Automatic titration detection method of organic matter content based on machine vision

This article proposes an automatic titration algorithm for organic matter content detection based on machine vision, which addresses the disadvantages of high risk factor, strong odor, significant pollution to laboratory environment, and slow efficiency of manual titration in organic matter detection. Firstly, by analyzing the color change characteristics during the titration process, machine learning techniques are used to classify the titration speed, and a titration experiment state recognition model is constructed to divide the titration speed into four categories and improve titration efficiency; Secondly, through a large number of titration experiments to collect relevant data and extract key feature parameters, an efficient titration algorithm based on histogram similarity was designed to accurately identify titration endpoints and improve detection accuracy. This study not only solves the limitations of manual operation in traditional titration methods, but also provides new ideas and methods for the automation and intelligence of chemical titration. The test results show that the titration error of the device is less than 0.2ml, and it has higher efficiency than manual titration, with good recognition rate and control accuracy.