Literature DB >> 25127615

Detection of potato brown rot and ring rot by electronic nose: from laboratory to real scale.

E Biondi1, S Blasioli2, A Galeone3, F Spinelli4, A Cellini5, C Lucchese6, I Braschi7.   

Abstract

A commercial electronic nose (e-nose) equipped with a metal oxide sensor array was trained to recognize volatile compounds emitted by potatoes experimentally infected with Ralstonia solanacearum or Clavibacter michiganensis subsp. sepedonicus, which are bacterial agents of potato brown and ring rot, respectively. Two sampling procedures for volatile compounds were tested on pooled tubers sealed in 0.5-1 L jars at room temperature (laboratory conditions): an enrichment unit containing different adsorbent materials (namely, Tenax(®) TA, Carbotrap, Tenax(®) GR, and Carboxen 569) directly coupled with the e-nose (active sampling) and a Radiello(™) cartridge (passive sampling) containing a generic Carbograph fiber. Tenax(®) TA resulted the most suitable adsorbent material for active sampling. Linear discriminant analysis (LDA) correctly classified 57.4 and 81.3% total samples as healthy or diseased, when using active and passive sampling, respectively. These results suggested the use of passive sampling to discriminate healthy from diseased tubers under intermediate and real scale conditions. 80 and 90% total samples were correctly classified by LDA under intermediate (100 tubers stored at 4°C in net bag passively sampled) and real scale conditions (tubers stored at 4°C in 1.25 t bags passively sampled). Principal component analysis (PCA) of sensorial analysis data under laboratory conditions highlighted a strict relationship between the disease severity and the responses of the e-nose sensors, whose sensitivity threshold was linked to the presence of at least one tuber per sample showing medium disease symptoms. At intermediate and real scale conditions, data distribution agreed with disease incidence (percentage of diseased tubers), owing to the low storage temperature and volatile compounds unconfinement conditions adopted.
Copyright © 2014 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  E-nose; LDA statistical analysis; PCA statistical analysis; Sensorial analysis; Volatile compounds

Mesh:

Substances:

Year:  2014        PMID: 25127615     DOI: 10.1016/j.talanta.2014.04.057

Source DB:  PubMed          Journal:  Talanta        ISSN: 0039-9140            Impact factor:   6.057


  11 in total

Review 1.  Potential Applications and Limitations of Electronic Nose Devices for Plant Disease Diagnosis.

Authors:  Antonio Cellini; Sonia Blasioli; Enrico Biondi; Assunta Bertaccini; Ilaria Braschi; Francesco Spinelli
Journal:  Sensors (Basel)       Date:  2017-11-11       Impact factor: 3.576

2.  Kiwi fruit (Actinidia chinensis) quality determination based on surface acoustic wave resonator combined with electronic nose.

Authors:  Liu Wei; Hui Guohua
Journal:  Bioengineered       Date:  2015-01-27       Impact factor: 3.269

3.  Chocolate Classification by an Electronic Nose with Pressure Controlled Generated Stimulation.

Authors:  Luis F Valdez; Juan Manuel Gutiérrez
Journal:  Sensors (Basel)       Date:  2016-10-20       Impact factor: 3.576

4.  Detection of Fungi and Oomycetes by Volatiles Using E-Nose and SPME-GC/MS Platforms.

Authors:  Jérémie Loulier; François Lefort; Marcin Stocki; Monika Asztemborska; Rafał Szmigielski; Krzysztof Siwek; Tomasz Grzywacz; Tom Hsiang; Sławomir Ślusarski; Tomasz Oszako; Marcin Klisz; Rafał Tarakowski; Justyna Anna Nowakowska
Journal:  Molecules       Date:  2020-12-05       Impact factor: 4.411

5.  Feasibility of Volatile Biomarker-Based Detection of Pythium Leak in Postharvest Stored Potato Tubers Using Field Asymmetric Ion Mobility Spectrometry.

Authors:  Gajanan S Kothawade; Sindhuja Sankaran; Austin A Bates; Brenda K Schroeder; Lav R Khot
Journal:  Sensors (Basel)       Date:  2020-12-21       Impact factor: 3.576

Review 6.  Emerging Methods of Monitoring Volatile Organic Compounds for Detection of Plant Pests and Disease.

Authors:  Samantha MacDougall; Fatih Bayansal; Ali Ahmadi
Journal:  Biosensors (Basel)       Date:  2022-04-13

7.  Enhancing Electronic Nose Performance Based on a Novel QPSO-KELM Model.

Authors:  Chao Peng; Jia Yan; Shukai Duan; Lidan Wang; Pengfei Jia; Songlin Zhang
Journal:  Sensors (Basel)       Date:  2016-04-11       Impact factor: 3.576

Review 8.  Plant Pest Detection Using an Artificial Nose System: A Review.

Authors:  Shaoqing Cui; Peter Ling; Heping Zhu; Harold M Keener
Journal:  Sensors (Basel)       Date:  2018-01-28       Impact factor: 3.576

9.  Classification for Penicillium expansum Spoilage and Defect in Apples by Electronic Nose Combined with Chemometrics.

Authors:  Zhiming Guo; Chuang Guo; Quansheng Chen; Qin Ouyang; Jiyong Shi; Hesham R El-Seedi; Xiaobo Zou
Journal:  Sensors (Basel)       Date:  2020-04-09       Impact factor: 3.576

10.  Odor Discrimination by Similarity Measures of Abstract Odor Factor Maps from Electronic Noses.

Authors:  Weiqing Guo; Haohui Kong; Junzhang Wu; Feng Gan
Journal:  Sensors (Basel)       Date:  2018-08-13       Impact factor: 3.576

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