Mostrar el registro sencillo del ítem
dc.contributor.author
Ayres, Lucas B.
dc.contributor.author
Gomez, Federico Jose Vicente
dc.contributor.author
Linton, Jeb R.
dc.contributor.author
Silva, María Fernanda
dc.contributor.author
Garcia, Carlos D.
dc.date.available
2021-12-22T11:38:34Z
dc.date.issued
2021-05
dc.identifier.citation
Ayres, Lucas B.; Gomez, Federico Jose Vicente; Linton, Jeb R.; Silva, María Fernanda; Garcia, Carlos D.; Taking the leap between analytical chemistry and artificial intelligence: A tutorial review; Elsevier Science; Analytica Chimica Acta; 1161; 5-2021; 1-19
dc.identifier.issn
0003-2670
dc.identifier.uri
http://hdl.handle.net/11336/149144
dc.description.abstract
The last 10 years have witnessed the growth of artificial intelligence into different research areas, emerging as a vibrant discipline with the capacity to process large amounts of information and even intuitively interact with humans. In the chemical world, these innovations in both hardware and algorithms have allowed the development of revolutionary approaches in organic synthesis, drug discovery, and materials’ design. Despite these advances, the use of AI to support analytical purposes has been mostly limited to data-intensive methodologies linked to image recognition, vibrational spectroscopy, and mass spectrometry but not to other technologies that, albeit simpler, offer promise of greatly enhanced analytics now that AI is becoming mature enough to take advantage of them. To address the imminent opportunity of analytical chemists to use AI, this tutorial review aims to serve as a first step for junior researchers considering integrating AI into their programs. Thus, basic concepts related to AI are first discussed followed by a critical assessment of representative reports integrating AI with various sensors, spectroscopies, and separation techniques. For those with the courage (and the time) needed to get started, the review also provides a general sequence of steps to begin integrating AI into their programs.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier Science
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ANALYTICAL
dc.subject
ARTIFICIAL INTELLIGENCE
dc.subject
DEEP LEARNING
dc.subject
SENSORS
dc.subject
SPECTROSCOPY
dc.subject.classification
Química Analítica
dc.subject.classification
Ciencias Químicas
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Taking the leap between analytical chemistry and artificial intelligence: A tutorial review
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2021-12-03T20:21:49Z
dc.journal.volume
1161
dc.journal.pagination
1-19
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Ayres, Lucas B.. CLEMSON UNIVERSITY (CLEMSON UNIVERSITY);
dc.description.fil
Fil: Gomez, Federico Jose Vicente. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Biología Agrícola de Mendoza. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias. Instituto de Biología Agrícola de Mendoza; Argentina
dc.description.fil
Fil: Linton, Jeb R.. IBM Watson and Cloud Platform; Estados Unidos
dc.description.fil
Fil: Silva, María Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Biología Agrícola de Mendoza. Universidad Nacional de Cuyo. Facultad de Ciencias Agrarias. Instituto de Biología Agrícola de Mendoza; Argentina
dc.description.fil
Fil: Garcia, Carlos D.. CLEMSON UNIVERSITY (CLEMSON UNIVERSITY);
dc.journal.title
Analytica Chimica Acta
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.aca.2021.338403
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0003267021002294?via%3Dihub
Archivos asociados