Advancements in Chemometrics and AI for Spectroscopy: Emerging Applications, Explainable AI, and Future Directions, Part II

The convergence of Artificial Intelligence (AI) and chemometrics is revolutionizing spectroscopy, shifting it from empirical methods to intelligent analytics. Recent advancements, particularly in explainable AI, multimodal deep learning, and generative modeling, enhance the accuracy and interpretability of spectroscopic analyses. This review outlines applications in agriculture, biomedicine, and environmental science, emphasizing platforms like SpectrumLab and SpectraML. Key applications include food authentication, where AI improves quality assessment, and biomedical diagnostics using AI-guided Raman spectroscopy to identify disease biomarkers. AI methodologies such as machine learning and deep neural networks enhance automation and predictive accuracy. Future developments focus on integrating large language models and physics-informed neural networks for automated interpretation. Overall, AI-powered spectroscopy combines data-driven insights with chemical understanding, paving the way for faster analyses and improved research methodologies, thus shaping the future trajectory of analytical chemistry. Key SEO terms include AI, spectroscopy, chemometrics, machine learning, and explainable AI.

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