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Classification of Substances Combining Standoff Laser Induced Fluorescence and Machine Learning

Kraus, Marian and Fellner, Lea and Gebert, Florian and Pargmann, Carsten and Walter, Arne and Duschek, Frank (2018) Classification of Substances Combining Standoff Laser Induced Fluorescence and Machine Learning. Journal of Light & Laser: Current Trends, 1 (1). Herald Scholarly Open Access.

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Official URL: http://www.heraldopenaccess.us/fulltext/Light-&-Laser-Current-Trends/Classification-of-Substances-Combining-Standoff-Laser-Induced-Fluorescence-and-Machine-Learning.php

Abstract

Contaminated objects and areas must be handled carefully depending on the underlying pollution. There are methods which require short distances, others the collection of samples or even direct contact to the hazardous, and some of the established techniques take long to reach a conclusion. A fast standoff method for predicting the potential hazard can be achieved by examining the laser induced fluorescence spectra of the substances of interest. The samples are excited by low-energy laser pulses of two alternating wavelengths. The datasets are measured for almost 50 agents, including fuels, pesticides and bacteria and represent the basis for a subsequent classification procedure. Therefore, the investigated materials are grouped in seven classes depending on their origin and utilization. The majority of the dataset is used in a training phase to create predictive models, which are tested with the remaining signals to qualify the classification. After all, the single spectra of the test set are classifed with an error rate less than 0.1 % in predicting the correct class. With a statement like this frst responders would be able to choose the right preventive measure for a rescue or decontamination procedure.

Item URL in elib:https://elib.dlr.de/120950/
Document Type:Article
Title:Classification of Substances Combining Standoff Laser Induced Fluorescence and Machine Learning
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Kraus, MarianMarian.Kraus (at) dlr.deUNSPECIFIED
Fellner, LeaLea.Fellner (at) dlr.deUNSPECIFIED
Gebert, FlorianFlorian.Gebert (at) dlr.deUNSPECIFIED
Pargmann, CarstenCarsten.Pargmann (at) dlr.dehttps://orcid.org/0000-0003-3688-6360
Walter, ArneArne.Walter (at) dlr.deUNSPECIFIED
Duschek, FrankFrank.Duschek (at) dlr.deUNSPECIFIED
Date:15 June 2018
Journal or Publication Title:Journal of Light & Laser: Current Trends
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:No
In ISI Web of Science:No
Volume:1
Publisher:Herald Scholarly Open Access
Status:Published
Keywords:Classification algorithms; Laser Induced Fluorescence (LIF); Machine learning; Standoff detection
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:fixed-wing aircraft
DLR - Research area:Aeronautics
DLR - Program:L AR - Aircraft Research
DLR - Research theme (Project):L - Laser Research and Technology
Location: Lampoldshausen
Institutes and Institutions:Institute of Technical Physics
Institute of Technical Physics > Atmospheric Propagation and Effect
Deposited By: Kraus, Marian
Deposited On:12 Nov 2018 10:18
Last Modified:12 Nov 2018 10:18

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