We report a new approach to analyze both vapor and liquid phases by utilizing a tapered fiber optic surface plasmon resonance (SPR) probe. This technique employs a fiber optic SPR probe with a modified geometry to tune the SPR coupling wavelength–angle pair. The observed composite spectrum included two distinct SPR dips associated with surface plasmons excited in the gas and liquid active regions. This sensor is able to detect refractive index changes in both vapor and liquid phases individually by simultaneous monitoring SPR coupling wavelengths from the two sensing surfaces.

Multivariate curve resolution-alternating least squares (MCR-ALS) applied to hyperspectral Raman imaging is extensively used to spatially and spectrally resolve the individual, pure chemical species within complex, heterogeneous samples. A critical aspect of performing MCR-ALS with hyperspectral Raman imaging is the selection of the number of chemical components within the experimental data. Several methods have previously been proposed to determine the number of chemical components, but it remains a challenging task that if done incorrectly, can lead to the loss of chemical information. In this work, we show that the choice of ‘optimal’ number of factors in the MCR-ALS model may vary depending on the relative contribution of the targeted species to the overall spectral intensity. In a data set consisting of 27 hyperspectral Raman images of TiO₂ polymorphs, it was observed that the more dominant species were best resolved with a parsimonious model. However, species with intensities near the noise level often needed more factors to be resolved than was predicted by standard methods. Based on the observations in this data set, we propose a new method that employs approximate reference spectra for determining optimal model complexity for identifying minor constituents with MCR-ALS.

Independently emerging fluorescence profiles of unknown, photochemically induced degradation products of several naturally non-fluorescent pesticides were monitored using single exposure excitation–emission fluorescence spectroscopy. Three-way parallel factor analysis (PARAFAC) was employed to uniquely resolve the pure fluorescent spectra of the overlapping photolysis products. The quantitative utility of EEM photolysis-based determinations was demonstrated by employing four-way PARAFAC models built from EEM time cubes of multiple fenvalerate samples. The 4-way PARAFAC models were then used to predict original pesticide concentrations resulting in conservative limit of detection and root mean square errors of calibration (RMSEC) of 3 µM each.

We present four unique prediction techniques, combined with multiple data pre-processing methods, utilizing a wide range of both oil types and oil peroxide values (PV) as well as incorporating natural aging for peroxide creation. Samples were PV assayed using a standard starch titration method, AOCS Method Cd 8-53, and used as a verified reference method for PV determination. Near-infrared (NIR) spectra were collected from each sample in two unique optical pathlengths (OPLs), 2 and 24 mm, then fused into a third distinct set. All three sets were used in partial least squares (PLS) regression, ridge regression, LASSO regression, and elastic net regression model calculation. While no individual regression model was established as the best, global models for each regression type and pre-processing method show good agreement between all regression types when performed in their optimal scenarios. Furthermore, small spectral window size boxcar averaging shows prediction accuracy improvements for edible oil PVs. Best-performing models for each regression type are: PLS regression, 25 point boxcar window fused OPL spectral information RMSEP = 2.50; ridge regression, 5 point boxcar window, 24 mm OPL, RMSEP = 2.20; LASSO raw spectral information, 24 mm OPL, RMSEP = 1.80; and elastic net, 10 point boxcar window, 24 mm OPL, RMSEP = 1.91. The results show promising advancements in the development of a full global model for PV determination of edible oils.