Issue 12, 2001
From the journal:

Analyst

Application of artificial neural networks to the classification of soils from São Paulo state using near-infrared spectroscopy

Paulo Henrique Fidêncio,ab   Itziar Ruisáncheza  and  Ronei Jesus Poppi*b  

* Corresponding authors

a Universitat Rovira i Virgilli, Departament de Química Analítica i Química Orgànica, Pça. Imperial Tàrraco 1, Tarragona, Spain

b Universidade Estadual de Campinas, Instituto de Química, CP 6154, Campinas, SP, Brazil
E-mail: ronei@iqm.unicamp.br
Fax: +55 19 37883023
Tel: +55 19 37883126

Abstract

This paper describes how artificial neural networks can be used to classify multivariate data. Two types of neural networks were applied: a counter propagation neural network (CP-ANN) and a radial basis function network (RBFN). These strategies were used to classify soil samples from different geographical regions in Brazil by means of their near-infrared (diffuse reflectance) spectra. The results were better with CP-ANN (classification error 8.6%) than with RBFN (classification error 11.0%).

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Article information

DOI
https://doi.org/10.1039/B107533K
Article type
Paper
Submitted
20 Aug 2001
Accepted
11 Oct 2001
First published
15 Nov 2001

Analyst, 2001,126, 2194-2200

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Application of artificial neural networks to the classification of soils from São Paulo state using near-infrared spectroscopy

P. H. Fidêncio, I. Ruisánchez and R. J. Poppi, Analyst, 2001, 126, 2194 DOI: 10.1039/B107533K

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