Search NASA⌕ Search

SEARCH · Search NASA

Results for “spectral data”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records

Spectral Data Fusion From Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and X-ray Fluorescence (XRF) Analyzers for Improved Detection of Cerium in a Simulated Dispersal Accident

Here, this work implements a mid-level data fusion methodology on spectral data from handheld X-ray fluorescence and laser-induced breakdown spectroscopy analyzers to quantify plutonium surrogate (CeO 2 ) contamination in soil samples for the first time. Spectral data from each analyzer were used independently to train supervised machine learning regressions to predict Ce concentration. Fused features from both data sets were then used to train the same models, comparing prediction performance by evaluating model precision and sensitivity. Fusing principal component scores from the two sensors yielded an order of magnitude improvement in precision and sensitivity of predictions made with an artificial neural network, compared to predictions made by models trained on independent sensor data. As a result, a boosted ensemble trained on the fused spectral features yielded an ideal predictor with root-mean-squared error on the order of 10 –6 and calculated limit of detection order 10 –5 wt %.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Neutral Loss Mass Spectral Data Enhances Molecular Similarity Analysis in $\mathrm{METLIN}$

We report Neutral loss (NL) spectral data presents a mirror of MS 2 data and is a valuable yet largely untapped resource for molecular discovery and similarity analysis. Tandem mass spectrometry (MS 2 ) data is effective for the identification of known molecules and the putative identification of novel, previously uncharacterized molecules (unknowns). Yet, MS 2 data alone is limited in characterizing structurally related molecules. To facilitate unknown identification and complement the METLIN-MS 2 fragment ion database for characterizing structurally related molecules, we have created a MS 2 to NL converter as a part of the METLIN platform. The converter has been used to transform METLIN’s MS 2 data into a neutral loss database (METLIN-NL) on over 860 000 individual molecular standards. The platform includes both the MS 2 to NL converter and a graphical user interface enabling comparative analyses between MS 2 and NL data. Examples of NL spectral data are shown with oxylipin analogues and two structurally related statin molecules to demonstrate NL spectra and their ability to help characterize structural similarity. Mirroring MS 2 data to generate NL spectral data offers a unique dimension for chemical and metabolite structure characterization.

59 BASIC BIOLOGICAL SCIENCES↗

LIBS Spectral Data for a Mixed Actinide Fuel Pellet Containing Uranium, Plutonium, Neptunium and Americium (V.1)

Laser-induced breakdown spectroscopy (LIBS) was used to analyze a mixed actinide fuel pellet containing 75% UO 2 /20% PuO 2 /3% AmO 2 /2% NpO 2 . The preliminary data shown here is the first report of LIBS analysis of a mixed actinide fuel pellet, to the authors knowledge. The LIBS spectral data was acquired in a plutonium facility at Los Alamos National Laboratory where the sample was contained within a glove box. The initial installation of the glove box was not intended for complete ultraviolet (UV), visible (VIS) and near infrared (NIR) transmission, therefore the LIBS spectrum is truncated in the UV and NIR regions due to the optical transmission of the window port and filters that were installed. The optical collection of the emission from the LIBS plasma will be optimized in the future. However, the preliminary LIBS data acquired is worth reporting due to the uniqueness of the sample and spectral data. The analysis of several actinides in the presence of each other is an important feature of this analysis since traditional methods must chemically separate uranium, plutonium, neptunium, and americium prior to analysis. Due to the historic nature of the sample fuel pellet analyzed, the provided sample composition of 75% UO 2 /20% PuO 2 /3% AmO 2 /2% NpO 2 cannot be confirm without further analytical processing. Uranium, plutonium, and americium emission lines were abundant and easily assigned while neptunium was more difficult to identify. There may be several reasons for this observation, other than knowing the exact sample composition of the fuel pellet. First, the atomic emission wavelength resources for neptunium are limited and such techniques as hollow cathode discharge lamp have different dynamics than the plasma used in LIBS which results in different emission spectra. Secondly, due to the complex sample of four actinide elements, which all have very dense electronic energy levels, there may be reactions and interactions occurring within the plasma, such as collisional energy transfer, that might be a factor in the reduction in neptunium emission lines. Neptunium has to be analyzed alone using LIBS to further understand the dynamics that may be occurring in the plasma of the mixed actinide fuel pellet sample. The LIBS data suggests that the emission spectrum for the mixed actinide fuel pellet is not simply the sum of the emission spectra of the pure samples but is dependent on the species present in the plasma and the interactions and reactions that occur within the plasma. Finally, many of the neptunium lines are in the near infrared region which is drastically reduced in intensity by the current optical setup and possibly the sensitivity of the emission detector in the spectral region. Once the optics are replaced and the optical collection system is modified and optimized, the probability of observing emission lines for neptunium might be increased significantly. The mixed actinide fuel pellet was analyzed under the experimental conditions listed in Table 1. The LIBS spectra of the fuel pellet are shown in Figures 1-49. The spectra are labeled with the observed wavelength and atomic species (both neutral (I) and ionic (II)). Table 2 is a complete list of the observed and literature based emission wavelengths. The literature wavelengths have references including NIST Atomic Spectra Database (NIST), B.A. Palmer et al. 'An Atlas of Uranium Emission Intensities in a Hollow Cathode Discharge' taken at the Kitt Peak National Observatory (KPNO), R.L. Kurucz 1995 Atomic Line Data from the Smithsonian Astrophysical Observatory (SAO), J. Blaise et al. 'The Atomic Spectrum of Plutonium' from Argonne National Laboratory (BFG), and M. Fred and F.S. Tomkins, 'Preliminary Term Analysis of Am I and Am II Spectra' (FT). The dash (-) shown under Ionic State indicates that the ionic state of the transition was not available. In the spectra, the dash (-) is replaced with a question mark (?). Peaks that are not assigned are most likely real features and not noise but cannot be confidently assigned to a transition without further investigation. Several peaks have multiple assignments due to limited resolution of the spectrometer used (20,000, $\lambda$/$\Delta$$\lambda$) and without the availability, at this point in time, of pure PuO 2 , AmO 2 , and NpO 2 to confirm the identity of the peaks. A different spectrometer was used in the plutonium facility to collect the mixed actinide fuel pellet data (Echelle 3000) than the DUO 2 , ThO 2 and uranium ore previously reported (Echelle 4000) which accounts for the slight shift in the observed wavelength of the uranium emission lines.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Evaluation of artificial neural network performance for classification of potato plants infected with potato virus Y using spectral data on multiple varieties and genotypes

Potato virus Y (Potyviridae, PVY) is a plant virus that poses a significant threat to potato producers on a global basis. The pathogen has disrupted seed potato supplies and negatively impacted yield and quality of commercial potato crops. The potato industry currently manages PVY infection levels via insecticide applications, regional seed certification programs that rely on field scouting to visually assess individual plants for infection status, and destructive and costly tissue sampling coupled with laboratory assays. Despite these efforts, PVY continues to confound potato industry stakeholders resulting in economic harm. Remote sensing and machine learning provide for the development of new tools to more accurately detect and spatially quantify PVY-infected plants versus the current state of the art. However, there is a need to understand how the occurrence of many different potato varieties impact the dynamics of developing models to detect potato plants impacted with PVY and their potential effectiveness. This study evaluates classification modelling outcomes using spectral datasets collected in different temporal and spatial environments (greenhouse and a production field) on multiple potato varieties consisting of labelled instances of plants infected with PVY and those not infected with the virus. A modelling framework was developed to support iterative modelling runs using artificial neural network (ANN) architectures configured as binary classifiers to develop sample populations to support statistical analysis on model performance using specific spectral subsets. When using spectral data to detect PVY-infected plants, ANN models achieved the highest mean accuracy of 0.894 on a single variety. Conversely, the same ANN model architecture only achieved a mean accuracy of 0.575 on a spectral data set representing 29 potato breeding lines. Additionally, statistical analysis indicates spectral regions including the red edge, near infrared and shortwave infrared contain more important spectral features for the ANN classifier introduced in this research.

60 APPLIED LIFE SCIENCES↗

Spectral Data from Multiplying Assemblies

In this report we focus on the data obtained from various assemblies using fissionable materials. These assemblies have gamma and neutron spectral data at delayed critical (DC), prompt critical (PC), and subcritical for various assemblies that use fissile and/or fissionable material as the primary source of radiation. There are a few categories of data that have been measured. The data was measured using many types of detectors. The data may have been differential fluxes, integral fluxes, or currents. Unfortunately some of these assemblies may not have data in an easily accessible format, or we do not have a model suitable for calculations. All have to be examined in more detail to determine which meet acceptability and applicability criteria.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗