Purpose – This study aims to identify leather type and authenticity through optical coherence tomography.
Design/methodology/approach – Optical coherence tomography images taken from genuine and faux
leather samples were used to create an image dataset, and automated machine learning algorithms were also
used to distinguish leather types.
Findings – The optical coherence tomography scan results in a different image based on leather type. This
information was used to determine the leather type correctly by optical coherence tomography and automatic
machine learning algorithms. Please note that this system also recognized whether the leather was genuine or
synthetic. Hence, this demonstrates that optical coherence tomography and automatic machine learning can be
used to distinguish leather type and determine whether it is genuine.
Originality/value – For the first time to the best of the authors’ knowledge, spectral-domain optical coherence
tomography and automated machine learning algorithms were applied to identify leather authenticity in a
noncontact and non-invasive manner. Since this model runs online, it can readily be employed in automated
quality monitoring systems in the leather industry. With recent technological progress, optical coherence
tomography combined with automated machine learning algorithms will be used more frequently in automatic
authentication and identification systems.