Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods

González-Navarro, Félix F.; Stoytcheva, Margarita; Rentería-Gutiérrez, Livier; Belanche-Muñoz, Lluís A.; Flores-Ríos, Brenda L.; Ibarra-Esquer, Jorge E.

doi:10.3390/s16111483

Cited by 41 publications

(15 citation statements)

References 24 publications

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“…Estimation and visualization of spatio-temporal dynamics. The dynamics of water quality variables can be often hard to predict due to its very changing nature [ 24 , 25 ]. The spatio-temporal interpolation of measurements in different stations and in different campaigns has been previously shown not to be limited to a mere visualization, but instead it represents the estimation of the variable dynamics, which allows the researcher to get deep further knowledge into the relevant information conveyed in the already available data, such as temporal space trends or physical and chemical characteristics of pollutants that are closely related to environmental conditions and the geographical areas though which the stretches of the river circulate.…”

Section: Discussionmentioning

confidence: 99%

Water Quality Sensing and Spatio-Temporal Monitoring Structure with Autocorrelation Kernel Methods

Vizcaíno

Muñoz-Romero

Cumbal

et al. 2017

Sensors

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Pollution on water resources is usually analyzed with monitoring campaigns, which consist of programmed sampling, measurement, and recording of the most representative water quality parameters. These campaign measurements yields a non-uniform spatio-temporal sampled data structure to characterize complex dynamics phenomena. In this work, we propose an enhanced statistical interpolation method to provide water quality managers with statistically interpolated representations of spatial-temporal dynamics. Specifically, our proposal makes efficient use of the a priori available information of the quality parameter measurements through Support Vector Regression (SVR) based on Mercer’s kernels. The methods are benchmarked against previously proposed methods in three segments of the Machángara River and one segment of the San Pedro River in Ecuador, and their different dynamics are shown by statistically interpolated spatial-temporal maps. The best interpolation performance in terms of mean absolute error was the SVR with Mercer’s kernel given by either the Mahalanobis spatial-temporal covariance matrix or by the bivariate estimated autocorrelation function. In particular, the autocorrelation kernel provides with significant improvement of the estimation quality, consistently for all the six water quality variables, which points out the relevance of including a priori knowledge of the problem.

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Section: Discussionmentioning

confidence: 99%

Water Quality Sensing and Spatio-Temporal Monitoring Structure with Autocorrelation Kernel Methods

Vizcaíno

Muñoz-Romero

Cumbal

et al. 2017

Sensors

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“…Luo et al made use of convolutional neural network algorithms for colorimetric sensing of organic carbon in water [ 246 ]. Gonzalez et al provided a comparative analysis of various machine-learning algorithms for improving the accuracy of a glucose oxidase biosensor [ 247 ]. By using artificial neural networks and support-vector machine algorithms, Ali et al demonstrated successful impedance-based classification of Escherichia coli and Salmonella typhimurium by using silver nanowires on polyimide substrates [ 248 ].…”

Section: Nano-biosensors For Cancer Detection and Future Prospectimentioning

confidence: 99%

Nanostructures for Biosensing, with a Brief Overview on Cancer Detection, IoT, and the Role of Machine Learning in Smart Biosensors

Banerjee

Maity

Mastrangelo

2021

Sensors

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Biosensors are essential tools which have been traditionally used to monitor environmental pollution and detect the presence of toxic elements and biohazardous bacteria or virus in organic matter and biomolecules for clinical diagnostics. In the last couple of decades, the scientific community has witnessed their widespread application in the fields of military, health care, industrial process control, environmental monitoring, food-quality control, and microbiology. Biosensor technology has greatly evolved from in vitro studies based on the biosensing ability of organic beings to the highly sophisticated world of nanofabrication-enabled miniaturized biosensors. The incorporation of nanotechnology in the vast field of biosensing has led to the development of novel sensors and sensing mechanisms, as well as an increase in the sensitivity and performance of the existing biosensors. Additionally, the nanoscale dimension further assists the development of sensors for rapid and simple detection in vivo as well as the ability to probe single biomolecules and obtain critical information for their detection and analysis. However, the major drawbacks of this include, but are not limited to, potential toxicities associated with the unavoidable release of nanoparticles into the environment, miniaturization-induced unreliability, lack of automation, and difficulty of integrating the nanostructured-based biosensors, as well as unreliable transduction signals from these devices. Although the field of biosensors is vast, we intend to explore various nanotechnology-enabled biosensors as part of this review article and provide a brief description of their fundamental working principles and potential applications. The article aims to provide the reader a holistic overview of different nanostructures which have been used for biosensing purposes along with some specific applications in the field of cancer detection and the Internet of things (IoT), as well as a brief overview of machine-learning-based biosensing.

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“…In recent years, various computational approaches have been employed in drug discovery and biosensor development. These encompass QSAR, virtual screening, molecular docking, homology modelling, and machine learning approaches ( Gonzalez-Navarro et al, 2016 ; Green et al, 2019 ). Some of these methods have their own advantages and drawbacks.…”

Section: Routes For Improvementmentioning

confidence: 99%

Green chemistry and coronavirus

Ahmadi

Rabiee

Fatahi

et al. 2021

Sustainable Chemistry and Pharmacy

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The novel coronavirus pandemic has rapidly spread around the world since December 2019. Various techniques have been applied in identification of SARS-CoV-2 or COVID-19 infection including computed tomography imaging, whole genome sequencing, and molecular methods such as reverse transcription polymerase chain reaction (RT-PCR). This review article discusses the diagnostic methods currently being deployed for SARS-CoV-2 identification including optical biosensors and point-of-care diagnostics that are on the horizon. These innovative technologies may provide a more accurate, sensitive and rapid diagnosis of SARS-CoV-2 to manage the present novel coronavirus outbreak, and could be beneficial in preventing any future epidemics. Furthermore, the use of green synthesized nanomaterials in the optical biosensor devices could leads to sustainable and environmentally-friendly approaches for this crisis.

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Glucose Oxidase Biosensor Modeling and Predictors Optimization by Machine Learning Methods

Cited by 41 publications

References 24 publications

Water Quality Sensing and Spatio-Temporal Monitoring Structure with Autocorrelation Kernel Methods

Water Quality Sensing and Spatio-Temporal Monitoring Structure with Autocorrelation Kernel Methods

Nanostructures for Biosensing, with a Brief Overview on Cancer Detection, IoT, and the Role of Machine Learning in Smart Biosensors

Green chemistry and coronavirus

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