Quantitative determination of total pigments in red meats using hyperspectral imaging and multivariate analysis

Food Chem. 2015 Jul 1:178:339-45. doi: 10.1016/j.foodchem.2015.01.071. Epub 2015 Jan 21.

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.

Keywords: Beef; Duck; Goose; Hyperspectral imaging; Partial least squares regression; Regression coefficients; Successful projections algorithm; Total pigments.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Cattle
  • Ducks
  • Geese
  • Image Processing, Computer-Assisted / methods*
  • Least-Squares Analysis
  • Meat / analysis*
  • Muscle, Skeletal / chemistry*
  • Pigments, Biological / chemistry*
  • Spectroscopy, Near-Infrared / methods*

Substances

  • Pigments, Biological