Artificial intelligence (AI) and automation techniques have promoted the rapid development of scientific fields such as chemistry, biomedicine, and materials science, where multiple variables and tremendous data collection are required in experiments. By incorporating machine learning (ML), an independently devised digital control system, and integrating custom-developed software into the sucrose hydrolysis experiment, intelligent identification of the polarimeter’s field of view and automatic data acquisition of the sucrose hydrolysis reaction are achieved. This innovation revolutionizes traditional experimental practices by replacing manual recognition and operation with automated processes, effectively addressing the inherent time-consuming and labor-intensive nature of conventional methods and thereby significantly improving experimental efficiency and accuracy. This novel, portable, and economical ML-based optical rotation measurement device will promote innovation in chemical experiment teaching models in the era of AI.
Xie et al. (Thu,) studied this question.