aceSeparation of coeluted compounds in capillary gas chromatography: customization via machine learning retention time prediction
Gas Chromatography is a versatile separation technique widely used in analytical chemistry for constituent determination. However, gas chromatography compound identification is not directly feasible unless the method is coupled with complementary techniques such as mass spectrometry or with referencing methods like retention indices. Statistical retention time prediction of compounds based on the gas chromatography experimental and instrumental parameters could facilitate the gas chromatography characterization…
Adaptive boosting support vector regression trained on GC parameters predicts retention times with high accuracy, enabling optimization of capillary gas chromatography to separate coeluted C1-C12 hydrocarbon isomers.
Model was developed and tested on a limited chemical space of 61 C1-C12 hydrocarbons and 608 data points from specific sources, constraining generalizability beyond those compounds and settings.
Evidence
- Peer-reviewedAnalytical and Bioanalytical Chemistry2026-08-26
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Truvace Impact Record TRV-2026-0903, v1: “Separation of coeluted compounds in capillary gas chromatography: customization via machine learning retention time prediction.” Truvace, 2026-08-27. /record/TRV-2026-0903 (accessed at citation time). sha256 fc179e31d0e80f15…
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