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Intelligent Recognition of the Polarimeter's Field of View and Automatic Optical Rotation Measurement of Sucrose Hydrolysis Using Machine Learning

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Title: Intelligent Recognition of the Polarimeter's Field of View and Automatic Optical Rotation Measurement of Sucrose Hydrolysis Using Machine Learning
Language: English
Authors: Huan Xie (ORCID 0009-0000-3609-3227), Yanghaotian Wu, Zhenyu Chen, Zhongyun Wu, Yu-Qing Zheng (ORCID 0000-0003-3727-565X), Zhirong Liu (ORCID 0000-0001-5070-8048), Jinrong Xu
Source: Journal of Chemical Education. 2026 103(3):1586-1594.
Availability: Division of Chemical Education, Inc. and ACS Publications Division of the American Chemical Society. 1155 Sixteenth Street NW, Washington, DC 20036. Tel: 800-227-5558; Tel: 202-872-4600; e-mail: eic@jce.acs.org; Web site: http://pubs.acs.org/jchemeduc
Peer Reviewed: Y
Page Count: 9
Publication Date: 2026
Document Type: Journal Articles
Reports - Descriptive
Education Level: Higher Education
Postsecondary Education
Descriptors: Artificial Intelligence, Computer Uses in Education, Science Experiments, Automation, Optics, Measurement, Science Instruction, College Science
DOI: 10.1021/acs.jchemed.5c01414
ISSN: 0021-9584
1938-1328
Abstract: 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.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1499680
Database: ERIC
FullText Text:
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  Data: Intelligent Recognition of the Polarimeter's Field of View and Automatic Optical Rotation Measurement of Sucrose Hydrolysis Using Machine Learning
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  Data: <searchLink fieldCode="AR" term="%22Huan+Xie%22">Huan Xie</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0000-3609-3227">0009-0000-3609-3227</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yanghaotian+Wu%22">Yanghaotian Wu</searchLink><br /><searchLink fieldCode="AR" term="%22Zhenyu+Chen%22">Zhenyu Chen</searchLink><br /><searchLink fieldCode="AR" term="%22Zhongyun+Wu%22">Zhongyun Wu</searchLink><br /><searchLink fieldCode="AR" term="%22Yu-Qing+Zheng%22">Yu-Qing Zheng</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3727-565X">0000-0003-3727-565X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Zhirong+Liu%22">Zhirong Liu</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-5070-8048">0000-0001-5070-8048</externalLink>)<br /><searchLink fieldCode="AR" term="%22Jinrong+Xu%22">Jinrong Xu</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Chemical+Education%22"><i>Journal of Chemical Education</i></searchLink>. 2026 103(3):1586-1594.
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  Data: Division of Chemical Education, Inc. and ACS Publications Division of the American Chemical Society. 1155 Sixteenth Street NW, Washington, DC 20036. Tel: 800-227-5558; Tel: 202-872-4600; e-mail: eic@jce.acs.org; Web site: http://pubs.acs.org/jchemeduc
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  Data: 10.1021/acs.jchemed.5c01414
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  Data: 0021-9584<br />1938-1328
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  Data: 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.
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      – SubjectFull: Computer Uses in Education
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      – SubjectFull: Science Experiments
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