Directory UMM :Journals:Journal of Stored Products Research:Vol 36.Issue4.oct2000:

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Review

Volatiles as an indicator of fungal activity and

dierentiation between species, and the potential

use of electronic nose technology for early detection

of grain spoilage

N. Magan

a,

*, P. Evans

b

a

Applied Mycology Group, Cran®eld Biotechnology Centre, Cran®eld University, Cran®eld, Bedford MK43 0AL, UK b

Department of Instrumentation and Analytical Science, Faraday Building, UMIST, PO BOX 88, Sackville Street, Manchester M60 1QD, UK

Accepted 10 November 1999

Abstract

There is signi®cant interest in methods for the early detection of quality changes in cereal grains. The development of electronic nose technology in recent years has stimulated interest in the use of characteristic volatiles and odours as a rapid, early indication of deterioration in grain quality. This review details the current status of this area of research. The range of volatiles produced by spoilage fungi in vitro and on grain are described, and the key volatile groups indicative of spoilage are identi®ed. The relationship between current grain quality descriptors and the general classes of o-odours as de®ned in the literature, e.g. sour, musty, are not very accurate and the possible correlation between these for wheat, maize and other cereals, and volatiles are detailed. Examples of dierentiation of spoilage moulds and between grain types using an electronic nose instrument are described. The potential for rapid and remote grain classi®cation and future prospects for the use of such technology as a major descriptor of quality are discussed.72000 Elsevier Science Ltd. All rights reserved.

Keywords:Fungi; Stored grain; Volatiles; Electronic nose technology; Odour detection

www.elsevier.com/locate/jspr

* Corresponding author. Tel.: +44-01234-750111; fax: +44-01234-750907.

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Contents

1. Introduction . . . 320

2. Fungal volatiles. . . 321

3. Volatiles in naturally contaminated grain . . . 326

4. Volatiles as taxonomic markers . . . 328

5. Descriptors of odour quality . . . 328

5.1. Classi®cation of o-odours in cereals . . . 329

6. Grain classi®cation using electronic noses . . . 333

7. Discussion . . . 336

8. Conclusions . . . 337

Acknowledgements . . . 337

References . . . 338

1. Introduction

Combine harvesting of both temperate and tropical cereals is most ecient when the ripened grain is slightly moist. Subsequent drying to safe moisture contents (e.g. for wheat this is 14%

00.70 water activity) is essential to prevent initiation of fungal activity which can result in

heating of the stored grain, and ultimately end in spontaneous heating and complete loss of the grain. The activity of spoilage fungi can also result in the production of mycotoxins by certain genera, which prevents such moulded grain from being used for either animal or human consumption. Thus it is very important to detect fungal deterioration in stored cereal grains at an early stage. This would facilitate and improve existing management of grain stores. It would also allow remedial measures to be more eectively implemented, allowing signi®cant losses and grain downgrading to be avoided.

Many rapid methods for the detection of fungal spoilage in grain have been examined. These include quantifying the eects of degradation on grain components, changes in fungal enzyme activity, respiratory activity of moulds, changes in chemical components, particularly of chitin, ergosterol and ATP, of moulds during growth in grain, immuno¯uorescence, DNA and immunoassays, electrochemical methods and very recently, photoacoustic FTIR methods (Tothill et al., 1992; Magan, 1993; Gordon et al., 1998). However, some of these at best correlate poorly with fungal growth or biomass in colonised grain. Many of these techniques are also time-consuming, expensive or not sensitive enough for the early detection of fungal activity. A speci®c biochemical marker with adequate reproducibility to detect early spoilage would help prevent major losses as a result of moulding of stored grain by fungi due to poor storage management.

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substrates such as grain. In the 1970s Kaminski et al. (1972, 1974, 1975) demonstrated that spoilage fungi produced volatiles, which were characteristic and dierent from those produced by bacteria or the seeds themselves. Indeed, in this period several studies suggested that the monitoring of the appearance of volatiles might be a good early indicator of quality loss and mycotoxin formation in grain (Stawicki et al., 1973; Richard-Molard et al., 1976; Abramson et al., 1980).

The primary objective of this review is to examine some of the key studies carried out on the use of volatiles as an indicator of the potential for spoilage. To this end it will consider a number of areas: (a) the range of fungal volatiles produced by spoilage fungi; (b) the presence of volatiles in naturally contaminated grain as an early indicator of spoilage; (c) the possible relationship between odour discriminators and volatiles produced by spoilage fungi on grain; (d) early detection of the activity of spoilage moulds in grain substrates; and (e) the potential for using electronic nose technology for detection of spoilage of grain. This review will not extensively cover the analytical methods used to quantify volatile production by spoilage fungi.

2. Fungal volatiles

Linton and Wright (1993) reviewed the possible reasons for the production of fungal volatiles. Production of a volatile may be a way of removing inhibitory intermediates from the metabolism under unfavourable conditions. They also suggested that volatiles might have inhibitory eects on other fungi and act as self-regulators of growth and development. For example, in Geotrichum candidum Link, volatiles were found to in¯uence all stages of growth, while those of Trichoderma harzianum Rifai have been demonstrated to inhibit the mycotoxigenic Fusarium moniliforme Sheldon, a common coloniser of maize. Ecologically, the situation can become more complex as certain fungal volatiles also attract insects (Hedlund et al., 1995).

Most of the early work on the detection of volatiles was done using gas chromatography (GC) and mass spectrometry (MS). For GC analysis most workers have used a polar stationary phase for the separation of the fungal alcohols, aldehydes, ketones and esters. Kaminski et al. (1985, 1987) also developed a spectrophotometric method for the accurate quanti®cation of volatile carbonyl compounds.

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Table 1

Volatile compounds found in cultures of fungi grown on dierent grain and cereal substratesa

Volatiles Grain medium Fungal species Reference

Alcohols

1-Pentanol Barley Pen.viridicatum Wilkins and Scholl (1989) 2-Methyl-1-butanol Wheat E.amstelodami BorjessoÈn et al. (1989) 3-Methyl-1-butanol Wheat Asp.¯avus, Asp.niger, Asp.ochraceus,

Asp. ¯avus Harris et al. (1986)

Wheat Alt.alternata, E.repens, Asp.¯avus, Asp.versicolor, Pen.chrysogenum, Pen.cyclopium, Fus.moniliforme, Fus.semitectum

Tuma et al. (1989)

Wheat Asp.¯avus, Pen.cyclopium BorjessoÈn et al. (1989) Barley Pen.aurantiogriseum, Pen.verrucosum,

Pen.roquefortii, Asp.¯avus, Asp.niger Harris et al. (1986)

Wheat Alt.alternata, E.repens, Asp.¯avus,

Maize Asp. ¯avus, Asp. parasiticus, Pen. chrysogenum, Alt.spp

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2-Octan-1-ol Wheat Asp.¯avus, Asp.ochraceus, Asp.oryzae,

Asp.¯avus, Asp.niger Harris et al. (1986)

Carbonyls

Acetaldehyde Wheat/maize Asp.¯avus, Asp.parasiticus, Pen.chrysogenum, Alt. spp

Wasowicz (1988)

2-Pentane Wheat E.amstelodami BorjessoÈn et al. (1989) 3-Octanone Wheat Asp.¯avus, Asp.ochraceus, Asp.oryzae,

Wheat E.repens, Pen.cyclopium Tuma et al. (1989) Nonanal Whole wheat

bread

2-Methylo-acetophenone

Barley Pen.coprophilum Wilkins and Scholl (1989)

Hydrocarbons

Dimethyl benzene Whole wheat bread

Trimethyl hexane Whole wheat bread

Pen.roquefortii Harris et al. (1986)

2, 4-Dimethyl hexane Wheat Fus.culmorum BorjessoÈn et al. (1989) Styrene Barley Pen.aurantiogriseum, Pen.verrucosum,

Pen.viridicatum, Pen.coprophilum

Wilkins and Scholl (1989)

Naphthalene Whole wheat Asp.niger Harris et al. (1986)

Miscellaneous

Ethyl acetate Wheat Fus.culmorum BorjessoÈn et al. (1989) 2-Methyl-furan Wheat E.amstelodami, Asp.¯avus, Pen.cyclopium BorjessoÈn et al. (1989) 2-(1-Pentyl)-furan Barley Pen.aurantiogriseum, Pen.verrucosum,

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species. One other important fact of note was that in many cases the water availability of the substrate was not accurately controlled, or measured. This could have a signi®cant impact on and modify the importance of individual volatile compounds produced by a speci®c species on a temperate or tropical cereal.

Both temperature and culture age can also have an eect on volatile production, e.g. Kaminski et al. (1972, 1974) found that in dierent cultures of fungi grown on autoclaved wheat meal at 26±288C for 5 days, volatile alcohols represented 79±96% of the total volatiles. The alcohol, 1-octen-3-ol was found to be predominant (35±93%). BorjessoÈn et al. (1989) found that alcohols, particularly ethanol, constituted 80% of the total volatile concentration in spoilage fungi, with the exception of the xerophilic species Eurotium amstelodami Mangin where about 50% were alcohols. Subsequent detailed studies by BorjessoÈn et al. (1990, 1992) found that there were dierences in the fungal volatiles produced by grain fungi (Penicillium spp, Aspergillus ¯avus Link, A. versicolor (Vuill)Tiraboschi and A. candidus Link) on agar and on moist temperate cereals over a 14 day incubation period using GC. Interestingly, there was less of a dierence between grain substrate type and age of cultures than between species. They found that over 80% of the volatiles were alcohols, except in the case of Eurotium amstelodami. In grain cultures ethanol accounted for more than 90% of the total alcohols produced. They also found that with the ubiquitous Penicillium species, P. aurantiogriseum Dierckx, the production of the 8-carbon alcohols, 2-methyl-1-propanol and 3-methyl-1-butanol was higher in grain than in agar cultures. Other studies (Sinha et al., 1988; Tuma et al., 1989) revealed methyl-1-butanol and 1-octen-ol to be predominant along with the ketone, 3-octanone. Since then other alcohols such as 3-octanol have been identi®ed (Nilsson et al., 1996). More recently, Pasanen et al. (1996) have suggested that volatile organic compounds, particularly ketones, may also be useful markers of the activity of mycotoxigenic species such as Fusarium sporotrichoides Sherbako and Penicillium verrucosum Dierckx. This suggests that patterns of volatile production could also be a powerful tool for the rapid and early detection of the activity of mycotoxigenic species. Wilkins and Scholl (1989) identi®ed volatiles from Penicilliumspp grown on irradiated barley (20±25% moisture content (m.c.)) for 7 days. Of the compounds identi®ed from P. aurantiogriseum, P. verrucosum and P. viridicatum Westl., 3-methyl-1-butanol, styrene and 1-octen-3-ol were predominant.

The main esters found are acetates, which include ethyl acetate, isobutyl acetate and isopentyl acetate (Larsen and Frisvad, 1995). They also identi®ed aromatic ethers such as 1-methoxy-3-methylbenzene. Besides esters, pyrazines such as methoxy pyrazines (Larsen and Frisvad, 1994), sulphurous compounds such as dimethyl disulphide (BorjessoÈn et al., 1993) and a number of mono- and sesqui-terpenes are produced. BorjessoÈn et al. (1989, 1990) also identi®ed heterocyclic compounds such as 3-methylfuran.

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the literature concerning the main fungal volatiles produced in inoculated grain cultures on maize, barley and wheat available.

The metabolic pathway leading to the formation of volatiles gives important clues to the relationship between various groups of volatiles, non-volatiles and mycotoxins (Fig. 1). Ethanol is produced under anaerobic conditions. Alcohols are derived from amino acids via the Ehrlich pathway. 1-octen-3-ol and other eight and ten carbon compounds are synthesised by oxidation of linoleic acid (acetate±fatty acids±alcohol pathway). The breakdown of lipids through fungal lipase activity results in free fatty acids. These are oxidised to b-keto acids, which are subsequently decarboxylated to methyl ketones. The corresponding secondary alcohols are formed by the reduction of methyl ketones. Lactones are produced from g-keto acids. Enzyme catalysed reactions between alcohols and acyl-CoA compounds result in the formation of esters. Pyrazines are thought to be synthesised by the condensation reaction between acetoin and ammonia. The dimethylsulphide is produced from methionine. The mevalonic acid pathway gives rise to a variety of terpenes such as geosmin and 2-methylisoborneol.

Table 2

Fungal volatiles identi®ed in inoculated cereal cultures by dierent authorsa

Fungal volatile Maize Cereal barley Wheat

1-Butanol c

2-Butanol c, d

3-Methyl-1-butanol a b c, d

1-Pentanol a b

1-Hexanol a c

2-Octen-1-ol c

1-Octanol c

3-Octanol a c

1-Octen-3-ol a b

Phenyl ethanol c

2-Ethyl-5-methyl-phenol b

Hexanal a

2-(2-Furyl) pentanal b

Benzaldehyde c

3-Octanone c

2-Hydroxy-3-butanone a

Nonanone c

2-Methyl-acetophenone b

Butyl acetate c

Amyl acetate c

Octyl acetate c

2-Methylfuran d

2-(1-Pentyl)furan b

3-Methyl anisole b

a

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3. Volatiles in naturally contaminated grain

A number of studies have identi®ed the background odours produced by dierent cereal types and cultivars. These have shown that a wide range of volatiles including alcohols, esters, aldehydes, ketones, alkanes, alkenes, furans, lactams, phenols, pyrazines, and pyrroles are produced by wheat, maize and rice grains (Kaminski and Wasowicz, 1991). However, few studies have examined the detailed accumulation of volatiles produced during fungal colonisation of cereal grain.

For example, Richard-Molard et al. (1976) followed the accumulation of fungal volatiles: 1-octen-3-ol, 3-octanone, 3-octanol, 3-methyl-1-butanol, in airtight stored maize at dierent temperatures in France. They found a sequential increase in the quantities of these volatiles produced with storage time. In naturally ventilated wheat, barley and oats in Canada, Abramson et al. (1980) identi®ed 1-octanol, 3-octanone and 3-methyl-1-butanol in moist grain (21% m.c.) in farm granaries over a 20-week storage period. They found a 10±15 times increase in volatiles by the seventh week of storage, with levels decreasing by the sixteenth week. They also found that 1-octanol was predominant, comprising 51±81% of the total volatiles. The highest concentrations of fungal volatiles were found in oats, but the widest range of volatiles occurred in barley. In another study Abramson et al. (1983) compared volatiles produced in barley grain stored at 16 and 20% m.c. for 66 weeks. The maximum was

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produced in the sixth week of storage, with signi®cantly higher levels of volatiles produced in the higher m.c. barley. The key volatiles were 3-methyl-1-butanol, 3-octanone, 1-octanol and 1-octen-3-ol.

Similar studies by Sinha et al. (1988) and Tuma et al. (1989) of wheat stored at a range of m.c.s' (15.5±25%) in unventilated and ventilated storage bins demonstrated that regardless of season, volatiles could be detected and correlated with fungal growth and activity in the stored grain. The highest levels of 3-methyl-1-butanol occurred in the 25% m.c. wheat and correlated signi®cantly with total counts of bacteria,Penicilliumspp and Fusarium spp. At 20% m.c. there was also correlation with the Eurotium glaucus group of species, which are important in initiating spoilage. In non-ventilated bins 1-octen-3-ol production was correlated with Penicillium spp in wheat at both 20 and 25% m.c. Interestingly, higher levels of volatiles were present in seed with a low viability suggesting that volatiles could be a very good early indicator of loss of seed viability and spoilage in stored grain. However, very little information is available on the correlation between fungal volatiles, quality loss and mycotoxin formation as an early indicator in storage. Pasanen et al. (1996) demonstrated some relationship between fungal volatiles and mycotoxins. They cultivated Fusarium sporotrichoides on straw, wheat and oat grains and found that on both grain types, a similar composition of volatiles were produced. Equal amounts of terpenes and ketones, and to a minor extent alcohols were produced. Interestingly, large amounts of trichothecene mycotoxins were also synthesised. These are synthesised via the terpenoid route, the same pathway used for the production of terpenes. They therefore suggested that the production of large amounts of ketones was due to the high lipid content of grain. Furthermore they showed that there were dierences in the volatile composition between toxigenic and non-toxigenic strains of Penicillium verrucosum. The isolate that synthesised ochratoxins showed an accelerated production of ketones compared to the non-toxigenic isolate. Ochratoxins are derived from polyketides and phenylalanines. Thus, similarity between the polyketides and fatty acid pathways may result in increased production of ketones in the mycotoxinogenic isolate.

Attempts have also been made to relate fungal volatile production in dierent grain types to total fungal populations (colony forming units, cfu) (Wasowicz, 1988). They monitored 100 g samples of wheat and maize of 17% m.c. stored for up to 70 days in 1 litre containers using direct gas chromatography. Fungal cfus increased from 102 to 106, and 103 to 107 gÿ1 respectively. At 21% m.c. they increased to 8 108 and 8 1010 gÿ1 wheat and maize

Table 3

Mean speci®c and total fungal volatiles in wheat and maize grain of dierent moisture contents (m.c.) after 28 days storage (adapted from Kaminiski and Wasowicz, 1991)

Volatiles (ppm)

Grain treatments Total volatiles 3-octanone 1-octen-3-ol

Wheat grain Ð 17% m.c. 15.0 4.5 < 0.25

Wheat grain Ð 21% m.c. 27.5 3.5 1.4

Maize grain Ð 17% m.c. 30.0 7.5 0.4

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respectively. The temporal changes in total and dominant volatiles in these two grain types are shown in Table 3. It was notable that there were dierences between the volatiles produced in wheat and maize. This could partially be a result of the dierence in starchy and lipid/sugar rich grains.

Some studies have also been carried out to try to correlate the volatile concentrations found in sound cereals and those found in musty/sour cereals with total cfus and other methods of quantifying mould presence. Wasowicz (1988) showed that in sound and musty wheat grain the volatiles present were qualitatively the same. However, some volatiles present in musty wheat grain were not detected in sound grain, and some also occur in signi®cantly higher concentrations in musty grain. They found that 2-methylisoborneol and geosmin, mainly produced by actinomycete species were mainly responsible for the musty odour found, with amounts of 3-octanone, 1-octen-3-ol, 3-octanol, and 1-octanol also present in high concentrations. These studies all point to the potential for using fungal volatiles as an early indication of grain quality during storage.

4. Volatiles as taxonomic markers

Studies on a range of fungal species revealed that speci®c volatiles such as 3-methylfuran are produced in similar amounts regardless of the fungal species and grain substrate used (BorjessoÈn et al., 1992). However, other volatile compounds, especially terpenes, were both species- and strain-speci®c (Larsen and Frisvad, 1995; Nilsson et al. 1996; BorjessoÈn et al., 1989, 1993; Larsen, 1997) e.g. Aspergillus species produced thujopsene, which was absent from the Penicillium cultures. Aspergillus candidus produced a monoterpene not found in the other fungi examined, suggesting that monoterpene pro®les could be used to dierentiate between fungi. Volatile pro®les can be used rather than individual volatile compounds to classify fungi at a species level, as a combination of volatiles is often unique to each species (Larsen and Frisvad, 1994; Larsen, 1997; Wilkins et al., 1997; Korpi et al., 1998). Larsen (1997) was able to classify Penicillium species within 2 days after inoculation and in a mixed culture of P. roquefortiiand P. commune Thom. in a ratio of 1000:1, with identi®cation after 3 days. Larsen and Frisvad (1995) were also able to use these patterns to reclassify similar groups of Penicilliumspp into separate clusters based on the production of geosmin. Since it is clear that there are a range of characteristic volatile odours produced by fungi when colonising grain the question is whether there is any relationship between these patterns and the odour descriptors actually used in the grain trade, particularly in the USA.

5. Descriptors of odour quality

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5.1. Classi®cation of o-odours in cereals

In Europe the grading of grain is covered by ISO 605:1991 (E) 1991. It states that odour should be determined as soon as possible after sampling but de®nes no parameters other than the fact that grain possessing a foreign odour should be rejected. If no odour is detected the sample is sealed and retested after 24 h. After this stage sub-samples of ca 5 g may be taken, ground and then heated to no more than 608C before re-evaluating the odour. Further examinations for foreign odours may be carried out during or after grinding.

In the United States an inspector determines the odour of cereal grains. Detection of any taint in the grain odour immediately reduces the grading of the grain to the lowest possible, namely, that of sample grain. This obviously has a serious eect on the economic value of the crop. The outcome of a grading may however be subject to an appeals process. A review of the current Grain Inspection Handbook (USDA, 1990) reveals that whilst odour classi®cation is described, little is known about the volatiles giving rise to the o-taints. Numerous sources of possible taint cause are listed in the handbook. For example the following categories are quoted:

Heat damage ``May arise from a variety of situations and can be a reason for downgrading the grain without odour analysis. Instances of its occurrence stem from:

(i) grain stored with too high a ®eld moisture content;

(ii) moisture migration due to convective air currents in storage; (iii) localised infestations of grain insects''.

These situations all favour mould development and reduce the nutritional value of the grain. Drier damage is less severe and given the classi®cation of `damaged by heat' but may also lead to o-taints.

Germ damage ``Results only in colouration of oils from the grain''. Sprout damage ``May ultimately lead to the presence of moulds''. Mould damage ``Can occur in the ®eld or in storage''.

Scab damage ``Arises from ®eld damage by Fusariumsp. and usually has mould in the germ of the seed. May also include vomitoxin (deoxynivalenol) which can lead to poisoning problems''.

The grain may be inspected for odour at three stages of the quality sampling process; (i) when sampling the whole of the lot (ii) before removal of foreign material and broken seed or (iii) after removal of foreign material and broken cereal. In all cases the same standard applies to the sample. Two samples are taken, a working sample and a ®le sample that may be referred to in any subsequent appeals. The following guidelines constitute the de®nitions of o-taint available to an inspector for the four grains considered in this report.

(a) Corn (250 g sample)

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(b) Sorghum (30 g sample)

As per Table 4 except that Smut and Garlic odours are covered by a separate exclusion based upon a visual inspection to determine the level of smut or garlic bulblets present. A separate classi®cation of Garlicky or Smutty Sorghum is de®ned in these instances.

(c) Soybean (125 g sample)

As per Table 4 except that smoke is also a category in the Sour de®nition. This application of smoke is only valid under sour for canola, ¯axseed, soybean and sun¯ower.

(d) Wheat (50 g sample)

As per Table 4 except that the Smut and Garlic odour de®nitions used for Sorghum also apply for wheat samples. Smutty and Garlicky Wheat classi®cations may be used in these instances.

For all types of grain, fumigant and insecticide odours are liable for special consideration. The sample is aerated and retested over a 4-h period and the sample reclassi®ed if the odour dissipates within that period. Marginal outcomes require a consensus decision between inspectors. In the ®nal report, grain failing for odour classi®cation is graded only as musty, sour or commercially oensive foreign odours (COFO). Kansas State University grain grading standards (Hermann and Kuhl, 1997) apply the following criteria:

Musty: may be subdivided into mould and insect. This classi®cation typically applies when certain grain boring insects or mould are present;

Sour: may arise from insect infestation of fermenting mouldy grain; COFO: from petroleum products or from mis- or overuse of fumigants.

Relating the o-taint responsible for the rejection of grain samples to speci®c volatile species has been a dicult problem that to date remains unsolved. Weinberg (1986) found no

Table 4

Odour classi®cation for corn samples (USDA, 1990)

Odour classi®cation examples

Sour Musty COFOa

Boot Ground Animal hides

Fermenting Insect Decaying animal and vegetable matter Insect (acrid) Mouldy Fertiliser

Pigpen Fumigant

Insecticide Oil products Skunk Smoke Strong weed

a

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relationship between the chemical composition measured by GC±MS and the perceived odour.

BorjessoÈn et al. (1992) studied the volatiles evolved by six fungal species on grains (see Table 5). Three Penicillium sp. were chosen along with three Aspergillus spp. All six produced unique pro®les by GC±MS analysis. Whilst several shared numerous common volatiles all six had characteristic metabolic products, the most signi®cant example being P. brevicompactum Dierckx which produced acetone at an astonishing rate. The relationship between accumulated CO2 evolution and fungal growth was found to be signi®cant. The correlation between

ergosterol and volatile production was also signi®cant but the correlation between colony forming units (cfu) and volatile metabolites was less strong.

Smith et al. (1994) presented the best attempt yet to de®ne the vocabulary used to describe odours associated with grain (Table 6). A set of reference odours descriptive of/or alluding to the grain descriptor is provided for thirty-one de®ned terms. Five non-speci®c descriptors still remain and were the most commonly applied when the system was evaluated against 400 grain samples (105 wheat, 116 corn, 75 soybean and 104 sorghum).

Descriptors often associated with the same broad category were found to be distinctly dierent from each other, e.g., over-ripe fermented fruit and sweaty socks are dierent but both may be described as being sour. Another example is the case of an odour not being

Table 5

Rate of volatile metabolite production from six fungal species grown on wheat (adapted from BorjessoÈn et al. (1992))

Production of metabolite (ng/ha_{) on wheat from fungi}

Volatile P. brevi compactum P. glabrum P. roque fortii A. ¯avus A. versi color A. candidus

Acetone 24,000 2-Propanol 298

3-Methylfuran 4.8 26 12 30 27 32

Nitromethane 9.4

2-Methyl-1-propanol 10 0.67 83 13 2.9 9.3 3-Pentanone 1.7 32

2-Methyl-1-butanol 12 1.4 8 3.8 3.6

1-Penten-3-ol 2.6 4.3

Octadiene 6.1 2.3 3.8

2-Butanone 28 22

Dimethylbenzene 7.5 2.9 1.3

Ethylbenzene 0.83 0.6

Limonene 0.2 0.47 2.1 1.7

3-Octanone 12

1-Octen-3-ol 3.2

Monoterpene 1 14

Sesquiterpene 1 7

Sesquiterpene 2 15

Thujopsene 0.96 1.5 0.62

a

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Table 6

Correlation between odour classi®cation systems and volatile metabolites from fungi on graina

Grain

Blackberry WONF Ethyl acetate Ethyl acetate (]3RA654) Methyl butanoate

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distinct enough to categorise precisely, being given a description of musty which could include `damp basement', `earthy humus', `mouldy' or `mushroom', all separate de®nitions. The terms de®ned are not exhaustive but encompass the commonest o-odour sources encountered.

6. Grain classi®cation using electronic noses

In recent years there has been signi®cant research interest in the development of electronic nose technology for food, agricultural and environmental applications (Gardner and Bartlett, 1994; Bartlett et al., 1997). Whilst not truly mimicking the natural olfactory system, electronic noses borrow from the mechanism of natural olfaction. A range of general non-speci®c sensors is exposed to a vapour and the pattern of response generated by all of the sensors is used to characterise the odour. However, it must be stressed that the sensors do not exactly reproduce the sensations encountered by the human nose. Consequently, noses must be trained or calibrated against known odours in order to correlate them against human perceptions of odour.

Typically an electronic nose consists of three elements: a sensor array which is exposed to the volatiles, conversion of the sensor signals to a readable format, and software analysis of the data to produce characteristic outputs related to the odour encountered. The output from the sensor array may be interpreted via a variety of methods such as pattern recognition algorithms, principal component analysis, discriminant function analysis, cluster analysis and arti®cial neural networks to discriminate between samples. The data obtained from the sensor array are comparative and generally not quantitative or qualitative in any way.

A variety of sensors are available for use in electronic nose systems. The most common types encountered are metal oxide or conducting polymer based. Conducting polymers oer the advantage that they are able to respond rapidly and reversibly at ambient temperatures. They are non-speci®c but can be highly sensitive, responding to a range of dierent compounds. The conductivity of the polymer changes when molecules are absorbed at the sensor surface. The sensors respond strongly to the presence of alcohols, ketones, fatty acids and esters, but have reduced responses to fully oxidised species such as CO2, NO2, and H2O.

Two types of studies have been carried out with electronic nose technology. Firstly, attempts have been made to dierentiate between spoilage fungi, based on their in vitro volatile

Benzofuran, 1H-Indene, 2-Ethyl pyridine,

2-Ethyl-3,5-dimethylpyrazine, COFO Fumigant Naphthalene

O,O,S-Trimethyl-phosphorodithioc acid

Naphthalene

O,O,O-Trimethyl-phosphorodithioc acid COFO Weed 2-Isobutyl-thiazole ?-Bergamotene

a

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production patterns, and secondly, studies have been carried out to try and distinguish between dierent grain samples and correlate the output with more traditional mould enumeration techniques. Recently, in vitro studies on a milled wheat-based medium showed that it was possible to distinguish between germinating spores of dierent xerophilic grain and food spoilage fungi within 48 h, prior to visible growth (Fig. 2, Keshri et al., 1998). The volatile pro®les obtained from replicate plates were compared for six dierent fungi using a 14 surface response sensor array system. Butanol and agar blanks were used as controls. By using discriminant function analysis (DFA) it was possible to dierentiate between ®ve dierent species but not between some relatedEurotiumspp, based on the volatile pro®les.

Initial studies with a range of dierent unmoulded dry grain types by examining the volatile odours produced by dry grains placed in 9 cm Petri plates in approx. 750 ml bags using the same electronic nose system suggested that it was possible to distinguish between some types, using DFA, but that there was some overlap between rape seed and barley (Fig. 3; G. Keshri and N. Magan, unpublished data, 1998). Some detailed studies have been carried out on grain samples where accurate correlations have been made with traditional methods of analysing grain contamination (Pisanelli et al., 1994, BorjessoÈn et al., 1996; Jonsson et al., 1997). The UMIST group has previously successfully discriminated between good and bad samples of wheat, with an unknown taint (Pisanelli et al., 1994) using a 20 element array of sensors using conducting polymer technology and a neural network discrimination architecture.

Recently, BorjessoÈn and Olsson (1998) have examined the correlation between the ergosterol and mycotoxin content of 40 barley samples of which approximately two thirds possessed some

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kind of o-odour. An array of 10 MOSFET sensors (metal oxide semiconductor ®eld eect transistors), 6 MOS sensors (metal oxide semiconductor (Taguchi type) sensors and a CO2

sensor were used. Samplers of approx. 33 g were used at 508C. A correlation with ochratoxin content was achieved when 1977 harvested barley only was used, as well as with deoxynevalenol content. Samples from grain harvested in dierent years caused diculties with correlations. Ergosterol was much easier to estimate, with a 0.75 correlation coecient achieved when using partial squares to analyse the data.

A sensory array device based upon electrochemical sensors developed by Stetter et al. (1993) was evaluated using samples previously evaluated by quali®ed USDA grain inspectors. The array could be trained to classify the odours from the samples correctly 83% of the time using a neural network simulation. The sampling protocol was quite complex and required pre-concentration of volatile fractions from heated samples before measurements were taken.

BorjessoÈn et al. (1996) used metal oxide semiconductor ®eld eect transistor sensors (MOSFET), SnO2 semiconductors and infrared detectors (to measure CO2 production in

samples) to evaluate mould activity in grain. They examined a range of normal, mouldy/musty, acid/sour, and burnt samples (235 samples) of wheat, barley and oats and compared this with

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the two classes (good, bad) used by grain inspectors. They found that the electronic nose correctly classi®ed 75% of samples when using the four classes, and 90% with the two-class system, i.e. good or bad only.

Jonsson et al. (1997) described the use of an electronic nose to dierentiate between various samples of oats, barley and rye quality. The general categories used were based on the Swedish Board of Agriculture classi®cations which are summarised as normal; musty; mouldy; acid; sour; burnt; or foreign and the intensities as weak, pronounced or strong (Statute Book, 1991). Samples of wheat with varying levels of ergosterol and fungal and bacterial cfus were also evaluated. Good correlations between the electronic nose system and inspectors were found for mouldy, weakly and strongly musty oats. Reasonable correlations were also found when oats, barley and rye were presented as good/musty mixtures leading to the conclusion that a basis for describing odour intensity was achievable. There were also some correlations between volatile patterns and quantitative measures of fungal growth as measured by the biochemical marker ergosterol, and the total fungal cfu counts with the arti®cial neural network predicted values.

7. Discussion

The key to eective utilisation of the new technology such as electronic noses is the capability for early and rapid detection of mould activity to enable remedial measures to be taken, and to be able to eectively distinguish between good and poor quality grain. Correlations with existing criteria used for discriminating grades are also essential. Trials carried out by one electronic nose company (Osmotec plc) with grain graded by USDA inspectors displayed broad correlation with the descriptors available, but signi®cant variations, particularly within the musty grade (Dr A. S. McNeish, Osmetec plc, personal communication 1999). An important factor to consider when using an electronic nose system is that generally its sensitivity spectrum is dierent from the human nose. Thus an electronic nose will not identify odours using identical criteria to a human panel. It can be seen from Table 5 that broad correlations exist between o-odour classes and the presence of speci®c fungal infections. The odours described by Smith et al. (1994) are intended purely as reference odours. They are examples of the kind of smell encountered when classifying grain rather than actual components of the grain odour itself.

The majority of fungi produce volatiles that are identi®ed with musty odours in grain. The dominant class of compound in this category is the C-8 alcohols. The primary odour expected for musty grain would therefore be associated with a mushroom-like smell (the C-8 alcohols) with subtle taints of other associated compounds such as aromatics and lower molecular weight alcohols. Whilst Seitz et al. (1998) identi®ed esters as sour descriptor components, only ethyl acetate was found in other studies of fungal infection of grains.

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often used during the harvesting and storage of grain. Therefore, grain treated with these common chemicals can easily be evaluated. This category (within COFO) should consequently be de®ned easily.

In trying to apply an electronic nose to the problem of classifying o-odours in grain a consistent standard is required. The broad categories oered by the USDA system are applicable but the sub-gradings need re®ning, those within the musty category particularly so, since ``mouldy'' is a broad sub-section with a range of potential causes. The ``insect'' category is best retained in one category only (musty) unless the description in sour is rede®ned with another term or speci®c type of insect infestation.

Descriptive surrogates are available that represent most of the sub-categories (Smith et al., 1994) which should facilitate better correlation between electronic nose measurements and the grading of inspectors. However, these surrogates are not truly representative of the odours encountered. Better surrogates are required based upon the actual volatiles encountered. At present the USDA grading system does not contravene any of the other systems used. The Swedish system separates mouldy from musty, and adds an acid category, which could be placed in the COFO category. Similarly, the de®nitions of Seitz et al. (1998) are classes and sub-classes of the USDA system. No classi®cation is oered in the ISO/BSI European standard.

8. Conclusions

This review has shown that a wide variety of volatiles are produced by fungi, either in vitro or when growing on agricultural grain substrates. Potential exists for distinguishing between species of fungi based on characteristic volatile patterns, which may be important when key spoilage fungi may be responsible for the production of harmful mycotoxins. It may be possible to use electronic nose systems to try and distinguish between grain colonised by mycotoxigenic and non-mycotoxigenic species, and this area needs further investigation.

Generally, bacteria are not a problem in intermediate moisture content stored cereals, and thus more detailed focusing is required on fungi, particularly xerophilic and xerotolerant species known to initiate moulding in both temperate and tropical cereals. The odours encountered are seldom the result of one single fungus or set of circumstances. However, knowledge of the volatiles produced by the most common agents and events responsible for grain damage can be used to eectively approach the problem of early detection of deterioration caused by either fungi or insect pests.

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Acknowledgements

The authors would like to acknowledge the Ministry of Agriculture Fisheries and Food for funding under contract number MAFF CTD 9701: Detection of contaminants in bulk and in-transit grain by sensor and physical methods.

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