title: Non-destructive prediction of thiobarbituricacid reactive substances (TBARS) value for freshness evaluation of chicken meat using hyperspectral imaging.
abstract: This study examined the potential of hyperspectral imaging (HSI) for rapid prediction of 2-thiobarbituric acid reactive substances (TBARS) content in chicken meat during refrigerated storage. Using the spectral data and the reference values of TBARS, a partial least square regression (PLSR) model was established and yielded acceptable results with regression coefficients in prediction (Rp) of 0.944 and root mean squared errors estimated by prediction (RMSEP) of 0.081. To simplify the calibration model, ten optimal wavelengths were selected by successive projections algorithm (SPA). Then, a new SPA-PLSR model based on the selected wavelengths was built and showed good results with Rp of 0.801 and RMSEP of 0.157. Finally, an image algorithm was developed to achieve image visualization of TBARS values in some representative samples. The encouraging results of this study demonstrated that HSI is suitable for determination of TBARS values for freshness evaluation in chicken meat.
authors.affiliation: College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, PR China.
authors.forename: Zhenjie
authors.initials: Z
authors.lastname: Xiong
authors.affiliation: College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, PR China; Food Refrigeration and Computerised Food Technology, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, Ireland. Electronic address: dawen.sun@ucd.ie.
authors.forename: Da-Wen
authors.initials: DW
authors.lastname: Sun
authors.affiliation: College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, PR China.
authors.forename: Hongbin
authors.initials: H
authors.lastname: Pu
authors.affiliation: College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, PR China.
authors.forename: Anguo
authors.initials: A
authors.lastname: Xie
authors.affiliation: College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, PR China.
authors.forename: Zhong
authors.initials: Z
authors.lastname: Han
authors.affiliation: College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, PR China.
authors.forename: Man
authors.initials: M
authors.lastname: Luo
mesh_terms: D000465:Algorithms; D000818:Animals; D002138:Calibration; D002645:Chickens; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D011202:Poultry Products / Q000032:analysis*; D012016:Reference Values; D017392:Thiobarbituric Acid Reactive Substances
chemical_list: D017392:Thiobarbituric Acid Reactive Substances
keywords: Chicken meat; Freshness; Hyperspectral imaging; Non-destructive analysis; Partial least square regression; Successive projections algorithm; TBARS