Abstract
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 odour, significant pollution to laboratory environment and slow efficiency of manual titration in organic matter detection. First, by analysing the colour 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; Second, 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 showed that the device had a titration error of less than 0.2 ml and was more efficient than manual titration. When comparing the results with manual titration, no statistically significant difference was observed when paired t-test was applied at a 95% confidence level. Therefore, it has been confirmed that it has good recognition rate and control accuracy.