Infrared spectroscopy and multivariate analysis: Classification of mixed fusarium species solani and oxysporum isolates at the species level

A. Salman, E. Shufan, I. Lapidot, L. Tsror, S. Mordechai, U. Sharaha, M. Huleihel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Fungi are microorganisms that are divided into groups and subgroups according to their similarity, genera, species, and strains. Fusarium is considered a phytopathogen that attacks variety of crops throughout the world, causing diseases resulting in severe economic losses. Many of the Fusarium species cause similar symptoms, making it impossible to distinguish among them based on symptoms alone. Fungicides are commonly the most effective treatment of these pathogens, and their use is effective and could prevent or decrease its severity and spread when detected early. Currently, classical methods (microbiological, molecular) are time-consuming. In this study, we aimed to distinguish among three different groups: Fusarium oxysporum, Fusarium solani, and a mixture of both species. Thus, we used Fourier transform infrared microscopy combined with principal component analysis (PCA) and linear discriminant analysis (LDA) classifier. Using the first ten PCs, our classification results showed a 94% success rate in distinguishing among the three groups.

Original languageEnglish
Title of host publication2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509021529
DOIs
StatePublished - 4 Jan 2017
Event2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016 - Eilat, Israel
Duration: 16 Nov 201618 Nov 2016

Publication series

Name2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016

Conference

Conference2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
Country/TerritoryIsrael
CityEilat
Period16/11/1618/11/16

Keywords

  • Fungi
  • LDA classifier
  • PCA
  • Species
  • infrared spectroscopy
  • multivariate analysis

ASJC Scopus subject areas

  • Computer Science Applications
  • Hardware and Architecture
  • Artificial Intelligence
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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