Multispectral LIF-Based Standoff Detection System for the Classification of CBE Hazards by Spectral and Temporal Features

Abstract

Laser-induced fluorescence (LIF) is a well-established technique for monitoring chemical processes and for the standoff detection of biological substances because of its simple technical implementation and high sensitivity. Frequently, standoff LIF spectra from large molecules and bio-agents are only slightly structured and a gain of deeper information, such as classification, let alone identification, might become challenging. Improving the LIF technology by recording spectral and additionally time-resolved fluorescence emission, a significant gain of information can be achieved. This work presents results from a LIF based detection system and an analysis of the influence of time-resolved data on the classification accuracy. A multi-wavelength sub-nanosecond laser source is used to acquire spectral and time-resolved data from a standoff distance of 3.5 m. The data set contains data from seven different bacterial species and six types of oil. Classification is performed with a decision tree algorithm separately for spectral data, time-resolved data and the combination of both. The first findings show a valuable contribution of time-resolved fluorescence data to the classification of the investigated chemical and biological agents to their species level. Temporal and spectral data have been proven as partly complementary. The classification accuracy is increased from 86% for spectral data only to more than 92%.

Current State of Laser-Induced Fluorescence Spectroscopy for Designing Biochemical Sensors

“…A multi-wavelength sub-nanosecond laser source was used to acquire spectral and time-resolved data from a standoff distance of 3.5 m from seven different bacterial species and six types of oil. Classification performed with a decision tree algorithm showed that accuracy was increased from 86% for spectral data only to more than 92% when combined with time-resolved data [49].…”

Potential of laser‐induced fluorescence‐light detection and ranging for future stand‐off virus surveillance

“…In addition, the specificity of the Rayleigh‐LIF‐LiDAR platform could be enhanced by using multiple channels of resolution for acquiring the LIF spectra (Kaye et al ., 2005; Ruske et al ., 2017). The specificity of the hyphenated LiDAR platform could further be enhanced by acquiring data on fluorescence lifetimes (time‐resolved LIF data) of the viruses and other aerosol particles, as was recently demonstrated for detection, classification and identification of some CBE agents (Fellner et al ., 2020).…”

“…Even though some studies have presented < 10 m and > 10 m as short and long stand‐off range, respectively (Bogue, 2018), this review adopts the detection distances < 20 m, 20–100 m and > 100 m as short, medium and long stand‐off detection range, respectively, in consonance with many other reports (Sedlacek et al ., 2002; Huestis et al ., 2010; Babichenko et al ., 2018; Fellner et al ., 2020). Stand‐off detection methods are suitable for operations in high risk and harsh environments, as they could provide information about CBE hazards in real‐time from safe distances of several centimetres to up to a kilometre (Jonsson et al ., 2009; Babichenko et al ., 2018; Fellner et al ., 2020). Based on these advantages, stand‐off detection will be an ideal approach for viruses, which can be highly contagious and dangerous.…”

Determination of composition of mixed biological samples using laser-induced fluorescence and combined classification/regression models

“…High classification performances (more than 90%) have been obtained in the most of applications, remote sensing included. On the contrary, no results and detailed investigations about the concentration measurements and classification of mixture samples are available in the literature [4,[8][9][10].…”