Digital images, color spaces, color appearance models and performance metrics data - Chairul Ichsan et al

This dataset accompanies the manuscript "Comparative Performance of Tristimulus and Color Appearance Models in Smartphone Digital Colorimetry for Soil CO2 Detection" by Chairul Ichsan, Khoirun Nisa, Siti Rodiah, and M. Mahfudz Fauzi Syamsuri (submitted to Talanta). It provides raw and processed data from experiments using a phenol red-based optical sensor exposed to controlled CO2 concentrations (0–7.8729 wt%) in soil, captured via smartphone for digital colorimetry analysis. Key components include: Color parameters extracted from tristimulus spaces (sRGB, CIELAB, CIE XYZ) and color appearance models (CIECAM02, CIECAM16, CAM16UCS, ZCAM), such as lightness, chroma, hue angle, etc. Machine learning regression performance metrics (RMSE, MAPE, R², LOD) for models like Partial Least Squares (PLS) and Bayesian Ridge, evaluated with Leave-One-Out Cross-Validation. Embedded sample digital images of the sensor at various CO2 levels. The data is structured in an XLSX file with three sheets: "color spaces, CAMs & metrics" (raw parameters and error analysis), "ML reg performance" (model benchmarks and LOD notes), and "digital images samples" (concentrations with embedded images). This dataset enables reproduction of the study's findings on CAM superiority for low-cost, field-deployable CO2 monitoring in CCS verification and precision agriculture. For full details, refer to the README.txt file included in the upload. Data collection methods: Controlled chamber experiments with high-purity CO2 (99.9%), phenol red sensor, and Samsung Galaxy A12 smartphone imaging. Processing used Python libraries (Pillow, Scikit-Image, Colour-Science). Limitations: Small sample size (n=6 concentrations); metrics are estimates. Keywords: Carbon Dioxide Monitoring, Color Appearance Model (CAM), Digital Colorimetry, Smartphone Sensor, Soil Gas Detection, Machine Learning Regression. Funding: None.本数据集配套于Chairul Ichsan、Khoirun Nisa、Siti Rodiah以及M. Mahfudz Fauzi Syamsuri合著的、投稿至《Talanta》的论文《智能手机数字比色法用于土壤CO₂检测中三刺激与颜色外观模型的对比性能研究》。本数据集包含基于酚红的光学传感器在土壤中受控CO₂浓度（0~7.8729 wt%）环境下开展实验所得到的原始与处理后数据，实验通过智能手机采集图像以进行数字比色分析。 核心数据集内容包括： 从三刺激空间（sRGB、CIELAB、CIE XYZ）以及颜色外观模型（Color Appearance Model, CAM）中提取的颜色参数，包括明度、彩度、色相角等； 针对偏最小二乘（Partial Least Squares, PLS）、贝叶斯岭回归等模型的机器学习回归性能指标（均方根误差RMSE、平均绝对百分比误差MAPE、决定系数R²、检出限LOD），采用留一交叉验证法进行评估； 不同CO₂浓度下传感器的嵌入式数字样本图像。 数据集以XLSX文件形式存储，包含三个工作表："color spaces, CAMs & metrics"（原始参数与误差分析）、"ML reg performance"（模型基准性能与检出限说明）以及"digital images samples"（对应浓度的嵌入式图像）。本数据集可复现本研究关于CAM在碳捕获与封存（Carbon Capture and Storage, CCS）验证、精准农业领域低成本野外部署CO₂监测中表现更优的结论。如需完整细节，请参阅上传文件中包含的README.txt文档。 数据采集方法：采用高纯度CO₂（99.9%）开展受控箱实验，搭配酚红传感器与三星Galaxy A12智能手机进行图像采集；数据处理使用Python库（Pillow、Scikit-Image、Colour-Science）。 局限性：样本量较小（仅6个浓度梯度）；性能指标为估算值。 关键词：二氧化碳监测、颜色外观模型（CAM）、数字比色法、智能手机传感器、土壤气体检测、机器学习回归。资助：无。