Simultaneous determination of tenderness and Escherichia coli contamination of pork using hyperspectral scattering technique.
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Yankun Peng | Sagar Dhakal | Kuanglin Chao | Feifei Tao | Yongyu Li | Yankun Peng | Feifei Tao | Yong-yu Li | S. Dhakal | K. Chao
[1] Yankun Peng,et al. Prediction of apple fruit firmness and soluble solids content using characteristics of multispectral scattering images , 2007 .
[2] Kiyoe Yoda,et al. An outbreak of Campylobacter jejuni food poisoning caused by secondary contamination in cooking practice at a high school. , 2006, Japanese journal of infectious diseases.
[3] Y. R. Chen,et al. Multispectral detection of fecal contamination on apples based on hyperspectral imagery: Part II. Application of hyperspectral fluorescence imaging , 2002 .
[4] G. Nychas,et al. Meat spoilage during distribution. , 2008, Meat science.
[5] Gang Yao,et al. Heating induced optical property changes in beef muscle , 2008 .
[6] D. Kell,et al. Rapid and Quantitative Detection of the Microbial Spoilage of Meat by Fourier Transform Infrared Spectroscopy and Machine Learning , 2002, Applied and Environmental Microbiology.
[7] H J Swatland,et al. A review of probes and robots: implementing new technologies in meat evaluation. , 1994, Journal of animal science.
[8] Kurt C. Lawrence,et al. Dynamic Thresholding Method for Improving Contaminant Detection Accuracy with Hyperspectral Images , 2005 .
[9] Kurt C. Lawrence,et al. Hyperspectral Image Classification for Fecal and Ingesta Identification by Spectral Angle Mapper , 2004 .
[10] Y. R. Chen,et al. Near-infrared reflectance analysis for predicting beef longissimus tenderness. , 1998, Journal of animal science.
[11] W. R. Windham,et al. Hyperspectral Imaging for Detecting Fecal and Ingesta Contaminants on Poultry Carcasses , 2002 .
[12] L. Mariey,et al. Discrimination, classification, identification of microorganisms using FTIR spectroscopy and chemometrics , 2001 .
[13] Wei Wang,et al. Rapid detection of total viable count of chilled pork using hyperspectral scattering technique , 2010, Defense + Commercial Sensing.
[14] Alan M. Lefcourt,et al. A novel integrated PCA and FLD method on hyperspectral image feature extraction for cucumber chilling damage inspection , 2004 .
[15] Jing Zhang,et al. Hyperspectral scattering profiles for prediction of the microbial spoilage of beef , 2009, Defense + Commercial Sensing.
[16] 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.
[17] M. Doyle,et al. Escherichia coli O157:H7 and its significance in foods. , 1991, International journal of food microbiology.
[18] C. Maltin,et al. Slow fiber cluster pattern in pig longissimus thoracis muscle: implications for myogenesis. , 2003, Journal of animal science.
[19] 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.
[20] R H Dainty,et al. Chemical/biochemical detection of spoilage. , 1996, International journal of food microbiology.
[21] K. Honikel,et al. Reference methods for the assessment of physical characteristics of meat. , 1998, Meat science.
[22] R. Rødbotten,et al. Prediction of beef quality attributes from early post mortem near infrared reflectance spectra , 2000 .
[23] A. Stolle,et al. Incidence of Salmonella in minced meat produced in a European Union-approved cutting plant. , 2001, Journal of food protection.
[24] C. Davis. Lasers and Electro-optics: Fundamentals and Engineering , 1996 .
[25] Royston Goodacre,et al. Rapid identification of closely related muscle foods by vibrational spectroscopy and machine learning. , 2005, The Analyst.
[26] Wei Wang,et al. Prediction of Pork Meat Total Viable Bacteria Count Using Hyperspectral Imaging System and Support Vector Machines , 2008 .
[27] Royston Goodacre,et al. Rapid and quantitative detection of the microbial spoilage of muscle foods: current status and future trends. , 2001 .
[28] M. S. Kim,et al. MULTISPECTRAL DETECTION OF FECAL CONTAMINATION ON APPLES BASED ON HYPERSPECTRAL IMAGERY: PART I. APPLICATION OF VISIBLE AND NEAR–INFRARED REFLECTANCE IMAGING , 2002 .
[29] Renfu Lu,et al. MODELING MULTISPECTRAL SCATTERING PROFILES FOR PREDICTION OF APPLE FRUIT FIRMNESS , 2005 .
[30] Gang Yao,et al. Distribution of optical scattering properties in four beef muscles , 2008 .
[31] Jiang Fa-chao,et al. Hyperspectral scattering profiles for prediction of beef tenderness. , 2009 .
[32] Jianwei Qin,et al. Measurement of the Absorption and Scattering Properties of Turbid Liquid Foods Using Hyperspectral Imaging , 2007, Applied spectroscopy.
[33] Jiewen Zhao,et al. [The determination of beef tenderness using near-infrared spectroscopy]. , 2006, Guang pu xue yu guang pu fen xi = Guang pu.
[34] R. Goodacre,et al. The rapid differentiation of Streptomyces isolates using Fourier transform infrared spectroscopy , 2006 .
[35] 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.