Seiffert, UdoJadhav, Ashish Shivajirao2024-11-192024-11-192024https://open-science.ub.ovgu.de/handle/684882692/121https://doi.org/10.24352/ub.ovgu-2024-096tapcorderv1/ ├── data/ │ ├── used/ │ │ ├── used00001.png │ │ └── used00002.png │ ├── new/ │ │ ├── new00001.png │ │ └── new00002.png │ └── clean/ │ ├── clean00001.png │ └── clean00002.png └── README.mdThis 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.enDiffractionImaging dataDataset