  We consider the use of optical coherence tomography (OCT) imaging to predict
the quality of meat. We find that intramuscular fat (IMF) absorbs infrared
light about nine times stronger than muscle, which enables us to estimate fat
content in intact meat samples. The method is made very efficient by extracting
relevant information from the three-dimensional high-resolution images
generated by OCT using principal component analysis (PCA). The principal
components are then used as regressors into a support vector regression (SVR)
prediction model. The SVR model is found to predict IMF content stably and
accurately, with an R^2 value of 0.94. Our study paves the way for automated,
contact-less, non-destructive, real time classification of the quality of meat
samples.
