Advances in Applications of Machine Learning for Colorimetric Analysis
10.19756/j.issn.0253-3820.251202
- VernacularTitle:机器学习在比色分析中的应用研究进展
- Author:
Yu-Han YAN
1
;
Quan-Feng WANG
;
Yu-Tong LAI
;
De-Min YANG
;
Chang XIA
Author Information
1. 重庆师范大学化学学院,重庆 401331
- Keywords:
Colorimetric analysis;
Machine learning;
Quantitative detection;
Artificial intelligence;
Review
- From:
Chinese Journal of Analytical Chemistry
2025;53(11):1797-1807
- CountryChina
- Language:Chinese
-
Abstract:
Colorimetric analysis is a detection and quantification method based on observable color changes in response to analytes,which offers significant advantages including visually detectable signals,straightforward operation,rapid response,and low cost.Consequently,it plays a crucial role in a variety of fields.With increasingly diverse and complex application,colorimetric analysis requires continuous improvement in sensitivity,adaptability to diverse detection environments,and complex data handling capabilities.In recent years,the development of artificial intelligence technology,particularly within its core domain of machine learning(ML),has led to significant advancements in colorimetric analysis.The ML-assisted colorimetric analysis enables high-throughput and high-sensitivity detection,alongside automated analysis,thereby providing novel strategies to overcome the inherent limitations.This review categorized machine learning techniques and summarized their application in colorimetric analysis,introducing two fundamental categories of supervised learning,and unsupervised learning based on the division of core learning paradigms.The research progress of ML-assisted colorimetric analysis in the fields of environmental monitoring,biochemical detection,and food safety were summarized.Finally,the current challenges facing by this research area were analyzed and the research prospect of ML-assisted colorimetric analysis was outlined.
