Accurate determination of total oxidant concentration [Ox]tot in nonthermal plasma treated aqueous systems remains a critical challenge due to the transient nature of reactive oxygen and nitrogen species and the subjectivity of conventional titration methods used for [Ox]tot determination. This study introduces a color based computer analysis method that integrates advanced image processing with machine learning to quantify colorimetric changes in potassium iodide solutions during oxidation. A custom built visual acquisition system recorded high resolution video of the color transitions occurring during plasma treatment while the change in oxidant concentration was simultaneously monitored using a standard titrimetric method. Extracted image frames were processed through a structured pipeline to obtain RGB, HSV, and Lab color features. Statistical analysis revealed strong linear relationships between selected color features and measured oxidant concentrations, particularly for HSV saturation, Lab a and b channels, and the blue component of RGB. These features were subsequently used to train and validate multiple machine learning models including linear regression, ridge regression, random forest, gradient boosting, and neural networks. Linear regression and gradient boosting demonstrated the highest predictive accuracy with R2 values exceeding 0.99. Dimensionality reduction from nine features to smaller feature subsets preserved predictive performance while improving computational efficiency. Comparison with experimental titration measurements showed that the proposed system predicts total oxidant concentration in potassium iodide solution with very high accuracy, achieving R2 values above 0.998 even under reduced feature conditions.

翻译：准确测定非热等离子体处理水体系中总氧化剂浓度[Ox]tot仍是一个关键挑战，这主要源于活性氧氮物种的瞬态特性以及传统滴定法测定[Ox]tot时存在的主观性。本研究提出一种基于颜色的计算机分析方法，该方法将先进的图像处理技术与机器学习相结合，用于量化氧化过程中碘化钾溶液比色变化。通过定制构建的视觉采集系统记录等离子体处理期间颜色转变的高分辨率视频，同时使用标准滴定法同步监测氧化剂浓度变化。提取的图像帧通过结构化流程进行处理，获取RGB、HSV和Lab颜色特征。统计分析显示选定颜色特征与实测氧化剂浓度之间存在强线性关系，尤其在HSV饱和度、Lab色彩空间的a和b通道以及RGB的蓝色分量方面表现显著。这些特征随后被用于训练和验证多种机器学习模型，包括线性回归、岭回归、随机森林、梯度提升和神经网络。线性回归与梯度提升模型展现出最高的预测精度，其R2值超过0.99。将九个特征降维至更小的特征子集在保持预测性能的同时提升了计算效率。与实验滴定测量结果的对比表明，所提出的系统能以极高精度预测碘化钾溶液中的总氧化剂浓度，即使在特征缩减条件下仍能实现超过0.998的R2值。