Identification and discrimination of bacterial strains by laser induced breakdown spectroscopy and neural networks

Marcos-Martinez, D.; Ayala, Juan A.; Izquierdo-Hornillos, R.; Villena, F. Javier Manuel de; Cáceres, J. O.

doi:10.1016/j.talanta.2011.01.069

Cited by 74 publications

(37 citation statements)

References 21 publications

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“…If the output is P a true positive (TP) or false positive (FP) is observed if the actual value is P or N, respectively. Conversely, a true negative (TN) or a false negative (FN) is observed if the predicted output is N and the actual value is N or P, respectively [10].…”

Section: Resultsmentioning

confidence: 99%

“…The connections are controlled by a weight that modulates the output from the neuron before inputting its numerical content to a neuron in the next layer. Multilayer perceptron, feedforward, supervised neural networks have been widely used in classification process [31], as well as, to model systems with a similar level of complexity [9,10]. A NN classification model is estimated by calibrating with a set of reference samples to calculate the optimum weights and biases that better represent the target classes.…”

Section: Neural Network (Nn)mentioning

confidence: 99%

“…LIBS technique has a broad scope due to the possibility of analyzing solids, liquids or gases, being able to acquire quickly a large amount of data. LIBS provides a spectral fingerprint of the elemental composition of the sample, which allows to obtain classification results in real time [8,10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of supervised chemometric methods for sample classification by Laser Induced Breakdown Spectroscopy

Moncayo

Manzoor

Navarro-Villoslada

et al. 2015

Chemometrics and Intelligent Laboratory Systems

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Section: Resultsmentioning

confidence: 99%

Section: Neural Network (Nn)mentioning

confidence: 99%

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Evaluation of supervised chemometric methods for sample classification by Laser Induced Breakdown Spectroscopy

Moncayo

Manzoor

Navarro-Villoslada

et al. 2015

Chemometrics and Intelligent Laboratory Systems

Self Cite

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“…This means that the problem of associating each spectrum with the corresponding composition might be described as the approximation of a functional relationship between an input in  3606 and an output in  4 (representing the concentrations of the four main elements in the alloy), based on a set of data (900 in our case). The strategy of using all the spectral data as the input of the Neural Network has been used, in the past, but this approach is not appropriate, for its high computational cost and the likelihood of function overfitting [17]. The output of a functional relationship must thus be determined by a subset of the input variables.…”

Section: Problem Descriptionmentioning

confidence: 99%

An artificial neural network approach to laser-induced breakdown spectroscopy quantitative analysis

D’Andrea¹,

Pagnotta

Grifoni

et al. 2014

Spectrochimica Acta Part B: Atomic Spectroscopy

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The usual approach to laser-induced breakdown spectroscopy (LIBS) quantitative analysis is based on the use of calibration curves, suitably built using appropriate reference standards. More recently, statistical methods relying on the principles of artificial neural networks (ANN) are increasingly used. However, ANN analysis is often used as a 'black box' system and the peculiarities of the LIBS spectra are not exploited fully. An a priori exploration of the raw data contained in the LIBS spectra, carried out by a neural network to learn what are the significant areas of the spectrum to be used for a subsequent neural network delegated to the calibration, is able to throw light upon important information initially unknown, although already contained within the spectrum. This communication will demonstrate that an approach based on neural networks specially taylored for dealing with LIBS spectra would provide a viable, fast and robust method for LIBS quantitative analysis. This would allow the use of a relatively limited number of reference samples for the training of the network, with respect to the current approaches, and provide a fully automatizable approach for the analysis of a large number of samples

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“…A variety of label-free methods have been implemented to identify bacteria including laser-induced breakdown spectroscopy [16][17][18], Fourier transform infrared spectroscopy [19,20], Raman spectroscopy [1,21], and autofluorescence [22,23].…”

Section: Introductionmentioning

confidence: 99%

The use of microfluidics and dielectrophoresis for separation, concentration, and identification of bacteria

et al. 2016

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Traditional bacterial analyses take one to two days under favorable conditions where the bulk of the time is spent waiting for bacteria to divide and grow until visual colonies can be observed for identification. In the case of bacteria with slow doubling times, this process can take weeks. This delay in analysis is unacceptable, especially in cases of life threatening diseases or emergencies. It is clear that in order to decrease the analysis time of the bacteria, the culturing and growth step must be circumvented. The goal of this research is to design, build, and test a device that could decrease the analysis time of bacteria using label-free methods of dielectrophoresis and Raman spectroscopy.Testing for device design was performed with clinical samples in mind, which consist of bacteria grown in a variety of environmental conditions (i.e. available food sources, growth stage, temperature, etc.) and accompanied by sample debris. Raman spectra of bacteria grown in varying media and metabolic stages were collected and analyzed. Results indicate that growth phase and media have an impact on Raman spectra iv and is distinguishable by linear discriminant analysis (LDA). Despite these spectral differences, it was found that LDA classification of closely related bacteria remains fairly high (90%) regardless of growth phase. Sample debris were also considered in device design and accommodated for by dielectrophoresis. Devices were built with the goal to isolate bacteria from a mixed sample and simultaneously acquire Raman spectra for identification.For this dissertation, a device was designed, built, and tested that incorporates dielectrophoresis for particle isolation and Raman spectroscopy for identification. The device was modeled in COMSOL to ensure that an appropriate electrical field gradient could be obtained to isolate bacteria from 5 µm diameter polystyrene spheres. The device was built and successfully trapped bacteria away from polystyrene spheres and Raman spectra of the bacteria were collected while trapped. These results indicate a clear potential for contactless dielectrophoresis-Raman devices to isolate and identify bacteria from sample debris, and thereby decrease the analysis time of bacteria. Typical bacterial analysis involves culturing and visualizing colonies on an array of agar plates. The growth patterns and colors among the array are used to identify the bacteria. For fast growing bacteria such as Escherichia coli, analysis will take one to two days. However, slow growing bacteria such as mycobacteria can take weeks to identify.In addition, there are some species of bacteria that are viable but nonculturable. This lengthy analysis time is unacceptable for life-threatening infections and emergency situations. It is clear that to decrease the analysis of the bacteria, the culturing and growth steps must be avoided. The goal of this research is to design, build, and test a device that could decrease the analysis time of bacteria.

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Identification and discrimination of bacterial strains by laser induced breakdown spectroscopy and neural networks

Cited by 74 publications

References 21 publications

Evaluation of supervised chemometric methods for sample classification by Laser Induced Breakdown Spectroscopy

Evaluation of supervised chemometric methods for sample classification by Laser Induced Breakdown Spectroscopy

An artificial neural network approach to laser-induced breakdown spectroscopy quantitative analysis

The use of microfluidics and dielectrophoresis for separation, concentration, and identification of bacteria

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