Development of AI-Driven Spectroscopic Analysis for Rapid Chemical Identification

Thesis Abstract

The rapid and accurate identification of chemical substances remains a critical challenge in analytical chemistry, particularly within environmental monitoring, pharmaceutical quality control, and industrial process management. Traditional spectroscopic techniques such as infrared (IR), ultraviolet-visible (UV-Vis), and Raman spectroscopy, while highly informative, often require expert interpretation and time-consuming data analysis, limiting their utility in high-throughput and real-time scenarios. This study aims to develop an artificial intelligence (AI)-driven analytical framework that enhances the speed, accuracy, and automation of chemical identification from spectroscopic data. The specific objectives include (1) designing a robust machine learning (ML) model capable of classifying spectral data into distinct chemical categories; (2) integrating the model into a user-friendly software platform for real-time chemical analysis; and (3) evaluating the performance of the system across diverse spectroscopic techniques and compound classes. A mixed-methods research design combining quantitative and qualitative approaches was employed. The quantitative phase involved collecting spectral data from a curated database of 1,200 chemical samples, encompassing pharmaceuticals, polymers, and environmental contaminants, using IR, Raman, and UV-Vis spectroscopy. These samples were systematically selected based on diversity in chemical structure and spectral complexity. Spectral data were pre-processed through normalization, baseline correction, and feature extraction, employing wavelet transforms and principal component analysis (PCA). Machine learning algorithms including support vector machines (SVM), random forest (RF), and deep convolutional neural networks (CNN) were trained and validated using stratified k-fold cross-validation to ensure generalizability. The qualitative phase involved semi-structured interviews with spectroscopic and AI experts to elucidate usability considerations and interpretability of the developed system. Data analysis employed statistical measures such as accuracy, precision, recall, and F1 score to evaluate model performance. Hyperparameter tuning was conducted via grid search to optimize classification efficacy. The analytical framework was complemented by the Theory of Technological Acceptance (TAM) to understand potential user adoption. Anticipated results suggest that the AI-enhanced spectroscopic analysis model will outperform traditional data interpretation methods, achieving classification accuracies exceeding 95% across multiple analytical techniques. The CNN-based model, in particular, is expected to demonstrate superior scalability and real-time processing capabilities. The system’s robustness will be validated through blind testing on external datasets and diverse sample matrices. This research is expected to make significant contributions to the field by providing an integrated AI-based approach that reduces dependence on expert interpretation, accelerates chemical identification, and broadens the application scope of spectroscopic analyses. Specifically, it advances current understanding of how machine learning techniques can be optimized for multi-modal spectral data and operationalized into practical diagnostic tools. The findings will inform best practices in spectral data preprocessing, model selection, and deployment, thereby guiding future research endeavors and industrial applications. Furthermore, the study will offer insights into user acceptance and the potential integration of AI-driven spectroscopic analysis into routine laboratory workflows. The main conclusion underscores that AI-driven spectral analysis can revolutionize chemical identification processes by significantly enhancing speed and accuracy while minimizing reliance on specialized expertise. Based on these findings, it is recommended that laboratories adopt AI-integrated spectroscopic systems for rapid screening and quality assurance. Future research should explore the extension of this framework to portable devices, the integration of complementary analytical modalities, and the adaptation of models for novel, emerging chemical entities to ensure broad-spectrum applicability and continuous improvement.