title: Quantitative determination of total pigments in red meats using hyperspectral imaging and multivariate analysis.
abstract: This study investigated the potential of hyperspectral imaging (HSI) for quantitative determination of total pigments in red meats, including beef, goose, and duck. Partial least squares regression (PLSR) was applied to correlate the spectral data with the reference values of total pigments measured by a traditional method. In order to simplify the PLSR model based on the full spectra, eleven optimal wavelengths were selected using successive projections algorithm (SPA). The new SPA-PLSR model yielded good results with the coefficient of determination (R(2)p) of 0.953, root mean square error (RMSEP) of 9.896, and ratio of prediction to deviation (RPD) of 4.628. Finally, distribution maps of total pigments in red meats were developed using an image processing algorithm. The overall results from this study indicated HSI had the capability for predicting total pigments in red meats.
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: 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: 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: 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; D002417:Cattle; D004372:Ducks; D005777:Geese; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016018:Least-Squares Analysis; D008460:Meat / Q000032:analysis*; D018482:Muscle, Skeletal / Q000737:chemistry*; D010860:Pigments, Biological / Q000737:chemistry*; D019265:Spectroscopy, Near-Infrared / Q000379:methods*
chemical_list: D010860:Pigments, Biological
keywords: Beef; Duck; Goose; Hyperspectral imaging; Partial least squares regression; Regression coefficients; Successful projections algorithm; Total pigments