A method for nondestructive prediction of pork meat quality and safety attributes by hyperspectral imaging technique

Abstract Rapid and nondestructive methods for predicting meat quality and safety attributes are of great concerns at present. A Hyperspectral imaging technique was investigated for evaluating pork meat tenderness and Escherichia coli (E. coli) contamination in this study. Totally 31 samples were used for hyperspectral imaging in the spectral range of 400–1100 nm. A novel method by Modified Gompertz function was exploited to extract the scattering characteristics of pork meat from the spatially-resolved hyperspectral images. Gompertz parameters α, β, e and δ which can represent different optical meanings were derived by curve-fitting to the original scattering profiles. The fitting coefficients were all around 0.99 between 470 and 960 nm, which indicating the effective interpretation by Gompertz function. Multi-linear regression models were established using both individual parameters and integrated parameters, and the results showed that Gompertz parameter δ was superior to other individual parameters for both pork meat tenderness and E. coli contamination, and the integrated parameter can perform better than individual parameters. The validation results (RCV) by the integrated parameter method were 0.949 and 0.939 for pork meat tenderness and E. coli contamination respectively. The study demonstrated that hyperspectral imaging technique combined with Gompertz function was potential for rapid determination of pork meat tenderness and E. coli contamination, and so hopefully to provide a promising tool for monitoring the multiple attributes concerning meat quality and safety.

[1]  A F Goetz,et al.  Imaging Spectrometry for Earth Remote Sensing , 1985, Science.

[2]  Shiv O. Prasher,et al.  Categorization of pork quality using Gabor filter-based hyperspectral imaging technology , 2010 .

[3]  Moon S. Kim,et al.  Development of simple algorithms for the detection of fecal contaminants on apples from visible/near infrared hyperspectral reflectance imaging , 2007 .

[4]  Zuhaimy Ismail,et al.  Nonlinear Growth Models for Modeling Oil Palm Yield Growth , 2005 .

[5]  Royston Goodacre,et al.  Rapid and quantitative detection of the microbial spoilage of muscle foods: current status and future trends. , 2001 .

[6]  Feifei Tao,et al.  Classification of Pork Quality Characteristics by Hyperspectral Scattering Technique , 2012 .

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

[8]  Jianwei Qin,et al.  Measurement of the Absorption and Scattering Properties of Turbid Liquid Foods Using Hyperspectral Imaging , 2007, Applied spectroscopy.

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

[10]  W. R. Windham,et al.  Partial Least Squares Regression of Hyperspectral Images for Contaminant Detection on Poultry Carcasses , 2006 .

[11]  Takaaki Satake,et al.  Possibility of using near infrared spectroscopy for evaluation of bacterial contamination in shredded cabbage , 2008 .

[12]  A. Sjöberg,et al.  HACCP-based food quality control and rapid detection methods for microorganisms , 1996 .

[13]  Royston Goodacre,et al.  Rapid identification of closely related muscle foods by vibrational spectroscopy and machine learning. , 2005, The Analyst.

[14]  Gang Yao,et al.  Heating induced optical property changes in beef muscle , 2008 .

[15]  Wei Wang,et al.  [A rapid nondestructive measurement method for assessing the total plate count on chilled pork surface]. , 2010, Guang pu xue yu guang pu fen xi = Guang pu.

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

[17]  Colm P. O'Donnell,et al.  Hyperspectral imaging – an emerging process analytical tool for food quality and safety control , 2007 .

[18]  Rakesh K. Singh,et al.  Application of Artificial Neural Networks to Predict the Oxidation of Menhaden Fish Oil Obtained from Fourier Transform Infrared Spectroscopy Method , 2011 .

[19]  Jiewen Zhao,et al.  [The determination of beef tenderness using near-infrared spectroscopy]. , 2006, Guang pu xue yu guang pu fen xi = Guang pu.

[20]  R. Lu,et al.  Analysis of spatially resolved hyperspectral scattering images for assessing apple fruit firmness and soluble solids content , 2008 .

[21]  S. Oshita,et al.  Rapid detection of Escherichia coli contamination in packaged fresh spinach using hyperspectral imaging. , 2011, Talanta.

[22]  Gang Yao,et al.  Monitoring sarcomere structure changes in whole muscle using diffuse light reflectance. , 2006, Journal of biomedical optics.

[23]  W. R. Windham,et al.  Hyperspectral Imaging for Detecting Fecal and Ingesta Contaminants on Poultry Carcasses , 2002 .

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

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

[26]  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.

[27]  Wei Wang,et al.  Rapid detection of total viable count of chilled pork using hyperspectral scattering technique , 2010, Defense + Commercial Sensing.

[28]  Willy Verstraete,et al.  Evaluation of the Gompertz function to model survival of bacteria introduced into soils. , 1995 .

[29]  Shiv O. Prasher,et al.  Use of visible spectroscopy for quality classification of intact pork meat , 2007 .

[30]  Gauri S. Mittal,et al.  Rapid Detection of Microorganisms Using Image Processing Parameters and Neural Network , 2010 .

[31]  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.

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

[33]  C. Davis Lasers and Electro-optics: Fundamentals and Engineering , 1996 .

[34]  Yankun Peng,et al.  Potential prediction of the microbial spoilage of beef using spatially resolved hyperspectral scattering profiles , 2011 .

[35]  J. Sofos,et al.  Challenges to meat safety in the 21st century. , 2008, Meat science.

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

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

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

[39]  Renfu Lu,et al.  Integrated spectral and image analysis of hyperspectral scattering data for prediction of apple fruit firmness and soluble solids content , 2011 .

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

[41]  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.

[42]  R. Chakraborty,et al.  Validity of modified Gompertz and Logistic models in predicting cell growth of Pediococcus acidilactici H during the production of bacteriocin pediocin AcH , 2007 .

[43]  Moon S. Kim,et al.  Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations , 2004 .

[44]  Yoshinori Kawagoe,et al.  Near infrared spectroscopy integrated with chemometrics for rapid detection of E. coli ATCC 25922 and E. coli K12 , 2010 .

[45]  R. Lu,et al.  Measurement of the optical properties of fruits and vegetables using spatially resolved hyperspectral diffuse reflectance imaging technique , 2008 .

[46]  S. Prasher,et al.  Pork quality and marbling level assessment using a hyperspectral imaging system , 2007 .

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

[48]  Douglas Fernandes Barbin,et al.  Non-destructive assessment of microbial contamination in porcine meat using NIR hyperspectral imaging , 2013 .

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

[50]  C. Maltin,et al.  Slow fiber cluster pattern in pig longissimus thoracis muscle: implications for myogenesis. , 2003, Journal of animal science.

[51]  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.

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

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

[54]  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.

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

[56]  M. Rose,et al.  The Gompertz equation as a predictive tool in demography , 1995, Experimental Gerontology.

[57]  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 .

[58]  Gang Yao,et al.  Distribution of optical scattering properties in four beef muscles , 2008 .