Towards real time assessment of intramuscular fat content in meat using optical fibre-based optical coherence tomography

TL;DR

This paper presents a non-destructive, real-time method using optical coherence tomography combined with PCA and SVR to accurately predict intramuscular fat content in meat samples, facilitating automated meat quality assessment.

Contribution

It introduces a novel OCT-based approach with PCA and SVR for fast, contactless prediction of meat fat content, achieving high accuracy and stability.

Findings

R^2 value of 0.94 for IMF prediction

OCT effectively detects fat absorption differences

Method enables automated meat quality classification

Abstract

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.