Chrysoprase Gemstone Colorimetric Data for Machine Learning Color Grading

51 chrysoprase gemstone samples and 676 synthetic green reference points measured for CIE L*a*b* color values using an X-Rite SP62 spectrophotometer. Author Yuansheng Jiang published this dataset on figshare under a CC-BY-4.0 license, last updated on 2026-05-15. The data was used to train and validate machine learning models, including logistic regression and neural networks, for objective gemstone color grading.

Use Cases

• Training color classification models based on CIE L*a*b* colorimetric data.
• Benchmarking machine learning algorithms (e.g., logistic regression, neural networks, random forest) on a small, structured physical measurement dataset.
• Developing reproducible, objective grading frameworks for gemstones or other colored materials.
• Studying the application of K-means clustering and Fisher discriminant analysis for color data validation.

Strengths

• Includes 51 real chrysoprase samples alongside 676 synthetically generated green reference points, providing a mixed data foundation.
• Machine learning models trained on this data achieved a reported macro F1-score of 99.59% in mixed cross-validation.
• Color data is objectively measured using a standardized spectrophotometer (X-Rite SP62) and the CIE L*a*b* color space.

Limitations

• The dataset is very small at 5.5 KB, with an unknown number of rows, limiting its scale for large-scale model training.
• Column-level documentation is absent; field semantics and structure must be inferred after download from the XLS file.
• The dataset's focus is narrow, covering only one specific gemstone (chrysoprase), which may limit generalizability.

Provenance

Source

Yuansheng Jiang

Collection Method

Color data measured using an X-Rite SP62 spectrophotometer; synthetic reference points generated using the GemDialogue Color Reference system.

Freshness

Last updated 2026-05-15 17:30:46; freshness should be verified.