Mass Spectrometry Data Processing and Feature Extraction in Drug Analysis Application of Data Mining Algorithms in Drug Quality Control

Abstract

This study explores the integration of mass spectrometry (MS) data processing, feature extraction, and data mining algorithms for drug analysis and quality control in the pharmaceutical industry. Leveraging MS technology's precision and sensitivity, coupled with advanced computational methodologies, the study aims to enhance drug formulation classification, impurity detection, and quantitative analysis. The experimental validation of this integrated approach demonstrates its effectiveness in accurately classifying different drug formulations, detecting outlier samples, and quantifying impurity levels with high precision and reliability. Supervised learning algorithms, such as Support Vector Machine (SVM) classifiers, facilitate formulation classification, while unsupervised clustering algorithms identify outlier samples. Regression models enable quantitative analysis of impurity levels, contributing to regulatory compliance and ensuring the safety and efficacy of drug products. The systematic integration of MS data processing, feature extraction, and data mining algorithms offers transformative capabilities for pharmaceutical research, development, and manufacturing, promising safer, more effective, and higher-quality drug products in the future.