AI in analytical chemistry: Advancements, challenges, and future directions

• Published in: Talanta (Oxford) Vol. 274; p. 125949
• Main Author: Cardoso Rial, Rafael
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01-07-2024
• Subjects: Analytical chemistry; Artificial intelligence; Chromatography; Machine learning; Spectroscopy; Analytical chemistry; Spectroscopy; Artificial intelligence; Chromatography; Machine learning
• ISSN: 0039-9140; 1873-3573
• DOI: 10.1016/j.talanta.2024.125949

This article explores the influence and applications of Artificial Intelligence (AI) in analytical chemistry, highlighting its potential to revolutionize the analysis of complex data sets and the development of innovative analytical methods. Additionally, it discusses the role of AI in interpreting large-scale data and optimizing experimental processes. AI has been fundamental in managing heterogeneous data and in advanced analysis of complex spectra in areas such as spectroscopy and chromatography. The article also examines the historical development of AI in chemistry, its current challenges, including the interpretation of AI models and the integration of large volumes of data. Finally, it forecasts future trends and the potential impact of AI on analytical chemistry, emphasizing the need for ethical and secure approaches in the use of AI.
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•AI revolutionizes data analysis in analytical chemistry.•Advanced AI aids complex spectroscopy and chromatography interpretation.•AI in chromatography enhances compound identification and quantification.•Machine learning predicts properties, enhancing drug and material discovery.•Explainable AI models improve interpretability in analytical chemistry.