Chemometric classification techniques as a tool for solving problems in analytical chemistry

Marta Bevilacqua; Riccardo Nescatelli; Remo Bucci; Andrea D. Magrì; Antonio L. Magrì; Federico Marini

doi:10.5740/jaoacint.sgebevilacqua

Chemometric classification techniques as a tool for solving problems in analytical chemistry

Marta Bevilacqua, Riccardo Nescatelli, Remo Bucci, Andrea D. Magrì, Antonio L. Magrì, Federico Marini^*

^*Corresponding author af dette arbejde

16 Citationer (Scopus)

Abstract

Supervised pattern recognition (classification) techniques, i.e., the family of chemometric methods whose aim is the prediction of a qualitative response on a set of samples, represent a very important assortment of tools for solving problems in several areas of applied analytical chemistry. This paper describes the theory behind the chemometric classification techniques most frequently used in analytical chemistry together with some examples of their application to real-world problems.

Originalsprog	Engelsk
Tidsskrift	Journal of AOAC International
Vol/bind	97
Udgave nummer	1
Sider (fra-til)	19-28
Antal sider	10
ISSN	1060-3271
DOI	https://doi.org/10.5740/jaoacint.sgebevilacqua
Status	Udgivet - 2014
Udgivet eksternt	Ja

Adgang til dokumentet

10.5740/jaoacint.sgebevilacqua

https://academic.oup.com/jaoac/article-pdf/97/1/19/32425745/jaoac0019.pdf

Citationsformater

@article{cb37eb9336fe498eb49eefd6e021e44f,

title = "Chemometric classification techniques as a tool for solving problems in analytical chemistry",

abstract = "Supervised pattern recognition (classification) techniques, i.e., the family of chemometric methods whose aim is the prediction of a qualitative response on a set of samples, represent a very important assortment of tools for solving problems in several areas of applied analytical chemistry. This paper describes the theory behind the chemometric classification techniques most frequently used in analytical chemistry together with some examples of their application to real-world problems.",

author = "Marta Bevilacqua and Riccardo Nescatelli and Remo Bucci and Magr{\`i}, {Andrea D.} and Magr{\`i}, {Antonio L.} and Federico Marini",

year = "2014",

doi = "10.5740/jaoacint.sgebevilacqua",

language = "English",

volume = "97",

pages = "19--28",

journal = "Journal of AOAC International",

issn = "1060-3271",

publisher = "Association of Analytical Communities",

number = "1",

}

TY - JOUR

T1 - Chemometric classification techniques as a tool for solving problems in analytical chemistry

AU - Bevilacqua, Marta

AU - Nescatelli, Riccardo

AU - Bucci, Remo

AU - Magrì, Andrea D.

AU - Magrì, Antonio L.

AU - Marini, Federico

PY - 2014

Y1 - 2014

N2 - Supervised pattern recognition (classification) techniques, i.e., the family of chemometric methods whose aim is the prediction of a qualitative response on a set of samples, represent a very important assortment of tools for solving problems in several areas of applied analytical chemistry. This paper describes the theory behind the chemometric classification techniques most frequently used in analytical chemistry together with some examples of their application to real-world problems.

AB - Supervised pattern recognition (classification) techniques, i.e., the family of chemometric methods whose aim is the prediction of a qualitative response on a set of samples, represent a very important assortment of tools for solving problems in several areas of applied analytical chemistry. This paper describes the theory behind the chemometric classification techniques most frequently used in analytical chemistry together with some examples of their application to real-world problems.

U2 - 10.5740/jaoacint.sgebevilacqua

DO - 10.5740/jaoacint.sgebevilacqua

M3 - Journal article

C2 - 24672856

AN - SCOPUS:84893674159

SN - 1060-3271

VL - 97

SP - 19

EP - 28

JO - Journal of AOAC International

JF - Journal of AOAC International

IS - 1

ER -

Chemometric classification techniques as a tool for solving problems in analytical chemistry

Abstract

Adgang til dokumentet

Fingeraftryk

Citationsformater