Optical coherence tomography to predict the quality of meat

Abi Thampi; Sam Hitchman; Stephane Coen; Frédérique Vanholsbeeck

doi:10.1117/12.2526897

19 July 2019 Optical coherence tomography to predict the quality of meat

Abi Thampi, Sam Hitchman, Stephane Coen, Frédérique Vanholsbeeck

Proceedings Volume 11078, Optical Coherence Imaging Techniques and Imaging in Scattering Media III; 110781T (2019) https://doi.org/10.1117/12.2526897
Event: European Conferences on Biomedical Optics, 2019, Munich, Germany

Abstract

The key quality parameters of meat that determine our eating experience are the percentage of intramuscular fat (IMF), the tenderness and the pH of meat. Existing methods to determine the quality of meat are chemical or mechanical which are often slow, destructive and have huge sampling errors. Polarisation sensitive Optical coherence tomography (PS-OCT) is a new fast, non-contact and non-destructive technique with a few microns resolution up to a few millimeters deep in tissues as opaque as meat. We use PS-OCT to measure the attenuation and birefringence of meat samples to determine their IMF content and tenderness. The attenuation of light in fat and muscle was studied to find that the attenuation coefficient in fat is ≈ 9 times greater than in muscle, which provides a way to study and predict the IMF content in meat samples. Also the phase image tells about the changes in the polarisation of light due to the birefringence of muscle, which varies with tenderness and can be analysed to predict the tenderness of meat. To make make prediction models, the OCT results are compared to gas chromatography ame ionization detection (GC-FID) fat estimate and Warner-Bratzler tenderness estimate results.

Citation Download Citation

Abi Thampi, Sam Hitchman, Stephane Coen, and Frédérique Vanholsbeeck "Optical coherence tomography to predict the quality of meat", Proc. SPIE 11078, Optical Coherence Imaging Techniques and Imaging in Scattering Media III, 110781T (19 July 2019); https://doi.org/10.1117/12.2526897

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