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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 #377139
Research Project: Assessment and Improvement of Poultry Meat, Egg, and Feed Quality

Location: Quality and Safety Assessment Research Unit

Title: Visible and near infrared hyperspectral imaging for non-destructive grading and classification of chicken breast fillets

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

Submitted to: ASABE Annual International Meeting
Publication Type: Proceedings
Publication Acceptance Date: 6/15/2018
Publication Date: 8/8/2018
Citation: Jia, B., Wang, W., Yoon, S.C., Zhuang, H., Yang, Y., Jiang, H. 2018. Visible and near infrared hyperspectral imaging for non-destructive grading and classification of chicken breast fillets. ASABE Annual International Meeting. Paper No. 1800826. https://doi.org/10.13031/aim.201800826.
DOI: https://doi.org/10.13031/aim.201800826

Interpretive Summary: DFD (dark, firm, and dry) and PSE (pale, soft, and exudative) are two major meat-quality defects in the poultry industry. It is known that the exhaustion and stress before slaughter results in PSE or DFD meat. DFD meat is prone to microbial contamination and PSE meat is regarded as defective because of its pale appearance, and soft texture. Therefore, because of the low economic value of PSE or DFD meat, rapid sensing and sorting of PSE and DFD meat will be valuable to the poultry industry. The current study developed a visible and near infrared hyperspectral imaging technology for grading and classification of DFD, normal, and PSE chicken breast fillets. The developed hyperspectral image classification model is based on spectral pre-treatments and partial least square-discriminant analysis. The performance of the classification model was about 80% accuracy in classifying the PSE, normal, and DFD meat categories.

Technical Abstract: This study investigated the potential of visible and near infrared (Vis/NIR) hyperspectral imaging (HSI) for grading and classification of pale, soft, and exudative (PSE), dark, firm, and dry (DFD), and normal chicken breast fillets. Hyperspectral images of boneless and skinless chicken breast samples were acquired with spectra in the wavelengths between 400 and 1000 nm. All samples were divided into PSE, normal, and DFD categories based on their color and pH values. Spectral pre-processing algorithms of Savitzky-Golay (S-G) smoothing, S-G first and second derivative processing, and standard normal variate (SNV) were applied to the spectral data obtained from region of interest (ROI) to reduce noises and enhance the performance of partial least square-discriminant analysis (PLS-DA) models. Full-wavelength model based on the second derivative processed spectra obtained the highest correct classification rate (CCR) of prediction set with value of 84.62 %. Twelve wavelengths were selected from full wavelengths by using Successive projection algorithm (SPA) to build new PLS-DA classification model. CCR value of prediction set was 84.62 % for the simplified model, the same as that for the full-wavelength model. Results suggest that Vis/NIR HSI can be used as a useful tool to grade and classify chicken breast meat.