Chroma Fusion: Integrating AI with Chromatography for Advanced Analysis

Artificial Intelligence (AI) and Machine Learning (ML) are transforming analytical chemistry, especially chromatographic techniques. AI contributes to enhancing method development, optimizing separation conditions, and automating data analysis. This review explores recent advances in the application of AI in chromatography, including retention time prediction, peak detection, data alignment, and integration with chemometric models. Challenges such as data quality and model interpretability are discussed, along with future directions that highlight AI’s role in achieving smart, automated chromatographic systems. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into chromatography has transformed the field of analytical chemistry, offering unprecedented opportunities for method development, optimization, and data analysis. This review provides a comprehensive overview of the recent advances in AI applications in chromatography, highlighting the potential of AI-driven approaches to enhance the efficiency, accuracy, and robustness of chromatographic analyses. In addition, AI-driven data analysis has revolutionized the field, enabling automated peak detection, integration, and quantification. Advanced ML algorithms, such as deep learning and convolutional neural networks, have been applied to chromatographic data, allowing for the extraction of valuable insights and patterns that may have gone undetected using traditional data analysis methods.