Deep learning in analytical chemistry › Научные исследования в СПбГУ

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Deep learning in analytical chemistry. / Debus, Bruno; Parastar, Hadi; Harrington, Peter; Kirsanov, Dmitry.

в: TrAC - Trends in Analytical Chemistry, Том 145, 116459, 12.2021.

Результаты исследований: Научные публикации в периодических изданиях › Обзорная статья › Рецензирование

Harvard

Debus, B, Parastar, H, Harrington, P & Kirsanov, D 2021, 'Deep learning in analytical chemistry', TrAC - Trends in Analytical Chemistry, Том. 145, 116459. https://doi.org/10.1016/j.trac.2021.116459

APA

Debus, B., Parastar, H., Harrington, P., & Kirsanov, D. (2021). Deep learning in analytical chemistry. TrAC - Trends in Analytical Chemistry, 145, [116459]. https://doi.org/10.1016/j.trac.2021.116459

Vancouver

Debus B, Parastar H, Harrington P, Kirsanov D. Deep learning in analytical chemistry. TrAC - Trends in Analytical Chemistry. 2021 Дек.;145. 116459. https://doi.org/10.1016/j.trac.2021.116459

Author

Debus, Bruno ; Parastar, Hadi ; Harrington, Peter ; Kirsanov, Dmitry. / Deep learning in analytical chemistry. в: TrAC - Trends in Analytical Chemistry. 2021 ; Том 145.

BibTeX

@article{423c0004620d4cbb89e968bee20d10c5,

title = "Deep learning in analytical chemistry",

abstract = "In recent years, extensive research in the field of Deep Learning (DL) has led to the development of a wide array of machine learning algorithms dedicated to solving complex tasks such as image classification or speech recognition. Due to their unprecedented ability to explore large volumes of data and extract meaningful hidden structures, DL models have naturally drawn attention from various fields in science. Analytical chemistry, in particular, has successfully benefited from the application of DL tools for extracting qualitative and quantitative information from high-dimensional and complex chemical measurements. This report provides introductory reading for understanding DL machinery and reviews recent analytical applications of these powerful algorithms.",

keywords = "Chemometrics, Convolutional neural networks, Data analysis, Deep learning, Machine learning, CONVOLUTIONAL NEURAL-NETWORKS",

author = "Bruno Debus and Hadi Parastar and Peter Harrington and Dmitry Kirsanov",

note = "Publisher Copyright: {\textcopyright} 2021 Elsevier B.V.",

year = "2021",

month = dec,

doi = "10.1016/j.trac.2021.116459",

language = "English",

volume = "145",

journal = "TrAC - Trends in Analytical Chemistry",

issn = "0165-9936",

publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Deep learning in analytical chemistry

AU - Debus, Bruno

AU - Parastar, Hadi

AU - Harrington, Peter

AU - Kirsanov, Dmitry

PY - 2021/12

Y1 - 2021/12

N2 - In recent years, extensive research in the field of Deep Learning (DL) has led to the development of a wide array of machine learning algorithms dedicated to solving complex tasks such as image classification or speech recognition. Due to their unprecedented ability to explore large volumes of data and extract meaningful hidden structures, DL models have naturally drawn attention from various fields in science. Analytical chemistry, in particular, has successfully benefited from the application of DL tools for extracting qualitative and quantitative information from high-dimensional and complex chemical measurements. This report provides introductory reading for understanding DL machinery and reviews recent analytical applications of these powerful algorithms.

AB - In recent years, extensive research in the field of Deep Learning (DL) has led to the development of a wide array of machine learning algorithms dedicated to solving complex tasks such as image classification or speech recognition. Due to their unprecedented ability to explore large volumes of data and extract meaningful hidden structures, DL models have naturally drawn attention from various fields in science. Analytical chemistry, in particular, has successfully benefited from the application of DL tools for extracting qualitative and quantitative information from high-dimensional and complex chemical measurements. This report provides introductory reading for understanding DL machinery and reviews recent analytical applications of these powerful algorithms.

KW - Chemometrics

KW - Convolutional neural networks

KW - Data analysis

KW - Deep learning

KW - Machine learning

KW - CONVOLUTIONAL NEURAL-NETWORKS

UR - http://www.scopus.com/inward/record.url?scp=85117830366&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/47211a26-0b83-31b4-a13d-4071b743743e/

U2 - 10.1016/j.trac.2021.116459

DO - 10.1016/j.trac.2021.116459

M3 - Review article

AN - SCOPUS:85117830366

VL - 145

JO - TrAC - Trends in Analytical Chemistry

JF - TrAC - Trends in Analytical Chemistry

SN - 0165-9936

M1 - 116459

ER -

ID: 88217289