Data sets of order three or more are increasingly common in areas ranging from biomedical imaging to threat detection, and are output from a number of spectroscopy (e.g. NIR, Raman, Excitation Emission Fluorescence) and spectrometry (e.g. SIMS) methods. Various chemometrics methods can be used to reduce the dimensionality of these data sets, and the resulting compressed data can then be visualized. These methods include Principal Components Analysis (PCA), Multivariate Curve Resolution (MCR), and Maximal Autocorrelation Factors (MAF) as well as numerous data clustering methods (e.g. HCA, DBSCAN, KNN) and classification techniques (e.g. PLS-DA, SIMCA). These methods can also be combined with traditional image analysis techniques such as particle analysis. This talk gives examples of how up front chemometric modeling can be used to extract relevant information which can then be visualized in two and three dimensions, and in time.
In-vivo examinations of cervical tissue were performed using Evoked Tissue Fluorescence (ETF) as part of a clinical trial. These examinations were performed in conjunction with conventional colposcopy, along with of biopsies of suspect cancerous or pre-cancerous tissue. The ETF data consisted of 22 images of the cervix taken at combinations of 3 excitation wavelengths and 9 emission wavelengths. The ultimate goal was to use these images to accurately classify tissue between normal states (normal squamous and normal columnar) and pre-cancerous and cancerous states (squamous metaplasia, and high and low Squamous Intraepithelial Lesion, HSIL and LSIL). Several chemometric methods were applied to the data for the purpose of preprocessing (e.g. image alignment), eliminating patient to patient variability (e.g. GLS and specialized centering), classification of tissues (hierarchical PLS-DA) and elucidating the underlying signatures of the tissues and background components (PARAFAC).
Nevada Nanotech Systems, Inc. (Nevada Nano) has developed a multi-sensor solution to Chemical, Biological, Radiological, Nuclear and Explosives (CBRNE) detection that combines the Molecular Property Spectrometer™ (MPS™)—a micro-electro-mechanical chip-based technology capable of measuring a variety of thermodynamic and electrostatic molecular properties of sampled vapors and particles—and a compact, high-resolution, solid-state gamma spectrometer module for identifying radioactive materials, including isotopes used in dirty bombs and nuclear weapons. By conducting multiple measurements, the system can provide a more complete characterization of an unknown sample, leading to a more accurate identification. Positive identifications of threats are communicated using an integrated wireless module. Currently, system development is focused on detection of commercial, military and improvised explosives, radioactive materials, and chemical threats. The system can be configured for a variety of CBRNE applications, including handheld wands and swab-type threat detectors requiring short sample times, and ultra-high sensitivity detectors in which longer sampling times are used. Here we provide an overview of the system design and operation and present results from preliminary testing.
Hyperspectral images in the long wave-infrared can be used for quantification of analytes in stack plumes. One approach uses eigenvectors of the off-plume covariance to develop models of the background that are employed in quantification. In this paper, it is shown that end members can be used in a similar way with the added advantage that the end members provide a simple approach to employ non-negativity constraints. A novel approach to end member extraction is used to extract from 14 to 53 factors from synthetic hyperspectral images. It is shown that the eigenvector and end member methods yield similar quantification performance and, as was seen previously, quantification error depends on net analyte signal.
Mismatch between the temperature of the spectra used in the estimator and the actual plume temperature was also studied. A simple model used spectra from three different temperatures to interpolate to an “observed” spectrum at the plume temperature. Using synthetic images, it is shown that temperature mismatch generally results in increases in quantification error. However, in some cases it caused an off-set of the model bias that resulted in apparent decreases in quantification error.
In this paper we will demonstrate chemometric approaches that can be applied to data from a well-understood polymer- coated acoustic wave vapor sensor array to extract information about the properties of detected vapors, whether that vapor was in the training set or not. Derivation of the approach and simulation using `synthetic' data are presented.
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