用紫外光谱加机器学习，快速准确判断水质参数。
Water Quality Estimation Through Machine Learning Multivariate Analysis
- 结合紫外可见光谱与机器学习，自动分析水质。
- 模型可解释性强，能识别各波长对结果的贡献。
- 适合关注水质安全与监管合规的研究者和从业者。
水的质量对农食产业至关重要，广泛用于灌溉、畜牧及加工环节。随着该领域数字化进程加快，水质的自动化评估日益重要。本文将紫外-可见（UV-Vis）光谱技术与机器学习相结合，用于水质评估，以保障用水安全并符合相关法规。同时，通过引入SHapley Additive exPlanations（SHAP）方法增强模型可解释性，揭示不同波长吸光度对预测结果的贡献。实验表明，该方法能实现快速、准确且可解释的关键水质参数评估，具有实际应用潜力。
原文摘要 · Abstract (English)
The quality of water is key for the quality of agrifood sector. Water is used in agriculture for fertigation, for animal husbandry, and in the agrifood processing industry. In the context of the progressive digitalization of this sector, the automatic assessment of the quality of water is thus becoming an important asset. In this work, we present the integration of Ultraviolet-Visible (UV-Vis) spectroscopy with Machine Learning in the context of water quality assessment aiming at ensuring water safety and the compliance of water regulation. Furthermore, we emphasize the importance of model interpretability by employing SHapley Additive exPlanations (SHAP) to understand the contribution of absorbance at different wavelengths to the predictions. Our approach demonstrates the potential for rapid, accurate, and interpretable assessment of key water quality parameters.
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