Evaluation of broiler breast fillets with the woody breast condition using expressible fluid measurement combined with deep learning algorithm

Abstract In this study, the relationship between expressible fluid (EF) measurements and the woody breast (WB) condition in broiler breast fillets (pectoralis major) was investigated and the deep learning algorithm (DLA) was evaluated to predict degrees of the WB condition based on EF images. Fillet samples were collected from a commercial plant and categorized into normal (no WB), moderate WB, and severe WB groups. EF of fresh and frozen samples were measured using the filter paper press method. The features of the images were analyzed using traditional manual method, gray level co-occurrence matrix (GLCM) method and the DLA method, respectively. The results show that there were significant differences in average EF measurements between three WB categories (P

[1]  Seung-Chul Yoon,et al.  Fusion of Spectra and Texture Data of Hyperspectral Imaging for the Prediction of the Water-Holding Capacity of Fresh Chicken Breast Filets , 2018 .

[2]  Xinglian Xu,et al.  Effect of salt content on gelation of normal and wooden breast myopathy chicken pectoralis major meat batters , 2017 .

[3]  W. Shao,et al.  Meat quality traits and proteome profile of woody broiler breast (pectoralis major) meat , 2018, Poultry science.

[4]  Seung-Chul Yoon,et al.  Rapid classification of intact chicken breast fillets by predicting principal component score of quality traits with visible/near-Infrared spectroscopy. , 2018, Food chemistry.

[5]  E. Puolanne,et al.  Myodegeneration With Fibrosis and Regeneration in the Pectoralis Major Muscle of Broilers , 2014, Veterinary pathology.

[6]  Zhenjie Xiong,et al.  Combination of spectra and texture data of hyperspectral imaging for differentiating between free-range and broiler chicken meats , 2015 .

[8]  F. Soglia,et al.  Wooden-Breast, White Striping, and Spaghetti Meat: Causes, Consequences and Consumer Perception of Emerging Broiler Meat Abnormalities. , 2019, Comprehensive reviews in food science and food safety.

[9]  M. Irie,et al.  Rapid method for determining water-holding capacity in meat using video image analysis and simple formulae. , 1996, Meat science.

[10]  F. Soglia,et al.  Meat quality in fast-growing broiler chickens , 2015 .

[11]  B. Hargis,et al.  White striping and woody breast myopathies in the modern poultry industry: a review. , 2016, Poultry science.

[12]  C. Coon,et al.  Incidence of broiler breast myopathies at 2 different ages and its impact on selected raw meat quality parameters , 2017, Poultry science.

[13]  Chen Li,et al.  High performance vegetable classification from images based on AlexNet deep learning model , 2018 .

[14]  H. Zhuang,et al.  Instrumental texture characteristics of broiler pectoralis major with the wooden breast condition. , 2016, Poultry science.

[15]  H. Zhuang,et al.  The wooden breast condition results in surface discoloration of cooked broiler pectoralis major2 , 2018, Poultry science.

[16]  Abdelouahab Moussaoui,et al.  Deep Learning for Tomato Diseases: Classification and Symptoms Visualization , 2017, Appl. Artif. Intell..

[17]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[18]  J. Zamorano,et al.  Contribution to improving the meat water holding capacity test by the filter paper press method. A comparison of three methods for measuring areas. , 1997, Meat science.

[19]  Raja Purushothaman,et al.  Tomato crop disease classification using pre-trained deep learning algorithm , 2018 .

[20]  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.

[21]  Daniel Lévy,et al.  Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks , 2016, ArXiv.

[22]  Mona A. S. Ali,et al.  Identifying Two of Tomatoes Leaf Viruses Using Support Vector Machine , 2015 .

[23]  Jiewen Zhao,et al.  Rapid detection of total viable count (TVC) in pork meat by hyperspectral imaging , 2013 .

[24]  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.

[25]  G R Trout,et al.  Techniques for measuring water-binding capacity in muscle foods-A review of methodology. , 1988, Meat science.

[26]  Seung-Chul Yoon,et al.  Tenderness classification of fresh broiler breast fillets using visible and near-infrared hyperspectral imaging. , 2018, Meat science.

[27]  M. Aaslyng,et al.  Classification of wooden breast myopathy in chicken pectoralis major by a standardised method and association with conventional quality assessments , 2018 .

[28]  C. Coon,et al.  Instrumental compression force and meat attribute changes in woody broiler breast fillets during short‐term storage , 2018, Poultry science.

[29]  J. Meullenet,et al.  Changes in broiler breast fillet tenderness, water-holding capacity, and color attributes during long-term frozen storage. , 2008, Journal of food science.

[30]  Seung-Chul Yoon,et al.  Toward a Fusion of Optical Coherence Tomography and Hyperspectral Imaging for Poultry Meat Quality Assessment , 2016 .

[31]  Marcel Salathé,et al.  Using Deep Learning for Image-Based Plant Disease Detection , 2016, Front. Plant Sci..

[32]  Seung-Chul Yoon,et al.  Detection of aflatoxin B 1 (AFB 1 ) in individual maize kernels using short wave infrared (SWIR) hyperspectral imaging , 2017 .