Lipids

, Volume 42, Issue 7, pp 679–685

Classification of Adipose Tissue Species using Raman Spectroscopy

Authors

  • J. Renwick Beattie
    • School of Chemistry and Chemical EngineeringQueen’s University
    • School of Chemistry and Chemical EngineeringQueen’s University
  • Claus Borggaard
    • Danish Meat Research Institute
  • Anna M. Fearon
    • School of Agriculture and Food ScienceQueen’s University
  • Bruce W. Moss
    • School of Agriculture and Food ScienceQueen’s University
Original Article

DOI: 10.1007/s11745-007-3059-z

Cite this article as:
Beattie, J.R., Bell, S.E.J., Borggaard, C. et al. Lipids (2007) 42: 679. doi:10.1007/s11745-007-3059-z

Abstract

In this study multivariate analysis of Raman spectra has been used to classify adipose tissue from four different species (chicken, beef, lamb and pork). The adipose samples were dissected from the carcass and their spectra recorded without further preparation. 102 samples were used to create and compare a range of statistical models, which were then tested on 153 independent samples. Of the classical multivariate methods employed, Partial Least Squares Discriminant Analysis (PLSDA) performed best with 99.6% correct classification of species in the test set compared with 96.7% for Principal Component Linear Discrimination Analysis (PCLDA). Kohenen and Feed-forward artificial neural networks compared well with the PLSDA, giving 98.4 and 99.2% correct classification, respectively.

Keywords

Raman spectroscopyGas chromatographyClassificationSpeciationAdiposeFatOilLipidFatty acidTriglycerideFAME

Abbreviations

PCA

Principal components analysis

PLSDA

Partial least squares discriminant analysis

LDA

Linear discriminant analysis

GC

Gas chromatography

FAME

Fatty acid methyl ester

PUFA

Polyunsaturated fatty acid

MUFA

Monounsaturated fatty acid

ANN

Artificial neural network

Copyright information

© AOCS 2007