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

Yu-xia, HU; Zhao, Nanjing; Gan, Tingting; Jian-nan, Duan; Yu, Hao; Meng, Dong; Liu, Jianguo; Liu, Wenqing

doi:10.1155/2017/4039048

Cited by 15 publications

(5 citation statements)

References 36 publications

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“…Therefore, variations in the chemical composition of the bacteria associated with protein and nucleic acid will determine changes in the absorption at different wavelengths in the UV‐Vis spectra in the range between 200 and 400 nm (Cheung et al, 2011; Markovitsi et al, 2010). Previous authors showed that the ratio between the wavelengths at 280:260 nm could be used as an indicator for changes in the proportion of nucleic acid and proteins during the growth of bacteria (Hu et al, 2017; Wilfinger et al, 1997). Therefore, changes observed in these wavelength regions can be attributed to the cells growing at different ratios.…”

Section: Resultsmentioning

confidence: 99%

A high‐throughput and machine learning resistance monitoring system to determine the point of resistance for Escherichia coli with tetracycline: Combining UV‐visible spectrophotometry with principal component analysis

Chapman

Orrell-Trigg

Kwoon

et al. 2021

Biotech & Bioengineering

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UV‐visible spectroscopy (UV‐Vis) is routinely used in microbiology as a tool to check the optical density (OD) pertaining to the growth stages of microbial cultures at the single wavelength of 600 nm, better known as the OD600. Typically, modern UV‐Vis spectrophotometers can scan in the region of approximately 200–1000 nm in the electromagnetic spectrum, where users do not extend the use of the instrument's full capability in a laboratory. In this study, the full potential of UV‐Vis spectrophotometry (multiwavelength collection) was used to examine bacterial growth phases when treated with antibiotics showcasing the ability to understand the point of resistance when an antibiotic is introduced into the media and therefore understand the biochemical changes of the infectious pathogens. A multiplate reader demonstrated a high throughput experiment (96 samples) to understand the growth of Escherichia coli when varied concentrations of the antibiotic tetracycline was added into the well plates. Principal component analysis (PCA) and partial least squares discriminant analysis were then used as the data mining techniques to interpret the UV‐Vis spectral data and generate machine learning “proof of principle” for the UV‐Vis spectrophotometer plate reader. Results from this study showed that the PCA analysis provides an accurate yet simple visual classification and the recognition of E. coli samples belonging to each treatment. These data show significant advantages when compared to the traditional OD600 method where we can now understand biochemical changes in the system rather than a mere optical density measurement. Due to the unique experimental setup and procedure that involves indirect use of antibiotics, the same test could be used for obtaining practical information on the type, resistance, and dose of antibiotic necessary to establish the optimum diagnosis, treatment, and decontamination strategies for pathogenic and antibiotic resistant species.

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Section: Resultsmentioning

confidence: 99%

A high‐throughput and machine learning resistance monitoring system to determine the point of resistance for Escherichia coli with tetracycline: Combining UV‐visible spectrophotometry with principal component analysis

Chapman

Orrell-Trigg

Kwoon

et al. 2021

Biotech & Bioengineering

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“…However, at approximately 305 nm, all of the microorganisms displayed a dramatic spike in the increase of absorbance. This increase was more noticeable in the bacteria than the yeasts and may have been caused by the proteins and nucleic acids of the microorganisms [19]. Taken together, these criteria could be used to distinguish bacteria from yeast.…”

Section: Distinctive Qualities Of Microorganism Graphsmentioning

confidence: 91%

“…For all bacteria, absorbance increased at near infrared wavelengths. For several bacteria, absorbance increased in the visible light spectrum (380-700 nm) and for some, the increase occurred at ultraviolet (<380 nm) wavelengths (Figures 11,12,13,14,15,16,17,18,19,20,21,22,23,24,25, and 26, Table 8). Staphylococcus epidermidis exhibited the smallest increase in absorbance at near infrared wavelengths, which was barely noticeable from 1100 nm and decreased until 900 nm (Figure 12).…”

Section: Distinctive Qualities Of Microorganism Graphsmentioning

confidence: 99%

Microorganism adhesion using silicon dioxide: An experimental study

Lozins

Selga

Ozoliņš

2020

Heliyon

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In this study, spectrophotometry was used to measure changes in the absorbance properties of yeast, Grampositive, and Gram-negative bacteria after their attachment to silicon dioxide microparticles (silica). The goal of this study was to determine whether spectrophotometry is an effective method to distinguish these microorganisms from one another and determine whether they have an affinity for silicon dioxide. The experiments were performed by examining the light absorption properties of yeast, Gram-positive and Gram-negative bacteria in a spectrophotometer, both with and without silicon dioxide microparticles. The experiments produced a number of promising results. First, the spectrophotometer graphs of yeast were noticeably different from those of both Grampositive and Gram-negative bacteria. Second, the absorption of light in both Gram-positive and Gram-negative bacteria occurred at near infrared range (700-1500 nm) and, unlike yeast, the wavelengths increased when silicon dioxide microparticles were added to the suspension. When silicon dioxide microparticles were added to yeast, the absorption of light decreased during the entire wavelength interval of the spectrophotometer measurement. These results indicate that bacteria have an affinity for silicon dioxide, and that spectrophotometry may be used to distinguish yeast from bacteria and, possibly, different bacterial types from one another.

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“…The generation of an accurate identification model for each microorganism must be based on a set of spectra that represents as many spectral variations as possible, which can be exhibited by each microorganism. The optical density at each wavelength point in the multi-wavelength transmission spectrum of bacterial suspensions is a function of the number, shape, size, internal structure, and the chemical composition of bacteria (Hu et al, 2017). Therefore, the bacterial identification model is characterized by the measured wavelength, and the optical density at the wavelength is the characteristic value, that is, the sample set is (λ i and τ(λ i )).…”

Section: Bacterial Identification Modelsmentioning

confidence: 99%

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

Cited by 15 publications

References 36 publications

A high‐throughput and machine learning resistance monitoring system to determine the point of resistance for Escherichia coli with tetracycline: Combining UV‐visible spectrophotometry with principal component analysis

A high‐throughput and machine learning resistance monitoring system to determine the point of resistance for Escherichia coli with tetracycline: Combining UV‐visible spectrophotometry with principal component analysis

Microorganism adhesion using silicon dioxide: An experimental study

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

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