Evaluation of the Nutritional Changes Caused by Huanglongbing (HLB) to Citrus Plants Using Laser-Induced Breakdown Spectroscopy

Huanglongbing (HLB) is the most recent and destructive bacterial disease of citrus and has no cure yet. A promising alternative to conventional methods is to use laser-induced breakdown spectroscopy (LIBS), a multi-elemental analytical technique, to identify the nutritional changes provoked by the d...

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Bibliographic Details
Published in:Applied spectroscopy 2017-07, Vol.71 (7), p.1471-1480
Main Authors: Ranulfi, Anielle Coelho, Romano, Renan Arnon, Bebeachibuli Magalhães, Aida, Ferreira, Ednaldo José, Ribeiro Villas-Boas, Paulino, Marcondes Bastos Pereira Milori, Débora
Format: Article
Language:English
Subjects:
Agriculture
Calcium - analysis
Calcium - metabolism
Citrus - chemistry
Citrus - metabolism
Citrus - physiology
Magnesium - analysis
Magnesium - metabolism
Nutritional Sciences
Plant Diseases
Plant Leaves - chemistry
Plant Leaves - metabolism
Plant Leaves - physiology
Spectrum Analysis - methods
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Summary:Huanglongbing (HLB) is the most recent and destructive bacterial disease of citrus and has no cure yet. A promising alternative to conventional methods is to use laser-induced breakdown spectroscopy (LIBS), a multi-elemental analytical technique, to identify the nutritional changes provoked by the disease to the citrus leaves and associate the mineral composition profile with its health status. The leaves were collected from adult citrus trees and identified by visual inspection as healthy, HLB-symptomatic, and HLB-asymptomatic. Laser-induced breakdown spectroscopy measurements were done in fresh leaves without sample preparation. Nutritional variations were evaluated using statistical tools, such as Student's t-test and analysis of variance applied to LIBS spectra, and the largest were found for Ca, Mg, and K. Considering the nutritional profile changes, a classifier induced by classification via regression combined with partial least squares regression was built resulting in an accuracy of 73% for distinguishing the three categories of leaves.
ISSN:0003-7028
1943-3530
DOI:10.1177/0003702817701751