Click on “Download PDF” for the PDF version or on the title for the HTML version.

If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.

Prediction of quality traits of Chicken Breast Fillets by Different spectral range of Hyperspectral Imaging

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2018 ASABE Annual International Meeting  1800828.(doi:10.13031/aim.201800828)
Authors:   Yi Yang, Wei Wang, Seung-chul Yoon, Hong Zhuang, Hongzhe Jiang, Beibei Jia
Keywords:   Chicken breast fillet, Hyperspectral imaging, Partial least-squares regression (PLSR), Quality trait.


Different spectral ranges of hyperspectral imaging were used in this study for the prediction of quality traits of chicken meat. By comparing the prediction ability of partial least square regression (PLSR) models, it is concluded that the range of visible and near infrared (Vis-NIR, 400-900 nm) is more suitable for the prediction of L* (Rcv=0.93 and RMSEcv=1.59), a* (Rcv=0.89 and RMSEcv=0.38), b* (Rcv=0.86 and RMEcv=0.88), and pH values (Rcv=0.80 and RMEcv=0.15). And near infrared (NIR, 1000-2500 nm) is more suitable for the prediction of drip loss (Rcv=0.72 and RMEcv=0.83), expressible fluid (Rcv=0.57 and RMEcv=2.07), and salt-induced water gain (Rcv=0.72 and RMEcv=18.30). While the predictive abilities of moisture were not good in both two spectral ranges (Rcv < 0.30). Our results of this study demonstrated that different spectral range can be used in the prediction of different quality traits in order to archive better prediction performance.

(Download PDF)    (Export to EndNotes)