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

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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.

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Acknowledgements

The authors are appreciative for the support of the University of Arkansas Division of Agriculture for providing scientific support.

Funding

This research was supported by the programs of Natural Science Foundation of the Higher Education Institutions of Anhui Province (KJ2021A1073, KJ2018ZD041).

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*Authors’ contributions - XS: Conceptualization, Investigation, Methodology, Software, Writing - original draft review and editing. CJM: Writing - review and editing. JPC-C: Writing - review and editing. YB: Writing - review and editing. JY: Writing - review and editing. YD: Investigation.

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Correspondence to Xiao Sun.

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Sun, X., Maynard, C.J., Caldas-Cueva, J.P. et al. Use of image analysis to evaluate morphometric measurements of broiler breast fillets affected by the woody breast condition. J Food Sci Technol 59, 4457–4465 (2022). https://doi.org/10.1007/s13197-022-05525-x

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