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ARS Home » Southeast Area » Athens, Georgia » U.S. National Poultry Research Center » Quality & Safety Assessment Research » Research » Publications at this Location » Publication #368609

Research Project: Assessment and Improvement of Poultry Meat, Egg, and Feed Quality

Location: Quality & Safety Assessment Research

Title: Integration of spectral and textural features of visible and near-infrared hyperspectral imaging for differentiating between normal and white striping broiler breast meat

Author
item JIANG, HONGZHE - China Agricultural University
item Yoon, Seung-Chul
item Zhuang, Hong
item WANG, WEI - China Agricultural University
item LI, YUFENG - Chinese Academy Of Sciences
item YANG, YI - China Agricultural University

Submitted to: Spectrochimica Acta
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/15/2019
Publication Date: 7/17/2019
Citation: Jiang, H., Yoon, S.C., Zhuang, H., Wang, W., Li, Y., Yang, Y. 2019. Integration of spectral and textural features of visible and near-infrared hyperspectral imaging for differentiating between normal and white striping broiler breast meat. Spectrochimica Acta. 213:118-126.

Interpretive Summary: White striping (WS) is an emerging global meat-quality defect in the poultry industry. Compared with normal meat, chicken meat with the WS condition is characterized by the appearance of white striations parallel to meat fibers on the surface of the breast and thigh muscles. Chicken breast meat with the WS condition shows decreased protein content and increased fat content and reduces consumer's purchase intent. Therefore, differentiation between normal and WS chicken breast fillets could benefit meat producers, meat processors, retailers, and/or consumers. Visual assessments are currently used for the differentiation between normal and WS meat. However, the disadvantage of the visual assessments is subjective, labor-intensive, and time-consuming. Hyperspectral imaging (HSI) is a new and rapid method and has shown promising success in visualizing meat quality attributes. Therefore, the objective of the present study was to evaluate HSI for classification of chicken breast meat with the WS condition. Our experiments demonstrated that HSI methodology differentiated between normal and WS chicken breast fillets with the correct classification rate of 91.7%. These results suggest that HSI can be used to classify chicken breast meat with the WS condition non-destructively. White striping (WS) is an emerging global meat-quality defect in the poultry industry. Compared with normal meat, chicken meat with the WS condition is characterized by the appearance of white striations parallel to meat fibers on the surface of the breast and thigh muscles. Chicken breast meat with the WS condition shows decreased protein content and increased fat content and reduces consumer's purchase intent. Therefore, differentiation between normal and WS chicken breast fillets could benefit meat producers, meat processors, retailers, and/or consumers. Visual assessments are currently used for the differentiation between normal and WS meat. However, the disadvantage of the visual assessments is subjective, labor-intensive, and time-consuming. Hyperspectral imaging (HSI) is a new and rapid method and has shown promising success in visualizing meat quality attributes. Therefore, the objective of the present study was to evaluate HSI for classification of chicken breast meat with the WS condition. Our experiments demonstrated that HSI methodology differentiated between normal and WS chicken breast fillets with the correct classification rate of 91.7%. These results suggest that HSI can be used to classify chicken breast meat with the WS condition non-destructively.

Technical Abstract: White striping (WS), an emerging muscle myopathy in poultry industry, is gaining increasing attention globally. In this study, visible and near-infrared hyperspectral imaging (HSI, 400–1000 nm) was investigated for developing an optical sensing technique to differentiate WS broiler breast fillets (pectoralis major) from normal fillets. The minimum noise fraction (MNF), followed by an inverse MNF (IMNF), was conducted to improve the signal-to-noise ratio of hyperspectral images during the pre-processing process. Three regions of interest (ROIs) were selected at cranial, middle and caudal locations within each fillet image. Spectral principal component analysis (PCA) revealed that PC2 and PC3 were effective for the differentiation and key wavelengths (450, 492, 541, 581, 629, 869 and 980 nm) were selected from the corresponding PC loadings. Spatial texture features on corresponding score images were obtained using gray level co-occurrence matrix (GLCM) and grayscale histogram statistics (GHS), respectively. Partial least squares discriminant analysis (PLS-DA) models were evaluated with various inputs including spectral (full and key wave lengths), textural and fused features. GLCM features improved performance of multispectral imaging with the highest correct classification rate (CCR) of 91.7%, AUC value (0.917), and Kappa coefficient (0.833) for prediction set. Considering the complexity and heterogeneity of meat samples at different locations, the optimal sampling location was also analyzed and results provided the evidence that the cranial location worked most effectively for the differentiation between normal and WS samples. Overall, results confirmed the great potential of HSI in range of 400–1000 nm in differentiation between normal and WS chicken breast meat.