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  • 20 MS VIBRATIONAL SPECTROSCOPY 2020 V1 PS CLMM FLM-1 MRM Submit

    Rights statement: This is the author’s version of a work that was accepted for publication in Vibrational Spectroscopy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Vibrational Spectroscopy, 111, 2020 DOI: 10.1016/j.vibspec.2020.103171

    Accepted author manuscript, 112 KB, Word document

    Embargo ends: 13/04/22

    Available under license: CC BY-NC-ND

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Attenuated total reflection Fourier-transform infrared spectroscopy coupled with chemometrics directly detects pre- and post-symptomatic changes in tomato plants infected with Botrytis cinerea

Research output: Contribution to journalJournal articlepeer-review

Published
Article number103171
<mark>Journal publication date</mark>1/11/2020
<mark>Journal</mark>Vibrational Spectroscopy
Volume111
Number of pages12
Publication StatusPublished
Early online date13/10/20
<mark>Original language</mark>English

Abstract

Sensor-based detection of pests and pathogens in a high throughput and non-destructive manner is essential for mitigating crop loss. Infrared (IR) sensors in the form of vibrational spectroscopy provide both biochemical information about disease, as well as a large number of variables for chemometrics. This approach is highly adaptable to most biological systems including interactions between plants and their environments. Fast-acting necrotrophic fungal pathogens present a specific group of pests with adverse effects on food production and supply and are therefore pertinent to food security. Botrytis cinerea and Solanum lycopersicum are models for the study of fungal and crop biology respectively. Herein we use a compact mid-IR spectrometer with attenuated total reflection (ATR) attachment to measure the plant-microbe interaction between S. lycopersicum and B. cinerea on leaves, in vivo of intact plants. Chemometric models including exploratory principal component analysis (PCA) solely, and as a classifier in combination with linear discriminant analysis (PCA-LDA) are applied. Fingerprint spectra (1800−900 cm−1) were excellent discriminators of plant disease in both visually symptomatic as well pre-symptomatic plants. Major biochemical alterations in leaf tissue as a result of infection are discussed. Diagnostic potential for automatic decision-making platforms is shown by high accuracy rates of 100 % for detecting plant disease at various stages of progression.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Vibrational Spectroscopy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Vibrational Spectroscopy, 111, 2020 DOI: 10.1016/j.vibspec.2020.103171