Meat Quality Evaluation by Hyperspectral Imaging Technique: An Overview
暂无分享,去创建一个
Da-Wen Sun | Gamal Elmasry | Douglas F Barbin | Paul Allen | P. Allen | Da‐Wen Sun | G. ElMasry | D. Barbin | G. Elmasry
[1] Ashok Samal,et al. Optical scattering in beef steak to predict tenderness using hyperspectral imaging in the VIS-NIR region , 2008 .
[2] Angelo Zanella,et al. Supervised Multivariate Analysis of Hyper-spectral NIR Images to Evaluate the Starch Index of Apples , 2009 .
[3] Kurt C. Lawrence,et al. Embedded bone fragment detection in chicken fillets using transmittance image enhancement and hyperspectral reflectance imaging , 2008 .
[4] P J Cullen,et al. Recent applications of Chemical Imaging to pharmaceutical process monitoring and quality control. , 2008, European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V.
[5] David Casasent,et al. Fusion algorithm for poultry skin tumor detection using hyperspectral data. , 2007, Applied optics.
[6] Kit L. Yam,et al. A simple digital imaging method for measuring and analyzing color of food surfaces , 2004 .
[7] Y. R. Chen,et al. HYPERSPECTRAL REFLECTANCE AND FLUORESCENCE IMAGING SYSTEM FOR FOOD QUALITY AND SAFETY , 2001 .
[8] Kurt C. Lawrence,et al. Improved Hyperspectral Imaging System for Fecal Detection on Poultry Carcasses , 2007 .
[9] K. Dzama,et al. Some biochemical aspects pertaining to beef eating quality and consumer health: A review , 2009 .
[10] Yud-Ren Chen,et al. Hyperspectral imaging for safety inspection of food and agricultural products , 1999, Other Conferences.
[11] Jean-Louis Damez,et al. Beef meat electrical impedance spectroscopy and anisotropy sensing for non-invasive early assessment of meat ageing , 2008 .
[12] M. P��������,et al. Ability of NIR spectroscopy to predict meat chemical composition and quality – a review , 2004 .
[13] M. Hunt,et al. Current research in meat color. , 2005, Meat science.
[14] M. Kreuzer,et al. Quality of retail beef from two grass-based production systems in comparison with conventional beef. , 2006, Meat science.
[15] Y. R. Chen,et al. USE OF HYPER– AND MULTI–SPECTRAL IMAGING FOR DETECTION OF CHICKEN SKIN TUMORS , 2000 .
[16] Gerhard Feiner,et al. Meat Products Handbook: Practical Science and Technology , 2006 .
[17] 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.
[18] Da-Wen Sun,et al. Improving quality inspection of food products by computer vision: a review , 2004 .
[19] Kurt C. Lawrence,et al. Real-time multispectral imaging system for online poultry fecal inspection using UML , 2006, SPIE Optics East.
[20] Seong G. Kong,et al. Principal component analysis for poultry tumor inspection using hyperspectral fluorescence imaging , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..
[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] 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.
[23] P. Allen,et al. Development of a computed tomographic calibration method for the determination of lean meat content in pig carcasses. , 2006, Acta veterinaria Hungarica.
[24] 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 .
[25] Yankun Peng,et al. Hyperspectral Scattering Profiles for Prediction of Beef Tenderness , 2008 .
[26] W. R. Windham,et al. Hyperspectral Imaging for Detecting Fecal and Ingesta Contaminants on Poultry Carcasses , 2002 .
[27] Renfu Lu,et al. Gloss Evaluation of Curved-surface Fruits and Vegetables , 2009 .
[28] Kurt C. Lawrence,et al. Bone fragment detection in chicken breast fillets using diffuse scattering patterns of back-illuminated structured light , 2006, SPIE Optics East.
[29] Michael Ngadi,et al. Pork Quality Classification Using a Hyperspectral Imaging System and Neural Network , 2007 .
[30] G. Monin,et al. Influence of intramuscular fat content on the quality of pig meat - 1. Composition of the lipid fraction and sensory characteristics of m. longissimus lumborum. , 1999, Meat science.
[31] Ashok Samal,et al. Visible/near-infrared hyperspectral imaging for beef tenderness prediction , 2008 .
[32] Daniel E. Guyer,et al. Near-infrared hyperspectral reflectance imaging for detection of bruises on pickling cucumbers , 2006, Computers and Electronics in Agriculture.
[33] Jens T. Thielemann,et al. Non-Contact Transflectance near Infrared Imaging for Representative on-Line Sampling of Dried Salted Coalfish (Bacalao) , 2006 .
[34] Da-Wen Sun,et al. Modelling vacuum cooling process of cooked meat: part 1: analysis of vacuum cooling system , 2002 .
[35] Dietrich Knorr,et al. Characterization of High-Hydrostatic-Pressure Effects on Fresh Produce Using Chlorophyll Fluorescence Image Analysis , 2009 .
[36] T. Kenny,et al. Effect of rapid and conventional cooling methods on the quality of cooked ham joints. , 2000, Meat science.
[37] D. S. Hale,et al. NATIONAL CONSUMER RETAIL BEEF STUDY: INTERACTION OF TRIM LEVEL, PRICE AND GRADE ON CONSUMER ACCEPTANCE OF BEEF STEAKS AND ROASTS , 1989 .
[38] Kurt C. Lawrence,et al. Performance of hyperspectral imaging system for poultry surface fecal contaminant detection. , 2006 .
[39] Mieke Uyttendaele,et al. Wageningen Academic Publishers , 2005 .
[40] Wayne Daley,et al. Fusion of visible and X-ray sensing modalities for the enhancement of bone detection in poultry products , 2000, SPIE Optics East.
[41] Achim Kohler,et al. Noncontact salt and fat distributional analysis in salted and smoked salmon fillets using X-ray computed tomography and NIR interactance imaging. , 2009, Journal of agricultural and food chemistry.
[42] Di Wu,et al. Study on infrared spectroscopy technique for fast measurement of protein content in milk powder based on LS-SVM , 2008 .
[43] P. Allen,et al. Prediction of beef palatability from colour, marbling and surface texture features of longissimus dorsi , 2010 .
[44] Moon S. Kim,et al. DETECTION OF SKIN TUMORS ON CHICKEN CARCASSES USING HYPERSPECTRAL FLUORESCENCE IMAGING , 2004 .
[45] Daniel E. Guyer,et al. Comparison of Artificial Neural Networks and Statistical Classifiers in Apple Sorting using Textural Features , 2004 .
[46] Da-Wen Sun,et al. Learning techniques used in computer vision for food quality evaluation: a review , 2006 .
[47] Wei Wang,et al. Prediction of Pork Meat Total Viable Bacteria Count Using Hyperspectral Imaging System and Support Vector Machines , 2008 .
[48] 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.
[49] Da-Wen Sun,et al. Automatic segmentation of beef longissimus dorsi muscle and marbling by an adaptable algorithm. , 2009, Meat science.
[50] K. Heia,et al. Detection of nematodes in cod (Gadus morhua) fillets by imaging spectroscopy. , 2007, Journal of food science.
[51] Seong-Gon Kong,et al. Inspection of poultry skin tumor using hyperspectral fluorescence imaging , 2003, International Conference on Quality Control by Artificial Vision.
[52] S. Prasher,et al. Prediction of drip-loss, pH, and color for pork using a hyperspectral imaging technique. , 2007, Meat science.
[53] J. Tan,et al. Beef Marbling and Color Score Determination by Image Processing , 1996 .
[54] J. Tan,et al. Classification of tough and tender beef by image texture analysis. , 2001, Meat science.
[55] J. Qin,et al. Detecting pits in tart cherries by hyperspectral transmission imaging , 2004, SPIE Optics East.
[56] G Monin,et al. Recent methods for predicting quality of whole meat. , 1998, Meat science.
[57] K. Honikel,et al. Reference methods for the assessment of physical characteristics of meat. , 1998, Meat science.
[58] Jing Zhang,et al. Hyperspectral scattering profiles for prediction of the microbial spoilage of beef , 2009, Defense + Commercial Sensing.
[59] Jens Petter Wold,et al. Prediction of Ice Fraction and Fat Content in Super-Chilled Salmon by Non-Contact Interactance near Infrared Imaging , 2009 .
[60] Seyed Mohammad Ali Razavi,et al. Application of Image Analysis and Artificial Neural Network to Predict Mass Transfer Kinetics and Color Changes of Osmotically Dehydrated Kiwifruit , 2011 .
[61] E. Agulló,et al. Determination of Total Pigments in Red Meats , 1990 .
[62] P. Purslow,et al. Modelling quality variations in commercial Ontario pork production. , 2008, Meat science.
[63] 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.
[64] Da-Wen Sun,et al. Modelling vacuum cooling process of cooked meat—part 2: mass and heat transfer of cooked meat under vacuum pressure , 2002 .
[65] Renfu Lu,et al. Detection of Internal Defect in Pickling Cucumbers Using Hyperspectral Transmittance Imaging , 2008 .
[66] Kurt C. Lawrence,et al. Effectiveness of Hyperspectral Imaging System for Detecting Cecal Contaminated Broiler Carcasses , 2005 .
[67] Prof. Dr. Joseph F. Zayas. Functionality of Proteins in Food , 1996, Springer Berlin Heidelberg.
[68] J. Gómez-Sanchís,et al. Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables , 2011 .
[69] T. Schroeder,et al. In‐Store Valuation of Steak Tenderness , 2001 .
[70] Seong G. Kong,et al. Band Selection of Hyperspectral Images for Automatic Detection of Poultry Skin Tumors , 2007, IEEE Transactions on Automation Science and Engineering.
[71] Bosoon Park,et al. Simple Algorithms for the Classification of Visible/Near-Infrared and Hyperspectral Imaging Spectra of Chicken Skins, Feces, and Fecal Contaminated Skins , 2003, Applied spectroscopy.
[72] Richard D. Driver. Quantification and threshold detection in real-time hyperspectral imaging , 2009, Defense + Commercial Sensing.
[73] K. J. Chen,et al. Segmentation of beef marbling based on vision threshold , 2008 .
[74] K. Lundström,et al. Variation in light scattering and water-holding capacity along the porcine Longissimus dorsi muscle. , 1985, Meat science.
[75] D. Beermann. ASAS Centennial paper: a century of pioneers and progress in meat science in the United States leads to new frontiers. , 2009, Journal of animal science.
[76] G. Valdimarsson,et al. Detection of parasites in fish muscle by candling technique , 1985 .
[77] T. J. Shankar,et al. A Case Study on Optimization of Biomass Flow During Single-Screw Extrusion Cooking Using Genetic Algorithm (GA) and Response Surface Method (RSM) , 2010 .
[78] Kurt C. Lawrence,et al. Comparison between visible/NIR spectroscopy and hyperspectral imaging for detecting surface contaminants on poultry carcasses , 2004, SPIE Optics East.
[79] F. Toldrá,et al. The use of muscle enzymes as predictors of pork meat quality , 2000 .
[80] Ultrasonic determination of chicken composition. , 1999, Journal of agricultural and food chemistry.
[81] M. Ngadi,et al. Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry , 2007 .
[82] Franco Pedreschi,et al. Color of Salmon Fillets By Computer Vision and Sensory Panel , 2010 .
[83] Gauri S. Mittal,et al. Rapid Detection of Microorganisms Using Image Processing Parameters and Neural Network , 2010 .
[84] Da-Wen Sun,et al. Recent applications of image texture for evaluation of food qualities—a review , 2006 .
[85] T. R. Dutson,et al. Quality Attributes and their Measurement in Meat, Poultry and Fish Products , 1995, Advances in Meat Research.
[86] A. Peirs,et al. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review , 2007 .
[87] Fred Godtliebsen,et al. Ridge detection with application to automatic fish fillet inspection , 2009 .
[88] Gerald W. Heitschmidt,et al. Detection of Ingesta on Pre-Chilled Broiler Carcasses by Hyperspectral Imaging , 2005 .
[89] 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.
[90] David Casasent,et al. Contaminant detection on poultry carcasses using hyperspectral data: Part II. Algorithms for selection of sets of ratio features , 2007, SPIE Optics East.
[91] Federico Pallottino,et al. Image Analysis Techniques for Automated Hazelnut Peeling Determination , 2010 .
[92] M. F. Furnols,et al. Estimation of lean meat content in pig carcasses using X-ray Computed Tomography and PLS regression , 2009 .
[93] Da-Wen Sun,et al. Recent developments in the applications of image processing techniques for food quality evaluation , 2004 .
[94] E Kalm,et al. Comparison of different methods for determination of drip loss and their relationships to meat quality and carcass characteristics in pigs. , 2004, Meat science.
[95] Silvia Serranti,et al. Hyperspectral imaging applied to complex particulate solids systems , 2008, Photonics Europe.
[96] Chun-Chieh Yang,et al. Machine vision system for online inspection of freshly slaughtered chickens , 2009 .
[97] Y. R. Chen,et al. Near-infrared reflectance analysis for predicting beef longissimus tenderness. , 1998, Journal of animal science.
[98] M. Ngadi,et al. Protein Denaturation in Pork Longissimus Muscle of Different Quality Groups , 2011 .
[99] W. R. Windham,et al. A Hyperspectral Imaging System for Identification of Faecal and Ingesta Contamination on Poultry Carcasses , 2003 .
[100] Da-Wen Sun,et al. Recent developments and applications of image features for food quality evaluation and inspection – a review , 2006 .
[101] Moon S. Kim,et al. Analysis of hyperspectral fluorescence images for poultry skin tumor inspection. , 2004, Applied optics.
[102] S. Prasher,et al. Pork quality and marbling level assessment using a hyperspectral imaging system , 2007 .
[103] 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.
[104] Jon Tschudi,et al. Rapid and non-invasive measurements of fat and pigment concentrations in live and slaughtered Atlantic salmon (Salmo salar L.) , 2008 .
[105] David Casasent,et al. Contaminant detection on poultry carcasses using hyperspectral data: Part I. Algorithms for selection of individual wavebands , 2007, SPIE Optics East.
[106] J D Tatum,et al. Online prediction of beef tenderness using a computer vision system equipped with a BeefCam module. , 2003, Journal of animal science.
[107] Moon S. Kim,et al. Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations , 2004 .
[108] L. Istasse,et al. Prediction of technological and organoleptic properties of beef Longissimus thoracis from near-infrared reflectance and transmission spectra. , 2004, Meat science.
[109] Clément Vigneault,et al. Spectral methods for measuring quality changes of fresh fruits and vegetables , 2008 .
[110] J. B. Morgan,et al. Using the near-infrared system to sort various beef middle and end muscle cuts into tenderness categories. , 2008, Journal of animal science.
[111] Gamal ElMasry,et al. High-speed assessment of fat and water content distribution in fish fillets using online imaging spectroscopy. , 2008, Journal of agricultural and food chemistry.
[112] Nuria Aleixos,et al. Erratum to: Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables , 2011 .
[113] W. R. Windham,et al. Contaminant classification of poultry hyperspectral imagery using a spectral angle mapper algorithm , 2007 .
[114] R. Winger,et al. Juiciness — its importance and some contributing factors , 1994 .
[115] Karl McDonald,et al. The effect of injection level on the quality of a rapid vacuum cooled cooked beef product , 2001 .
[116] D. E. Chan,et al. PREDICTION OF PORK QUALITY CHARACTERISTICS USING VISIBLE AND NEAR–INFRARED SPECTROSCOPY , 2002 .
[117] Michael Ngadi,et al. Wavelength Selection for Surface Defects Detection on Tomatoes by Means of a Hyperspectral Imaging System , 2006 .
[118] Jens Petter Wold,et al. Fat Distribution Analysis in Salmon Fillets Using Non-Contact near Infrared Interactance Imaging: A Sampling and Calibration Strategy , 2009 .
[119] Jean-Louis Damez,et al. Meat quality assessment using biophysical methods related to meat structure. , 2008, Meat science.
[120] Dejan Škorjanc,et al. Predicting pork water-holding capacity with NIR spectroscopy in relation to different reference methods , 2010 .
[121] R. G. Kauffman,et al. Muscle protein changes post mortem in relation to pork quality traits. , 1997, Meat science.
[122] F. Teuscher,et al. Application of computer image analysis to measure pork marbling characteristics. , 2005, Meat science.
[123] J. Aguilera,et al. Computer Vision and Stereoscopy for Estimating Firmness in the Salmon (Salmon salar) Fillets , 2010 .
[124] Kurt C. Lawrence,et al. Hyperspectral Imaging for Detecting Fecal and Ingesta Contamination on Poultry Carcasses , 2001 .
[125] K. Heia,et al. Detection of Parasites in Cod Fillets by Using SIMCA Classification in Multispectral Images in the Visible and NIR Region , 2001 .
[126] Ashok Samal,et al. Partial least squares analysis of near-infrared hyperspectral images for beef tenderness prediction , 2008 .
[127] H. J. Andersen,et al. Factors of significance for pork quality-a review. , 2003, Meat science.
[128] O. Sørheim,et al. Computed tomography for quantitative determination of sodium chloride in ground pork and dry-cured hams. , 2007, Journal of food science.
[129] R. G. Kauffman,et al. The use of filter paper to estimate drip loss of porcine musculature. , 1986, Meat science.
[130] H. Heymann,et al. Image texture features as indicators of beef tenderness. , 1999, Meat science.
[131] D. E. Chan,et al. High Throughput Spectral Imaging System for Wholesomeness Inspection of Chicken , 2008 .
[132] Da-Wen Sun,et al. Effect of evacuation rate on the vacuum cooling process of a cooked beef product , 2001 .
[133] David Casasent,et al. Hyperspectral feature selection and fusion for detection of chicken skin tumors , 2004, SPIE Optics East.
[134] Digvir S. Jayas,et al. Wavelet Analysis of Signals in Agriculture and Food Quality Inspection , 2010 .
[135] Tao Xiong,et al. A combined SVM and LDA approach for classification , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[136] J B Morgan,et al. Predicting beef tenderness using near-infrared spectroscopy. , 2008, Journal of animal science.
[137] Ning Wang,et al. Determination of pork quality attributes using hyperspectral imaging technique , 2005, SPIE Optics East.
[138] R. Lu,et al. DETECTION OF BRUISES ON APPLES USING NEAR – INFRARED HYPERSPECTRAL IMAGING , 2003 .
[139] R. Roehe,et al. Application of near infrared reflectance spectroscopy to predict meat and meat products quality: A review. , 2009, Meat science.
[140] Kurt C. Lawrence,et al. ALGORITHM DEVELOPMENT WITH VISIBLE/NEAR-INFRARED SPECTRA FOR DETECTION OF POULTRY FECES AND INGESTA , 2003 .
[141] M. López-Caballero,et al. Washing effect on the quality index method (QIM) developed for raw gilthead seabream (Sparus aurata) , 2001 .