Dataset Information

Use of image analysis to evaluate morphometric measurements of broiler breast fillets affected by the woody breast condition.

ABSTRACT: The objective of this study was to investigate the relationships between morphometric measurements and woody breast (WB) severity in breast fillets using image processing as an objective detection method for WB. Breast fillets were collected and categorized as normal (NORM), mild (MILD), moderate (MOD), and severe (SEV). Compression force and energy increased as WB severity increased alongside a decrease in severity through fillet regions (P < 0.05). Length and caudal thickness were highest in SEV and MOD groups (P < 0.05), and cranial thickness increased as WB severity increased (P < 0.05). The aerial area was the smallest in NORM fillets, while the planar area increased from NORM to MOD (P < 0.05). Fillet curvatures were highest in SEV and MOD fillets (P < 0.05). All measured parameters expressed strong correlation to WB scores (P < 0.05) except width. The results from this study may provide a basis for further assessment of the potential incorporation of these measurements into vision grading systems that may allow processors to sort fillets by WB severity in commercial plants.

Supplementary information

The online version contains supplementary material available at 10.1007/s13197-022-05525-x.

SUBMITTER: Sun X

PROVIDER: S-EPMC9525462 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Use of image analysis to evaluate morphometric measurements of broiler breast fillets affected by the woody breast condition.

Sun Xiao X Maynard Clay J CJ Caldas-Cueva Juan P JP Bai Yu Y You Jinjie J Dong Yan Y

Journal of food science and technology 20220621 11

The objective of this study was to investigate the relationships between morphometric measurements and woody breast (WB) severity in breast fillets using image processing as an objective detection method for WB. Breast fillets were collected and categorized as normal (NORM), mild (MILD), moderate (MOD), and severe (SEV). Compression force and energy increased as WB severity increased alongside a decrease in severity through fillet regions (<i>P</i> < 0.05). Length and caudal thickness were highe ...[more]

PMID: 36193456

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Project description:Spaghetti meat (SM), woody breast (WB), and white striping (WS) are myopathies affecting breast muscle of broiler chickens, and are characterized by a loss of myofibers and an increase in fibrous tissue. The conditions develop in intensive broiler chicken production systems, and cause poor meat process-ability and negative customer perception leading to monetary losses. The objectives of the present study were to describe the physical and histological characteristics of breast myopathies from commercial broiler chicken flocks in Ontario, Canada, and to assess the associations between the severity of myopathies with the physical and histological characteristics of the affected breast muscle fillets. Chicken breast fillets (n = 179) were collected over 3 visits from a processing plant and scored macroscopically to assess the severity of myopathies, following an established scoring scheme. For each fillet, the surface area, length, width, thickness, weight, and hardness (compression force) were measured. A subset of 60 fillets was evaluated microscopically. Multinomial logistic regression models were built to evaluate associations between physical parameters and macroscopic scores. The odds of SM co-occurring with severe WB (SM1WB2) were significantly associated with increased fillet thickness (OR = 1.59, 95% CI 1.31-1.94) and weight (OR = 1.06, 95% CI 1.03-1.09). Histologically, myopathies had overlapping lesions consisting of polyphasic myodegeneration, perivascular inflammatory cuffing and accumulation of fibrous tissue and fat. The pairwise correlation between macroscopic and microscopic scores was moderate (rho 0.45, P < 0.001). This is the first study to characterize breast myopathies in Canadian broiler flocks. Results show that the morphologic and microscopic changes of fillets from this cohort are similar to data from other countries, and provide database to benchmark these parameters in future studies. Our standardized categorization can be applied to broiler breast fillets in other regions of the world.

Project description:The objective of this research was the evaluation of Fourier transforms infrared spectroscopy (FT-IR) and multispectral image analysis (MSI) as efficient spectroscopic methods in tandem with multivariate data analysis and machine learning for the assessment of spoilage on the surface of chicken breast fillets. For this purpose, two independent storage experiments of chicken breast fillets (n = 215) were conducted at 0, 5, 10, and 15 °C for up to 480 h. During storage, samples were analyzed microbiologically for the enumeration of Total Viable Counts (TVC) and Pseudomonas spp. In addition, FT-IR and MSI spectral data were collected at the same time intervals as for microbiological analyses. Multivariate data analysis was performed using two software platforms (a commercial and a publicly available developed platform) comprising several machine learning algorithms for the estimation of the TVC and Pseudomonas spp. population of the surface of the samples. The performance of the developed models was evaluated by intra batch and independent batch testing. Partial Least Squares- Regression (PLS-R) models from the commercial software predicted TVC with root mean square error (RMSE) values of 1.359 and 1.029 log CFU/cm2 for MSI and FT-IR analysis, respectively. Moreover, RMSE values for Pseudomonas spp. model were 1.574 log CFU/cm2 for MSI data and 1.078 log CFU/cm2 for FT-IR data. From the implementation of the in-house sorfML platform, artificial neural networks (nnet) and least-angle regression (lars) were the most accurate models with the best performance in terms of RMSE values. Nnet models developed on MSI data demonstrated the lowest RMSE values (0.717 log CFU/cm2) for intra-batch testing, while lars outperformed nnet on independent batch testing with RMSE of 1.252 log CFU/cm2. Furthermore, lars models excelled with the FT-IR data with RMSE of 0.904 and 0.851 log CFU/cm2 in intra-batch and independent batch testing, respectively. These findings suggested that FT-IR analysis is more efficient than MSI to predict the microbiological quality on the surface of chicken breast fillets.

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Dataset Information

Use of image analysis to evaluate morphometric measurements of broiler breast fillets affected by the woody breast condition.

Supplementary information

Publications

Use of image analysis to evaluate morphometric measurements of broiler breast fillets affected by the woody breast condition.

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