RESEARCH ARTICLE
Characterization of volatile metabolites formed by molds on
barley by mass and ion mobility spectrome try
Alexander Erler
1
| Daniel Riebe
1
| Toralf Beitz
1
|
Hans-Gerd Löhmannsröben
1
| Daniela Grothusheitkamp
2
|
Thomas Kunz
2
| Frank-Jürgen Methner
2
1
Physical Chemistry, Univer sity of Potsdam,
Karl-Liebknecht-Str. 24-25 , 14476 Potsdam,
Germany
2
Department of Food Techno logy and Food
Chemistry, Technis che Universität Berlin,
Seestr. 13, 13353 Berlin, Germany
Correspondence
Hans-Gerd Löhmannsröben, University of
Potsdam, Physical Che mistry, Karl-Liebknecht-
Str. 24-25, 14476 Potsdam, Germany.
Email: loehm@uni-potsdam. de
Funding information
Federal Ministry of Food and Agricul ture,
Grant/Award Number: 2814 801811; Federal
Office for Agriculture and Food, Grant/Award
Number: 2814801811
Abstract
The contamination of barley by molds on the field or in storage leads to the spoilage
of grain and the production of mycotoxins, which causes major economic losses in
malting facilities and breweries. Therefore, on-site detection of hidden fungus con-
taminations in grain storages based on the detection of volatile marker compounds is
of high interest. In this work, the volatile metabolites of 10 different fungus species
are identified by gas chromatography (GC) combined with two complementary mass
spectrometric methods, namely, electron impact (EI) and chemical ionization at
atmospheric pressure (APCI)-mass spectrometry (MS). The APCI source utilizes soft
X-radiation, which enables the selective protonation of the volatile metabolites
largely without side reactions. Nearly 80 volatile or semivolatile compounds from
different substance classes, namely, alcohols, aldehydes, ketones, carboxylic acids,
esters, substituted aromatic compounds, alkenes, terpenes, oxidized terpenes, sesqui-
terpenes, and oxidized sesquiterpenes, could be identified. The profiles of volatile
and semivolatile metabolites of the different fungus species are characteristic of
them and allow their safe differentiation. The application of the same GC parameters
and APCI source allows a simple method transfer from MS to ion mobility spectrome-
try (IMS), which permits on-site analyses of grain stores. Characterization of IMS
yields limits of detection very similar to those of APCI-MS. Accordingly, more than
90% of the volatile metabolites found by APCI-MS were also detected in IMS. In
addition to different fungus genera, different species of one fungus genus could also
be differentiated by GC-IMS.
KEYWORDS
APCI, fungus, gas chromatography, ion mobility spectrometry, mass spectrometry, mold, soft
X-ray
Received: 19 November 2019 Revised: 17 December 2019 Accepted: 24 December 2019
DOI: 10.1002/jms.4501
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This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any
medium, provided the original work is properly cited and is not used for commercial purposes.
© 2020 The Authors. Journal of Mass Spectrometry published by John Wiley & Sons Ltd
J Mass Spectrom. 2020;55:e 4501. wileyonlinelibrar y.com/journal/jms 1o f1 0
https://doi.org/10. 1002/jms.4501

1 | INTRODUC TION
Barley, as one example of a cereal grain, can be infested by molds on
the field or in storage, leading to the spoilage of grain and the produc-
tion of mycotoxins. This contamination causes major economic losses
in malting facilities and breweries.
1
One example of a field pest is the
genus Fusarium , whereas Aspergillus is an example of storage pests.
Penicillium can be attributed to both categories depending on the
exact species. Fusarium is among the most important grain pathogens.
Besides harvest losses, a major issue is the formation of mycotoxins.
In addition to the nonvolatile mycotoxin metabolites, a wide spectrum
of 50 volatile and 53 semivolatile metabolites (microbial volatile
organic compounds [mVOC]) is also produced by different Fusarium
species.
2-4
An important class of semivolatile metabolites are the ses-
quiterpenes, which serve as building blocks in the biosynth esis of
mycotoxins.
5-7
Other fungi, encountered as field or storage pest alike,
are various Alternaria,
8-11
Aspergillus,
12-14
and Penicillium species.
In the laboratory, the detection and identificat ion of fungi is
based on cell cultivation, which is usually time-consuming. Thus,
approaches based on the detection of molecules, such as MALDI-
MS
15
and subsequent database matching of the fingerpri nt spectra,
are being developed. An alternative is the search for characteristic
volatile marker compounds in the headspace above the fungi. Advan-
tages of this method are the possibilit y of detecting hidden fungus
contaminations in grain storages and contributing to the characteriza-
tion of the metabolome of the fungi. The search for the characteristic
volatile markers is based on active or passive headspace sampling and
subsequent analysis by gas chromatography (GC) and electron ioniza-
tion (EI)-mass spectrometry (MS).
8,16
The identification of the metabo-
lites occurs mainly via the fragment ion patterns by NIST database
matching. However, for many substances, no molecular ion peak is
found, reducing the reliability of the assignment. This issue is
addressed by atmospheric pressure chemical ionization (APCI),
17
where protonated molecular ions are predominantly formed. In this
regard, EI and APCI are complementary ionization methods. Commer-
cial APCI sources in MS are based on corona discharges. Although the
corona discharge source is inexpensive, it has some disadvantages
such as additional, competing ionization processes and the limited life-
time of the corona needle. Our group already demonstrate d the appli-
cation of an alternative APCI source based on soft X-radiation in
MS. In two publications detailing the detection of explosives in the
negative mode
18
and the detection of volatile metabolites of fungi in
the positive mode,
19
we could show that the underlying ionization
mechanism is more selective. These miniature X-ray sources, which
are not subject to any legal regulations in Germany (photon energy
E
X-ray
< 5 keV), have already been introduced as alternative s for radio-
active
63
Ni sources in ion mobility (IM) spectrometers, eg, by the com-
pany Bruker Daltonics (Leipzig, Germany).
Since APCI mass spectrometers are instruments usually confined
to the laboratory, mobile instruments are required for on-site analyses
of grain stores. Ion mobility spectrometry (IMS) is an analytical
method with the potential for headspace (HS) screening of environ-
mental and biological samples, which has already been
demonstrated.
20
Additionally, handheld IM spectrometers are com-
mercially available. One application is the HS investigation of olive oils
by IMS, which allows the detection of components (terpenes) in olive
oil,
21
the classification of olive oils,
22
and nontargeted olive oil profil-
ing.
23
Other examples are the detection of odors in the
environment,
24
off-flavors in foods,
25
fungal infestations of wood,
26
the identification of human pathogenic bacteria and fungi,
27,28
and
breath analysis.
29
In this wo rk, ou r previo us HS- GC- EI/A PCI -MS inv est igatio ns of
diff ere nt fungi on agar
19
were ex ten ded to fu ngu s-c onta min ated
barle y grain s. The aim was the ident ifica tio n of volat ile fungus me tabo-
lite s. In a second st ep , the MS method was tra nsferr ed to IMS, an d HS-
GC-AP CI- IM sp ectro me tric inv es tig ation s of fungi -co nta minat ed bar ley
grain s were ca rried ou t. These exp er ime nts demo nstra ted the po ten tial
of IMS for on-si te monit or ing of hidden fu ngu s contam ina tio ns in grain
stor ag es. The applic at ion of the same GC and X-ray-b as ed APCI sou rce
in both hyp he nat ion me thods all ow s a straig ht metho d tra nsf er.
2 | EXPERI MENTAL PART
2.1 | Microbiological sample preparation
Th e inve stiga te d samp le s were comp os ed of ster il ized bar le y grai n
inoc ul ated with a spore su sp ensi on of the co rre sp ondi ng fu ngu s. The
gra in co ntain ed ab ou t 30% of wa ter. Af te r the ino cu lat io n, the sa mple
vial s we re rota te d for 15 minut es to en su re ev en distr ibu ti on of the
sp ore su sp ensi on ; 6 g of th e sa mple wer e pl aced in si de a HS via l. Th e
su spen si on was pro duc ed from di ff erent br eed in g medi a (eac h inclu d-
ing a fu ngus) tha t were su sp ende d in a 0.9% Na Cl so lutio n and fil tere d
su bseq ue ntl y. Th e fungu s cu lt ure s used were ob taine d fr om cu lture
coll ect io ns (Leibn iz Inst itu te DSMZ -G erman Co lle ct ion of Microo rg an -
is ms and Cel l Cul tur es; VL B-B re we ry Re sea rch and Educa tio n Cente r
Berl in ) or wer e is ola ted fro m co ntami na te d grai n samp le s. Th e inv es ti-
gat ed field pe st s were Fus ariu m cul mo ru m (DSM Z 621 91) , Fusar ium
gram in eari um (V LB re fere nc e stock s) , Fusar ium sp . (isol at e from
deox y nival en ol- conta mi na te d dia stase wheat , ca lle d here F. DW 14),
and Altern ar ia alte rna ta (i solat e fro m brewi ng barl ey). The in vestiga ted
stor age pest s were Aspe rgill us niger (D SM Z 22593) , Aspe rgillu s ficuum
(DSM Z 932/N RRL 3135 ), Asperg illu s ve rsico lor (DSMZ 63 292), an d
thre e differe nt Peni cilium sp p. (isol ate from brewi ng barle y, called he re
P. Pen A , P. Pen 14, an d P. Pe n R). Thro ugh DNA analy sis, P. Pen R wa s
found to be ei ther Pe nicil lium ca menbert ii or Penicil lium gri seoful vum and
A. a lternat a was iden tifie d. This anal ysis was ca rried ou t at the Re search
Cen ter Weihe nste phan for Br ewing a nd Food Qu ality (T echnica l Uni-
vers ity of Munich ) via polyme rase ch ain react ion (PCR) seq uencing of
the rDNA an d com paris on with the Blas t Sear ch databa se.
2.2 | HS characterization
After a growth period of 8 days, the HS vials were hermetically sealed
and left for another 48 hours, stopping the fungus growth. A solid-
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phase microextraction (SPME) fiber, coated with divenylbenzene,
carboxen, and polydimethylsiloxan, was used for sampling. The vola-
tile compounds in the HS were adsorped onto the fiber over a period
of 1 hour at a temperature of 45  C. Desorption was performed in the
GC injector at 250  C for 1 minute.
HS investigations of fungi were carried out with GC-EI-MS
(7890A GC, 5975C MSD, Agilent Technologies) and GC-APCI-MS
(5890 Series II GC, Hewlett Packard, LTQ XL, Thermo Fisher
Scientific), which were already described in detail previously.
19
For
both MS methods, the same type of GC columns, but different inner
diameters (15 m × 0.25/0.32 mm × 1.0 μ m), containing
poly(5% diphenyl-95% dimethylsiloxan) as the active phase was used
for the preseparation of the substances. The same GC temperature
program was used as well: initial period of 5 minutes at 32  C followed
by a heating phase with 10  C/min and a final period at 200  C for
8 minutes.
The handheld IM spectrometer (Roadrunner, Bruker Daltonics,
Leipzig) has an APCI source based on soft X-radiati on (miniature X-ray
tube, 40 mm length, rhodium target on a beryllium window), with an
energy of 2.7/2.8 keV ( L
α
/ L
β
-transitions of Rh) and is therefore
exempt from registration in Germany. The same type of source was
used in the APCI-MS system (LTQ XL, Thermo Fisher Scientific),
where it was integrated into a home-built ionization chamber.
18,19
The original application scenario of the Roadrunner instrument is the
detection of explosives and drugs on surfaces. In order to couple the
spectrometer (95 mm length of the drift tube) to a GC (the same GC
as used in GC-APCI-MS), modific ations had to be made. The original
thermal desorption unit was replaced by a home-mad e heated
(180  C) inlet system for the GC capillary. Additionally, the internal
drift gas cycle was modified, and an external drift gas (nitrogen,
400 mL/min) was applied. The resolution of the spectrometer in the
GC configuration is around 40. The Roadrunner spectrometer was set
to the maintenance mode, which allows long-term GC measurements.
The temperature program in the GC was slightly changed in order to
improve the resolution: initial period of 30  C prior to a heating phase
with 20  C/min and a final period at 200  C for 10 minutes. A custom
python script was written for format conversion of the Bruker data
file to the data formats of OpenMS and Origin where the data evalua-
tion and visualization was performed.
The spectra resulting from GC-EI-MS, GC-APCI-MS, and GC-
APCI-IMS measurements were correlated using the GC retention
time. Because an alkane standard generally applied for determination
of retention indices cannot be used in APCI-MS or APCI-IMS, a fatty
acid methyl ester mixture consisting of C
n
H
2 n +1
COOCH
3
( n = 8-20 for
even n ) was used. Furthermore, GC-APCI-MS and GC-APCI-IMS were
characterized by a mixture of representative volatile fungus metabo-
lites. This mVOC standard contain ed 3-methylbutanol (a, 2mM),
2-hexanone (b, 50 μ M), α -pinene (c, 300 μ M), benzaldehyde (d, 50 μ M),
1-octen-3-ol (e, 200 μ M), 2-phenylet hanol (f, 150 μ M), longipinene (g,
20 μ M), and caryophyllene (h, 20 μ M). All substances were purchased
from Sigma-Aldrich.
3 | RESULT S
3.1 | Characterization of the GC-IM spectrometer
The aim of this work was the characterization of the mVOC spectrum
in the HS above barley grains contaminated with fungi by a mobile
analytical instrument based on GC-IMS. In order to enable a direct
method transfer from previous GC-MS experiments, the same gas
chromatograph, column , and method parameters (flow rates, tempera-
ture program) were used in GC-IMS. This allows the simple assign-
ment of mVOC already identified by GC-MS to the peaks in the 2D-
GC-IM spectra through correlation of the retention indices. Further-
more, the method was characterized by a standard that contains rep-
resentatives (volatile metabolites) of the most important substance
classes in the fungi HS. The IM spectrometer used was a handheld
FIGURE 1 (A) Two-dimensional spectrum (drift time t
Drift
vs retention time t
Ret
) of the representative volatile fungus metabolites mixture and
(B) GC-chromatogram compute d by summati on of GC traces at various drift times (corresponding to substance maxima in IM spectra) of the same
mixture. GC, gas chromatography; IM, ion mobility [Colour figure can be viewed at wileyonlinelibrary.com]
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instrument equipped with the same soft X-radiation source that was
previously utilized in APCI-MS. This allows a simple transfer of
method parameters from the mass spectrometer to the IM spectrome-
ter. Because the original application purpose of the highly specialized
handheld instrument is the detection of explosives and narcotics in
baggage check-ins of airports, which are sampled by swipes, a GC
connection is not intended in the instrument. Therefo re, the inlet part
and internal gas flows of the instrument had to be modified. While
the focus of the modification of the inlet part was on the prevention
of cold spots, the optimization of the gas flows improves sensitivity
and resolving power of the instrument. The sampling procedure is
based on the adsorption of volatile compounds on a SPME fiber.
Figure 1A shows the 2D spectrum of the representative volatile
fungus metabolites mixture, which includes mVOC of the most impor-
tant substance classes. The 2D spectrum consists of the two dimen-
sions retention time and drift time. For most compounds, two peaks
FIGURE 2 Calibration plots (double-log representati on) of selected compoun ds of the mVOC mixture injected into the GC-APCI-IMS (A) as
1- μ L liquid sample and (B) after desorption of the SPME fiber in the injector after total evaporation of 2- μ L liquid sample. A refers to the peak
area, c refers to the concentration in the injected liquid, and refers c
sol
to the concentration in the solution below the headspace. APCI,
atmospheric pressure chemical ionization; GC, gas chromatography; IMS, ion mobility spectrometry; mVOC, microbial volatile organic compounds
[Colour figure can be viewed at wileyon linelibrary.com]
FIGURE 3 LOD of eight mVOC for three detection methods: GC-
EI-mass spectrometry (MS), GC-APCI-MS, and GC-IMS. APCI,
atmospheric pressure chemical ionization; EI, electron impact; GC, gas
chromatography; IMS, ion mobility spectrometry; LOD, limits of
detection; MS, mass spectrometry; mVOC, microbial volatile organic
compounds
FIGURE 4 Heatmap of the mVOC detected in the headspace of
four fungus species belonging to different genera, color code: green —
detection by EI-MS and APCI-MS, black — detection by APCI-MS only,
and blue — detection by EI-MS only; substance numbers according to
the following substance classes: 1-5 alcohols, 6-8 aldehydes, 9-12
ketones, 13 carboxylic acid, 14-16 esters, 17-34 substitu ted aromatic
compounds, 35-36 alkenes, 37-40 terpenes, 41 oxidized terpenes,
42-72 sesquiterpenes, 73-74 oxidized sesquiterpenes, and 75-78
other compounds; refer to Table S1. APCI, atmospheric pressure
chemical ionization; EI, electron impact; IMS, ion mobility
spectrometry; MS, mass spectrome try; mVOC, microbial volatile
organic compounds [Colour figure can be viewed at
wileyonlinelibrary.com ]
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are found, which can be assigned to the protonated monomer and
dimer ions. All peaks are well separated and have a symmetric shape
in both dimensions. The signal intensity (current I ) as sum of the
selected ion traces in the retention time dimension is displayed in
Figure 1B. The GC peaks are highly symmetric, which is an indication
of the absence of memory effects in the IM spectrome ter.
The formation of mVOC as volatile metabolite s of fungi can
occur over a wide and mostly unknown concentration range.
Another challenge is the strong variation of the response values of
the compounds in APCI.
19
Therefore, a quantitative description of
the mVOC space would require an extensive calibration of each
substance. However, many compounds are not commercially avail-
able. Calibration plots of five representative mVOC deriving from
GC-APCI-IMS measurements are shown in Figure 2, as examples of
the concentration ranges and sensitivities of the mVOC detection.
The calibration plot after direct injection of liquids is shown in
Figure 2A, and the calibration plot after total evaporation of 2- μ L
solution and subsequent adsorpti on of the mVOC on an SPME fiber
is displayed in Figure 2B. While the different calibration curves in
Figure 2A are the result of different respons e factors of the APCI
source, the calibration curves in Figure 2B additionally include the
effect of different adsorption equilibria of the mVOC on the SPME
fiber. Linear detection ranges cover two orders of magnitude. The
estimated limits of detection (LOD) are slightly lower for direct
injection of liquids and are in the upper nanomolar range. The small
differences of both curves indicate efficient sampling of the mVOC
by SPME.
The mVOC were identified by GC-EI-MS and GC-APCI-MS inves-
tigations. The results of the MS methods can only be transferred to
IMS if the detection ranges of both methods are similar. Furthermore,
IMS is only useful for on-site detection and identification of fungi if
the LOD are low enough. In Figure 3, the LOD of eight mVOC mea-
sured by the mass- and IMS-based methods are compared. The most
important result is that the LOD of APCI-MS and APCI-IMS are in the
same range except for the sesquiterpenes. It is interesting to note that
both in-house modified APCI instruments are more sensitive than the
commercial EI-MS instrument. This is very likely an effect of the dif-
ferent ionization efficiencies of the two sources.
FIGURE 5 Heatmap of the mVOC detected by APCI-MS in the
headspace of four fungus species belonging to different genera, color
code: red — major components, blue — minor components, and green —
traces. APCI, atmospheric pressure chemical ionization; MS, mass
spectrometry; mVOC, microbial volatile organic compounds [Colour
figure can be viewed at wileyonlinelibrary.com]
FIGURE 6 Representation of specific and nonspecific metabolites found by APCI-MS with regard to (A) the results of this work and (B) the
literature. Seven of the found substances were described as specific in the literature as well (dark brown bars). APCI, atmospheric pressure
chemical ionization; MS, mass spectrometry [Colour figure can be viewed at wileyonlinelibrary.com]
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3.2 | MS investigation of mVOC formed by fungi
on barley grain
In our previous publication,
19
the results of HS monitoring of fungi on
different agar substrates by MS were reported. The result of the com-
plementary EI/APCI investig ation was an overview of all detected
mVOC sorted by substance classes. Marker substances allowing the
specific detection of individual fungi were searche d in this dataset. In
the current work, these investigations were additionally performed
for barley grains contaminated by fungi. In the first step, the mVOC
were identified by both GC-EI-MS and GC-APCI-MS measurements.
Specific marker substances were identified from the resulting mVOC
lists. These experiments were extended to IM spectrometric measure-
ments in order to verify the potential of this mobile on-site analytical
method for monitoring grain stores. In addition to the differentiation
of fungus genera, the potential of IMS for the differentiation of sev-
eral species of one fungus genus was explored.
The mVOC detected by EI-MS and APCI-MS are presented in a
heatmap (Figure 4). The four columns represent the detected volatile
metabolites for the different fungus species Aspergillus , Fusarium , Peni-
cillium , and Alternaria , which all belong to different genera. The color
code indicates the MS methods used to detect the mVOC. Seventy-
eight substances were found and are arranged according to their sub-
stance classes. These substance classes include alcohols (five mVOC),
aldehydes (three mVOC), ketones (four mVOC), carboxylic acids (one
mVOC), esters (three mVOC), substituted aromatic compound s
(18 mVOC), alkenes (two mVOC), terpenes (four mVOC), oxidized ter-
penes (one mVOC), sesquiterpenes (31 mVOC), oxidized sesquiter-
penes (two mVOC), and additional nonidentified substances. A
detailed list of all compounds can be found in Table S1. Most sub-
stances (91%) were detected by both ionization methods. Another
heatmap providing a semiquantitative representation of the APCI-MS
results is shown in Figure 5. Due to the varying and often unknown
response factors, the mVOC concentrations were determined only
semiquantitative ly and classified as major components (red), minor
components (blue), and traces (green) in this work.
Compared with the HS measurements on agar,
19
the number of
detected mVOC has slightly changed due to the influence of the dif-
ferent substrates. In detail, the transition from different agar
FIGURE 7 Two-dimensional spectra of the mVOC from P. Pen 14 obtained by (A) GC-APCI-MS and (B) GC-APCI-IMS (gray — [M + H]
+
,
orange — [M − OH]
+
, and red — [2M + H]
+
). APCI, atmospheric pressure chemical ionizatio n; GC, gas chromatography; IMS, ion mobility
spectrometry; MS, mass spectrometry; mVOC, microbial volatile organic compounds [Colour figure can be viewed at wileyon linelibrary.com]
FIGURE 8 Heatmap of the mVOC detected by APCI-IMS in the
headspace of four fungus species belonging to different genera, color
code: red — major components, blue — minor components, and green —
traces. APCI, atmospheric pressure chemical ionization; IMS, ion
mobility spectrometry; mVOC, microbial volatile organic compounds
[Colour figure can be viewed at wileyon linelibrary.com]
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FIGURE 9 Heatmaps for three Penicill ium species detected by (A) APCI-MS and (B) APCI-IMS; three Aspergillus species detected by (C) APCI-
MS and (D) APCI-IMS; and three Fusarium species detected by (E) APCI-M S and (F) APCI-IMS. APCI, atmospheric pressure chemical ionization;
IMS, ion mobility spectrometry; MS, mass spectrometry
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substrates to barley results in a decrease of the total number of
mVOC detected by GC-EI-MS from 86 to 78, while the total number
of mVOC detected by GC-APCI-MS increases from 67 to 73. This
finding indicates that the number of unpolar mVOC decreases and the
number of the more polar substances slightly increases.
A brief glance already reveals that the mVOC for the four fun-
gus species form characteristic pattern s that differ significantly
from each other. Within the framewo rk of the four fungus species
investigated, 50 mVOC of all 78 mVOC detec ted by EI-MS and
45 mVOC of all 73 mVOC detected by APCI-MS are specific to
the four fungi and can thus potentially be used as marker sub-
stances. More details are shown in Figure 6A for the APCI-MS
experiments. Fifteen mVOC are specific to Aspergillus spp.,
16 mVOC are specific to Fusarium spp., 11 mVOC are specific to
Penicillium spp., and four mVOC are specific to Alternaria spp. The
remaining mVOC were found in the HS of at least two fungi and
were classified as nonspecific. Several substances classifi ed as spe-
cific in this work were reportedly detected in the HS of other
microorganisms in the literature (see Table S2) and have to be
reclassified as nonspecific. Therefore, the number of specific sub-
stances is strongly reduced. Then, as shown in Figure 6B, seven
mVOC are specific to Aspergillus spp., nine mVOC are specific to
Fusarium spp., seven mVOC are specific to Penicillium spp., and
three mVOC are specific to Alternaria spp. In the future with con-
tinuing research worldwide, the number of substances specific to a
fungus species will furthermore decrease since more mVOC will be
discovered in the HS of other microorganisms, which are now clas-
sified as specific mVOC. Otherwise, this procedure of comparison
with literature results is conservative because the number of non-
specific substances found represents an upper limit, since not all
microorganisms will occur together at one place in the field or in
storage.
3.3 | Detection of fungi by IMS based on mVOC
HS investigations were also carried out by GC-IMS. Analogous to the
2D GC-APCI-MS spectra ( m / z vs t
Ret
), two-dimensional ( t
Drift
vs t
Ret
)
spectra are also obtained in GC-APCI-IMS; 2D spectra comparing GC-
APCI-MS and GC-APCI-IMS for one species are shown in Figure 7.
IMS has a lower resolution than MS, but in connection with the GC
preseparation, most marker peaks can reliably be separated from sur-
rounding mVOC peaks.
A detailed examinatio n shows that 92% of the mVOC peaks
found in APCI-MS are also detected by APCI-IMS. The mVOC
detected in the HS of the four fungi can be summari zed in a heatmap
(Figure 8).
Similar to Figure 5, different mVOC patterns measured by APCI-
IMS were obtained for the four fungus species belonging to different
genera. Furthermore, characteristic marker substances were identified
that only appear in the HS of the corresponding fungus species.
Regarding the four fungi, 43 of 66 mVOC found in APCI-IMS are
potential specific marker compounds. In detail, 14 specific mVOC for
Aspergillus spp., 13 specific mVOC for Fusarium spp., 11 specific
mVOC for Penicilium spp., and five specific mVOC for Alternaria spp.
were found by APCI-IMS.
The mVOC patterns of fungi belonging to different genera fea-
ture strong variations. Contrary to this, the distinction of different
fungus species of one genus should pose a larger challenge. This ques-
tion was investigated for the example of three species, each of the
three fungus genera Penicillium , Aspergillus , and Fusarium , applying
both APCI-MS and APCI-IMS. APCI-IMS is able to detect most of the
compounds, which are detected by APCI-MS. In detail, the match
between mVOC detected by IMS and MS is 88% for Aspergillus , 87%
for Fusarium , and 93% for Penicillium. This finding is also supported by
Figure 9. The left column shows the mVOC patterns detected by
APCI-MS, and the right column show the corresponding mVOC pat-
terns detected by APCI-IMS. Both are very similar.
A brief survey of the mVOC signatures of the three fungus genera
(see Figure 9A,C,E) shows strong difference s as was discussed in
detail above. As expected, the differences become smaller if the
respective three species of each fungus genus Penicillium , Aspergillus ,
and Fusarium (eg, in Figure 9A,C,E) are compared. This finding is espe-
cially pronounced in Figure 9A. Of the 15 compounds detected for P.
Pen A, three mVOC are also observed in the HS of the other two Peni-
cillium species, nine mVOC are also observed in the HS of one of the
other two Penicillium species, and three mVOC are only observed for
P. Pen A. Thus, the latter three compounds can be regarded as poten-
tial marker compounds for Penicillium Pen A. Summarizing all
heatmaps in Figure 9, for Aspergillus 28% of the mVOC are potential
markers for species A. niger , 4% for species A. versicolor , and 32% for
species A. ficuum. For Fusarium , 43% of the mVOC are potential
markers for species F. culmorum , 22% for species F. graminarium , and
FIGURE 10 Score plot of the PCA of the GC traces (GC-IMS) of
the three Aspergillus species (color code: Aspergillus ficuum — blue
triangles, Aspergillus niger — red circles, and Aspergillus versicolor — green
squares). GC, gas chromatography; IMS, ion mobility spectrometry;
PCA, principal component analysis. [Colour figure can be viewed at
wileyonlinelibrary.com ]
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13% for species F. DW 14. Finally, for Penicillium , 17% of the mVOC
are potential markers for species P. Pen R, 10% for species P. Pen A,
and 17% for species P. Pen 14.
As an alternative to the very time-consuming marker search,
principal component analysis (PCA) of the GC traces of three fun-
gus species was carried out. The aim was to establish a fast, non-
supervised classification method for fungi based on the GC traces
of the GC-IMS measurements, without detailed time-consuming
evaluation of all spectra. This analysis based on PCA was demon-
strated for the three Aspergillus species as shown in Figure 10. In
the score plot, the first and third principal compon ents are dis-
played, which together account for 49% of the variance. Each point
in the score plot represents one fungus sample. The following color
code was applied: A. ficuum (blue), A. niger (red), and A. versicolor
(green). The different fungus samples cluster in three different
groups according to the corresponding three Aspergillus species.
These three different clusters are clearly separated. Therefore,
unknown samples can potentially be classified by PCA using the
GC traces without further time-consuming data evaluation. It is
worth noting that the PCA classification was possible for fungus
species of one genus and that fungi of different genera could also
be differentiated.
4 | CONCLUSIONS
The investigation of volatile and semivolatile metabolites in the HS
above barley allows the detection and identification of fungi. The
metabolites were identified by complementary GC-EI-MS and GC-
APCI-MS investigations. The mVOC profiles of the fungi investi-
gated have a different pattern, allowing their differentiation. In
these mVOC patterns, characteristic marker substances could be
found for each fungus. The detection of these marker substances
provides a reliable method for the identification of the
corresponding fungus. IMS, a technique that can be performed with
commercially available handhe ld instruments, potentially allows the
on-site detection of fungus contaminations in grain stores. Based
on the application of the same GC parameters and APCI source, a
simple method transfer from MS to IMS is possible. Despite the
lower resolution of IMS in comparison to MS, most mVOC could
be completely separated in GC-APCI-IMS. A characterization of
GC-APCI-IMS yields comparable LOD to GC-APCI-MS. This high
sensitivity is also reflected in the detection of nearly 90% of the
mVOC detected by GC-APCI-MS by GC-APCI-IMS. In addition to
the distinction of different fungus genera, different species of one
fungus genus can be distinguished by GC-APCI-based IM
spectrometry.
ACKNOWLEDGEMENTS
The authors gratefully acknowledge the financial support for this
research received from the German Federal Ministry of Food and
Agriculture and the administrational support from the Federal
Office of Agriculture and Food (BLE, grant no. 2814801811).
ORCID
Alexander Erler https://orcid.org/000 0-0001-5732-3685
Daniel Riebe https://orcid.org/0000-0002-1234 -6733
Toralf Beitz https://orcid.org/0000-0002-5537 -913X
Hans-Gerd Löhmannsröb en https://orcid.org/00 00-0002-3304-
5104
Daniela Grothusheitkamp https://orcid.org/00 00-0002-0258-5853
Frank-Jürgen Methner https://orcid.org/000 0-0001-7195-1709
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SUPPORTING INFORMATION
Additional supporting information may be found online in the
Supporting Information section at the end of this article.
How to cite this article: Erler A, Riebe D, Beitz T, et al.
Characterization of volatile metabolites formed by molds on
barley by mass and ion mobility spectrometry. J Mass
Spectrom . 2020;55:e4501. https://d oi.org/10.1002/jms.4501
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