Automated spectral interpretation using artificial intelligence teachniques

Abstract

Spectral interpretation is an essential task in analytical chemistry used to determine molecular structures and chemical compositions. Traditional interpretation methods rely heavily on expert knowledge and manual analysis, which can be time-consuming and error-prone. With the rapid growth of data generated by spectroscopic instruments, artificial intelligence (AI) techniques such as machine learning and deep learning have emerged as powerful tools for automated spectral analysis. AI models can learn patterns from large spectral datasets and automatically predict molecular structures, classify compounds, and detect anomalies. This paper reviews the application of AI techniques in automated spectral interpretation, discusses commonly used algorithms, and highlights future research directions in this domain.