Method of detecting mould in air: a new tool for mushroom growers?

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  • Published: Mar 21, 2016
  • Author: Ryan De Vooght-Johnson
  • Channels: Laboratory Informatics / Chemometrics & Informatics
thumbnail image: Method of detecting mould in air: a new tool for mushroom growers?

A mushroom of many different forms

Although fungi themselves, mushrooms are vulnerable to infectious diseases caused by fungal pathogens. For example, Trichoderma (present in soils) can cause green mould disease, which causes dark green patches to develop on mushrooms.

Mushrooms, the fleshy fruiting body of fungi, are eaten worldwide. They are especially important in European, Japanese and Chinese cuisine and popular with vegetarians owing to their ‘meaty’ texture.

The most widely cultivated and popular is the common mushroom (Agaricus bisporus). This mushroom, cultivated in over 70 countries, is the type you’re most likely to see in supermarkets. The white form is also known as the button, white or table mushroom, while the brown form is known as the Italian or chestnut mushroom, and you may recognise the mature form as the portobello mushroom.

Although fungi themselves, mushrooms are vulnerable to infectious diseases caused by fungal pathogens. For example, Trichoderma (present in soils) can cause green mould disease, which causes dark green patches to develop on mushrooms. This disease limits growth and can cause major yield losses – epidemics in Britain in the 1980s/90s generated losses of up to £4 million. Similarly, wet bubble disease, caused by Mycogone perniciosa, causes a dense white growth to appear, distorting mushroom tissue and causing crop losses.

Detecting the presence of these pathogens is therefore an important goal for mushroom growers as it may help them to detect disease before it has spread throughout the crop. One way of achieving this is to identify the compounds they emit in the air. These ‘microbial volatile organic compounds’, or MVOCs, can be detected using a number of techniques, including solid-phase microextraction of the headspace (the air around a sample) combined with GC-MS. This technique, named HS-SPME-GC-MS, is frequently used in food science.

However, in its current form, the method is far from efficient. Because moulds and the substrates they grow on (e.g., mushrooms) contain many different compounds, identification is a complicated and time-consuming process. Analysis of chromatograms alone can take hours, as complex samples like mushroom tissue often produce over 100 peaks.

Removing the need for processing

To reduce analysis time, researchers have developed chemometric methods, which can rapidly compare chromatograms without the need for lengthy evaluation processes such as de-convolution.

In a study recently published in the Journal of Chemometrics, researchers applied these approaches to the common mushroom and its two major pathogens: Mycogone perniciosa and Trichoderma aggressivum. “We introduce a fast and easy approach to compare unprocessed chromatograms and create two-dimensional plots to visualise the samples,” summarises Attila Gere, author of the study and food scientist at Szent István University in Budapest.

The method begins with SPME of the headspace, followed by analysis of the MVOCs by GC-MS. Afterwards, a chemometrics technique called detrended fluctuation analysis or DFA (usually applied to analyse time series data) is applied.

An integrated quality control system

Using GC-MS, the researchers obtained total ion chromatograms for the mushroom and the two mould types. They also used two outliers – compost and red wine – to test the accuracy of the technique.

As expected, by visual comparison alone they could not identify any markers or reliably identify the different samples. Usually in this situation, time-consuming feature extraction processes would be necessary to separate and identify the samples, such as deconvolution algorithms.

Instead, the researchers distinguished the moulds using DFA – which meant they didn’t have to process the chromatograms at all. A 2D plot was created to show the relationship between the samples. This effectively separated the mushroom samples from the disease-causing fungi, plus the anomalies (wine and compost).

However, because DFA is a relatively unusual choice for this task, the researchers validated the results using a more common technique – principal component analysis (PCA). Surprisingly, DFA outperformed the more established technique. “Comparison of DFA to PCA showed more accurate groupings,” Gere adds. Where PCA struggled to differentiate between the mushroom and the fungus that causes wet bubble disease, DFA clearly separated them.

The method described here can rapidly identify infected mushroom compost. This will be of great interest to mushroom growers, as it could allow them to detect disease-causing agents by simply monitoring the air – which could help avoid potential crop losses. In the future, the researchers say it could be integrated into a quality control system for mushroom cultivation.

More broadly speaking, the method provides a fast, accurate and easy way of distinguishing unprocessed chromatograms without the need for feature extraction of any kind. As DFA is a universal technique, it could be applied to unprocessed chromatograms from other samples and other analytical equipment.

Related Links

J. Chemometrics, 2016, Early View paper. Radványi et al. Discrimination of mushroom disease-related mould species based solely on unprocessed chromatograms.

Wiki: Mushrooms

Wiki: Agaricus bisporus

Pests and diseases of mushrooms

Green mould disease

Wet bubble disease

Article by Ryan De Vooght-Johnson

The views represented in this article are solely those of the author and do not necessarily represent those of John Wiley and Sons, Ltd.

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