AI Medical Compendium Journal:
Journal of chromatography. A

Showing 1 to 10 of 66 articles

Temporal evolution stages classification and aging time prediction of gel-pen ink using GC-IMS combined with machine learning for forensic science applications.

Journal of chromatography. A
Determining the temporal evolution of inks remains a critical challenge in forensic document analysis. The temporal evolution stages classification and aging time prediction of gel-pen ink were investigated by integrating gas chromatography-ion mobil...

Multi-response optimization and validation analysis in the detection of acetochlor and butachlor by HPLC based on D-optimal design methodology.

Journal of chromatography. A
Acetochlor and butachlor, widely used herbicides, pose environmental and health risks through water contamination. This study developed a high-performance liquid chromatography (HPLC) method for analyzing these compounds, employing a multi-response a...

Accelerating mechanistic model calibration in protein chromatography using artificial neural networks.

Journal of chromatography. A
In the manufacturing of therapeutic monoclonal antibodies (mAbs), mechanistic models can aid the evaluation and selection of suitable chromatography operating conditions during process development. However, model calibration remains a common bottlene...

Machine learning-driven insights into retention mechanism in IAM chromatography of anticancer sulfonamides: Implications for biological efficacy.

Journal of chromatography. A
Machine learning (ML) tools offer new opportunities in drug discovery, especially for enhancing our understanding of molecular interactions with biological systems. This study develops a comprehensive quantitative structure-retention relationship (QS...

Machine learning for predicting retention times of chiral analytes chromatographically separated by CMPA technique.

Journal of chromatography. A
Chiral mobile phase additive (CMPA) technique is an attractive method for chromatographic enantioseparation of chiral analytes. However, establishing chromatographic separation and analysis methods for given chiral analytes often requires extensive t...

Compressed chromatographic fingerprint of Artemisiae argyi Folium empowered by 1D-CNN: Reduce mobile phase consumption using chemometric algorithm.

Journal of chromatography. A
INTRODUCTION: High-Performance Liquid Chromatography (HPLC) is widely used for its high sensitivity, stability, and accuracy. Nonetheless, it often involves lengthy analysis times and considerable solvent consumption, especially when dealing with com...

Reinforcement learning for automated method development in liquid chromatography: insights in the reward scheme and experimental budget selection.

Journal of chromatography. A
Chromatographic problem solving, commonly referred to as method development (MD), is hugely complex, given the many operational parameters that must be optimized and their large effect on the elution times of individual sample compounds. Recently, th...

Application of physics-informed neural networks to predict concentration profiles in gradient liquid chromatography.

Journal of chromatography. A
Chromatography is one of the key methods in the analysis of mixture compositions, in the testing of chemical purity, as well as in the production of highly pure compounds. For this reason, it finds an important place in many industries. Currently, on...

An quality evaluation method based on three-dimensional integration and machine learning: Advanced data processing.

Journal of chromatography. A
This study presents an innovative approach for the quality evaluation of traditional Chinese medicine (TCM) by integrating three-dimensional (3D) data processing with machine learning, aimed at enhancing the efficiency and accuracy of HPLC-DAD data a...

Improved workflow for constructing machine learning models: Predicting retention times and peak widths in oligonucleotide separation.

Journal of chromatography. A
This study presents an improved workflow to support the development of machine learning models to predict oligonucleotide retention times, peak widths and thus peak resolutions, from larger datasets where manual processing is not feasible. We explore...