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Detection of lethal bronzing disease in cabbage palms (Sabal palmetto) using a low-cost electronic nose

  • Martin J. Oates
  • , Nawaf Abu-Khalaf
  • , Carlos Molina-Cabrera
  • , Antonio Ruiz-Canales
  • , Jose Ramos
  • , Brian W. Bahder

Research output: Contribution to journalArticlepeer-review

Abstract

Lethal Bronzing Disease (LB) is a disease of palms caused by the 16SrIV-D phytoplasma. A low-cost electronic nose (eNose) prototype was trialed for its detection. It includes an array of eight Taguchi-type (MQ) sensors (MQ135, MQ2, MQ3, MQ4, MQ5, MQ9, MQ7, and MQ8) controlled by an Arduino NANO® microcontroller, using heater voltages that vary sinusoidally over a 2.5 min cycle. Samples of uninfected, early symptomatic, moderate symptomatic, and late symptomatic infected palm leaves of the cabbage palm were processed and analyzed. MQ sensor responses were subjected to a 256 element discrete Fourier transform (DFT), and harmonic component amplitudes were reviewed by principal component analysis (PCA). The experiment was repeated three times, each showing clear evidence of differences in sensor responses between the samples of uninfected leaves and those in the early stages of infection. Within each experiment, four groups of responses were identified, demonstrating the ability of the unit to repeatedly distinguish healthy leaves from diseased ones; however, detection of the severity of infection has not been demonstrated. By selecting appropriate coefficients (here demonstrated with plots of MQ5 Cos1 vs. MQ8 Sin3), it should be possible to build a ruleset classifier to identify healthy and unhealthy samples.

Original languageEnglish
Article number188
JournalBiosensors
Volume10
Issue number11
DOIs
StatePublished - Nov 2020

Bibliographical note

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

ASJC Scopus Subject Areas

  • Analytical Chemistry
  • Biotechnology
  • Biomedical Engineering
  • Instrumentation
  • Engineering (miscellaneous)
  • Clinical Biochemistry

Keywords

  • Diagnostics
  • Discrete Fourier Transform (DFT)
  • Electronic nose
  • Plant disease
  • Principal Component Analysis (PCA)

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