METHOD FOR IDENTIFYING THE EQUIVALENT FRAME OF A SOLUTION BY ANALYZING TITRATION VIDEO IMAGES USING AN AUTOMATIC APPROACH

Determination of equivalence in inorganic chemistry can be achieved by various methods, such as colorimetric titration, conductimetric titration and pH-metric titration. However, these traditional methods often require repetition to guarantee reliable results, increasing the time and cost of the experiment. In response to this limitation, some authors have suggested a semi-automatic approach based on colorimetric titration, although this also has subjective aspects. The aim of this article is to propose a new method based on intuitive observation to identify the equivalent frame in a titration video. This approach relies on the use of the KNN classifier to automate the detection of the reference frame within a predefined confidence interval. To facilitate this automation, a dataset was built up from experiments at the Groupe Chimie de lEau et Substances Naturelles (GCESNA) laboratory at the Institut National Polytechnique HouphouetBoigny (INP-HB).The performance of the KNN algorithm was effectively assessed by evaluating it against the performance indicators of precision, recall and F-measure. The results obtained are as follows: Precision: 92.2%, Recall: 91.7% and F-measure: 92.0%. These experimental results demonstrate the effectiveness of the KNN algorithm in frame classification.

无机化学中的当量点测定可通过多种方法实现，例如比色滴定法、电导滴定法与pH电位滴定法。然而，这类传统方法往往需要重复操作以保障结果可靠，这会延长实验周期并提升实验成本。针对这一局限，已有学者提出基于比色滴定法的半自动解决方案，但该方案仍存在主观判断的弊端。本文旨在提出一种基于直观观测的全新方法，用于识别滴定视频中的当量点帧。该方法依托K近邻（KNN）分类器，实现预定义置信区间内参考帧的自动化检测。为推进该自动化流程，研究团队从侯普埃特·博伊尼国立理工学院（INP-HB）的水化学与天然物质研究组（GCESNA）实验室的实验数据中构建了本数据集。本研究通过精准率、召回率与F1值三项性能指标，对K近邻算法的性能开展了有效评估。所得实验结果如下：精准率为92.2%，召回率为91.7%，F1值为92.0%。上述实验结果证明了K近邻算法在帧分类任务中的有效性。