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Abstract
The growing demand for rapid and reliable DNA analysis in genomics, diagnostics, and pharmaceutical research has driven the evolution of analytical technologies toward automation and intelligence. Ultraviolet (UV) spectroscopy has long served as a fundamental technique for DNA quantification and purity assessment due to its simplicity, speed, and cost-effectiveness. However, conventional UV methods are limited by their dependence on fixed absorbance values and ratio-based interpretations, which often fail to capture subtle spectral variations and complex sample interferences. The integration of artificial intelligence (AI) into UV spectroscopy has introduced a paradigm shift, enabling comprehensive spectral analysis, automated interpretation, and predictive modeling. AI-driven approaches, including machine learning and deep learning algorithms, can extract meaningful insights from full-spectrum data, enhancing accuracy and reproducibility. This review explores the concept of smart spectroscopy, focusing on AI-driven UV analysis for high-throughput DNA screening. It discusses the principles of UV spectroscopy, the role of AI in spectral interpretation, advancements in data processing, applications across various domains, and future perspectives. The convergence of UV spectroscopy and AI represents a powerful strategy for next-generation molecular analysis.