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Spectrochim Acta A Mol Biomol Spectrosc. 2019 Apr 15;213:118-126. doi: 10.1016/j.saa.2019.01.052. Epub 2019 Jan 17.

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

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College of Engineering, China Agricultural University, Beijing 100083, China.
Quality & Safety Assessment Research Unit, U.S. National Poultry Research Center, USDA-ARS, 950 College Station Rd., Athens, GA 30605, USA.
College of Engineering, China Agricultural University, Beijing 100083, China. Electronic address:
Multidisciplinary Initiative Center, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China. Electronic address:


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 wavelengths), 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.


Broiler breast meat; Near-infrared spectroscopy, hyperspectral imaging; Sampling location; Texture; White striping

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