Machine Learning on Smartphone-Captured Diffraction Data

This study presents a novel approach for classifying oily or cream-like substances using diffraction data captured on a smartphone camera, applied specifically to assessing engine oil quality.Utilising the COMPOLYTICS TapCorder approach, optical diffraction patterns were analysed with a tailored feature extraction method.The performance of three machine learning paradigms -Multilayer Perceptrons (MLP), Learning Vector Quantization (LVQ), and Radial Basis Function Networks (RBFN) -was analysed in classifying new and used oil samples.MLP achieved the highest accuracy, while LVQ required the least computation time, highlighting trade-offs relevant for consumer-focused applications.This work clearly demonstrates the feasibility of accessible, low-cost chemical substance analysis via smartphone-based systems.