Micro-Structural Analysis of Tablet Surface Layers by Intelligent Laser Speckle Classification (ILSC) Technique: an Application in the Study of both Surface Defects and Subsurface Granule Structures

Abstract

As a consequence of the latest developments in laser technologies it is now possible to develop a low-cost and accurate tablet inspection system by the unification of optical and artificial intelligence methods. The functionality of the proposed system is based on a sequence of texture analysis of laser speckle images (using laser sources of 650 and 808 nm, VIS/IR) followed by the optimization of texture parameters using Bayesian Networks (BN). In the first part of this work, a Bayesian inference method was used to detect microscale tablet defects that are generated “progressively” during production whereas in the second part a Bayesian classifier method was used to discriminate between tablets made from different granule sizes. In part two, it was shown that (i) the comparatively higher energy (5 mW) IR laser light generates different speckle effects than the lower energy visible (Red 3 mW) by interacting with deeper subsurface of the tablets and (ii) by using multi-classifier systems (MCS) to fuse the Bayesian classifiers from both types of speckle images it was possible to achieve a higher discrimination power (88% classification accuracy) for distinguishing between tablets made from different granule sizes than one can achieve from a single image type. It is suggested that this unified method has the potential to provide for a comprehensive analysis of both tablet quality attributes, on the one hand, and failure modes, on the other, that might be used in the development of a low-cost tablet inspection system.