Work

Machine Learning on Smartphone-Captured Diffraction Data

Year: 2025

Type: conference-paper

Abstract: 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.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-34

Authors: Udo Seiffert, Ashish Shivajirao Jadhav, Andreas Backhaus

Institution: Otto-von-Guericke-Universität Magdeburg

Language: English

Topic: Spectroscopy and Chemometric Analyses

Subfield: Analytical Chemistry

Field: Chemistry

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W4409480491

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