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Rapid classification of alloyed steel by means of optical emission spectroscopy and a supervised learning procedure.

Authors
Type
Published Article
Journal
Talanta
0039-9140
Publisher
Elsevier
Publication Date
Volume
41
Issue
7
Pages
1137–1141
Identifiers
PMID: 18966049
Source
Medline

Abstract

A supervised learning procedure for classification of steel samples analyzed by means of optical emission spectroscopy was developed. The method should be applicable in simple portable spectrometers, for various groups of materials and working relatively fast. Data vectors extracted from digital spectra of unknown samples are compared with average vectors evaluated from the data vectors of repeated measurements of reference samples. The classification is carried out on the basis of the multivariate distance between the data vector of the unknown sample and the nearest average reference vector and its deviation. The supervised learning procedure was tested by 10 steel samples which could be successfully classified.

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