A Hybrid Least Squares and Principal Component Analysis Algorithm for Raman Spectroscopy

Raman spectroscopy is a powerful technique for detecting and quantifying analytes in chemical mixtures. A critical part of Raman spectroscopy is the use of a computer algorithm to analyze the measured Raman spectra. The most commonly used algorithm is the classical least squares method, which is popular due to its speed and ease of implementation. However, it is sensitive to inaccuracies or variations in the reference spectra of the analytes (compounds of interest) and the background. Many algorithms, primarily multivariate calibration methods, have been proposed that increase robustness to such variations. In this study, we propose a novel method that improves robustness even further by explicitly modeling variations in both the background and analyte signals. More specifically, it extends the classical least squares model by allowing the declared reference spectra to vary in accordance with the principal components obtained from training sets of spectra measured in prior characterization experiments. The amount of variation allowed is constrained by the eigenvalues of this principal component analysis. We compare the novel algorithm to the least squares method with a low-order polynomial residual model, as well as a state-of-the-art hybrid linear analysis method. The latter is a multivariate calibration method designed specifically to improve robustness to background variability in cases where training spectra of the background, as well as the mean spectrum of the analyte, are available. We demonstrate the novel algorithm’s superior performance by comparing quantitative error metrics generated by each method. The experiments consider both simulated data and experimental data acquired from in vitro solutions of Raman-enhanced gold-silica nanoparticles

Quantitative characterization of individual microdroplets using surface-enhanced resonance Raman scattering spectroscopy

Surface-enhanced resonance Raman scattering (SERRS) spectroscopy is a highly sensitive optical technique capable of detecting multiple analytes rapidly and simultaneously. There is significant interest in SERRS detection in micro- and nanotechnologies, as it can be used to detect extremely low analyte concentrations in small volumes of fluids, particularly in microfluidic systems. There is also rapidly growing interest in the field of microdroplets, which promises to offer the analyst many potential advantages over existing technologies for both design and control of microfluidic assays. While there have been rapid advances in both fields in recent years, the literature on SERRS-based detection of individual microdroplets remains lacking. In this paper, we demonstrate the ability to quantitatively detect multiple variable analyte concentrations from within individual microdroplets in real time using SERRS spectroscopy. We also demonstrate the use of a programmable pump control algorithm to generate concentration gradients across a chain of droplets