The TapCorder Data Set

Seiffert, Udo; Jadhav, Ashish Shivajirao

The TapCorder Data Set

dc.contributor.author	Seiffert, Udo
dc.contributor.author	Jadhav, Ashish Shivajirao
dc.date.accessioned	2024-11-19T13:18:34Z
dc.date.available	2024-11-19T13:18:34Z
dc.date.issued	2024
dc.description	tapcorderv1/ ├── data/ │ ├── used/ │ │ ├── used00001.png │ │ └── used00002.png │ ├── new/ │ │ ├── new00001.png │ │ └── new00002.png │ └── clean/ │ ├── clean00001.png │ └── clean00002.png └── README.md
dc.description.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(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.
dc.identifier.uri	https://open-science.ub.ovgu.de/handle/684882692/121
dc.identifier.uri	https://doi.org/10.24352/ub.ovgu-2024-096
dc.language.iso	en
dc.publisher	Otto-von-Guericke Universität Magdeburg
dc.rights.uri	https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.subject	Diffraction
dc.subject	Imaging data
dc.title	The TapCorder Data Set
dc.type	Dataset