Improved identification and classification accuracy of wooden breast by jointly using near-infrared spectroscopy and compression speed

Li, Zhen; Wu, Lei; Yang, Zongyun; Wang, Peng; Xu, Xinglian

doi:10.1016/j.foodres.2022.111795

Cited by 2 publications

(1 citation statement)

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“…Geronimo et al [27] reported an accuracy of 97.5% in classifying normal (no WB) and WB-affected breast meat using selected six wavelengths based on NIRS. Li et al [28] assessed the feasibility of NIRS and compression speed models for classifying between normal and WB-affected samples, obtaining an overall accuracy of 81.58%. NIRS is promising for online application; though, as a point measurement technique, it only acquires information from a sample area and is inadequate or suboptimal for sensing the spatial heterogeneity of samples and characterizing WB conditions.…”

Section: Introductionmentioning

confidence: 99%

Detection of woody breast condition in broiler breast fillets using multispectral scattering imaging

Cai,

2024

Sensing for Agriculture and Food Quality and Safety XVI

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Muscular myopathies such as woody or wooden breast (WB), which impair the eating quality and marketability of poultry products, are threatening the profitability of poultry industries worldwide, with an estimated annual loss exceeding $500 million for the United States (U.S.) poultry industry. WB-affected fillets are characterized by abnormal tissue hardness and muscle rigidity with varying degrees of severity. The assessment of WB conditions at processing facilities currently relies on tactile palpation combined with a visual examination by trained personnel. This approach is subjective, labor-intensive, costly, and may induce contamination due to physical contact. Optical imaging technology offers a promising alternative for objective and non-invasive quality assessment of broiler meat. This study presents a proof-of-concept evaluation of a new scattering imaging technique that captures light-scattering characteristics of meat tissues for the detection of WB conditions in broiler breast fillets. Broadband scattering images, generated under the illumination of a highly focused broadband beam, were acquired from broiler meat samples. Two types of image features, i.e., 1) deep-learning-based and 2) hand-crafted scattering features, were extracted for building classification models using regularized linear discriminant analysis to differentiate meat samples into two categories, i.e., "Normal (no WB)" and "Defective", according to WB conditions. Deep-learning-based features yielded an overall classification accuracy of 80.9%, while an improved accuracy of 88.7% was obtained by hand-crafted scattering features, representing a significant improvement of 7.8% (P < 0.01). Furthermore, feature selection based on minimum redundancy maximum relevance (MRMR) was conducted to select a subset of scattering image features for discriminant modeling, leading to a further accuracy improvement to 90.5% with top-ranked 65 features. This study has demonstrated the promise of the light scattering imaging technique for WB detection in broiler breast meats.

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Section: Introductionmentioning

confidence: 99%