三刺激和颜色外观模型在低成本智能手机数字色度测量中用于土壤CO2检测的比较性能
Chairul Ichsan1, Khoirun Nisa1, Siti Rodiah1
1Department of Chemistry, Faculty of Science and Technology, Universitas Islam Negeri Raden Fatah Palembang, Seberang Ulu I, Palembang, 30252, South Sumatera, Indonesia. chairulichsan_uin@radenfatah.ac.id.
The Analyst
|February 25, 2026
概括
先进的色彩外观模型 (CAMs) 改善了基于智能手机的在土壤中的二氧化碳 (CO2) 泄漏的检测. 与机器学习相结合,CAM16UCS提供了一种高度敏感,低成本的环境监测和碳捕获验证方法.
科学领域:
- 环境科学 环境科学
- 分析化学 分析化学
- 传感器技术 传感器技术
背景情况:
- 检测二氧化碳 (CO2) 泄漏对于碳捕获和储存 (CCS) 验证和土壤健康监测至关重要.
- 基于智能手机的数字色度测量为传统方法提供了具有成本效益的替代方案,但由于颜色表示而面临准确性挑战.
- 现有的方法在可变的场条件下受到颜色空间选择的限制.
研究的目的:
- 系统地将传统的三刺激色彩空间与先进的色彩外观模型 (CAM) 进行比较,以量化土壤二氧化碳度.
- 为了评估机器学习回归模型的性能,与不同颜色表示集成,用于二氧化碳检测.
- 建立一个灵敏,可扩展和具有成本效益的协议,用于使用数字色度测量检测二氧化碳泄漏.
主要方法:
- 基于红的光学传感器被用来检测控制的土壤环境中的二氧化碳度 (0-7.8729 wt%).
- 对传感器的智能手机图像进行处理,使用三刺激模型 (sRGB,CIELAB,CIE XYZ) 和CAMs (CIECAM02,CIECAM16,CAM16UCS) 提取色彩数据.
- 机器学习回归模型 (PLS,贝叶斯脊) 经过训练,并使用leave-one-out交叉验证进行验证.
主要成果:
- 颜色外观模型 (CAMs),特别是CAM16UCS,在准确性方面明显超过了传统的三刺激模型.
- CAM16UCS-部分最小平方 (PLS) 组合实现了0.0427重%CO2的上限检测 (LOD).
- 这种CAM集成的方法超过了光谱相光学基准 (0.0780重量%) 和最佳三刺激模型CIELAB-PLS (0.0463重量%).
结论:
- 与CAM集成的数字色度测量提供了一种高度敏感和强大的方法来量化土壤二氧化碳度.
- 这种方法克服了传统色彩空间的局限性,在可变条件下提供更高的精度.
- 开发的协议已被验证用于快速,低成本的环境监测,CCS验证和精密农业应用.
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