Predicting Silicate Glass Geochemistry Using Raman Spectroscopy and Supervised Machine Learning: Partial Least Square Applications to Amorphous Raman Spectra.

Here, Raman spectroscopy is used to develop a univariate partial least squares (PLS) calibration capable of quantifying geochemistry in synthetic and natural silicate glass samples. The calibration yields eight oxide-specific models that allow predictions of silicon dioxide (SiO), sodium oxide (NaO), potassium oxide (KO), calcium oxide (CaO), titanium dioxide (TiO), aluminum oxide (AlO), ferrous oxide (FeO), and magnesium oxide (MgO) (wt%) in glasses spanning a wide range of compositions, while also providing correlation-coefficient matrices that highlight the importance of specific Raman channels in the regression of a particular oxide. The PLS suite is trained on 48 of the 69 total glasses, and tested against 21 validation samples (i.

Non-destructive raman spectroscopic determination of freshwater mollusk composition, growth, and damage repair.

Increased acidification of aquatic habitats due to climate change is damaging mollusks. Non-destructive methods for analysis are necessary to study these endangered species. We analyzed five gastropods using Raman spectroscopy.

In Vitro and In Vivo SERS Biosensing for Disease Diagnosis.

For many disease states, positive outcomes are directly linked to early diagnosis, where therapeutic intervention would be most effective. Recently, trends in disease diagnosis have focused on the development of label-free sensing techniques that are sensitive to low analyte concentrations found in the physiological environment. Surface-enhanced Raman spectroscopy (SERS) is a powerful vibrational spectroscopy that allows for label-free, highly sensitive, and selective detection of analytes through the amplification of localized electric fields on the surface of a plasmonic material when excited with monochromatic light.