Fakultät für Elektrotechnik und Informationstechnik

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The TapCorder Data Set
(Otto-von-Guericke Universität Magdeburg, 2024) Seiffert, Udo; Jadhav, Ashish Shivajirao
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(R) 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.