Blockchain-Based Mass Spectrometry Data Processing and Feature Extraction in Drug Quality Control with Data Mining Model for Deep Learning Process

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Dr. T.Venkata Naga Jayudu
Dr. Giribabu Sadineni
Jajjara Bhargav
P.Jayaselvi
Dr.T.Sunitha
Masthan Rao Kale*

Abstract

Mass spectrometry data processing and feature extraction are vital in drug analysis, particularly for ensuring drug quality and safety. These techniques allow for the detailed identification and quantification of chemical compounds within pharmaceutical products, supporting the detection of impurities, active ingredients, and potential contaminants. Data mining algorithms enhance this process by sifting through large datasets, identifying patterns, and extracting key features that are critical for quality control. Algorithms such as clustering, classification, and anomaly detection can isolate relevant data points, aiding in precise compound characterization. By automating the extraction of critical features from complex spectra, data mining facilitates faster and more accurate quality assessment, reducing human error and enhancing compliance with regulatory standards. This paper introduces Ethereum Blockchain Clustering (EBC) as a novel approach to drug quality assessment in the pharmaceutical industry. Leveraging the capabilities of blockchain technology and mass spectrometry data analysis, EBC offers a transparent and decentralized platform for managing and analyzing drug quality attributes. Through a series of simulations and case studies, we demonstrate the effectiveness of EBC in categorizing drug samples, extracting relevant features, and assessing their potency, purity, and stability. The results highlight the potential of EBC to enhance transparency, traceability, and trust within the pharmaceutical supply chain, ultimately contributing to improved patient safety and healthcare outcomes. in our simulations, we observe mean drug potency values ranging from 97.8% to 99.2%, mean drug purity values ranging from 95.1% to 97.8%, and mean drug stability ranging from 23.2 to 25.8 months across different scenarios and clusters. These results highlight the potential of EBC to enhance transparency, traceability, and trust within the pharmaceutical supply chain, ultimately contributing to improved patient safety and healthcare outcomes.

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