2018/05/20 by Ningren Han, Rajeev J. Ram, Han, Ningren +1
Biochemistry, Genetics and Molecular Biology · Chemistry · Engineering · #Applications (stat.AP) #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Fault Detection and Control Systems #Spectroscopy Techniques in Biomedical and Chemical Research #Spectroscopy and Chemometric Analyses #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.1805.07688
openalex publication_date 2018/05/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this work, we propose a two-stage algorithm based on Bayesian modeling and computation aiming at quantifying analyte concentrations or quantities in complex mixtures with Raman spectroscopy. A hierarchical Bayesian model is built for spectral signal analysis, and reversible-jump Markov chain Monte Carlo (RJMCMC) computation is carried out for model selection and spectral variable estimation. Processing is done in two stages. In the first stage, the peak representations for a target analyte spectrum are learned. In the second, the peak variables learned from the first stage are used to estimate the concentration or quantity of the target analyte in a mixture. Numerical experiments validated its quantification performance over a wide range of simulation conditions and established its advantages for analyte quantification tasks under the small training sample size regime over conventional multivariate regression algorithms. We also used our algorithm to analyze experimental spontaneous Raman spectroscopy data collected for glucose concentration estimation in biopharmaceutical process monitoring applications. Our work shows that this algorithm can be a promising complementary tool alongside conventional multivariate regression algorithms in Raman spectroscopy-based mixture quantification studies, especially when collecting a large training dataset with high quality is challenging or resource-intensive.