Optical Methods and Techniques for Meat Quality Inspection

Meat and meat products are closely associated with the daily eating habits of people around the world. Quality monitoring of meat and meat products is essential to ensure public health. In recent years, the meat industry has employed state-of-the art, high-speed processing technology, and meat processors need rapid, non-destructive, easy-to-use technology to monitor the safety and quality of meats and meat products for economic benefit. Optical technology has been gaining importance in research and industrial applications for real-time, non-destructive, accurate measurement of the quality attributes of meat and meat products. Hyperspectral imaging, multispectral imaging, visible-near-infrared (Vis/NIR) spectroscopy, and machine vision are used in research and industry for detection of the physical, chemical, sensory, and microbiological attributes of meat and meat products. These optical technologies have shown potential for accurate detection of individual attributes as well as multiple attributes simultaneously. This article reviews these optical technologies for detecting the quality attributes of meat (especially beef, pork, lamb, and poultry). This article also discusses the prevailing challenges for practical application of optical technologies and future research prospects.

[1]  Shigeki Nakauchi,et al.  Near Infrared Spectroscopy and Hyperspectral Imaging for Prediction and Visualisation of Fat and Fatty Acid Content in Intact Raw Beef Cuts , 2010 .

[2]  Da-Wen Sun,et al.  Meat Quality Evaluation by Hyperspectral Imaging Technique: An Overview , 2012, Critical reviews in food science and nutrition.

[3]  Muhua Liu,et al.  Quantification of egg yolk contamination in egg white using UV/Vis spectroscopy: Prediction model development and analysis , 2014 .

[4]  Mirosław Słowiński,et al.  Lightness of the color measured by computer image analysis as a factor for assessing the quality of pork meat. , 2011, Meat science.

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

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

[7]  J. Pierna,et al.  NIR hyperspectral imaging spectroscopy and chemometrics for the detection of undesirable substances in food and feed , 2012 .

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

[9]  Piotr Baranowski,et al.  Classification models of bruise and cultivar detection on the basis of hyperspectral imaging data , 2014 .

[10]  E A Navajas,et al.  On-line application of visible and near infrared reflectance spectroscopy to predict chemical-physical and sensory characteristics of beef quality. , 2009, Meat science.

[11]  Sang-Heon Lee,et al.  Review on fruit harvesting method for potential use of automatic fruit harvesting systems , 2011 .

[12]  M. M. Reis,et al.  Early on-line classification of beef carcasses based on ultimate pH by near infrared spectroscopy. , 2014, Meat science.

[13]  Hongbin Pu,et al.  Feasibility of using hyperspectral imaging to predict moisture content of porcine meat during salting process. , 2014, Food chemistry.

[14]  Howland D. T. Jones,et al.  Label-free measurement of algal triacylglyceride production using fluorescence hyperspectral imaging. , 2014 .

[15]  M. Castellari,et al.  Analysis of seven purines and pyrimidines in pork meat products by ultra high performance liquid chromatography-tandem mass spectrometry. , 2010, Journal of chromatography. A.

[16]  D. Andueza,et al.  Comparison of visible and near infrared reflectance spectroscopy to discriminate between pasture-fed and concentrate-fed lamb carcasses. , 2008, Meat science.

[17]  Maocheng Zhao,et al.  Visualizing quantitatively the freshness of intact fresh pork using acousto-optical tunable filter-based visible/near-infrared spectral imagery , 2013 .

[18]  Kay Sowoidnich,et al.  Application of Diode-Laser Raman Spectroscopy for In situ Investigation of Meat Spoilage , 2010 .

[19]  José Blasco,et al.  In-line sorting of irregular potatoes by using automated computer-based machine vision system , 2012 .

[20]  Meegalla R. Chandraratne,et al.  Prediction of lamb tenderness using image surface texture features , 2006 .

[21]  Moon S. Kim,et al.  Multispectral detection of organic residues on poultry processing plant equipment based on hyperspectral reflectance imaging technique , 2007 .

[22]  Guo Hui,et al.  Software design of marbling grades system for beef. , 2012 .

[23]  Jiewen Zhao,et al.  Freshness measurement of eggs using near infrared (NIR) spectroscopy and multivariate data analysis , 2011 .

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

[25]  Douglas Fernandes Barbin,et al.  Prediction of chicken quality attributes by near infrared spectroscopy. , 2015, Food chemistry.

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

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

[28]  Noha Morsy,et al.  Robust linear and non-linear models of NIR spectroscopy for detection and quantification of adulterants in fresh and frozen-thawed minced beef. , 2013, Meat science.

[29]  David W. Penman,et al.  Determination of stem and calyx location on apples using automatic visual inspection , 2001 .

[30]  Jiancheng Jia,et al.  Prediction of lean and fat composition in swine carcasses from ham area measurements with image analysis. , 2010, Meat science.

[31]  K. J. Chen,et al.  Segmentation of beef marbling based on vision threshold , 2008 .

[32]  Renfu Lu,et al.  Assessment of internal quality of blueberries using hyperspectral transmittance and reflectance images with whole spectra or selected wavelengths , 2014 .

[33]  F. Peña,et al.  Marbling in the longissimus thoracis muscle from lean cattle breeds. Computer image analysis of fresh versus stained meat samples. , 2013, Meat science.

[34]  Meegalla R. Chandraratne,et al.  Prediction of lamb carcass grades using features extracted from lamb chop images , 2006 .

[35]  Yankun Peng,et al.  Nondestructive assessment of beef-marbling grade using hyperspectral imaging technology , 2011, 2011 International Conference on New Technology of Agricultural.

[36]  X. Serra,et al.  Texture parameters of dry-cured ham m. biceps femoris samples dried at different levels as a function of water activity and water content. , 2005, Meat science.

[37]  K. J. Chen,et al.  Color grading of beef fat by using computer vision and support vector machine , 2010 .

[38]  Huirong Xu,et al.  Use of signal to noise ratio and area change rate of spectra to evaluate the Visible/NIR spectral system for fruit internal quality detection , 2014 .

[39]  Yao-Ze Feng,et al.  Determination of total viable count (TVC) in chicken breast fillets by near-infrared hyperspectral imaging and spectroscopic transforms. , 2013, Talanta.

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

[41]  Arun K Bhunia,et al.  Multiplex fiber optic biosensor for detection of Listeria monocytogenes, Escherichia coli O157:H7 and Salmonella enterica from ready-to-eat meat samples. , 2013, Food microbiology.

[42]  Yigang Xu,et al.  A multiplex polymerase chain reaction coupled with high-performance liquid chromatography assay for simultaneous detection of six foodborne pathogens , 2012 .

[43]  S. Lanteri,et al.  Detection of minced beef adulteration with turkey meat by UV-vis, NIR and MIR spectroscopy , 2013 .

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

[45]  P. Allen,et al.  Development of a hybrid image processing algorithm for automatic evaluation of intramuscular fat content in beef M. longissimus dorsi. , 2008, Meat science.

[46]  S. Prasher,et al.  Prediction of drip-loss, pH, and color for pork using a hyperspectral imaging technique. , 2007, Meat science.

[47]  J. Tan,et al.  Classification of tough and tender beef by image texture analysis. , 2001, Meat science.

[48]  Jing Zhang,et al.  Hyperspectral scattering profiles for prediction of the microbial spoilage of beef , 2009, Defense + Commercial Sensing.

[49]  Renfu Lu,et al.  Hyperspectral laser-induced fluorescence imaging for assessing apple fruit quality , 2007 .

[50]  Yankun Peng,et al.  Simultaneous determination of tenderness and Escherichia coli contamination of pork using hyperspectral scattering technique. , 2012, Meat science.

[51]  Suranjan Panigrahi,et al.  Design and development of a metal oxide based electronic nose for spoilage classification of beef , 2006 .

[52]  Yao-Ze Feng,et al.  Near-infrared hyperspectral imaging in tandem with partial least squares regression and genetic algorithm for non-destructive determination and visualization of Pseudomonas loads in chicken fillets. , 2013, Talanta.

[53]  N R Lambe,et al.  Prediction of sensory characteristics of lamb meat samples by near infrared reflectance spectroscopy. , 2007, Meat science.

[54]  Chaoxin Zheng,et al.  Predicting shrinkage of ellipsoid beef joints as affected by water immersion cooking using image analysis and neural network , 2007 .

[55]  Jens Michael Carstensen,et al.  Potential of multispectral imaging technology for rapid and non-destructive determination of the microbiological quality of beef filets during aerobic storage. , 2014, International journal of food microbiology.

[56]  Michael Ngadi,et al.  Prediction of pork marbling scores using pattern analysis techniques , 2013 .

[57]  J. Lu,et al.  Evaluation of pork color by using computer vision. , 2000, Meat science.

[58]  Keun-Taik Lee.  Quality and safety aspects of meat products as affected by various physical manipulations of packaging materials. , 2010, Meat science.

[59]  Gamal ElMasry,et al.  Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging. , 2013, Food chemistry.

[60]  E. Boselli,et al.  Determination of phospholipid molecular species in pork meat by high performance liquid chromatography-tandem mass spectrometry and evaporative light scattering detection. , 2008, Meat science.

[61]  J E Guerrero-Ginel,et al.  A feasibility study on the use of near-infrared spectroscopy for prediction of the fatty acid profile in live Iberian pigs and carcasses. , 2009, Meat science.

[62]  Hongbin Pu,et al.  Hierarchical variable selection for predicting chemical constituents in lamb meats using hyperspectral imaging , 2014 .

[63]  D E Gerrard,et al.  Utilization of image processing to quantitate surface metmyoglobin on fresh beef. , 1996, Meat science.

[64]  Da‐Wen Sun,et al.  Automatic measurement of pores and porosity in pork ham and their correlations with processing time, water content and texture. , 2006, Meat science.

[65]  Chun-Chieh Yang,et al.  Fast line-scan imaging system for broiler carcass inspection , 2007 .

[66]  Ismail Kavdir,et al.  Detecting corn tassels using computer vision and support vector machines , 2014, Expert Syst. Appl..

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

[68]  M. Z. Ilic,et al.  Improved high-performance liquid chromatographic method for analysis of histidine dipeptides anserine, carnosine and balenine present in fresh meat. , 1983, Journal of chromatography.

[69]  P. Allen,et al.  Identification of important image features for pork and turkey ham classification using colour and wavelet texture features and genetic selection. , 2010, Meat science.

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

[71]  Jerzy Hanuza,et al.  Raman spectroscopy in determination of horse meat content in the mixture with other meats. , 2014, Food chemistry.

[72]  Da-Wen Sun,et al.  Multi-classification of pizza using computer vision and support vector machine , 2008 .

[73]  Jens Petter Wold,et al.  Detection of rancidity in freeze stored turkey meat using a commercial gas-sensor array system , 2006 .

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

[75]  P. Zapotoczny,et al.  The use of computer-assisted image analysis in the evaluation of the effect of management systems on changes in the color, chemical composition and texture of m. longissimus dorsi in pigs. , 2014, Meat science.

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

[77]  Hongbin Pu,et al.  Prediction of Color and pH of Salted Porcine Meats Using Visible and Near-Infrared Hyperspectral Imaging , 2014, Food and Bioprocess Technology.

[78]  Fernando Mendoza,et al.  Supervised neural network classification of pre-sliced cooked pork ham images using quaternionic singular values. , 2010, Meat science.

[79]  N. Salama,et al.  Validation of HPLC method for determination of tetracycline residues in chicken meat and liver , 2011 .

[80]  Jiewen Zhao,et al.  Determination of total volatile basic nitrogen (TVB-N) content and Warner–Bratzler shear force (WBSF) in pork using Fourier transform near infrared (FT-NIR) spectroscopy , 2011 .

[81]  Kurt C. Lawrence,et al.  Embedded bone fragment detection in chicken fillets using transmittance image enhancement and hyperspectral reflectance imaging , 2008 .

[82]  Fang Cheng,et al.  On-line prediction of pH values in fresh pork using visible/near-infrared spectroscopy with wavelet de-noising and variable selection methods , 2012 .

[83]  Jianwei Qin,et al.  Investigation of Raman chemical imaging for detection of lycopene changes in tomatoes during postharvest ripening , 2011 .

[84]  R. Lu,et al.  Hyperspectral Scattering for assessing Peach Fruit Firmness , 2006 .

[85]  Yankun Peng,et al.  A method for nondestructive prediction of pork meat quality and safety attributes by hyperspectral imaging technique , 2014 .

[86]  P. G. Stiles,et al.  Liquid chromatographic identification of meats. , 1988, Journal - Association of Official Analytical Chemists.

[87]  B. Uttaro,et al.  Prediction of pork belly fatness from the intact primal cut , 2010 .

[88]  D. Andueza,et al.  Prediction of lamb meat fatty acid composition using near-infrared reflectance spectroscopy (NIRS). , 2011, Food chemistry.

[89]  Xia Yao,et al.  Monitoring leaf nitrogen status with hyperspectral reflectance in wheat , 2008 .

[90]  Saeid Minaei,et al.  Reflectance Vis/NIR spectroscopy for nondestructive taste characterization of Valencia oranges , 2012 .

[91]  George-John E. Nychas,et al.  Rapid qualitative and quantitative detection of beef fillets spoilage based on Fourier transform infrared spectroscopy data and artificial neural networks , 2010 .

[92]  Meegalla R. Chandraratne,et al.  Classification of lamb carcass using machine vision: Comparison of statistical and neural network analyses , 2007 .

[93]  J. Björkroth,et al.  Predominant enterobacteria on modified-atmosphere packaged meat and poultry. , 2013, Food microbiology.

[94]  M. Čandek-Potokar,et al.  Application of near infrared spectroscopy to predict chemical composition of meat and meat products. , 2010 .

[95]  J. Barat,et al.  Nondestructive assessment of freshness in packaged sliced chicken breasts using SW-NIR spectroscopy , 2011 .

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

[97]  Jess D. Reed,et al.  Use of digital images to estimate CIE color coordinates of beef , 2008 .

[98]  J. McGrath,et al.  Determination of sucrose content in sugar beet by portable visible and near-infrared spectroscopy. , 2015, Food chemistry.

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

[100]  Jun-Hu Cheng,et al.  Potential of hyperspectral imaging for non-invasive determination of mechanical properties of prawn (Metapenaeus ensis) , 2014 .

[101]  S. Balasubramanian,et al.  Neural networks-integrated metal oxide-based artificial olfactory system for meat spoilage identification , 2009 .

[102]  Bart Nicolai,et al.  Contactless and non-destructive differentiation of microstructures of sugar foams by hyperspectral scatter imaging , 2014 .

[103]  Gamal ElMasry,et al.  Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression , 2012 .

[104]  Eric Dufour,et al.  Synchronous front-face fluorescence spectroscopy as a promising tool for the rapid determination of spoilage bacteria on chicken breast fillet , 2011 .

[105]  Kurt C. Lawrence,et al.  Performance of hyperspectral imaging system for poultry surface fecal contaminant detection. , 2006 .

[106]  Dolores Pérez-Marín,et al.  Double integrating sphere measurements for estimating optical properties of pig subcutaneous adipose tissue , 2013 .

[107]  G. Calderón‐Domínguez,et al.  Determination of Trichinella spiralis in pig muscles using Mid-Fourier Transform Infrared Spectroscopy (MID-FTIR) with Attenuated Total Reflectance (ATR) and Soft Independent Modeling of Class Analogy (SIMCA). , 2012, Meat science.

[108]  O. G. Meza-Márquez,et al.  Application of mid-infrared spectroscopy with multivariate analysis and soft independent modeling of class analogies (SIMCA) for the detection of adulterants in minced beef. , 2010, Meat science.

[109]  Hassan Ghassemian,et al.  Applied machine vision and artificial neural network for modeling and controlling of the grape drying process , 2013 .

[110]  Gamal Elmasry,et al.  Near-infrared hyperspectral imaging and partial least squares regression for rapid and reagentless determination of Enterobacteriaceae on chicken fillets. , 2013, Food chemistry.

[111]  Chenglong Wang,et al.  Recognition of worm-eaten chestnuts based on machine vision , 2011, Math. Comput. Model..

[112]  Ashok Samal,et al.  Visible/near-infrared hyperspectral imaging for beef tenderness prediction , 2008 .

[113]  Wang Wei,et al.  Determination of pork freshness attributes by hyperspectral imaging technique , 2012 .

[114]  Renfu Lu,et al.  Detection of fruit fly infestation in pickling cucumbers using a hyperspectral reflectance/transmittance imaging system , 2013 .

[115]  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.

[116]  Seyed Saeid Mohtasebi,et al.  Application of machine-vision techniques to fish-quality assessment , 2012 .

[117]  A. Sahar,et al.  Use of Fourier transform-infrared spectroscopy to predict spoilage bacteria on aerobically stored chicken breast fillets , 2014 .

[118]  Quansheng Chen,et al.  Nondestructive detection of total volatile basic nitrogen (TVB-N) content in pork meat by integrating hyperspectral imaging and colorimetric sensor combined with a nonlinear data fusion , 2015 .

[119]  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 .

[120]  Huanyu Jiang,et al.  Machine vision techniques for the evaluation of seedling quality based on leaf area , 2013 .

[121]  M De Marchi,et al.  The relevance of different near infrared technologies and sample treatments for predicting meat quality traits in commercial beef cuts. , 2013, Meat science.

[122]  Ying Zhang,et al.  Automatic recognition vision system guided for apple harvesting robot , 2012, Comput. Electr. Eng..

[123]  Long Xue,et al.  Application of Particle Swarm Optimization (PSO) Algorithm to Determine Dichlorvos Residue on the Surface of Navel Orange with Vis-NIR Spectroscopy , 2012 .

[124]  Yankun Peng,et al.  Prediction of apple fruit firmness and soluble solids content using characteristics of multispectral scattering images , 2007 .

[125]  Moon S. Kim,et al.  A comparison of hyperspectral reflectance and fluorescence imaging techniques for detection of contaminants on spinach leaves , 2014 .

[126]  Yong He,et al.  Identification of crack features in fresh jujube using Vis/NIR hyperspectral imaging combined with image processing , 2014 .

[127]  E. Berg,et al.  Characterizing beef muscles with optical scattering and absorption coefficients in VIS-NIR region. , 2007, Meat science.

[128]  Yimin Wei,et al.  Classification of geographical origins and prediction of δ13C and δ15N values of lamb meat by near infrared reflectance spectroscopy. , 2012, Food chemistry.

[129]  H. J. Andersen,et al.  Early prediction of water-holding capacity in meat by multivariate vibrational spectroscopy. , 2003, Meat science.

[130]  Jiewen Zhao,et al.  Nondestructive measurement of total volatile basic nitrogen (TVB-N) content in salted pork in jelly using a hyperspectral imaging technique combined with efficient hypercube processing algorithms , 2013 .

[131]  M. Lopez-Sabater,et al.  Determination of inulin in meat products by high-performance liquid chromatography with refractive index detection. , 2000, Journal of chromatography. A.

[132]  Kurt C. Lawrence,et al.  Line-scan hyperspectral imaging system for real-time inspection of poultry carcasses with fecal material and ingesta , 2011 .

[133]  Vincent Leemans,et al.  Defects segmentation on 'Golden Delicious' apples by using colour machine vision , 1998 .

[134]  Liang Gong,et al.  Computer vision detection of defective apples using automatic lightness correction and weighted RVM classifier , 2015 .

[135]  Pengcheng Nie,et al.  Application of Time Series Hyperspectral Imaging (TS-HSI) for Determining Water Distribution Within Beef and Spectral Kinetic Analysis During Dehydration , 2013, Food and Bioprocess Technology.

[136]  Moon S. Kim,et al.  Detection of cuticle defects on cherry tomatoes using hyperspectral fluorescence imagery , 2013 .

[137]  Jean-Louis Damez,et al.  Beef meat electrical impedance spectroscopy and anisotropy sensing for non-invasive early assessment of meat ageing , 2008 .

[138]  Jiewen Zhao,et al.  Application of linear/non-linear classification algorithms in discrimination of pork storage time using Fourier transform near infrared (FT-NIR) spectroscopy , 2011 .

[139]  K. Chao,et al.  DESIGN OF A DUAL-CAMERA SYSTEM FOR POULTRY CARCASSES INSPECTION , 2000 .

[140]  Zhenjie Xiong,et al.  Application of Visible Hyperspectral Imaging for Prediction of Springiness of Fresh Chicken Meat , 2015, Food Analytical Methods.

[141]  Siddhartha S. Mehta,et al.  Vision-based control of robotic manipulator for citrus harvesting , 2014 .

[142]  Jiewen Zhao,et al.  Rapid detection of total viable count (TVC) in pork meat by hyperspectral imaging , 2013 .

[143]  M. Chmiel,et al.  Application of computer vision systems for estimation of fat content in poultry meat , 2011 .

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

[145]  Paul Allen,et al.  Prediction of moisture, color and pH in cooked, pre-sliced turkey hams by NIR hyperspectral imaging system , 2013 .

[146]  Wei Chen,et al.  Multispectral imaging for rapid and non-destructive determination of aerobic plate count (APC) in cooked pork sausages , 2014 .

[147]  Vassilis S. Kodogiannis,et al.  An intelligent based decision support system for the detection of meat spoilage , 2014, Eng. Appl. Artif. Intell..

[148]  M. Juárez,et al.  Use of near infrared spectroscopy for estimating meat chemical composition, quality traits and fatty acid content from cattle fed sunflower or flaxseed. , 2014, Meat science.

[149]  Yankun Peng,et al.  Prediction of beef quality attributes using VIS/NIR hyperspectral scattering imaging technique , 2012 .

[150]  M. B. R. Mollah,et al.  Digital image analysis to estimate the live weight of broiler , 2010 .

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

[152]  Renfu Lu,et al.  Hyperspectral and multispectral imaging for evaluating food safety and quality , 2013 .

[153]  George-John E. Nychas,et al.  Contribution of Fourier transform infrared (FTIR) spectroscopy data on the quantitative determination of minced pork meat spoilage , 2011 .

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

[155]  Chaoxin Zheng,et al.  Correlating colour to moisture content of large cooked beef joints by computer vision , 2006 .

[156]  Qian Zhang,et al.  Development of near infrared reflectance spectroscopy to predict chemical composition with a wide range of variability in beef. , 2014, Meat science.

[157]  Ashok Samal,et al.  Optical scattering with hyperspectral imaging to classify longissimus dorsi muscle based on beef tenderness using multivariate modeling. , 2013, Meat science.

[158]  F. Teuscher,et al.  Application of computer image analysis to measure pork marbling characteristics. , 2005, Meat science.

[159]  Gamal ElMasry,et al.  Chemical-free assessment and mapping of major constituents in beef using hyperspectral imaging , 2013 .

[160]  Karsten Heia,et al.  VIS/NIR spectroscopy for non-destructive freshness assessment of Atlantic salmon (Salmo salar L.) fillets , 2013 .

[161]  K. Chen,et al.  Prediction of troponin-T degradation using color image texture features in 10d aged beef longissimus steaks. , 2014, Meat science.

[162]  Ashok Samal,et al.  Partial least squares analysis of near-infrared hyperspectral images for beef tenderness prediction , 2008 .

[163]  J Sugiyama,et al.  Non-destructive evaluation of ATP content and plate count on pork meat surface by fluorescence spectroscopy. , 2013, Meat science.

[164]  J P Wold,et al.  On-line determination and control of fat content in batches of beef trimmings by NIR imaging spectroscopy. , 2011, Meat science.

[165]  J. Tan,et al.  Determination of animal skeletal maturity by image processing. , 2003, Meat science.

[166]  C. Plasencia,et al.  Design, development and application of a bioelectrochemical detection system for meat tenderness prediction. , 2011, Biosensors & bioelectronics.

[167]  X. Serra,et al.  Relationship between water content, NaCl content, pH and texture parameters in dry-cured muscles. , 2005, Meat science.

[168]  N. Perišić,et al.  Characterizing salt substitution in beef meat processing by vibrational spectroscopy and sensory analysis. , 2013, Meat science.

[169]  L. Hoffman,et al.  Prediction of the chemical composition of mutton with near infrared reflectance spectroscopy , 2007 .

[170]  J C Forrest,et al.  Development of technology for the early post mortem prediction of water holding capacity and drip loss in fresh pork. , 2000, Meat science.

[171]  Agapi I. Doulgeraki,et al.  Potential of a simple HPLC-based approach for the identification of the spoilage status of minced beef stored at various temperatures and packaging systems. , 2011, International journal of food microbiology.

[172]  Fernando Mendoza,et al.  Classification of pre-sliced pork and Turkey ham qualities based on image colour and textural features and their relationships with consumer responses. , 2010, Meat science.

[173]  S. Aggrey,et al.  Raman spectroscopy to assess water holding capacity in muscle from fast and slow growing broilers , 2014 .

[174]  Yankun Peng,et al.  A Nondestructive Method for Prediction of Total Viable Count in Pork Meat by Hyperspectral Scattering Imaging , 2014, Food and Bioprocess Technology.