Hazelnut Spread Contaminant Detection via Microwave Sensing at 10 GHz

A 2020 dataset from OpenML for evaluating classification models on independent and identically distributed tabular data. It was curated by the TabArena team for their Tabular ML IID Study, focusing on features from a single 10 GHz frequency for detecting contaminants in food products. The original research was published by Ricci et al. and Urbinati et al. in IEEE journals and conferences.

Use Cases

• Benchmarking classification algorithms based on microwave sensing features at 10 GHz.
• Developing models for food contaminant detection as described in the referenced IEEE papers.
• Evaluating model performance on independent and identically distributed (IID) tabular data for the TabArena study.

Strengths

• Dataset is licensed under CC BY-SA 4.0, allowing for sharing and adaptation.
• Based on peer-reviewed research published in IEEE journals and conferences in 2020 and 2021.
• Curated for a specific study (TabArena Tabular ML IID Study) with a defined focus on IID classification tasks.

Limitations

• Row count, column names, and sample data are unknown, limiting suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The label uses an original ordinal encoding that could not be reversed during curation.

Provenance

Source

Original data from OpenML (ID 45538), based on research by Ricci, Marco, et al. and Urbinati, Luca, et al.

Collection Method

Data likely gathered from microwave sensing experiments for food contaminant detection, as described in the referenced papers.

Time Range

Dataset year is 2020.

Freshness

Last updated date is unknown; freshness unverified.

Geography

Spatial coverage is unknown.