Development of a Biological Aerosol Detector Using Laser-Induced Fluorescence and a Particle Collection System

Choi, Kibong; Ha, Yeon-Chul; Lee, Hong Kyu; Lee, Jaekyung

doi:10.1080/10739149.2013.855639

Cited by 13 publications

(4 citation statements)

References 15 publications

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“…In more advanced prototypes, to excite fluorescence, two wavelengths (266, 355 nm) have been used [ 32 , 33 ] or the entire spectrum measured [ 34 ]. In the following years, further analyzers were built using the LIF method for the detection of biological analyzers [ 35 , 36 ]. So far, the most technologically advanced instrument is the LIF instrument, which uses fluorescence lifetime measurements in four spectral ranges using two excitation wavelengths (293 and 337 nm) [ 37 ].…”

Section: Introductionmentioning

confidence: 99%

Fluorescence Methods for the Detection of Bioaerosols in Their Civil and Military Applications

Kwaśny

Bombalska

Kaliszewski

et al. 2023

Sensors

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The article presents the history of the development and the current state of the apparatus for the detection of interferents and biological warfare simulants in the air with the laser-induced fluorescence (LIF) method. The LIF method is the most sensitive spectroscopic method and also enables the measurement of single particles of biological aerosols and their concentration in the air. The overview covers both the on-site measuring instruments and remote methods. The spectral characteristics of the biological agents, steady-state spectra, excitation–emission matrices, and their fluorescence lifetimes are presented. In addition to the literature, we also present our own detection systems for military applications.

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Section: Introductionmentioning

confidence: 99%

Fluorescence Methods for the Detection of Bioaerosols in Their Civil and Military Applications

Kwaśny

Bombalska

Kaliszewski

et al. 2023

Sensors

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“…This approach ensures continuous measurement and immediate response. Thus the analysis process can be facilitated and accelerated compared with other commonly used analytical procedures (Hill et al, 1999;Choi et al, 2014;Taketani et al, 2013;Feugnet et al, 2008). Besides advantages such as reagentless and real-time particle characterization, laser-based methods do not provide information on the chemical composition of aerosol.…”

Section: Introductionmentioning

confidence: 99%

“…Various analytical methods of particle discrimination and classification have been applied. It has been shown that principal component analysis (PCA), linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA) of fluorescence spectra greatly increase the discrimination of particles compared with methods based on spectra averaging or fluorescence threshold Kaliszewski et al, 2013;Pan et al, 2012;Savage et al, 2017;Crawford et al, 2015). Artificial neural networks (ANNs) comprise an emerging analytical approach that is becoming more widely and successfully applied in various life domains such as chemical analysis (Borecki et al, 2008), image recognition (Antowiak and Chałasińska-Macukow, 2003), data mining and weather forecasting (Purnomo et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Improved real-time bio-aerosol classification using artificial neural networks

et al. 2018

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Abstract. Air pollution has had an increasingly powerful impact on the everyday life of humans. More and more people are aware of the health problems that may result from inhaling air which contains dust, bacteria, pollens or fungi. There is a need for real-time information about ambient particulate matter. Devices currently available on the market can detect some particles in the air but cannot classify them according to health threats. Fortunately, a new type of technology is emerging as a promising solution. Laser-based bio-detectors are characterizing a new era in aerosol research. They are capable of characterizing a great number of individual particles in seconds by analyzing optical scattering and fluorescence characteristics. In this study we demonstrate the application of artificial neural networks (ANNs) to real-time analysis of single-particle fluorescence fingerprints acquired using BARDet (a Bio-AeRosol Detector). A total of 48 different aerosols including pollens, bacteria, fungi, spores, and nonbiological substances were characterized. An entirely new approach to data analysis using a decision tree comprising 22 independent neural networks was discussed. Applying confusion matrices and receiver operating characteristics (ROC) analysis the best sets of ANNs for each group of similar aerosols were determined. As a result, a very high accuracy of aerosol classification in real time was achieved. It was found that for some substances that have characteristic spectra, almost each particle can be properly classified. Aerosols with similar spectral characteristics can be classified as specific clouds with high probability. In both cases the system recognized aerosol type with no mistakes. In the future, it is planned that performance of the system may be determined under real environmental conditions, involving characterization of fluorescent and nonfluorescent particles.

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“…Initially the LIF based detection of airborne particles was developed for military applications and covered stand-of (LIDARS) and "in situ" devices which detect individual particles in flowing air (Choi, Ha, Lee, & Lee, 2014;DeFreez, 2009;Farsund, Rustad, & Skogan, 2012;Feugnet et al, 2008;Ho, 2002;Ho & Duncan, 2005;Jeys, Herzog, Hybl, Czerwinski, & Sanchez, 2007;Kaliszewski et al, 2013;Saari, Reponen, & Keskinen, 2014;Sivaprakasam, Huston, Scotto, & Eversole, 2004;Wojtanowski et al, 2015). Recently, many reports have described civilian applications of continuous, real-time fluorescence flow detectors including monitoring of pollens or determining air quality in a classroom and a hospital environment.…”

mentioning

confidence: 98%

A new real-time bio-aerosol fluorescence detector based on semiconductor CW excitation UV laser

Kaliszewski

Włodarski

Młyńczak

et al. 2016

Journal of Aerosol Science

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Development of a Biological Aerosol Detector Using Laser-Induced Fluorescence and a Particle Collection System

Cited by 13 publications

References 15 publications

Fluorescence Methods for the Detection of Bioaerosols in Their Civil and Military Applications

Fluorescence Methods for the Detection of Bioaerosols in Their Civil and Military Applications

Improved real-time bio-aerosol classification using artificial neural networks

A new real-time bio-aerosol fluorescence detector based on semiconductor CW excitation UV laser

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