Detection of Potato Storage Disease via Gas Analysis: A Pilot Study Using Field Asymmetric Ion Mobility Spectrometry

Soft rot is a commonly occurring potato tuber disease that each year causes substantial losses to the food industry. Here, we explore the possibility of early detection of the disease via gas/vapor analysis, in a laboratory environment, using a recent technology known as FAIMS (Field Asymmetric Ion...

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Main Authors: Massimo Rutolo, James A. Covington, John Clarkson, Daciana Iliescu
Format: Article
Language:English
Published: MDPI AG 2014-08-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/14/9/15939
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spelling doaj-b1305a24c5ef48b496ff493a285eaf842020-11-24T22:51:11ZengMDPI AGSensors1424-82202014-08-01149159391595210.3390/s140915939s140915939Detection of Potato Storage Disease via Gas Analysis: A Pilot Study Using Field Asymmetric Ion Mobility SpectrometryMassimo Rutolo0James A. Covington1John Clarkson2Daciana Iliescu3School of Engineering, University of Warwick, Coventry CV4 7AL, UKSchool of Engineering, University of Warwick, Coventry CV4 7AL, UKWarwick Crop Centre, School of Life Sciences, University of Warwick, Wellesbourne, Warwick CV35 9EF, UKSchool of Engineering, University of Warwick, Coventry CV4 7AL, UKSoft rot is a commonly occurring potato tuber disease that each year causes substantial losses to the food industry. Here, we explore the possibility of early detection of the disease via gas/vapor analysis, in a laboratory environment, using a recent technology known as FAIMS (Field Asymmetric Ion Mobility Spectrometry). In this work, tubers were inoculated with a bacterium causing the infection, Pectobacterium carotovorum, and stored within set environmental conditions in order to manage disease progression. They were compared with controls stored in the same conditions. Three different inoculation time courses were employed in order to obtain diseased potatoes showing clear signs of advanced infection (for standard detection) and diseased potatoes with no apparent evidence of infection (for early detection). A total of 156 samples were processed by PCA (Principal Component Analysis) and k-means clustering. Results show a clear discrimination between controls and diseased potatoes for all experiments with no difference among observations from standard and early detection. Further analysis was carried out by means of a statistical model based on LDA (Linear Discriminant Analysis) that showed a high classification accuracy of 92.1% on the test set, obtained via a LOOCV (leave-one out cross-validation).http://www.mdpi.com/1424-8220/14/9/15939FAIMSsoft rotpotato storage diseaseearly disease detection
collection DOAJ
language English
format Article
sources DOAJ
author Massimo Rutolo
James A. Covington
John Clarkson
Daciana Iliescu
spellingShingle Massimo Rutolo
James A. Covington
John Clarkson
Daciana Iliescu
Detection of Potato Storage Disease via Gas Analysis: A Pilot Study Using Field Asymmetric Ion Mobility Spectrometry
Sensors
FAIMS
soft rot
potato storage disease
early disease detection
author_facet Massimo Rutolo
James A. Covington
John Clarkson
Daciana Iliescu
author_sort Massimo Rutolo
title Detection of Potato Storage Disease via Gas Analysis: A Pilot Study Using Field Asymmetric Ion Mobility Spectrometry
title_short Detection of Potato Storage Disease via Gas Analysis: A Pilot Study Using Field Asymmetric Ion Mobility Spectrometry
title_full Detection of Potato Storage Disease via Gas Analysis: A Pilot Study Using Field Asymmetric Ion Mobility Spectrometry
title_fullStr Detection of Potato Storage Disease via Gas Analysis: A Pilot Study Using Field Asymmetric Ion Mobility Spectrometry
title_full_unstemmed Detection of Potato Storage Disease via Gas Analysis: A Pilot Study Using Field Asymmetric Ion Mobility Spectrometry
title_sort detection of potato storage disease via gas analysis: a pilot study using field asymmetric ion mobility spectrometry
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2014-08-01
description Soft rot is a commonly occurring potato tuber disease that each year causes substantial losses to the food industry. Here, we explore the possibility of early detection of the disease via gas/vapor analysis, in a laboratory environment, using a recent technology known as FAIMS (Field Asymmetric Ion Mobility Spectrometry). In this work, tubers were inoculated with a bacterium causing the infection, Pectobacterium carotovorum, and stored within set environmental conditions in order to manage disease progression. They were compared with controls stored in the same conditions. Three different inoculation time courses were employed in order to obtain diseased potatoes showing clear signs of advanced infection (for standard detection) and diseased potatoes with no apparent evidence of infection (for early detection). A total of 156 samples were processed by PCA (Principal Component Analysis) and k-means clustering. Results show a clear discrimination between controls and diseased potatoes for all experiments with no difference among observations from standard and early detection. Further analysis was carried out by means of a statistical model based on LDA (Linear Discriminant Analysis) that showed a high classification accuracy of 92.1% on the test set, obtained via a LOOCV (leave-one out cross-validation).
topic FAIMS
soft rot
potato storage disease
early disease detection
url http://www.mdpi.com/1424-8220/14/9/15939
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