A research on detection and identification of volatile organic compounds utilizing cataluminescence-based sensor array

Button mushrooms are one of the most commonly cultivated mushroom species facing different risks e.g.: viral, bacterial and fungal diseases. One of the most common problems is caused by Trichoderma aggressivum, or ‘green mould' disease. The presence or absence of mushroom disease-related moulds can sufficiently be detected from the air by headspace solid-phase microextraction coupled gas chromatography-mass spectrometry (HS SPME GC-MS) via their emitted microbial volatile organic compounds (MVOCs). In the present study, HS SPME GC-MS was used to explore the volatile secondary metabolites released by T. aggressivum f. europaeum on different nutrient-rich and -poor media. The MVOC pattern of green mould was determined, then media-dependent and independent biomarkers were also identified during metabolomic experiments. The presented results provide the basics of a green mould identification system which helps producers reducing yield loss, new directions for researchers in mapping the metabolomic pathways of T. aggressivum and new tools for policy makers in mushroom quality control.

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Development of proton-transfer ion trap-mass spectrometry: on-line detection and identification of volatile organic compounds in air

Journal of the American Society for Mass Spectrometry ◽

10.1016/j.jasms.2005.03.025 ◽

2005 ◽

Vol 16 (8) ◽

pp. 1316-1324 ◽

Cited By ~ 75

Author(s):

C. Warneke ◽

J. A. Gouw ◽

E. R. Lovejoy ◽

P. C. Murphy ◽

W. C. Kuster ◽

...

Keyword(s):

Mass Spectrometry ◽

Volatile Organic Compounds ◽

Proton Transfer ◽

Organic Compounds ◽

Ion Trap ◽

Line Detection ◽

Ion Trap Mass Spectrometry ◽

Detection And Identification ◽

Volatile Organic ◽

On Line

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Identification of plastic toys contaminated with volatile organic compounds using QCM gas sensor array

Talanta ◽

10.1016/j.talanta.2019.120701 ◽

2020 ◽

Vol 211 ◽

pp. 120701 ◽

Cited By ~ 3

Author(s):

E. Oleneva ◽

T. Kuchmenko ◽

E. Drozdova ◽

A. Legin ◽

D. Kirsanov

Keyword(s):

Volatile Organic Compounds ◽

Gas Sensor ◽

Organic Compounds ◽

Sensor Array ◽

Gas Sensor Array ◽

Volatile Organic ◽

Plastic Toys

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A fluorometric paper-based sensor array for the discrimination of volatile organic compounds (VOCs) with novel salicylidene derivatives

Dyes and Pigments ◽

10.1016/j.dyepig.2018.06.044 ◽

2018 ◽

Vol 159 ◽

pp. 378-383 ◽

Cited By ~ 6

Author(s):

Thiti Jarangdet ◽

Kornkanya Pratumyot ◽

Kittiwat Srikittiwanna ◽

Wijitar Dungchai ◽

Withawat Mingvanish ◽

...

Keyword(s):

Volatile Organic Compounds ◽

Organic Compounds ◽

Sensor Array ◽

Volatile Organic

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In-Situ Analysis of Volatile Organic Compounds in Biogas Fermentation Processes Using a Metal Oxide Gas Sensor Array

ECS Meeting Abstracts ◽

10.1149/ma2020-01282153mtgabs ◽

2020 ◽

Vol MA2020-01 (28) ◽

pp. 2153-2153

Author(s):

Binayak Ojha ◽

Divyashree Narayana ◽

Margarita Aleksandrova ◽

Heinz Kohler ◽

Matthias Schwotzer ◽

...

Keyword(s):

Volatile Organic Compounds ◽

Metal Oxide ◽

Gas Sensor ◽

Organic Compounds ◽

Sensor Array ◽

Fermentation Processes ◽

Gas Sensor Array ◽

Volatile Organic ◽

Metal Oxide Gas Sensor

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Selective Detection of Target Volatile Organic Compounds in Contaminated Air Using Sensor Array with Machine Learning: Aging Notes and Mold Smells in Simulated Automobile Interior Contaminant Gases

Sensors ◽

10.3390/s20092687 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2687

Author(s):

Toshio Itoh ◽

Yutaro Koyama ◽

Woosuck Shin ◽

Takafumi Akamatsu ◽

Akihiro Tsuruta ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Volatile Organic Compounds ◽

Organic Compounds ◽

Sensor Array ◽

Selective Detection ◽

K Nearest Neighbor ◽

Linear Discriminant ◽

Age Related ◽

Volatile Organic

We investigated the selective detection of target volatile organic compounds (VOCs) which are age-related body odors (namely, 2-nonenal, pelargonic acid, and diacetyl) and a fungal odor (namely, acetic acid) in the presence of interference VOCs from car interiors (namely, n-decane, and butyl acetate). We used eight semiconductive gas sensors as a sensor array; analyzing their signals using machine learning; principal-component analysis (PCA), and linear-discriminant analysis (LDA) as dimensionality-reduction methods; k-nearest-neighbor (kNN) classification to evaluate the accuracy of target-gas determination; and random forest and ReliefF feature selections to choose appropriate sensors from our sensor array. PCA and LDA scores from the sensor responses to each target gas with contaminant gases were generally within the area of each target gas; hence; discrimination between each target gas was nearly achieved. Random forest and ReliefF efficiently reduced the required number of sensors, and kNN verified the quality of target-gas discrimination by each sensor set.

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