[Construction of a machine learning ensemble prediction model for gas chromatographic retention index on stationary phases with different polarities].
Year: 2025
Type: other
Abstract: of 0.97, and a test set RMSE of 107.44. Williams plots were used to characterize the application domain of the model, with over 94% of the data lying within the domain, indicative of broad applicability and high predictive confidence. The successful development of this predictive retention-index model represents a significant advancement in the gas-chromatography field. The developed model offers several key benefits by integrating advanced machine learning techniques with comprehensive chemical- and physical-property data; it highly accurately predicts RI values across a wide range of polar stationary phases. The developed ensemble model exhibits superior robustness and predictive abilities compared to individual machine-learning models. The establishment of this model is of great scientific significance and practical value for improving the efficiency and accuracy of target and non-target gas-chromatographic analyses.
Source: PubMed
Authors: Qian-Yi Wang, Yong-le Zhu, Xue-Hua Li
Institution: Dalian University of Technology
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4408884675