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Application of Chemometrics in Biosensing : A Brief Review. / Martynko, Ekaterina; Kirsanov, Dmitry.

In: Biosensors, Vol. 10, No. 8, 100, 08.2020.

Research output: Contribution to journal › Review article › peer-review

Harvard

Martynko, E & Kirsanov, D 2020, 'Application of Chemometrics in Biosensing: A Brief Review', Biosensors, vol. 10, no. 8, 100. https://doi.org/10.3390/bios10080100

APA

Martynko, E., & Kirsanov, D. (2020). Application of Chemometrics in Biosensing: A Brief Review. Biosensors, 10(8), [100]. https://doi.org/10.3390/bios10080100

Vancouver

Martynko E, Kirsanov D. Application of Chemometrics in Biosensing: A Brief Review. Biosensors. 2020 Aug;10(8). 100. https://doi.org/10.3390/bios10080100

Author

Martynko, Ekaterina ; Kirsanov, Dmitry. / Application of Chemometrics in Biosensing : A Brief Review. In: Biosensors. 2020 ; Vol. 10, No. 8.

BibTeX

@article{073e3bcdfff8453d9ebabb4b4ef1d934,
title = "Application of Chemometrics in Biosensing: A Brief Review",
abstract = "The field of biosensing is rapidly developing, and the number of novel sensor architectures and different sensing elements is growing fast. One of the most important features of all biosensors is their very high selectivity stemming from the use of bioreceptor recognition elements. The typical calibration of a biosensor requires simple univariate regression to relate a response value with an analyte concentration. Nevertheless, dealing with complex real-world sample matrices may sometimes lead to undesired interference effects from various components. This is where chemometric tools can do a good job in extracting relevant information, improving selectivity, circumventing a non-linearity in a response. This brief review aims to discuss the motivation for the application of chemometric tools in biosensing and provide some examples of such applications from the recent literature.",
keywords = "ANN, Biosensor, Chemometrics, Classification, Multivariate regression, PCA, PLS, ANALYTICAL FIGURES, BIOELECTRONIC TONGUE, PHOTOBACTERIUM-PHOSPHOREUM, biosensor, ARRAY, PHENOLIC-COMPOUNDS, TOXICITY, classification, MIXTURES, chemometrics, ELECTROCHEMICAL BIOSENSORS, AMPEROMETRIC BIOSENSOR, IMPRINTED POLYMER, multivariate regression",
author = "Ekaterina Martynko and Dmitry Kirsanov",
note = "Funding Information: This research was funded by Russian Science Foundation grant #18-19-00151. Publisher Copyright: {\textcopyright} 2020 by the authors. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.",
year = "2020",
month = aug,
doi = "10.3390/bios10080100",
language = "English",
volume = "10",
journal = "Biosensors",
issn = "2079-6374",
publisher = "MDPI AG",
number = "8",

}

RIS

TY - JOUR

T1 - Application of Chemometrics in Biosensing

T2 - A Brief Review

AU - Martynko, Ekaterina

AU - Kirsanov, Dmitry

N1 - Funding Information: This research was funded by Russian Science Foundation grant #18-19-00151. Publisher Copyright: © 2020 by the authors. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2020/8

Y1 - 2020/8

N2 - The field of biosensing is rapidly developing, and the number of novel sensor architectures and different sensing elements is growing fast. One of the most important features of all biosensors is their very high selectivity stemming from the use of bioreceptor recognition elements. The typical calibration of a biosensor requires simple univariate regression to relate a response value with an analyte concentration. Nevertheless, dealing with complex real-world sample matrices may sometimes lead to undesired interference effects from various components. This is where chemometric tools can do a good job in extracting relevant information, improving selectivity, circumventing a non-linearity in a response. This brief review aims to discuss the motivation for the application of chemometric tools in biosensing and provide some examples of such applications from the recent literature.

AB - The field of biosensing is rapidly developing, and the number of novel sensor architectures and different sensing elements is growing fast. One of the most important features of all biosensors is their very high selectivity stemming from the use of bioreceptor recognition elements. The typical calibration of a biosensor requires simple univariate regression to relate a response value with an analyte concentration. Nevertheless, dealing with complex real-world sample matrices may sometimes lead to undesired interference effects from various components. This is where chemometric tools can do a good job in extracting relevant information, improving selectivity, circumventing a non-linearity in a response. This brief review aims to discuss the motivation for the application of chemometric tools in biosensing and provide some examples of such applications from the recent literature.

KW - ANN

KW - Biosensor

KW - Chemometrics

KW - Classification

KW - Multivariate regression

KW - PCA

KW - PLS

KW - ANALYTICAL FIGURES

KW - BIOELECTRONIC TONGUE

KW - PHOTOBACTERIUM-PHOSPHOREUM

KW - biosensor

KW - ARRAY

KW - PHENOLIC-COMPOUNDS

KW - TOXICITY

KW - classification

KW - MIXTURES

KW - chemometrics

KW - ELECTROCHEMICAL BIOSENSORS

KW - AMPEROMETRIC BIOSENSOR

KW - IMPRINTED POLYMER

KW - multivariate regression

UR - http://www.scopus.com/inward/record.url?scp=85089806819&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/8381be00-3426-376c-9c0d-fbf153603426/

U2 - 10.3390/bios10080100

DO - 10.3390/bios10080100

M3 - Review article

C2 - 32824611

AN - SCOPUS:85089806819

VL - 10

JO - Biosensors

JF - Biosensors

SN - 2079-6374

IS - 8

M1 - 100

ER -

ID: 70787234