Economics - AI in Transport

Abstract Artificial Intelligence (AI) has emerged as a transformative tool in scientific research, particularly in the analysis and interpretation of complex spectroscopic data. Traditional spectroscopic techniques such as Raman spectroscopy, infrared spectroscopy, nuclear magnetic resonance (NMR), and mass spectrometry generate large and complex datasets that require advanced analytical methods for accurate interpretation. AI-driven approaches, including machine learning and deep learning algorithms, offer powerful solutions for automated pattern recognition, feature extraction, and predictive analysis in spectroscopy. This review explores the integration of artificial intelligence with spectroscopic techniques, highlighting recent advancements, applications, and challenges in the field. The study discusses how AI enhances the accuracy, efficiency, and speed of spectral data analysis while reducing human intervention. Additionally, it examines various AI models used in spectroscopy and their potential in chemical analysis, biomedical diagnostics, and material science. The paper also addresses current limitations and future research directions for the effective implementation of AI-driven spectroscopic data interpretation.