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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 #353109

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

Location: Quality & Safety Assessment Research

Title: Fusion of spectra and texture data of hyperspectral imaging for the prediction of the water-holding capacity of fresh chicken breast filets

Author
item Yang, Yi - China Agricultural University
item Wang, Wei - China Agricultural University
item Zhuang, Hong
item Yoon, Seung-chul
item Jiang, Hongzhe - China Agricultural University

Submitted to: Applied Sciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/3/2018
Publication Date: 4/20/2018
Citation: Yang, Y., Wang, W., Zhuang, H., Yoon, S.C., Jiang, H. 2018. Fusion of spectra and texture data of hyperspectral imaging for the prediction of the water-holding capacity of fresh chicken breast filets. Applied Sciences. 8(4), 640. doi:10.3390/app8040640.
DOI: https://doi.org/10.3390/app8040640

Interpretive Summary: Water-holding capacity is an important quality attribute of meat. Water-holding capacity directly affects the yield of further-processed meat, consumer’s acceptance of meat products, and texture of cooked meat. It is one of mostly measured meat quality parameters. Traditionally, water-holding capacity is measured with different methods, such as cook loss, drip loss, thaw loss, expressible fluid, marinade uptake, marinade retention, and salt-induced water gain, depending on research and/or application interests. Although these methods provide direct information about meat performance under different conditions or treatments, the methods are laborious, time-consuming, and/or invasive. Therefore, the development of a rapid and non-destructive technique to assess water-holding capacity could be beneficial to research and industry. Hyperspectral imaging (HIS) is an emerging technology, which can rapidly and non-destructive predict quality of food products. Recently, considerable endeavors have been made in meat research and shown that HSI can successfully predict quality of pork, beef, lamb, and fish. Therefore, the objective of in this study was to investigate the potential to use spectral and textural data of visible and near-infrared HSI to predict water-holding capacity of fresh broiler breast meat measured with drip loss, expressible fluid, and salt-induced water. Our results show that water-holding capacity of broiler breast meat could be predicted with multivariable analysis of HIS spectral and textural data. The results vary with the method used to measure water-holding capacity. For drip loss and expressible fluid, combination of the textural data with the spectral data effectively improves the predictive ability compared with the individual data alone. However, for the salt-induced water gain, the predicting model with spectral data alone performs better than textural data and combination of the spectral data and the textural data. These results suggest that both HIS spectral and textural data should be considered in the development of HIS-based models for predicting water-holding capacity of meat and the model performances may vary with the methods used to measure the water-holding capacity.

Technical Abstract: This study investigated the fusion of spectra and texture data of hyperspectral imaging (HSI, 1000–2500 nm) for predicting the water-holding capacity (WHC) of intact, fresh chicken breast filets. Three physical and chemical indicators drip loss, expressible fluid, and salt-induced water gain were measured to be different WHC references of chicken meat. Different partial least squares regression (PLSR) models were established with corresponding input variables including the full spectra, key wavelengths, and texture variables, as well as the fusion data of key wavelengths and the corresponding texture variables, respectively. The results demonstrated that for drip loss and expressible fluid, texture data was an effective supplement to spectra data, and fusion data as an input variable could effectively improve the predictive ability of the independent prediction set (Rp = 0.80, RMSEp = 0.80; Rp = 0.56, RMSEp = 2.10). While the best model to predict salt-induced water gain was based on key wavelengths (Rp = 0.69, RMSEp = 18.04), this was mainly because salt-induced water gain was measured on mince samples, which lacked the important physical structure to represent the texture information of meat. Our results of this study demonstrated the potential to improve the evaluation of the WHC of chicken meat by HSI further.