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DaFiF: A complete dataset for fish's freshness problems
Eko Prasetyo1, Nanik Suciati2, Ni Putu Sutramiani3
1Department of Informatics, Faculty of Engineering, Universitas Bhayangkara Surabaya, Jl. Ahmad Yani 114, Surabaya 60231, Indonesia.
Data in Brief
|November 4, 2024
Summary
This study introduces a dataset for non-destructive fish freshness detection using sensor data and images. It aids in developing systems to assess fish quality and estimate deterioration for consumers and industry.
Area of Science:
- Food Science and Technology
- Sensory Science
- Data Science
Background:
- Assessing fish freshness is crucial for consumer safety and market value.
- Current methods often rely on subjective sensory evaluation, lacking objectivity.
- Non-destructive techniques offer an innovative approach to fish quality assessment.
Purpose of the Study:
- To present a comprehensive dataset for developing non-destructive fish freshness detection systems.
- To facilitate the creation of models for estimating fish quality deterioration.
- To support the standardization of fish freshness classification.
Main Methods:
- Collected sensor data (MQ 135, TGS 2602) and images over 11 days for mackerel, tilapia, and tuna.
- Performed organoleptic examinations based on Indonesian National Standard (SNI) 2729-2013 parameters.
- Generated daily sensor data and images for 21 fish per species.
Main Results:
- The dataset comprises multi-modal data (sensor, image, organoleptic) for three fish species.
- Enables development of machine learning models for fish freshness prediction.
- Provides a basis for regression modeling of freshness degradation.
Conclusions:
- The dataset is valuable for advancing non-destructive fish freshness assessment technologies.
- Facilitates objective quality control in the seafood industry.
- Supports research in food spoilage and preservation.
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