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

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%

Application of Standoff LIF to Living and Inactivated Bacteria Samples

To minimize the impact of an airborne bio-agent output, sensitive, specific and swift detection and identification are essential. A single method can hardly meet all of these requirements. Point sensors allow highly sensitive and specific identification but are localized and comparatively slow. Most laser-based standoff systems lack selectivity and specificity but provide real-time detection and classification in a wide region with additional information about location and propagation. A combination of both methods allows benefiting from their complementary assets and may be a promising solution to optimize detection and identification of hazardous substances. Here, we present progress for an outdoor bio-detector based on laser-induced fluorescence (LIF) developed at the DLR Lampoldshausen. After excitation at 280 and 355 nm, bacteria species express unique fluorescence spectra. Upon deactivation, the spectral features change depending on the applied method

Standoff laser induced fluorescence of living and inactivated bacteria

Biological hazards, such as bacteria, represent a non-assessable threat in case of an accident or a terroristic attack. Rapid detection and highly sensitive identification of released, suspicious substances at low false alarm rates are challenging requirements which one single technology cannot cope with. It has been shown that standoff detection using laser-induced fluorescence (LIF) can provide information on the class of bioorganic substances in real-time1. In combination with traditional, highly sensitive, but non-standoff methods, the time for identification of the threat can be optimized. This work is aimed at the selectivity of LIF technology for different bacterial strains. A second important aspect examines how to deal with inactivated bacteria and how their fluorescence signature changes after deactivation. LIF spectra of closely and more distantly related bacterial strains are presented as well as spectra of bacteria treated by different inactivation methods

Conceptual design for an ultra-sensitive bioaerosol detection system

The detection of aerosols in general and bioaerosols more specific has gained an increased importance in multiple fields. While environmental scientists are increasingly interested in the impacts of aerosols onto climatic effects, researchers in the security sector are looking for ways to remotely detect dangerous substances from safe distances. Additionally, due to the Corona pandemic, the detection of bioaerosols has gained significant relevance in sectors like public health, transportation, and aviation. As a result, more accurate, i.e. sensitive and specific, measurement equipment is needed. Here we present the design concept for a new sensor system designed to measure thin bioaerosol clouds. For the detection air samples are excited with laser light to generate a signal based on laser induced fluorescence. The fluorescence is collected in an integration sphere to optimize signal. Inside the integration sphere multiple sensors are placed, each combined with a filter to exclude all signals not belonging to a certain, agent specific wavelength interval. Through the intelligent combination of spectral intervals, a specific characteristic of the studied air sample is measured. Based on the measured characteristic a classification is performed to determine the category of the sample. Development aims at testing indoor air quality in real time

Fluorescence signatures of SARS CoV-2 spike S1 proteins and an human ACE-2: excitation-emission maps and fluorescence lifetimes

Significance: Fast and reliable detection of infectious SARS-CoV-2 virus loads is an important issue. Fluorescence spectroscopy is a sensitive tool to do so in clean environments. This presumes a comprehensive knowledge of fluorescence data. Aim: We aim at providing fully featured information on wavelength and time-dependent data of the fluorescence of the SARS-CoV-2 spike protein S1 subunit, its receptor-binding domain (RBD), and the human angiotensin-converting enzyme 2, especially with respect to possible optical detection schemes. Approach: Spectrally resolved excitation-emission maps of the involved proteins and measurements of fluorescence lifetimes were recorded for excitations from 220 to 295 nm. The fluorescence decay times were extracted by using a biexponential kinetic approach. The binding process in the SARS-CoV-2 RBD was likewise examined for spectroscopic changes. Results: Distinct spectral features for each protein are pointed out in relevant spectra extracted from the excitation-emission maps. We also identify minor spectroscopic changes under the binding process. The decay times in the biexponential model are found to be [Formula: see text] and [Formula: see text]. Conclusions: Specific material data serve as an important background information for the design of optical detection and testing methods for SARS-CoV-2 loaded media

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

Abstract Laser-induced fluorescence (LIF) provides the ability to distinguish organic materials by a fast and distant in situ analysis. When detecting the substances directly in the environment, e.g. in an aerosol cloud or on surfaces, additional fluorescence signals of other fluorophores occurring in the surrounding are expected to mix with the desired signal. We approached this problem with a simplified experimental design for an evaluation of classification algorithms. An upcoming question for enhanced identification capabilities is the case of mixed samples providing different signals from different fluorophores. For this work, mixtures of up to four common fluorophores (NADH, FAD, tryptophan, and tyrosine) were measured by a dual wavelength setup and spectrally analyzed. Classification and regression are conducted with neural networks and show an excellent performance in predicting the ratios of the selected ingredients

Laser induced fluorescence technologies applied for the standoff detection of bioaerosols

Standoff detection of hazardous bacteria is important for instant countermeasures which prevent further spread of pathogens. Laser induced fluorescence (LIF) technology is a suitable method for detection and localization of bacterial aerosol clouds from longer distances. LIF allows for the standoff differentiation of bacteria from other substances in the environment like pollen, fog, chemicals or smoke. This standoff technology promises excellent capabilities for a security concept: After localization of the aerosol source, samples of bacteria can be collected quickly and subsequently analyzed in a laboratory by other methods like DNA sequencing or polymerase chain reaction assays for identification. A LIF detection system for bioaerosol detection which provides laser pulses with wavelengths of 266 nm and 355 nm for excitations is evaluated on aerosols. The setup enables fast data acquisition and provides a complete dataset in less than a few seconds at repetition rates of 100 Hz. Detection of different test aerosols from distances longer than 20 m is shown. Aerosols in different concentrations and compositions are generated and fluorescence intensities for different laser energies applied for excitation are discussed. The discrimination of bacterial aerosols from other substances is shown

Comparison of Classification Methods for Spectral Data of Laser-induced Fluorescence

Online detection of CBRNE is a research field of growing importance due to its relevance for public security and defense. The selectivity of machine learning has reached maturity in order to distinguish very similar laser-induced fluorescence (LIF) spectra of different samples - establishing the basis for an automatic classification. The work in this contribution applies the classification process of decision trees, support vector machines and artificial neural networks to LIF spectra. Two experimental setups with two excitation wavelengths each (280 and 355 nm; 266 and 355 nm) and different spectral resolutions of about 1 nm and 12 nm, respectively, have been performed. In the first setup the discrimination of seven bacteria species with an accuracy of over 90 % is demonstrated. The data of the second setup with lower spectral resolution are equally sufficient for a subsequent classification. The results are compared and represented in a low-dimensional subspace for the purpose of visualization

Novel standoff detection system for the classification of chemical and biological hazardous substances combining temporal and spectral laser induced fluorescence techniques

In the effort to reduce the potential risk of human exposure to chemical and biological hazardous material, the demand increases for a detection system which rapidly identifies possible threats from a distance to avoid direct human contact to these materials. In this scope, we present a novel detection system which is able to simultaneously measure spectrally and temporally resolved laser induced fluorescence signals excited by two consecutive laser pulses with differ-ent central wavelengths at 266 nm and 355 nm. The setup enables fast data acqui-sition that provides a complete dataset in less than a few milliseconds at repetition rates of 100 Hz. Furthermore, with its modular design it can be transported easily for operation at different locations. First measurements indicate a high perfor-mance and a good distinguishability of bacterial specimen within a limited set of three representative bacterial species. Together with the consecutive classification procedure, the setup promises to become a valuable tool for standoff detection of bio-hazards

Prospects for biological and chemical substance classification using a standoff laser induced fluorescence detection system

After accidental or intended release of chemical or biological hazardous materials, fast detection and classification is mandatory to be able to rapidly take steps against negative consequences on humans. Standoff laser based devices are suited to localize and classify a thread on short timescales without the need of direct human contact to these materials. In this scope we present the classification prospects of our standoff detection system that utilizes laser induced fluorescence signals excited with laser pulses of two different UV excitation wavelengths. For fast data acquisition we use a rather limited spectral resolution of 32 channels per wavelength which is sufficient to achieve a good performance. The described system is able to perform a complete classification procedure including data acquisition, data analysis and classification and to get an assignment of the measured sample after a few seconds. The identification and classification prospects are evaluated by classifying measured sample spectra using a model which is based on a previously acquired reference database. Within the set, different representatives for bacterial and chemical agents as well as background materials as diesel and pollen are investigated. The performance, namely the classification accuracy and false alarm rates, is evaluated for differently sized classes down to an identification of the investigated set of materials