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Discrimination of mushroom disease‐related mould species based solely on unprocessed chromatograms
Author(s) -
Radványi Dalma,
Gere Attila,
Sipos László,
Kovács Sándor,
Jókai Zsuzsa,
Fodor Péter
Publication year - 2016
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2777
Subject(s) - mushroom , principal component analysis , solid phase microextraction , chemistry , chromatography , euclidean distance , agaricus bisporus , mass spectrometry , gas chromatography–mass spectrometry , pattern recognition (psychology) , mathematics , food science , artificial intelligence , statistics , computer science , geometry
Agaricus bisporus A15 species (bisporic button mushroom) and its two main mould diseases (Mycogone perniciosa and Trichoderma aggressivum) were analysed using headspace solid‐phase microextraction sampling coupled with gas chromatograpy–mass spectrometry analysis. The presence or absence of mushroom disease‐related moulds can easily be detected and monitored in the air by headspace solid‐phase microextraction gas chromatograpy–mass spectrometry via their emitted microbial volatile organic compounds. Detrended fluctuation analysis (DFA) was first applied to distinguish different mould samples based on their unprocessed total ion chromatograms, which describe the air polluted by microbial volatile organic compounds. A two‐dimensional plot, generated from the results of DFA, visualizes the relationship of the analysed samples. The pattern found by DFA was compared with the pattern found by the traditionally applied principal component analysis. Cluster analysis (using Euclidean distance and Ward's method) of the principal component analysis scores and DFA coefficients showed characteristic groupings. A methodology is introduced, which uses the unprocessed chromatogram, without any time‐consuming feature extraction and can indicate the presence of infected samples. The proposed methodology is able to give a support to the mushroom growers to indicate different mushroom disease‐related infection via air monitoring. Additionally, DFA is a sample‐independent and universal method. Hence, it can be also applied to distinguish unprocessed chromatograms produced by any other analytical equipment. Copyright © 2016 John Wiley & Sons, Ltd.