European Journal of Biomedical and Pharmaceutical Sciences

NEXT-GENERATION ANALYTICAL TECHNIQUES IN PHARMACEUTICAL ANALYSIS

Kolli Devi Varaha Satya Kumari, Dr. Kuna Mangamma*, Palivela Kumari, Golla Richa Durga Bhavani, Bollipalli Sanath

ABSTRACT

Recent developments in pharmaceutical analysis are highlighted in this study. It focuses on cutting-edge techniques that aid in the very accurate identification and measurement of pharmaceutical molecules, such as Raman light scattering spectroscopy, near-infrared (NIR) spectral technique, and mass spectral technique. These methods yield fast and accurate results with little sample preparation. Pharmaceutical analysis is necessary to verify the efficacy, safety, and quality of pharmaceuticals. Advances in artificial intelligence (AI), green analytical chemistry, and nanotechnology have increased testing's speed and accuracy. Numerous approaches, including spectroscopy, and chromatography, are included in this overview. It also talks about the field's prospects for the future and current trends. Artificial intelligence (AI), and especially deep learning algorithms, have advanced Raman spectroscopy by improving data processing, feature extraction, and model optimization, which not only increases the accuracy and efficiency of Raman spectroscopy detection but also significantly broadens its range of application, regardless of the computational demands, data requirements, or ethical considerations. Drug structures, drug forms, drug quality control, component identification, and drug-biomolecule interactions are just a few of the many uses of AI-guided Raman spectroscopy in biomedicine. As a result, AI techniques are essential to the advancement of Raman spectroscopy in clinical diagnostics and pharmaceutical research, providing fresh insights and instruments for illness management and pharmaceutical process control.

Keywords: Pharmaceutical Analysis, Artificial Intelligence, Raman spectroscopy, NIR, Green analytical chemistry, machine learning, CNNS, GNNS, GANS, transformer model.