Identifying leather type and authenticity by optical coherence tomography

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 fauxleather samples were used to create an image dataset, and automated machine learning algorithms were alsoused to distinguish leather types.Findings – The optical coherence tomography scan results in a different image based on leather type. Thisinformation was used to determine the leather type correctly by optical coherence tomography and automaticmachine learning algorithms. Please note that this system also recognized whether the leather was genuine orsynthetic. Hence, this demonstrates that optical coherence tomography and automatic machine learning can beused 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 coherencetomography and automated machine learning algorithms were applied to identify leather authenticity in anoncontact and non-invasive manner. Since this model runs online, it can readily be employed in automatedquality monitoring systems in the leather industry. With recent technological progress, optical coherencetomography combined with automated machine learning algorithms will be used more frequently in automaticauthentication and identification systems.