We provide an in-depth convolutional neural network (CNN) analysis of optical responses of liquid crystals (LCs) when exposed to different chemical environments. Our aim is to identify informative features that can be used to construct automated LC-based chemical sensors and that can shed some light on the underlying phenomena that governs and distinguishes LC responses. Previous work demonstrated that, by using features extracted from AlexNet, micrographs of different LC responses can be classified with an accuracy of 99%. Reaching such high levels of accuracy, however, required use of a large number of features (on the order of thousands), which was computationally intensive and which clouded the physical interpretability of the dominant features. To address these issues, here we report a study of the effectiveness of using features extracted from color images using VGG16, which is a more compact CNN than Alexnet. Our analysis reveals that features extracted from the first and second convolutional layers of VGG16 are sufficient to achieve a perfect classification accuracy on the same dataset used by Cao and coworkers, while reducing the number of features to less than a hundred. The number of features is further reduced to ten via recursive feature elimination with minimal loss in classification accuracy (5-10%). This feature reduction procedure reveals that differences in spatial color patterns are developed within seconds in the LC response. The results thus reveal that hue histograms provide an informative set of features that can be used to characterize LC micrographs of the sensor response. We also hypothesize that differences in spatial correlation length of LC textures detected by VGG16 with DMMP and water likely reflect differences in the anchoring energy of the LC on the surface of the sensor. This latter proposal hints at fresh approaches for the design of LC-based sensors based on characterization of spontaneous fluctuations in orientation (as opposed to changes in time-average orientation)
Convolutional Network Analysis of Optical Micrographs
24 January 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.