A Multimodal hyperspectral dataset of cocoa beans with physicochemical annotation
Kebin Contreras1, Mohamad Jouni2, Mauro Dalla Mura2,3
1Universidad Industrial de Santander, 68002, Bucaramanga, Colombia.
Scientific Data
|November 27, 2025
Summary
This study introduces a new spectral dataset for non-invasive cocoa bean quality assessment. The data enables automated classification of fermentation levels, improving objectivity and scalability.
Area of Science:
- Agricultural Science
- Spectroscopy
- Data Science
Background:
- Traditional cocoa quality assessment is subjective and destructive.
- Limited spectral datasets hinder objective, non-invasive quality evaluation.
- A comprehensive dataset is needed for scalable methodologies.
Purpose of the Study:
- To present a novel dataset of cocoa bean spectral properties at various spatiospectral resolutions.
- To enable non-invasive, objective cocoa quality assessment.
- To support the development of automated classification methods for cocoa bean quality.
Main Methods:
- Acquired 19 scenes using four imaging devices under open and closed conditions.
- Collected physicochemical data (moisture, polyphenols, cadmium) and hyperspectral images (350-1000 nm).
- Followed Colombian standard NTC 1252:2021 for bean fermentation classification (well, partially, poorly).
Main Results:
- The dataset successfully distinguishes between cocoa quality levels using spectral information.
- Classification accuracy was demonstrated under both invasive and non-invasive conditions.
- Statistical analysis supports the development of automated quality assessment tools.
Conclusions:
- The presented dataset is valuable for advancing non-invasive cocoa bean quality assessment.
- Hyperspectral imaging combined with machine learning can objectively classify cocoa fermentation.
- This work facilitates scalable and objective methodologies for the cocoa industry.
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