Online Monitoring of Solutions Within Microfluidic Chips: Simultaneous Raman and UV–Vis Absorption Spectroscopies

Abstract

Microfluidics is an appealing analytical tool in the global effort to close the nuclear fuel cycle. Using a microfluidic chip permits the analysis of greatly reduced sample volumes compared to what is necessary for traditional analytical methods. There is a commensurate reduction in disposal volume and cost. The development of novel sensors is necessary to take full advantage of the microchip configuration, where optical-spectroscopy-based approaches offer a powerful route to characterize chemical composition. This study uses simultaneously applied UV−vis and micro-Raman spectroscopies adapted to function on the microscale to analyze in situ both the Nd 3+ (UV−vis-active) and HNO 3 (Raman-active) concentrations in the same sample. An adjustable translation platform was designed to hold the micro-Raman probe above and perpendicular to the chip face and the UV−vis probe in the plane of the chip. These complimentary spectral techniques when processed through multivariate partial leastsquares (PLS) models gave an accurate picture of the widely varying solution concentrations as a function of time for each solution component. Solution matrix effects can drastically alter analyte signatures as measured by both UV−vis absorbance and Raman spectroscopy. PLS methods successfully modeled these spectral changes and accurately measured concentrations of components of interest within the microfluidic chip.

Measuring Nd(III) Solution Concentration in the Presence of Interfering Er(III) and Cu(II) Ions: A Partial Least Squares Analysis of Ultraviolet–Visible Spectra

“…However, previous study has shown that a PLS model can accurately quantify the [Nd 3þ ] even when the [HNO 3 ] varies so long as the variation is represented in the training set. 36,37 This observation can also be applied to other metal ions. Sample spectra were referenced to a deionized water spectrum using the UV-Vis instrumental software before exporting to Matlab for study.…”

“…4 Neodymium has also been used as a non-radioactive surrogate for Pu 3þ and Pu 4þ ion, both spectroscopically and chemically. 36 These properties make Nd 3þ an ideal candidate for chemometric analysis demonstrations. The high absorption band of Cu 2þ at >560 nm as a perturbation of the baseline for the Nd 3þ bands in that region and is also a common contaminant in fuel reprocessing systems.…”

Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning

“…It was necessary to preprocess the original spectra due to the uneven intensity of light sources at different wavelengths and the influence of instrument noise. In this paper, the spectra were processed using ten preprocessing methods, including multiplicative scatter correction (MSC) [ 23 ], standardized normal variate (SNV) [ 24 ], normalization [ 25 ], autoscales [ 26 ], mean centering (MC) [ 27 ], moving average method (MA) [ 28 ], detrend fluctuation analysis (Detrend) [ 29 ], Savitsky–Golay smoothing (SG) [ 30 ], Savitsky–Golay first derivative (SG-FD) [ 31 ], and Savitsky–Golay second derivative (SG-SD) [ 32 ]. To reduce calculation and increase calculation speed, competitive adaptive reweighted sampling (CARS) [ 33 ], principal components analysis (PCA) [ 34 ], and successive projections algorithm (SPA) [ 35 ] are preferable to extract feature wavelengths to reduce the dimensionality.…”

Nondestructive Detection for Egg Freshness based on Hyperspectral Scattering Image combined with Ensemble Learning

“…It is necessary to preprocess the original spectra due to the uneven intensity of light sources at different wavelengths and the influence of instrument noise. In this paper, the spectra are processed by ten pre-processing methods, including Multiplicative Scatter Correction (MSC) [18], Standardized Normal Variate (SNV) [19], Normalization [20], Autoscales [21], Mean Centering (MC) [22], Moving-Average Method (MA) [23], Detrend Fluctuation Analysis (Detrend) [24], Savitsky-Golay Smoothing (SG) [25], Savitsky-Golay-First Derivative (SG-FD) [26] and Savitsky-Golay-Second Derivative (SG-SD) [27]. to distinguish due to their obvious spoilage and unpleasant smell deterioration, so they will not be discussed in this article.…”