Rapid classification of intact chicken breast fillets by predicting principal component score of quality traits with visible/near-Infrared spectroscopy.

In this study visible/near-infrared spectroscopy (Vis/NIRS) was evaluated to rapidly classify intact chicken breast fillets. Five principal components (PC) were extracted from reference quality traits (L∗, pH, drip loss, expressible fluid, and salt-induced water gain). A quality grades classification method by PC1 score was proposed. With this method, 150 chicken fillets were properly classified into three quality grades, i.e., DFD (dark, firm and dry), normal, and PSE (pale, soft and exudative). Furthermore, PC1 score could be predicted using partial least squares regression (PLSR) model based on Vis/NIRS (R2p = 0.78, RPD = 1.9), without the measurement of any quality traits. Thresholds of PC1 classification method were applied to classify the predicted PC1 score values of each fillet into three quality grades. The classification accuracy of calibration and prediction set were 85% and 80%, respectively. Results revealed that PC1 score classification method is feasible, and with Vis/NIRS, this method could be rapidly implemented.

[1]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[2]  Guang-hong Zhou,et al.  A comparative study of chemical composition, color, and thermal gelling properties of normal and PSE-like chicken breast meat , 2015 .

[3]  Di Wu,et al.  Application of long-wave near infrared hyperspectral imaging for measurement of color distribution in salmon fillet , 2012 .

[4]  H. Zhuang,et al.  Measurement of water-holding capacity in raw and freeze-dried broiler breast meat with visible and near-infrared spectroscopy. , 2014, Poultry Science.

[5]  S. Barbut,et al.  Effects of pale, normal, and dark chicken breast meat on microstructure, extractable proteins, and cooking of marinated fillets. , 2005, Poultry science.

[6]  W. R. Windham,et al.  Principal component analysis of physical, color, and sensory characteristics of chicken breasts deboned at two, four, six, and twenty-four hours postmortem. , 2004, Poultry science.

[7]  H. Zhuang,et al.  Validation of a combi oven cooking method for preparation of chicken breast meat for quality assessment. , 2008, Journal of food science.

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

[9]  J. Kijowski,et al.  Impact of PSE and DFD meat on poultry processing - a review. , 2003 .

[10]  J. Jiao,et al.  Multivariate statistical evaluation of trace elements in groundwater in a coastal area in Shenzhen, China. , 2007, Environmental pollution.

[11]  Ying Ouyang,et al.  Evaluation of river water quality monitoring stations by principal component analysis. , 2005, Water research.

[12]  Hong Zhuang,et al.  Postmortem aging and freezing and thawing storage enhance ability of early deboned chicken pectoralis major muscle to hold added salt water. , 2012, Poultry science.

[13]  W. R. Windham,et al.  Prediction of physical, color, and sensory characteristics of broiler breasts by visible/near infrared reflectance spectroscopy. , 2004, Poultry science.

[14]  Ashutosh Kumar Singh,et al.  Factor analytical approaches for evaluating groundwater trace element chemistry data , 2003 .

[15]  C. Owens,et al.  The characterization and incidence of pale, soft, and exudative broiler meat in a commercial processing plant. , 2002, Poultry science.

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

[17]  S. Barbut,et al.  Effects of regular and modified starches on cooked pale, soft, and exudative; normal; and dry, firm, and dark breast meat batters. , 2005, Poultry science.

[18]  F. B. Wardlaw,et al.  EFFECT OF POSTMORTEM MUSCLE CHANGES ON POULTRY MEAT LOAF PROPERTIES , 1973 .

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

[20]  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.

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

[22]  G. Hemke,et al.  Prediction of pork quality using visible/near-infrared reflectance spectroscopy. , 2006, Meat science.

[23]  H. Zarei,et al.  Factor analysis of chemical composition in the Karoon River basin, southwest of Iran , 2013, Applied Water Science.

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

[25]  S. Barbut,et al.  Problem of pale soft exudative meat in broiler chickens. , 1997, British poultry science.

[26]  Di Wu,et al.  Non-destructive and rapid analysis of moisture distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared hyperspectral imaging , 2013 .

[27]  S. Brown,et al.  Variation in the colour of broiler breast fillets in the UK , 2000, British poultry science.

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

[29]  M. Shimokomaki,et al.  Broiler chicken PSE (Pale, Soft, Exudative) meat and water release during chicken carcass thawing and brazilian legislation , 2013 .

[30]  D. P. Smith,et al.  Pale poultry muscle syndrome. , 2009, Poultry science.

[31]  H. Zhuang,et al.  Comparisons of sensory descriptive flavor and texture profiles of cooked broiler breast fillets categorized by raw meat color lightness values. , 2010, Poultry science.

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

[33]  K. Honikel,et al.  Measurement of water-holding capacity and juiciness , 1994 .

[34]  R. Klont,et al.  Prediction of pork quality with near infrared spectroscopy (NIRS): 1. Feasibility and robustness of NIRS measurements at laboratory scale. , 2012, Meat science.