A new spectroscopy method for in situ rapid detection and classification of micro-organisms

García‐Rubio, Luis H.; Alupoaei, Catalina E.; Olivares, José A.; Stark, Peter; Garcia-Lopez, Alicia; Stephans, Christie; Berg, T.; Klungness, Greta; Gennaccaro, Angela; Huffman, Debra E.

doi:10.1117/12.571392

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…As indicated in Table 2, the amount of nucleic acid estimate is 7.1335 × 10 −14 g/cell, which is almost the same as the value reported in the literature (7.0 × 10 −14 , [40]). In addition, the protein content estimate is certainly within the range of values reported in the literature [41], which were obtained with entirely different approach.…”

Section: Spectral Analysis: Scattering Componentsupporting

confidence: 86%

Analytic Method on Characteristic Parameters of Bacteria in Water by Multiwavelength Transmission Spectroscopy

Yu-xia

Zhao

Gan

et al. 2017

Journal of Spectroscopy

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An analytic method together with the Mie scattering theory and Beer-Lambert law is proposed for the characteristic parameter determination of bacterial cells (Escherichia coli 10389) from multiwavelength transmission spectroscopy measurements. We calculate the structural parameters of E. coli cells, and compared with the microscopy, the relative error of cell volume is 7.90%, the cell number is compared with those obtained by plate counting, the relative error is l.02%, and the nucleic content and protein content of single E. coli cells are consistent with the data reported elsewhere. The proposed method can obtain characteristic parameters of bacteria as an excellent candidate for the rapid detection and identification of bacteria in the water.

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Section: Spectral Analysis: Scattering Componentsupporting

confidence: 86%

Analytic Method on Characteristic Parameters of Bacteria in Water by Multiwavelength Transmission Spectroscopy

Yu-xia

Zhao

Gan

et al. 2017

Journal of Spectroscopy

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“…The basic elements of the model, cell respiration and equilibrium considerations, have been tested against experimental data obtained in our laboratories and at Los Alamos National Laboratories (Claro Scientific, 2008, unpublished data). The logistic growth model is well accepted in the literature (Madigan et al, 2000) and has been successfully tested against measured particle count data from pure culture batch experiments (Alupoaei, 2001; Alupoaei et al, 2001, 2002a,b; Garcia‐Rubio et al, 2005). The respiration rates of leukocytes and platelets used in our model are consistent with those reported in literature.…”

Section: Introductionmentioning

confidence: 99%

A new method for the detection of microorganisms in blood cultures: Part I. Theoretical analysis and simulation of blood culture processes

Smith¹,

Serebrennikova

Huffman³

et al. 2008

Can J Chem Eng

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The physical and chemical changes occurring in blood as a consequence of microbial activity can be used as quantitative indicators of the presence of microorganisms in blood cultures. This paper reports on the theoretical analysis and computer simulation of the changes in the physical and chemical properties of blood expected as a result of the presence of microorganisms and explores the possibilities of spectrophotometric systems for the early detection of pathogens. It is concluded that multi-wavelength reflectance methods are suitable and that the approach reported herein can lead to considerable simplifications and cost reduction of blood culture systems.Les changements physiques et chimiques survenant dans le sang suiteà une activité microbienne peuventêtre utilisés comme indicateurs quantitatifs de la présence de microorganismes dans les cultures sanguines. On présente dans cet article l'analyse théorique et la simulation par ordinateur des changements dans les propriétés physiques et chimiques du sang issus de la présence de microorganismes et on explore les possibilités des systèmes spectrophotométriques pour la détection précoce des pathogènes. On conclut que les méthodes de réflectanceà longueurs d'onde multiple conviennent et que l'approche mentionnée ici peut conduireà des simplifications considérables et une réduction des coûts des systèmes de culture sanguine.

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“…A qualitative comparison of the spectra of the four bacteria reveals the discriminating power among them using the spectroscopy approach. It can be seen that the spectral patterns of K. pneumonia, E. coli, and S. typhi have similar features with their peak at approximately 260 nm, which is likely due to the similarities in shape, aggregation state, and chemical composition of these microorganisms (Garcia-Rubio et al, 2004;Alupoaei and García-Rubio, 2005). However, when their optical densities at some wavelength points are distinct, it is due to the differences in the size and content of the chemical composition (Adams et al, 1989).…”

Section: Multi-wavelength Transmission Spectra For Bacteriamentioning

confidence: 87%

Rapid identification of bacteria in water by multi-wavelength transmittance spectroscopy and the artificial neural network

Hu,

Zhu,

et al. 2024

Front. Environ. Sci.

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Background: Multi-wavelength transmittance spectroscopy, in combination with the artificial neural network, has been a novel tool used to identify and classify microorganisms in recent years.Methods: In our work, the transmittance spectra in the region from 200 to 900 nm for four bacterial species of interest, Escherichia coli (E. coli), Staphylococcus aureus (S. aureus), Klebsiella pneumoniae (K.pneumoniae), and Salmonella typhimurium (S. typhi), were recorded using an ultraviolet–visible spectrophotometer. Considering too much redundant data on the full-wave band spectra, the characteristic wavelength variables were selected using the competitive adaptive reweighting sampling (CARS) algorithm. Spectra of the initial training set of these targeted microorganisms were used to create identification models representing the spectral variability of each species using four kinds of neural networks, namely, backpropagation (BP), radial basis function network (RBF), generalized regression neural network (GRNN), and probabilistic neural network (PNN).Results: The blinded isolate spectra of targeted species were identified using the four identification models given above. Compared to fullband modeling, after using CARS to screen the wavelength variables, four identification models are established for the 35 preferred characteristic wavelengths, and the prediction performance of the four models is notably improved. Among them, the CARS–PNN model is the best, and the identification rates of all targeted bacteria were achieved with 100% accuracy; the calculation time is just approximately 0.04 s.Discussion: The use of CARS can effectively remove useless information from the spectra, reduce model complexity, and enhance model prediction performance. Multi-wavelength transmission spectroscopy, combined with the CARS–PNN method, can provide a new method for the rapid detection of bacteria in water and could be readily extended for bacterial microbiological detection in blood and food.

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A new spectroscopy method for in situ rapid detection and classification of micro-organisms

Cited by 3 publications

References 18 publications

Analytic Method on Characteristic Parameters of Bacteria in Water by Multiwavelength Transmission Spectroscopy

Analytic Method on Characteristic Parameters of Bacteria in Water by Multiwavelength Transmission Spectroscopy

A new method for the detection of microorganisms in blood cultures: Part I. Theoretical analysis and simulation of blood culture processes

Rapid identification of bacteria in water by multi-wavelength transmittance spectroscopy and the artificial neural network

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