Discrimination of Aspergillus spp., Botrytis cinerea and Penicillium expansum in grape berries by ATR-FT-IR spectroscopy

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Abstract

Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FT-IR) in conjunction with chemometric modelling and machine learning algorithms, was successfully applied to objectively differentiate Aspergillus carbonarius, A. niger, Botrytis cinerea or Pencillium expansum fungal mycelium and mature wine-grape berries (Vitis vinifera, cultivar Chardonnay) infected with either of these bunch rot pathogens. The differentiation of B. cinerea infected grape berries from those infected with either Aspergillus or Penicillium species shows promise as a tool for the rapid detection of the pathogen when grapes are received at the winery for processing. Support vector modelling provided superior class prediction for pathogen and control samples over other modelling techniques, while random forest models were successful in classifying samples infected with Aspergillus spp., illustrating the potential for these techniques to be applied to the assessment of bunch rot pathogens. The use of ATR-FT-IR shows potential for assessing the phytosanitary aspects of grapes
Original languageEnglish
Pages (from-to)68-76
Number of pages9
JournalAmerican Journal of Enology and Viticulture
Volume70
Issue number1
Early online date26 Sep 2018
DOIs
Publication statusPublished - Jan 2019

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Botrytis
Penicillium expansum
Penicillium
Vitis
Fourier transform infrared spectroscopy
Fourier Transform Infrared Spectroscopy
Aspergillus
Botrytis cinerea
reflectance
grapes
spectroscopy
Fruit
Spectrum Analysis
pathogens
microbial detection
Aspergillus carbonarius
wine grapes
artificial intelligence
chemometrics
Aspergillus niger

Cite this

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title = "Discrimination of Aspergillus spp., Botrytis cinerea and Penicillium expansum in grape berries by ATR-FT-IR spectroscopy",
abstract = "Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FT-IR) in conjunction with chemometric modelling and machine learning algorithms, was successfully applied to objectively differentiate Aspergillus carbonarius, A. niger, Botrytis cinerea or Pencillium expansum fungal mycelium and mature wine-grape berries (Vitis vinifera, cultivar Chardonnay) infected with either of these bunch rot pathogens. The differentiation of B. cinerea infected grape berries from those infected with either Aspergillus or Penicillium species shows promise as a tool for the rapid detection of the pathogen when grapes are received at the winery for processing. Support vector modelling provided superior class prediction for pathogen and control samples over other modelling techniques, while random forest models were successful in classifying samples infected with Aspergillus spp., illustrating the potential for these techniques to be applied to the assessment of bunch rot pathogens. The use of ATR-FT-IR shows potential for assessing the phytosanitary aspects of grapes",
keywords = "berry quality, bunch rot, gray mold, phytopathogen detection",
author = "Leigh Schmidtke and Lachlan Schwarz and Claudia Schueuermann and Christopher Steel",
year = "2019",
month = "1",
doi = "10.5344/ajev.2018.18048",
language = "English",
volume = "70",
pages = "68--76",
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TY - JOUR

T1 - Discrimination of Aspergillus spp., Botrytis cinerea and Penicillium expansum in grape berries by ATR-FT-IR spectroscopy

AU - Schmidtke, Leigh

AU - Schwarz, Lachlan

AU - Schueuermann, Claudia

AU - Steel, Christopher

PY - 2019/1

Y1 - 2019/1

N2 - Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FT-IR) in conjunction with chemometric modelling and machine learning algorithms, was successfully applied to objectively differentiate Aspergillus carbonarius, A. niger, Botrytis cinerea or Pencillium expansum fungal mycelium and mature wine-grape berries (Vitis vinifera, cultivar Chardonnay) infected with either of these bunch rot pathogens. The differentiation of B. cinerea infected grape berries from those infected with either Aspergillus or Penicillium species shows promise as a tool for the rapid detection of the pathogen when grapes are received at the winery for processing. Support vector modelling provided superior class prediction for pathogen and control samples over other modelling techniques, while random forest models were successful in classifying samples infected with Aspergillus spp., illustrating the potential for these techniques to be applied to the assessment of bunch rot pathogens. The use of ATR-FT-IR shows potential for assessing the phytosanitary aspects of grapes

AB - Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FT-IR) in conjunction with chemometric modelling and machine learning algorithms, was successfully applied to objectively differentiate Aspergillus carbonarius, A. niger, Botrytis cinerea or Pencillium expansum fungal mycelium and mature wine-grape berries (Vitis vinifera, cultivar Chardonnay) infected with either of these bunch rot pathogens. The differentiation of B. cinerea infected grape berries from those infected with either Aspergillus or Penicillium species shows promise as a tool for the rapid detection of the pathogen when grapes are received at the winery for processing. Support vector modelling provided superior class prediction for pathogen and control samples over other modelling techniques, while random forest models were successful in classifying samples infected with Aspergillus spp., illustrating the potential for these techniques to be applied to the assessment of bunch rot pathogens. The use of ATR-FT-IR shows potential for assessing the phytosanitary aspects of grapes

KW - berry quality

KW - bunch rot

KW - gray mold

KW - phytopathogen detection

UR - http://www.ajevonline.org/content/early/2018/09/20/ajev.2018.18048

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DO - 10.5344/ajev.2018.18048

M3 - Article

VL - 70

SP - 68

EP - 76

JO - American Journal of Enology and Viticulture

JF - American Journal of Enology and Viticulture

SN - 0002-9254

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