Tenderness prediction in porcine longissimus dorsi muscles using instrumental measurements along with NIR hyperspectral and computer vision imagery

Tenderness is an important attribute influencing consumer opinion about the eating quality of fresh meat. Manual assessment of tenderness requires lengthy procedures with tedious sample preparations. An objective, non-destructive, and rapid technique for assessing meat tenderness is required by the meat industry. In this study, the development of partial least squares (PLS) regression models to relate near-infrared (NIR) reflectance spectra and statistical features (mean, standard deviation, norm-1 energy, norm-2 energy, average residual, and entropy) from discrete wavelet transforms (DWT) of raw porcine longissimus dorsi muscle images, to slice shear force (SSF) instrumental measurements, was investigated. The coefficient of determination (R2) of the PLS regression model was 0.63 when only spectral information from hyperspectral (HS) images was analyzed, while PLS models using DWT features extracted from computer vision (CV) images yielded coefficient of determination of 0.48. By combining them, the R2 increased to 0.75. The study has shown the potential for NIR measurements combined with wavelet features from CV images to provide better correlations with muscle tenderness.

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

[2]  Gamal ElMasry,et al.  Quality classification of cooked, sliced turkey hams using NIR hyperspectral imaging system , 2011 .

[3]  Da-Wen Sun,et al.  Automatic segmentation of beef longissimus dorsi muscle and marbling by an adaptable algorithm. , 2009, Meat science.

[4]  D. Ahn,et al.  Relationship Between Weakening of Z‐disks and Liberation of Phospholipids During Postmortem Aging of Pork and Beef , 2003 .

[5]  J. A. Westerhuis,et al.  New Indicator for Optimal Preprocessing and Wavelength Selection of Near-Infrared Spectra , 2004, Applied spectroscopy.

[6]  Colm P. O'Donnell,et al.  Identification of mushrooms subjected to freeze damage using hyperspectral imaging. , 2009 .

[7]  Gerard Downey,et al.  Rapid Non-destructive Detection of Spoilage of Intact Chicken Breast Muscle Using Near-infrared and Fourier Transform Mid-infrared Spectroscopy and Multivariate Statistics , 2009, Food and Bioprocess Technology.

[8]  M. Dagnew,et al.  Measurement of nutrients in Saskatchewan hog manures using near-infrared spectroscopy , 2004 .

[9]  Da-Wen Sun,et al.  CLASSIFICATION OF TENDERNESS OF LARGE COOKED BEEF JOINTS USING WAVELET AND GABOR TEXTURAL FEATURES , 2006 .

[10]  Da-Wen Sun,et al.  Prediction of beef eating qualities from colour, marbling and wavelet surface texture features using homogenous carcass treatment , 2009, Pattern Recognit..

[11]  Da-Wen Sun,et al.  Colour calibration of a laboratory computer vision system for quality evaluation of pre-sliced hams. , 2009, Meat science.

[12]  J. Aguilera,et al.  Computer Vision and Stereoscopy for Estimating Firmness in the Salmon (Salmon salar) Fillets , 2010 .

[13]  Gamal ElMasry,et al.  Application of NIR hyperspectral imaging for discrimination of lamb muscles , 2011 .

[14]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[15]  S. Shackelford,et al.  Meat tenderness and muscle growth: is there any relationship? , 2002, Meat science.

[16]  Y R Chen,et al.  Two-dimensional visible/near-infrared correlation spectroscopy study of thawing behavior of frozen chicken meats without exposure to air. , 2001, Meat science.

[17]  G. G. Pohlman.  Soil Science Society of America , 1940 .

[18]  G. Geesink,et al.  Prediction of pork quality attributes from near infrared reflectance spectra. , 2003, Meat science.

[19]  M. Koohmaraie,et al.  Muscle proteinases and meat aging. , 1994, Meat science.

[20]  S. Shackelford,et al.  Evaluation of sampling, cookery, and shear force protocols for objective evaluation of lamb longissimus tenderness. , 2004, Journal of animal science.

[21]  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.

[22]  Gerard Downey,et al.  Application of Near and Mid-Infrared Spectroscopy to Determine Cheese Quality and Authenticity , 2008 .

[23]  S. Shackelford,et al.  Tenderness classification of beef: II. Design and analysis of a system to measure beef longissimus shear force under commercial processing conditions. , 1999, Journal of animal science.

[24]  T. Nishimura,et al.  Structural weakening of intramuscular connective tissue during postmortem aging of pork , 2008 .

[25]  M F Rothschild,et al.  Correlations among selected pork quality traits. , 2002, Journal of animal science.

[26]  P. Purslow,et al.  Modelling quality variations in commercial Ontario pork production. , 2008, Meat science.

[27]  K. Honikel,et al.  Reference methods for the assessment of physical characteristics of meat. , 1998, Meat science.

[28]  S. Wold,et al.  Some recent developments in PLS modeling , 2001 .

[29]  H. Heymann,et al.  Pork loin color relative to sensory and instrumental tenderness and consumer acceptance. , 2003, Meat science.

[30]  Gamal ElMasry,et al.  Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging , 2011 .

[31]  Gamal ElMasry,et al.  Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging. , 2012, Analytica chimica acta.

[32]  S. Engelsen,et al.  Characterization of marama bean (Tylosema esculentum) by comparative spectroscopy: NMR, FT-Raman, FT-IR and NIR , 2011 .

[33]  Gamal ElMasry,et al.  Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef , 2012 .

[34]  R. G. Kauffman,et al.  Muscle protein changes post mortem in relation to pork quality traits. , 1997, Meat science.

[35]  Jiang Li,et al.  Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction , 2002, IEEE Trans. Geosci. Remote. Sens..

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

[37]  J. R. Jensen,et al.  Remote Sensing of Urban/Suburban Infrastructure and Socio‐Economic Attributes , 2011 .

[38]  I. Hansson,et al.  Effects of outdoor rearing and sire breed (Duroc or Yorkshire) on carcass composition and sensory and technological meat quality. , 1997, Meat science.

[39]  M. J. Beriain,et al.  Meat color in retail displays with fluorescent illumination , 2005 .

[40]  Kristof Mertens,et al.  The Importance of Choosing the Right Validation Strategy in Inverse Modelling , 2010 .

[41]  V. Santé-Lhoutellier,et al.  Characterisation of PSE zones in semimembranosus pig muscle. , 2005, Meat science.

[42]  K. Jouppila,et al.  Muscle fiber properties and thermal stability of intramuscular connective tissue in porcine M. semimembranosus , 2009 .

[43]  A. Garrido-Varo,et al.  Near-infrared reflectance spectroscopy (NIRS) for the mandatory labelling of compound feedingstuffs: chemical composition and open-declaration , 2004 .

[44]  K. Chen,et al.  Predicting beef tenderness using color and multispectral image texture features. , 2012, Meat science.

[45]  J. Merks,et al.  Perspective of NIRS measurements early post mortem for prediction of pork quality. , 2005, Meat science.

[46]  P. Geladi,et al.  Hyperspectral NIR imaging for calibration and prediction: a comparison between image and spectrometer data for studying organic and biological samples. , 2006, The Analyst.

[47]  S. Shackelford,et al.  Repeatability of tenderness measurements in beef round muscles. , 1997, Journal of animal science.

[48]  Da-Wen Sun,et al.  Computer vision––an objective, rapid and non-contact quality evaluation tool for the food industry , 2004 .

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

[50]  X. Shao,et al.  Simultaneous Wavelength Selection and Outlier Detection in Multivariate Regression of Near-Infrared Spectra , 2005, Analytical sciences : the international journal of the Japan Society for Analytical Chemistry.

[51]  C. Hurburgh,et al.  NEAR-INFRARED SENSING OF MANURE NUTRIENTS , 2000 .

[52]  Engin Avci,et al.  Comparison of wavelet families for texture classification by using wavelet packet entropy adaptive network based fuzzy inference system , 2008, Appl. Soft Comput..

[53]  Digvir S. Jayas,et al.  Wavelet Analysis of Signals in Agriculture and Food Quality Inspection , 2010 .

[54]  D. F. Malley,et al.  Rapid Analysis of Hog Manure and Manure‐amended Soils Using Near‐infrared Spectroscopy , 2002 .

[55]  W. Shi,et al.  Wavelet-based image fusion and quality assessment , 2005 .

[56]  S. Shackelford,et al.  Relationship between shear force and trained sensory panel tenderness ratings of 10 major muscles from Bos indicus and Bos taurus cattle. , 1995, Journal of animal science.

[57]  S. Shackelford,et al.  Technical note: use of belt grill cookery and slice shear force for assessment of pork longissimus tenderness. , 2004, Journal of animal science.

[58]  Xavier Otazu,et al.  Multiresolution-based image fusion with additive wavelet decomposition , 1999, IEEE Trans. Geosci. Remote. Sens..

[59]  M. Ngadi,et al.  Protein Denaturation in Pork Longissimus Muscle of Different Quality Groups , 2011 .

[60]  Franco Pedreschi,et al.  Color of Salmon Fillets By Computer Vision and Sensory Panel , 2010 .

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

[62]  Gamal ElMasry,et al.  Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. , 2012, Analytica chimica acta.

[63]  Arivazhagan Selvaraj,et al.  Texture classification using wavelet transform , 2003, Pattern Recognit. Lett..

[64]  Tom Fearn,et al.  Practical Nir Spectroscopy With Applications in Food and Beverage Analysis , 1993 .

[65]  J. M. Lee,et al.  Characterization of Nutritional Value for Twenty-one Pork Muscles , 2008 .

[66]  Gerard Downey,et al.  Analysis of Meats , 2004 .

[67]  D. E. Chan,et al.  PREDICTION OF PORK QUALITY CHARACTERISTICS USING VISIBLE AND NEAR–INFRARED SPECTROSCOPY , 2002 .

[68]  Cheng-Jin Du,et al.  Comparison of three methods for classification of pizza topping using different colour space transformations , 2005 .

[69]  N. M. Faber,et al.  How to avoid over-fitting in multivariate calibration--the conventional validation approach and an alternative. , 2007, Analytica chimica acta.

[70]  Cheng-Jin Du,et al.  Prediction of beef eating quality from colour, marbling and wavelet texture features. , 2008, Meat science.

[71]  Kazuhiko Shiranita,et al.  Grading meat quality by image processing , 2000, Pattern Recognit..

[72]  K. Ruba Soundar,et al.  Texture classification with combined rotation and scale invariant wavelet features , 2005, Pattern Recognit..

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

[74]  Nuria Aleixos,et al.  Erratum to: Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables , 2011 .

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