• Dr Duggirala Mahendra
• KONDAVEETI VISHNU PRIYA
• VELAMALA KEERTHIKA
• KATIKALA DIVYA SRI
• M VIJAYA PAUL

Spectroscopic techniques such as Near-Infrared (NIR), Raman, and UV-Visible spectroscopy are increasingly used in pharmaceutical manufacturing due to their rapid, non-destructive, and high-throughput capabilities. However, interpreting complex spectral data in real time remains a significant challenge. The integration of Artificial Intelligence (AI) and Machine Learning (ML) with spectroscopic methods has revolutionized real-time quality assurance by enabling predictive analysis, anomaly detection, and process optimization. This paper explores the architecture and applications of AI-driven models for interpreting spectroscopic data in pharmaceutical settings. We present case studies highlighting the implementation of AI for real-time release testing (RTRT), impurity profiling, and continuous manufacturing. Various algorithms such as Principal Component Analysis (PCA), Partial Least Squares Regression (PLSR), Support Vector Machines (SVM), and Deep Neural Networks (DNN) are discussed for data modeling and classification. The paper also addresses regulatory concerns, model validation, and the future potential of AI in spectroscopy-driven quality control. This integration holds promise for transforming pharmaceutical analytics from reactive testing to proactive, real-time quality assurance.

AI in pharma, spectroscopy, NIR, Raman, UV-Vis, real-time release testing, quality assurance, machine learning, PCA, deep learning

"AI-Driven Spectroscopic Data Interpretation for Real-Time Pharmaceutical Quality Assurance", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.f759-f762, July-2025, Available :http://www.jetir.org/papers/JETIR2507582.pdf

"AI-Driven Spectroscopic Data Interpretation for Real-Time Pharmaceutical Quality Assurance", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. ppf759-f762, July-2025, Available at : http://www.jetir.org/papers/JETIR2507582.pdf