Abstract

"AI-DRIVEN DATA ANALYSIS FOR IDENTIFICATION OF IMPURITIES IN HPLC CHROMATOGRAMS & ARTIFICIAL INTELLIGENT SYSTEM FOR HPLC COLUMN SELECTION AND METHOD DEVELOPMENT"

Harshal A. Bachhav*, Ganesh B. Shirsath, Shivshankar R. Badhe, Deepika V. Kurne, Dr. Sandeep S. Sonawane

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Abstract

The integration of Artificial Intelligence (AI) in High-Performance Liquid Chromatography (HPLC) represents a transformative advancement in analytical chemistry. AI-driven data analysis enhances the identification of impurities in HPLC chromatograms, significantly improving accuracy and efficiency compared to traditional methods. Utilizing machine learning and deep learning algorithms, AI can detect minute impurities that may be overlooked by conventional techniques, thereby ensuring higher purity standards in pharmaceutical and chemical industries. The significance of AI in this domain extends to optimizing method development and column selection, crucial steps in HPLC processes. Intelligent systems can analyze vast datasets to recommend optimal HPLC columns and methods, reducing the trialand- error approach traditionally employed. This optimization not only saves time and resources but also enhances the reproducibility and robustness of HPLC analyses. Key findings in recent studies highlight the superior performance of AI algorithms in impurity identification, with neural networks and support vector machines demonstrating high precision in analyzing complex chromatograms. Moreover, AI-driven optimization techniques, such as genetic algorithms and reinforcement learning, have shown significant promise in method development, offering tailored solutions that adapt to specific analytical requirements. Future perspectives in this field point towards increased integration of AI with HPLC instrumentation, leading to fully automated and intelligent analytical workflows. This integration is expected to revolutionize quality control processes in pharmaceuticals, environmental monitoring, and food safety. Further research is anticipated to focus on enhancing the interpretability of AI models, ensuring regulatory compliance, and expanding the applicability of AI across diverse analytical challenges. In conclusion, the synergy between AI and HPLC heralds a new era of precision and efficiency in analytical chemistry. By leveraging AI for impurity identification and method optimization, industries can achieve unprecedented levels of accuracy, reliability, and productivity in their HPLC analyses.

Keywords: Artificial Intelligence (AI), High-Performance Liquid Chromatography (HPLC), Impurity Identification, Machine Learning, Method Optimization, Column Selection, Analytical Chemistry.