Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry.

Nanoporous materials are of great interest in many applications, such as catalysis, separation, and energy storage. The performance of these materials is closely related to their pore sizes, which are inefficient to determine through the conventional measurement of gas adsorption isotherms. Nuclear magnetic resonance (NMR) relaxometry has emerged as a technique highly sensitive to porosity in such materials.

An Interactive Spectral Analysis Tool for Chemical Identification and Quantification of Gas-Phase Species in Complex Spectra.

A spectral analysis tool has been developed to interactively identify and quantify individual gas-phase species from complex infrared absorbance spectra obtained from laboratory or field data. The SpecQuant program has an intuitive graphical interface that accommodates both reference and experimental data with varying resolution and instrumental lineshape, as well as algorithms to readily align the wavenumber axis of a sample spectrum with the raster of a reference spectrum. Using a classical least squares model in conjunction with reference spectra such as those from the Pacific Northwest National Laboratory (PNNL) gas-phase infrared database or simulated spectra derived from the HITRAN line-by-line database, the mixing ratio of each identified species is determined along with its associated estimation error.

PARASIAS: A new method for analyzing higher-order tensors with shifting profiles.

Higher-order tensor data analysis has been extensively employed to understand complicated data, such as multi-way GC-MS data in untargeted/targeted analysis. However, the analysis can be complicated when one of the modes shifts e.g.

The Often-Overlooked Power of Summary Statistics in Exploratory Data Analysis: Comparison of Pattern Recognition Entropy (PRE) to Other Summary Statistics and Introduction of Divided Spectrum-PRE (DS-PRE).

Unsupervised exploratory data analysis (EDA) is often the first step in understanding complex data sets. While summary statistics are among the most efficient and convenient tools for exploring and describing sets of data, they are often overlooked in EDA. In this paper, we show multiple case studies that compare the performance, including clustering, of a series of summary statistics in EDA.

Sensor Fusion: Comprehensive Real-Time, On-Line Monitoring for Process Control via Visible, Near-Infrared, and Raman Spectroscopy.

On-line monitoring based on optical spectroscopy provides unprecedented insight into the chemical composition of process streams or batches. Amplifying this approach through utilizing multiple forms of optical spectroscopy in sensor fusion can greatly expand the number and type of chemical species that can be identified and quantified. This is demonstrated herein, on the analysis of used nuclear fuel recycling streams: highly complex processes with multiple target and interfering analytes.

Identification of Uranium Minerals in Natural U-Bearing Rocks Using Infrared Reflectance Spectroscopy.

The identification of minerals, including uranium-bearing species, is often a labor-intensive process using X-ray diffraction (XRD), fluorescence, or other solid-phase or wet chemical techniques. While handheld XRD and fluorescence instruments can aid in field applications, handheld infrared (IR) reflectance spectrometers can now also be used in industrial or field environments, with rapid, nondestructive identification possible via analysis of the solid's reflectance spectrum providing information not found in other techniques. In this paper, we report the use of laboratory methods that measure the IR hemispherical reflectance of solids using an integrating sphere and have applied it to the identification of mineral mixtures (i.

Contact geometry affects lesion formation in radio-frequency cardiac catheter ablation.

One factor which may be important for determining proper lesion creation during atrial ablation is catheter-endocardial contact. Little information is available that relates geometric contact, depth and angle, to ablation lesion formation. We present an electrothermal computer model of ablation that calculated lesion volume and temperature development over time.

Cathether contact geometry affects lesion formation in radio-frequency cardiac catheter ablation.

One factor which may be important for determining proper lesion creation in an atrial ablation procedure is catheter-endocardial contact. Little information is available that relates geometric contact, depth and angle, to ablation lesion formation. We present an electrothermal computer model of ablation that calculates lesion volume and temperature development over time.

Passive standoff detection of RDX residues on metal surfaces via infrared hyperspectral imaging.

Hyperspectral images of galvanized steel plates, each containing a stain of cyclotrimethylenetrinitramine (RDX), were recorded using a commercial long-wave infrared imaging spectrometer. Demonstrations of passive RDX chemical detection at areal dosages between 16 and 90 microg/cm(2) were carried out over practical standoff ranges between 14 and 50 m. Anomaly and target detection algorithms were applied to the images to determine the effect of areal dosage and sensing distance on detection performance for target RDX.

A comparison of a common approach to partial least squares-discriminant analysis and classical least squares in hyperspectral imaging.

In hyperspectral analysis, PLS-discriminant analysis (PLS-DA) is being increasingly used in conjunction with pure spectra where it is often referred to as PLS-Classification (PLS-Class). PLS-Class has been presented as a novel approach making it possible to obtain qualitative information about the distribution of the compounds in each pixel using little a priori knowledge about the image (only the pure spectrum of each compound is needed). In this short note it is shown that the PLS-Class model is the same as a straightforward classical least squares (CLS) model and it is highlighted that it is more appropriate to view this approach as CLS rather than PLS-DA.

Strategies for detecting organic liquids on soils using mid-infrared reflection spectroscopy.

Stand-off monitoring for chemical spills can provide timely information for cleanup efforts, and mid-infrared reflection spectroscopy is one approach being investigated for spill detection. Using laboratory data, anomaly and target detection strategies were examined for the detection of four different low-volatility organic liquids on two different soil types. Several preprocessing and signal-weighting strategies were studied.

Multivariate curve resolution applied to infrared reflection measurements of soil contaminated with an organophosphorus analyte.

Multivariate curve resolution (MCR) is a powerful technique for extracting chemical information from measured spectra of complex mixtures. A modified MCR technique that utilized both measured and second-derivative spectra to account for observed sample-to-sample variability attributable to changes in soil reflectivity was used to estimate the spectrum of dibutyl phosphate (DBP) adsorbed on two different soil types. This algorithm was applied directly to measurements of reflection spectra of soils coated with analyte without resorting to soil preparations such as grinding or dilution in potassium bromide.

Error analysis for estimation of trace vapor concentration pathlength in stack plumes.

Near-infrared hyperspectral imaging is finding utility in remote sensing applications such as detection and quantification of chemical vapor effluents in stack plumes. Optimizing the sensing system or quantification algorithms is difficult because reference images are rarely well characterized. The present work uses a radiance model for a down-looking scene and a detailed noise model for dispersive and Fourier transform spectrometers to generate well-characterized synthetic data.