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

Yi, Yang; Wang, Wei; Zhuang, Hong; Yoon, Seung-Chul; Bowker, Brian; Jiang, Hongzhe; Pang, Bin

doi:10.1016/j.jfoodeng.2020.110133

Cited by 11 publications

(8 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The visible/near‐Infrared spectroscopy combined with PCA was evaluated to classify intact chicken breast fillets and the accuracies of calibration and prediction sets were 85 and 80%, respectively (Yang et al, 2018). Meanwhile, the expressible fluid measurement was applied to predicted degrees of the woody breast condition of broiler breast fillets in another study (Yang et al, 2021), which showed good results (high accuracies of 93.3 and 92.3% of independent validation sets of fresh and frozen treatments) after being classified by deep learning algorithm.…”

Section: Resultsmentioning

confidence: 99%

Categorization and authentication of Beijing‐you chicken from four breeds of chickens using near‐infrared hyperspectral imaging combined with chemometrics

Zhang

Gao

Jia

et al. 2020

J Food Process Engineering

View full text Add to dashboard Cite

show abstract

Section: Resultsmentioning

confidence: 99%

Categorization and authentication of Beijing‐you chicken from four breeds of chickens using near‐infrared hyperspectral imaging combined with chemometrics

Zhang

Gao

Jia

et al. 2020

J Food Process Engineering

View full text Add to dashboard Cite

show abstract

“…These researchers also used multilayer perceptron (a feed-forward network differing from the backpropagation network in BPNN) to classify the data set, and classification accuracy was 90.67% for WB. Yang et al ( 2021 ) analysed images derived from the expressible fluid of breast meat to classify WB using SVM (training and testing ratio is unreported) and DL (training to testing is 2 to 1). These researchers found fewer classification efficiencies for SVM algorithms in the testing set (38.25–63.89%), compared with the training set (40.41–81.94%) in three out of the four SVM classification methods used.…”

Section: Discussionmentioning

confidence: 99%

“…These researchers also used multilayer perceptron (a feedforward network differing from the backpropagation network in BPNN) to classify the data set, and classification accuracy was 90.67% for WB. Yang et al (2021) Connexions of random different nodes or units in a computing system to solve problems that are impossible to solve by conventional statistical methods are known as artificial neural networks and are based on the circuitry of the human brain. When applied to a processor framework, the subconscious network can execute unique functions (perception, speech synthesis, image recognition), which have proven to be useful in industrial applications (Alpaydin, 2010).…”

Section: Discussionmentioning

confidence: 99%

Acceptability of Artificial Intelligence in Poultry Processing and Classification Efficiencies of Different Classification Models in the Categorisation of Breast Fillet Myopathies

et al. 2021

View full text Add to dashboard Cite

Breast meat from modern fast-growing big birds is affected with myopathies such as woody breast (WB), white striping, and spaghetti meat (SM). The detection and separation of the myopathy-affected meat can be carried out at processing plants using technologies such as bioelectrical impedance analysis (BIA). However, BIA raw data from myopathy-affected breast meat are extremely complicated, especially because of the overlap of these myopathies in individual breast fillets and the human error associated with the assignment of fillet categories. Previous research has shown that traditional statistical techniques such as ANOVA and regression, among others, are insufficient in categorising fillets affected with myopathies by BIA. Therefore, more complex data analysis tools can be used, such as support vector machines (SVMs) and backpropagation neural networks (BPNNs), to classify raw poultry breast myopathies using their BIA patterns, such that the technology can be beneficial for the poultry industry in detecting myopathies. Freshly deboned (3–3.5 h post slaughter) breast fillets (n = 100 × 3 flocks) were analysed by hand palpation for WB (0-normal; 1-mild; 2-moderate; 3-Severe) and SM (presence and absence) categorisation. BIA data (resistance and reactance) were collected on each breast fillet; the algorithm of the equipment calculated protein and fat index. The data were analysed by linear discriminant analysis (LDA), and with SVM and BPNN with 70::30: training::test data set. Compared with the LDA analysis, SVM separated WB with a higher accuracy of 71.04% for normal (data for normal and mild merged), 59.99% for moderate, and 81.48% for severe WB. Compared with SVM, the BPNN training model accurately (100%) separated normal WB fillets with and without SM, demonstrating the ability of BIA to detect SM. Supervised learning algorithms, such as SVM and BPNN, can be combined with BIA and successfully implemented in poultry processing to detect breast fillet myopathies.

show abstract

“…The image analysis study [ 14 ] suggested that conformation changes in broiler carcasses were mainly related to a breast width increase as WB severity increased. Expressible fluid images were analyzed using deep learning to predict degrees of the WBC [ 16 ]. Image analysis was also used to measure intensity distributions and texture features in images of chicken breast fillets and combined with support vector machine for classification of normal or WB fillets with 91.8% accuracy, while near-infrared (NIR) spectroscopy showed higher performance with 97.5% accuracy [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Development of Imaging System for Online Detection of Chicken Meat with Wooden Breast Condition

Yoon

Bowker

Zhuang

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

In recent years, the wooden breast condition has emerged as a major meat quality defect in the poultry industry worldwide. Broiler pectoralis major muscle with the wooden breast condition is characterized by hardness upon human palpation, which can lead to decrease in meat value or even reduced consumer acceptance. The current method of wooden breast detection involves a visual and/or tactile evaluation. In this paper, we present a sideview imaging system for online detection of chicken breast fillets affected by the wooden breast condition. The system can measure a physical deformation (bending) of an individual chicken-breast fillet through high-speed imaging at about 200 frames per second and custom image processing techniques. The developed image processing algorithm shows the over 95% classification performance in detecting wooden breast fillets.

show abstract

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

Cited by 11 publications

References 27 publications

Categorization and authentication of Beijing‐you chicken from four breeds of chickens using near‐infrared hyperspectral imaging combined with chemometrics

Categorization and authentication of Beijing‐you chicken from four breeds of chickens using near‐infrared hyperspectral imaging combined with chemometrics

Acceptability of Artificial Intelligence in Poultry Processing and Classification Efficiencies of Different Classification Models in the Categorisation of Breast Fillet Myopathies

Development of Imaging System for Online Detection of Chicken Meat with Wooden Breast Condition

Contact Info

Product

Resources

About