# Exploratory multivariate analysis using R Language for method development in liquid chromatography

## TL;DR

This paper introduces an R-based multivariate analysis method to streamline the development of liquid chromatography methods for drug separation.

## Contribution

A novel R-based approach using factor analysis and clustering for chromatographic method development is introduced.

## Key findings

- The method identified key parameters affecting chromatographic separation of 15 drugs.
- Optimal stationary and mobile phase combinations were selected based on retention and resolution.
- A web app, ChromaFAMDeX, was developed to make the statistical methods more accessible.

## Abstract

The visual evaluation of data derived from screening and optimization experiments in the development of new analytical methods poses a considerable time investment and introduces the risk of subjectivity. This study presents a novel approach to processing such data, based on factor analysis of mixed data and hierarchical clustering — multivariate techniques implemented in the R programming language. The methodology is demonstrated in the early-stage screening and optimization of the chromatographic separation of 15 structurally diverse drugs that affect the central nervous system, using a custom R Language script. The presented explorative approach enabled the identification of key parameters affecting the separation and significantly reduced the time required to evaluate the comprehensive dataset from the screening experiments. Based on the data analysis results, the optimal combination of stationary phase and mobile phase composition was selected, considering retention, overall resolution, and peak shape of compounds. Additionally, compounds vulnerable to changes in selected chromatographic conditions were identified. As a complement to the presented R Language script, a web-based application ChromaFAMDeX has been developed to offer an intuitive interface that enhances the accessibility of the used statistical methods. Accompanying the publication, the R script and the link to the standalone application are provided, enabling replication and adaptation of the methodology.

The online version contains supplementary material available at 10.1007/s00216-024-05705-y.