Troy,et al.  Postmortem changes in muscle electrical properties of bovine M. longissimus dorsi and their relationship to meat quality attributes and pH fall. , 2000, Meat science.

[41]  Ryan Gosselin,et al.  A Bootstrap-VIP approach for selecting wavelength intervals in spectral imaging applications , 2010 .

[42]  J B Morgan,et al.  Efficacy of performing Warner-Bratzler and slice shear force on the same beef steak following rapid cooking. , 2010, Meat science.