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ARS Home » Southeast Area » Athens, Georgia » U.S. National Poultry Research Center » Quality and Safety Assessment Research Unit » Research » Publications at this Location » Publication #377143
Research Project: Assessment and Improvement of Poultry Meat, Egg, and Feed Quality

Location: Quality and Safety Assessment Research Unit

Title: Non-destructive assessment of cooking loss of fresh broiler breast fillets using shortwave infrared (SWIR) hyperspectral imaging

Author
item JIANG, HONGZHE - China Agricultural University
item WANG, WEI - China Agricultural University
item Zhuang, Hong
item Yoon, Seung-Chul
item YANG, YI - China Agricultural University
item JIA, BEIBEI - China Agricultural University

Submitted to: ASABE Annual International Meeting
Publication Type: Proceedings
Publication Acceptance Date: 6/15/2018
Publication Date: 8/8/2018
Citation: Jiang, H., Wang, W., Zhuang, H., Yoon, S.C., Yang, Y., Jia, B. 2018. Non-destructive assessment of cooking loss of fresh broiler breast fillets using shortwave infrared (SWIR) hyperspectral imaging. ASABE Annual International Meeting. Paper No. 1800805. https://doi.org/10.13031/aim.201800805.
DOI: https://doi.org/10.13031/aim.201800805

Interpretive Summary: Cooking loss of broiler breast fillets is one of major methods to measure water-holding capacity (WHC) that is an important quality trait, directly related to the eating quality of the poultry meat. We investigated the feasibility of hyperspectral imaging (HSI) in short-wave infrared (SWIR) spectral range between 1,000 and 2,500 nm for non-invasive assessment of cooking loss of fresh broiler breast fillets. Partial least square discriminant analysis (PLS-DA) and radial basis function-support vector machine (RBF-SVM) were evaluated with various preprocessing methods including first or second derivative, standard normal variate (SNV), and multiplicative scatter correction (MSC). Overall, RBF-SVM model with the optimal preprocessing of MSC showed the best performance: training accuracy of 78% and prediction accuracy of 71%.

Technical Abstract: Cooking loss (CL), which is one of the many diverse methods for water-holding capacity (WHC) determination, is an important quality trait directly relating to the eating quality of broiler breast fillets. In this study, hyperspectral imaging (HSI) in short-wave infrared (SWIR, 1000-2500 nm) range was investigated for non-invasive assessment of CL of fresh broiler breast fillets. A total of 74 SWIR hyperspectral images for fillets were acquired, and after images calibration, principal component analysis (PCA) followed by inverse PCA was conducted to eliminate bad lines and strip noise. Mean spectra were extracted from regions of interest (ROIs) identified by a mask. Fillets CL were determined by measuring the weight loss in cooking and CL categories were defined as high-CL when CL = 20%, and low-CL when CL ' 20%. Two classification methods of partial least square discriminant analysis (PLS-DA) and radial basis function-support vector machine (RBF-SVM) were applied, respectively, with various preprocessing methods including first or second derivative (Der1 and Der2), standard normal variate (SNV) and multiplicative scatter correction (MSC). Overall, RBF-SVM model with the optimal preprocessing of MSC was considered as the best model by showing correct classification rates (CCRs) of 0.78 and 0.71 for calibration and prediction sets, respectively. Conclusively, results suggest that SWIR HSI has limited benefits for assessing CL of fresh broiler breast fillets; however, the classification result should be enhanced in further study.