Contaminant-detection-in-packaged-cocoa-hazelnut-spread-jars-using-Microwaves-Sensing-and-

2400 instances of scattering parameter measurements for detecting contaminants in packaged food jars. The dataset was created by Luca Urbinati using microwave sensing at 9.0 GHz and contains 30 attributes per sample. It includes 1200 uncontaminated and 1200 contaminated instances across 11 different contaminant classes.

Use Cases

• Training machine learning models for contaminant classification based on 30 scattering parameters.
• Developing anomaly detection systems for packaged food inspection using microwave frequency data.
• Comparing the effectiveness of different microwave frequencies (9.0-11.0 GHz) for sensing various contaminant materials and positions.
• Researching non-invasive quality control methods for industrial food production lines.

Strengths

• Contains 2400 total instances with a balanced set of 1200 contaminated and 1200 uncontaminated samples.
• Includes 11 distinct contaminant classes with specific counts per class, such as 200 'air_surface' and 100 'small_metal_sphere_middle' instances.
• Data is part of a series measured at five specific microwave frequencies (9.0, 9.5, 10.0, 10.5, 11.0 GHz), allowing for comparative analysis.

Limitations

• Column-level documentation is absent; field semantics for the 30 scattering parameters must be inferred after download.
• Last update date is unknown; freshness unverified.
• The dataset description is generic across five frequency-specific datasets, which may cause confusion if the 9.0 GHz specificity is overlooked.

Provenance

Source

OpenML, author Luca Urbinati

Collection Method

Microwave sensing measurements of scattering parameters from a network of antennas, target object (jar), and medium.