Digital image analyses as an alternative tool for chicken quality assessment

Poultry meat colour is an important quality attribute for the rapid detection of “pale poultry syndrome”, as it is affected by conditions of animal welfare during pre-mortem period. The meat processing industry demands a fast and non-contact method for accurate meat colour assessment. In the present study, computer vision was tested as a potential tool to predict colour measurements compared to CIELab attributes of chicken breast (pectoralis major) obtained by analytical reference measurements. The proposed approach using computer vision was successful in avoiding pixels with little information (specular reflection) and based on an illumination normalisation step it was obtained an acceptable correlation between colorimeter measurements and the proposed framework (Delta E = 5.2). High correlation coefficients obtained between computer vision and colorimeter validate the approach for measuring L* colour component. Results for determination coefficient was R2 = 0.99 for L*. In addition, our framework reach R2 = 0.74 for a*, and R2 = 0.88 for b* component. Results suggest that computer vision methods base on an RGB device can become useful tool for fast quality assessment of chicken meat in large-scale processing plants.

[1]  Sandra Helena Prudencio,et al.  Consumer attitudes and preferences regarding pale, soft, and exudative broiler breast meat , 2012 .

[2]  Knut Conradsen,et al.  Comparison of a multispectral vision system and a colorimeter for the assessment of meat color. , 2015, Meat science.

[3]  Yu Ju Wu,et al.  The effect of substrate properties on print attributes for gravure printing--From proof to press , 2008 .

[4]  Christine Connolly,et al.  A study of efficiency and accuracy in the transformation from RGB to CIELAB color space , 1997, IEEE Trans. Image Process..

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

[6]  P. Allen,et al.  Texture appearance characterization of pre-sliced pork ham images using fractal metrics: Fourier analysis dimension and lacunarity , 2009 .

[7]  Da-Wen Sun,et al.  Recent developments in the applications of image processing techniques for food quality evaluation , 2004 .

[8]  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.

[9]  Giorgia Foca,et al.  Automated identification and visualization of food defects using RGB imaging: Application to the detection of red skin defect of raw hams , 2012 .

[10]  Edenir Rodrigues Pereira-Filho,et al.  Digital image analysis - an alternative tool for monitoring milk authenticity , 2013 .

[11]  Robin Torrence,et al.  Identification of starch granules using image analysis and multivariate techniques , 2004 .

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

[13]  Da-Wen Sun,et al.  Food colour measurement using computer vision , 2013 .

[14]  Adriana Lourenço Soares,et al.  The effects of the dark house system on growth, performance and meat quality of broiler chicken. , 2015, Animal science journal = Nihon chikusan Gakkaiho.

[15]  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.

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

[17]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

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

[19]  D. Mery,et al.  Color measurement in L ¿ a ¿ b ¿ units from RGB digital images , 2006 .

[20]  M O Smith,et al.  Characteristics of pale, soft, exudative broiler breast meat. , 2000, Poultry science.

[21]  Franco Pedreschi,et al.  A non-destructive digital imaging method to predict immobilized yeast-biomass , 2009 .

[22]  Manabendra Bhuyan,et al.  A computer based system for matching colours during the monitoring of tea fermentation , 2005 .

[23]  Patrick Jackman,et al.  Robust colour calibration of an imaging system using a colour space transform and advanced regression modelling. , 2012, Meat science.

[24]  Da-Wen Sun,et al.  Recent developments and applications of image features for food quality evaluation and inspection – a review , 2006 .

[25]  D. V. Byrne,et al.  Evaluation of pork colour: prediction of visual sensory quality of meat from instrumental and computer vision methods of colour analysis. , 2003, Meat science.

[26]  Daniel Filippini,et al.  Direct quantitative evaluation of complex substances using computer screen photo-assisted technology: the case of red wine. , 2007, Analytica Chimica Acta.

[27]  Fabio Napolitano,et al.  Measurement of meat color using a computer vision system. , 2013, Meat science.

[28]  Andrew R. East,et al.  Colour vision system evaluation of bicolour fruit: A case study with ‘B74’ mango , 2008 .

[29]  Gerard Downey,et al.  Rapid Non-destructive Detection of Spoilage of Intact Chicken Breast Muscle Using Near-infrared and Fourier Transform Mid-infrared Spectroscopy and Multivariate Statistics , 2009, Food and Bioprocess Technology.

[30]  J. Aguilera,et al.  Changes on image texture features of breakfast flakes cereals during water absorption , 2013, Food science and technology international = Ciencia y tecnologia de los alimentos internacional.

[31]  M De Marchi,et al.  Feasibility of the direct application of near-infrared reflectance spectroscopy on intact chicken breasts to predict meat color and physical traits. , 2011, Poultry science.

[32]  Bjarne K. Ersbøll,et al.  Supervised feature selection for linear and non-linear regression of L⁎a⁎b⁎ color from multispectral images of meat , 2014, Eng. Appl. Artif. Intell..

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

[34]  Da-Wen Sun,et al.  Improving quality inspection of food products by computer vision: a review , 2004 .

[35]  M. Shimokomaki,et al.  Protease activity and the ultrastructure of broiler chicken PSE (pale, soft, exudative) meat , 2010 .