Classification of adipose tissue species using Raman spectroscopy.
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.
Beattie
J Renwick
JR
School of Chemistry and Chemical Engineering, Queen's University, Belfast, BT9 5AG, Northern Ireland.
Bell
Steven E J
SE
Borggaard
Claus
C
Fearon
Anna M
AM
Moss
Bruce W
BW
D000273:Adipose Tissue / Q000737:chemistry*; D000818:Animals; D002417:Cattle; D002645:Chickens; D005231:Fatty Acids, Unsaturated / Q000032:analysis*; D016018:Least-Squares Analysis; D008460:Meat / Q000145:classification*; D015233:Models, Statistical; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D025341:Principal Component Analysis; D012756:Sheep; D013045:Species Specificity; D013059:Spectrum Analysis, Raman / Q000379:methods*; D013552:Swine
D005231:Fatty Acids, Unsaturated