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Related Experiment Video

Updated: May 17, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Chrysoprase color grading with machine learning: A systematic approach.

Yuansheng Jiang^1,2, Ying Guo¹, Vien Cheung²

¹School of Gemmology, China University of Geosciences (Beijing), Beijing, China.

|May 15, 2026

Summary

This study introduces a machine learning approach for objective chrysoprase color grading, overcoming subjective evaluation challenges. The developed model accurately classifies gemstone colors, enhancing standardization in the gem market.

Related Experiment Videos

Last Updated: May 17, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Area of Science:

Gemology
Computer Science
Materials Science

Background:

Gemstone quality and value are heavily influenced by color, but subjective perception leads to market inconsistencies.
Automating color grading is crucial for standardizing gemstone markets, especially for distinctive stones like chrysoprase.

Purpose of the Study:

To develop and validate a machine learning-based framework for automated, objective chrysoprase color grading.
To address the limitations of subjective color evaluation in the gemstone market.

Main Methods:

Collected CIE (1976) L*a*b* color data for chrysoprase samples and reference points using a spectrophotometer.
Employed K-means clustering and Fisher discriminant analysis for color data validation.
Trained and evaluated various machine learning algorithms (logistic regression, neural networks, etc.) for color classification.

Main Results:

Logistic regression and neural networks demonstrated high performance in color grading.
The selected logistic regression model achieved 100% accuracy on real samples and 99.59% macro F1-score in mixed cross-validation.
The proposed machine learning approach proves robust and reliable for objective gemstone color evaluation.

Conclusions:

Machine learning offers a feasible and reproducible method for structured gemstone color grading.
The developed framework can standardize chrysoprase color evaluation and potentially be adapted for other gemstones.
Objective color grading enhances market consistency and value assessment for gemstones.