Classification of Adipose Tissue Species using Raman Spectroscopy

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.

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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

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Correspondence to Steven E. J. Bell.

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Beattie, J.R., Bell, S.E.J., Borggaard, C. et al. Classification of Adipose Tissue Species using Raman Spectroscopy. Lipids 42, 679–685 (2007). https://doi.org/10.1007/s11745-007-3059-z

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Keywords

  • Raman spectroscopy
  • Gas chromatography
  • Classification
  • Speciation
  • Adipose
  • Fat
  • Oil
  • Lipid
  • Fatty acid
  • Triglyceride
  • FAME