Multiple UV wavelength excitation and fluorescence of bioaerosols

Sivaprakasam, Vasanthi; Huston, Alan L.; Scotto, Cathy S.; Eversole, Jay D.

doi:10.1364/opex.12.004457

Cited by 143 publications

(66 citation statements)

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“…Дополнительная информация об отнесении биологического агента к какому-либо классу, например, спо-рам, бактериям или белкам [2], способствует повышению эффективности мер медицинской помощи по-страдавшим.…”

Section: Introductionunclassified

“…Кроме того, для биоаэрозолей, относящихся к различным таксономическим группам [2], проявляются различия в спектральных характеристиках: форме, положению максимума, величине флуоресценции [9][10][11], что позволяет проводить их идентификацию [12].…”

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“…Таким образом, задача правильной идентификации патогенных частиц в составе аэрозоля с помощью ПОМ требует повышения селективности метода. В существующих устройствах ПОМ для повышения селективности включают в оптическую систему дополнительные источники излучения и (или) фотоде-текторы [2,7,8]. Например, применяют дополнительный источник возбуждения в области длин волн 330-380 нм, что позволяет, помимо спектра флуоресценции триптофана, регистрировать спектры флуо-ПРИМЕНЕНИЕ МЕТОДОВ ХЕМОМЕТРИКИ ДЛЯ АНАЛИЗА БИОАЭРОЗОЛЕЙ … Научно-технический вестник информационных технологий, механики и оптики, 2016, том 16, № 1 32 ресценции NADH и флавинов в границах спектрального диапазона 400-600 нм [7].…”

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Application of chemometrics for analysis of bioaerosols by flow-optical method

Evgeniy¹,

Evgeniy²,

Andrey³

et al. 2016

Naučno-teh. vestn. inf. tehnol. meh. opt.

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Section: Introductionunclassified

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Application of chemometrics for analysis of bioaerosols by flow-optical method

Evgeniy¹,

Evgeniy²,

Andrey³

et al. 2016

Naučno-teh. vestn. inf. tehnol. meh. opt.

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“…6 Examples of commonly used techniques in biodetection are, e.g., Laser Induced Fluorescence (LIF), [7][8][9] Flame Emission Spectroscopy (FES), 10 Raman spectroscopy, 11,12 and Spark or Laser Induced Breakdown Spectroscopy (SIBS/ LIBS), 9,[13][14][15] to mention a few. However, to further reduce the false positive/negative rates in alarm algorithms based on data acquired by the optical techniques mentioned above, some gain can be expected by optimizing which variables to monitor.…”

Section: Introductionmentioning

confidence: 99%

Impact of data reduction on multivariate classification models built on spectral data from bio-samples

Larsson

Andersson

Landström

2015

J. Anal. At. Spectrom.

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Multivariate data analysis methods have been used to evaluate single shot spectral data, obtained by laser induced breakdown spectroscopy (LIBS), from ten different biological samples (simulants and possible interferents in Biological Warfare Agent (BWA) detection applications). Spectral data as echellograms (2D CCD images) and extracted 1D spectra were used and the classification performance was studied as the number of input variables was altered. Principal component analysis (PCA) indicated a possibility to separate the samples due to spectral differences, and partial least squares discriminant analysis (PLS-DA) was applied to study the predictability in more detail. For full resolution 1D spectra, a normalization of the data mainly resulted in visual effects in the PCA score-plots without significant effect in predictability by the PLS-DA models, however, normalization improved the predictability if the amount of variables were heavily reduced. A quite strong data (variable) reduction could be performed on both the 1D and 2D data without losing significant predictability. Using similar amounts of variables, the prediction models performed better using the echellograms directly compared to the extracted 1D spectra. The problem of spectral data shift (relative 'database' spectra) was also investigated, where already small shifts cause the models to fail. However, after a selection of important variables and allowing certain regions for these variables, the impact of shift on predictability could be reduced.

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“…These detectors can reach close to real-time warning. Multiple excitations (266 and 355 nm) for the detection of bioaerosols have been reported [10] as well as the spectral characteristics of fluorescence from particles excited at 266 nm [11].…”

Section: Introductionmentioning

confidence: 99%

Development of Fluorescence-based LIDAR Technology for Biological Sensing

et al. 2005

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Results of our on-going development of biological warfare agents (BWA) detection systems based on spectral detection of ultraviolet (UV) laser induced fluorescence (LIF) are presented. A compact optical parametric oscillator (OPO) with intracavity sum-frequency mixing (SFM) to generate 293 nm UV laser irradiation was developed. The OPO/SFM device was pumped by a diode-pumped Nd:YAG laser (1064 nm), including subsequent second-harmonic generation (SHG) in an external periodically poled KTiOPO 4 (PPKTP) crystal. The laser generated 1.8 ns pulses at 100 Hz with an average power of 44 mW at 532 nm. The whole system could be used to deliver approximately 30 µJ laser irradiation per pulse (100 Hz) at 293 nm. The spectral detection part of the system consists of a grating and a photomultiplier tube (PMT) array with 32 channels, which can measure fluorescence spectra in the wavelength band from 250 nm to 800 nm. The detector system was designed along with a trigger laser to enable measurement of fluorescence spectra from an individual aerosol particle of simulants for BWA upon excitation with a single nanosecond laser pulse. We demonstrate the successful detection and spectral characterization of simulants for BWA, i.e., Bacillus atrophaeus (BG), Bacillus thuringiensis (BT), and Ovalbumin (OA).

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Multiple UV wavelength excitation and fluorescence of bioaerosols

Cited by 143 publications

References 0 publications

Application of chemometrics for analysis of bioaerosols by flow-optical method

Application of chemometrics for analysis of bioaerosols by flow-optical method

Impact of data reduction on multivariate classification models built on spectral data from bio-samples

Development of Fluorescence-based LIDAR Technology for Biological Sensing

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