Can Volatile Organic Metabolites Be Used to Simultaneously Assess Microbial and Mite Contamination Level in Cereal Grains and Coffee Beans?
et al. (2013) Can Volatile Organic Metabolites Be Used to Simultaneously Assess Microbial and
Mite Contamination Level in Cereal Grains and Coffee Beans? PLoS ONE 8(4): e59338. doi:10.1371/journal.pone.0059338
Can Volatile Organic Metabolites Be Used to Simultaneously Assess Microbial and Mite Contamination Level in Cereal Grains and Coffee Beans?
A ngelo C. Salvador 0
Ine s Baptista 0
Anto nio S. Barros 0
Newton C. M. Gomes 0
Angela Cunha 0
Adelaide Almeida 0
Silvia M. Rocha 0
Vishal Shah, Dowling College, United States of America
0 1 Departament of Chemistry, QOPNA, University of Aveiro, Campus Universita rio de Santiago , Aveiro , Portugal , 2 Departament of Biology and CESAM, University of Aveiro, Campus Universita rio de Santiago , Aveiro , Portugal
A novel approach based on headspace solid-phase microextraction (HS-SPME) combined with comprehensive twodimensional gas chromatography-time-of-flight mass spectrometry (GC6GC-ToFMS) was developed for the simultaneous screening of microbial and mite contamination level in cereals and coffee beans. The proposed approach emerges as a powerful tool for the rapid assessment of the microbial contamination level (ca. 70 min versus ca. 72 to 120 h for bacteria and fungi, respectively, using conventional plate counts), and mite contamination (ca. 70 min versus ca. 24 h). A full-factorial design was performed for optimization of the SPME experimental parameters. The methodology was applied to three types of rice (rough, brown, and white rice), oat, wheat, and green and roasted coffee beans. Simultaneously, microbiological analysis of the samples (total aerobic microorganisms, moulds, and yeasts) was performed by conventional plate counts. A set of 54 volatile markers was selected among all the compounds detected by GC6GC-ToFMS. Principal Component Analysis (PCA) was applied in order to establish a relationship between potential volatile markers and the level of microbial contamination. Methylbenzene, 3-octanone, 2-nonanone, 2-methyl-3-pentanol, 1-octen-3-ol, and 2-hexanone were associated to samples with higher microbial contamination level, especially in rough rice. Moreover, oat exhibited a high GC peak area of 2-hydroxy-6-methylbenzaldehyde, a sexual and alarm pheromone for adult mites, which in the other matrices appeared as a trace component. The number of mites detected in oat grains was correlated to the GC peak area of the pheromone. The HS-SPME/GC6GC-ToFMS methodology can be regarded as the basis for the development of a rapid and versatile method that can be applied in industry to the simultaneous assessment the level of microbiological contamination and for detection of mites in cereals grains and coffee beans.
Funding: Authors thank the Research Unit 62/94, QOPNA (project PEst-C/QUI/UI0062/2011) and to the Research Unit CESAM (FCTFundacao para a Ciencia e
Tecnologia - http://www.fct.pt/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
The food industry suffers enormous financial losses due to
complains arising out of off-flavours, which also leads to the loss of
consumers and suppliers confidence . Furthermore, the
globalization of the markets implies deep management and control
of the process as many kinds of foods are processed and consumed
far away from the site where they are cultivated. Apart from
offflavours, serious health problems can arise, due to the biological
deterioration as a possible consequence of storage of those grains.
While in storage, this biological deterioration can result of several
pests such as insects, rodents, mites and microorganisms (especially
Microbial volatile metabolites produced during storage and/or
processing of cereals or coffee have been used as markers of
microbial contamination . The metabolite volatile profile
seems to be closely related to the product safety and quality. For
example, the fungal metabolites can be related with the fungal
specie and the type of food matrix contaminated . An extensive
range of common volatiles linked to microbial spoilage was
reported, comprising a large diversity of alcohols, ketones,
aldehydes, esters, carboxylics acids, lactones, terpenes, sulphur
and nitrogen compounds . The most common volatiles
associated to microbial contamination in cereals and other types
of foods are 2-methyl-1-propanol, 3-methyl-1-butanol, 1-octanol,
1-octen-3-ol, 2-butanone, 3-octanone, 2-hexanone, 2-heptanone,
2-methylisoborneol, geosmin, limonene, dimethyl disulfide and
3methylfuran [2,612]. The information on the volatile profiles of
other organism associated to food spoilage such as microscopic
invertebrates is extremely scarce, although mites are known to
produce volatile compounds associated to specific minty odours
in contaminated foods .
Rapid and reproducible approaches for screening the volatile
biological metabolites in foods are an emerging concern, since the
conventional microscope observations and culture-based methods,
both used to detect microorganisms and the first to detect mites,
are time consuming, and laborious . A wide range of methods
have been used to extract volatile and semi-volatile compounds,
some of them based on solvent extraction, but since the 1990s,
solid phase microextraction (SPME) has been extensively used.
SPME is a rapid, easy, solvent-free and sensitive extraction/
concentration technique . For the analysis, one-dimensional
gas chromatographic (1DGC) processes are widely applied in
food products, although long GC runs are needed to achieve high
separation power and this technique typically shows peaks that are
the result of two or more co-eluted compounds. Considerable
research has been dedicated to improve the resolving power of a
GC system, where one possibility is to couple, through an interface
(modulator), two independent columns, i.e., with different
stationary phases, named as comprehensive two-dimensional gas
chromatography (GC6GC) [16,17]. The separation in first
dimension (1D) is usually driven by the boiling point properties
and polarity in the second one (2D). This technique shows a great
potential, as it grants high degree of separation, becoming a
suitable solution for the analysis of target compounds in complex
matrices. GC6GCToFMS has been successfully used in several
fields of analysis, including food matrix . Moreover, the
use of SPME combined with GC6GCToFMS for the screening
of food microbial contaminants was already been used, as in
moisture damage in cacao beans and for the evaluation of
cucumber spoilage, revealing a higher sensitivity [21,22].
This work reports the development of an approach based on
headspace (HS)-SPME combined with GC6GCToFMS for the
screening of biological (microbial and mites) contamination level
in solid foods. A full-factorial design for the optimization of
SPME experimental parameters was developed using five
standards reported as microbial growth markers . The
developed methodology was then applied to real matrices:
grains of three types of rice (rough, brown, and white rice), oat
and wheat and also to green and roasted coffee beans.
Simultaneously, the evaluation of microbial and mites
contamination was performed by quantification of colonies (total
aerobic microorganisms, yeasts and moulds) and by optical
microscopy counts, respectively.
Materials and Methods
Seven types of samples were analysed: green and roasted coffee
beans (Coffea arabica), rough rice (unprocessed raw rice), brown rice
(unpolished rice), and white rice (Oryza sativa L.), unprocessed raw
oat (Avena sativa) and unprocessed raw wheat (Triticum aestivum). The
samples were supplied from local warehouses who have kept them
in silos until the commercialization and/or transformation. After
sampling they were stored in the dark, under cool and dry
conditions, until analysis.
Reagents and Standards
For the experiments hereby reported, five chemical standards
were used: 3-octanone (99%; Aldrich-Chemie; Steinheim,
Germany), 1-octanol (96%; Merck-Schuchardt; Darmstadt,
Germany), 1-octen-3-ol (98%; Aldrich Chemical; Milwaukee,
U.S.A), 3-methyl-1-butanol ($99%; Aldrich-Chemie; Steinheim,
Germany), geosmin (98%; Wako; Neuss, Germany). A stock
solution was prepared with 3-octanone (120 mg L21), 1-octanol
(59 mg L21), 1-octen-3-ol (93 mg L21), 3-methyl-1-butanol
(180 mg L21), and geosmin (1.8 mg L21) in absolute ethanol,
and stored in a glass flask at 4uC.
Full-factorial Design for Optimization of SPME Parameters
Three SPME experimental parameters were tested:
temperature and time of extraction and SPME coating fibre. The SPME
holder for manual sampling and fibres were purchased from
Supelco (Aldrich, Bellefonte, PA, USA). Four coating fibres were
used: 85 mm polyacrylate coating (PA), 100 mm
polydimethylsiloxane coating (PDMS), 65 mm polydimethylsiloxane/divinylbenzene
coating (PDMS/DVB) and 50/30 mm divinylbenzene/carboxen/
polydimethylsiloxane coating (DVB/CAR/PDMS). SPME fibres
were preconditioned in the GC injector, according to the
recommendation of the manufacturer and daily conditioned for
10 min at 250uC.
An aliquot of 100 mL of the stock solution containing the five
chemical standards was placed in a 120 mL glass vial, and the vial
was capped with a PTFE septum and an aluminium cap
(Chromacol Ltd., Herts, UK). After the closure of the sample
vials, the SPME fibre was manually inserted into the sample vial
headspace for 10 and 30 min at 30.0, 40.0 and 50.0uC (60.1uC) in
a water bath. This procedure was repeated in triplicate for each
condition tested. All combinations of extraction time and
temperature were tested with the four coating fibres. The analyses
were carried out by gas chromatographyquadrupole mass
spectrometry (GCqMS). Blanks, corresponding to the analysis
of the coating fibre not submitted to any extraction procedure,
were run between sets of three analyses.
Analysis of Rice, Wheat and Oat Grains and Coffee Beans
For HS-SPME assay, aliquots of 6.516 g of each sample,
corresponding to a volume ca. 20 mL (1/b ratio of 0.5) were
placed into a 60 mL glass vial, and the vial was capped with a
PTFE septum and an aluminium cap. The vial was placed in a
thermostated water bath at 50.0uC, and then the DVB/CAR/
Results expressed in CFU g21, Mean of three independent assays, each one with three replicates (n = 9).
Figure 3. Typical GC6GCToFMS total ion chromatogram contour plots. Rough rice (A), brown rice (B), and white rice (C). Part of the
nalkanes series (C6C14) was superimposed on the contour plots. Compounds are numbered according to Table S1.
PDMS fibre was inserted in the headspace during the 30 min of
extraction. Three independent assays were conducted for each
type of grain. The analyses were carried out by GC6GCToFMS.
Blanks, corresponding to the analysis of the coating fibre not
submitted to any extraction procedure, were run between the sets
of three analyses.
After the extraction/concentration step of the five standards
under study from the stock solution, the SPME coating fibre was
manually introduced into the GC injection port at 250uC where it
was maintained for 3 min for desorption. The injection port was
lined with a 0.75 mm I.D. splitless glass liner. The analysis of
volatiles extracted by HS-SPME was carried in an Agilent
Technologies 6890 N Network gas chromatograph, equipped
with a 60 m60.25 mm I.D., 0.25 mm film thickness DB-FFAP
fused silica capillary column (J&W Scientific, Folsom, CA, USA),
connected to an Agilent 5973 quadrupole mass selective detector.
Splitless injections were used (3 min). Helium carrier gas had a
flow rate of 1.7 mL min21 and the column head pressure was
12 psi. The oven was programmed to start at 50uC (1 min) and
raised until 220uC (1 min) at 5uC min21. The mass spectrometer
was operated in the electron impact mode (EI) at 70 eV scanning
the range 33300 m/z at 3 scans s21, in a full scan acquisition
mode. The GC-qMS analysis was only applied to the five chemical
standards, and their identification in all assays were confirmed by
their retention times and mass spectra, which were also compared
with the library data system of the GCqMS equipment (Wiley
The SPME coating fibre containing the headspace volatile
compounds of the cereals and coffee samples was manually
introduced into the GC6GCToFMS injection port and
maintained at 250uC for desorption. The injection port was lined with a
0.75 mm I.D. splitless glass liner. Splitless injections were used
(30 s). LECO Pegasus 4D (LECO, St. Joseph, MI, USA)
GC6GCToFMS system consisted of an Agilent GC 7890A gas
chromatograph, with a dual stage jet cryogenic modulator
(licensed from Zoex) and a secondary oven. The detector was a
high-speed ToF mass spectrometer. An HP-5 30 m60.32 mm
I.D., 0.25 mm film thickness (J&W Scientific Inc., Folsom, CA,
USA) was used as 1D column and a DB-FFAP 0.79 m x 0.25 mm
I.D., 0.25 mm film thickness (J&W Scientific Inc., Folsom, CA,
USA) was used as the 2D column. The carrier gas was helium at a
constant flow rate of 2.50 mL min21. The primary oven
temperature was programmed from 40uC (1 min) to 140uC at
10uC min21, then, from 140uC to 200uC (1 min) at 7uC min21.
The secondary oven temperature program was 15uC offset above
the primary oven. The MS transfer line temperature was 250uC
and the MS source temperature was 250uC. The modulation time
was 5 s; the modulator temperature was kept at 20uC offset (above
primary oven). Also, the hot and cold pulse duration time was 0.80
and 1.70 s, respectively. The ToFMS was operated at a spectrum
storage rate of 100 spectra s21. The mass spectrometer was
operated in the EI mode at 70 eV using a range of m/z 33350
and the detector voltage was 21695 V. Total ion chromatograms
(TIC) were processed using the automated data processing
software ChromaTOF (LECO) at S/N threshold 6. Contour plots
were used to evaluate the general separation quality and for
manual peak identification. A signal-to-noise threshold of 100 was
used. In order to tentatively identify the different compounds, the
mass spectrum of each compound detected was compared to those
in mass spectral libraries which included an in-house library of
standards, and two commercial databases (Wiley 275 and US
National Institute of Science and Technology (NIST) V. 2.0
Mainlib and Replib). Furthermore, a manual inspection of the
mass spectra was done, combined with the use of additional data,
such as the retention index (RI) value, which was determined
according to the Van den Dool and Kratz RI equation . For
the determination of the RI, a C6 C20 n-alkanes series was used,
and these values were compared with values reported in the
literature for chromatographic columns similar to that used as the
1D column in the present work . A mass spectral match
factor, the tentatively identified compounds showed similarity
matches .900, was set to decide whether a peak was correctly
identified or not. The DTIC (Deconvoluted Total Ion Current)
GC6GC area data were used as an approach to estimate the
relative content of each volatile component. Reproducibility was
expressed as relative standard deviation (RSD).
Enumeration of Total Aerobic Microorganisms, Yeasts
For each sample, three independent assays were performed,
each one with three replicates. In each independent assay, three
10 g sub-samples of grain were suspended in 90 mL of Peptone
Water (Merck, Darmstad, Germany). The enumeration of total
aerobic microorganisms was based in the ISO standard 4833:2003
. After the preparation of the initial suspension and serial
dilutions, 1 mL of each sample was pour-plated (three replicates)
in Plate Count Agar (Merck4Food, Merck, Darmstad, Germany).
Culture plates were incubated for 7263 hours at 3061 C.
Following incubation, colonies were counted in the most suitable
dilution and the result was calculated from the average colony
counts in the three replicates and expressed as colony forming
units per gram (CFU.g21). The enumeration of yeasts and moulds
was performed according to the Portuguese Standard NP
3277:1987 . Three replicates of each sample were
spreadplated (0.5 and 0.1 mL aliquots) in Rose-Bengal Chloramphenicol
Agar (Merck, Darmstad, Germany). Culture plates were incubated
for 12062 hours at 2561 C. Following incubation, colonies of
yeasts and moulds were counted independently in the most
suitable volume. The results were calculated from the average
colony counts in the three replicates and expressed as colony
forming units per gram (CFU.g21).
Capture and Counting of Mite
Cereals grains and coffee beans were processed separately using
a Berlese funnel for 24 hours in order to isolate mites in 250 mL
erlenmeyers containing a alcoholic solution of 1/1 ethanol/
distillate water (v/v) . For each independent assay three
subsamples of 20 g each were analysed. The solution was filtered
through polycarbonate membranes 1.2 mm pore size (Millipore,
Bedford, USA) in a vacuum filtration manifold (Millipore,
Bedford, USA). Mite counting was conducted under optical
microscope (Leica DMLS, Leica Microsystems GmbH, Wetzlar,
Germany). During counting, the distinction between adult (male
and female), larval and nymphal stages was based on
physiognomic characteristics . Essentially, larva and adult mites were
identified by the number of legs, where the former is six-legged
and the latter is eight-legged. Moreover, the gender discrimination
is based on the posterior part, where males have a concave shape,
and females an irregular shape.
Principal Component Analysis
In order to assess a possible relationship between the volatile
metabolites and sample microbial contamination, PCA was
applied to the auto-scaled areas of the 54 volatiles identified by
HS-SPME/GC6GCToFMS presented in the 7 types of matrices
under study (grains of rough, brown and white rice, oat, wheat,
and green and roasted coffee beans), each one corresponding to
three independent assays, and also to the values of microbial
contamination (colonies of total aerobic microorganisms - TAM,
yeasts, and moulds) . The goal of this approach was to extract
the main sources of variability and hence to help on the
characterisation of the dataset.
Results and Discussion
Full-factorial Design for Optimization of SPME
In order to optimize the SPME procedure, a full-factorial design
was implemented, which comprised the evaluation of three
extraction temperatures (30.0, 40.0 and 50.0 C), two extraction
times (10 and 30 min) and four coating SPME fibres (PA, PDMS,
PDMS/DVB, and DVB/CAR/PDMS). The results of these
analyses are represented in Fig. 1, where each bubble corresponds
to the total chromatographic area of the five standards under study
inherent to three different variables (extraction temperature,
extraction time and the SPME fibre type). The bubble plot
showed in Fig. 1 allows a straightforward comparison of the overall
extraction efficiency, as a larger bubble represents a higher total
chromatographic area. Independently of the used fibre, under the
ranges of time and temperature studied, the higher extraction
temperature and time led to higher chromatographic area, i.e.
higher extraction efficiency. However, for the conditions tested,
the higher extraction temperatures promoted higher GC peak
areas than the higher extraction times, which suggest that the
effect of the extraction temperature was more important, on the
extraction efficiency, than extraction time (Fig. 1).
With the exception observed at 50.0 C, for 30 min, where PA
and DVB/CAR/PDMS fibres exhibited similar chromatographic
areas (Fig. 1), PA fibre generally presented the higher extraction
efficiency compared to all other fibres under study. The extraction
at 50.0 C, for 30 min with the SPME coating fibre DVB/CAR/
PDMS was selected for further volatile metabolite microbial
extractions. For these conditions, RSD was considered acceptable
(9.5%). DVB/CAR/PDMS fibre was selected instead of PA fibre,
because PA stationary phase retains the volatile compounds
through absorption, while DVB/CAR/PDMS stationary phase
has a synergistic effect between adsorption and absorption. Despite
of the similar results for the two fibres, the mutually synergetic
effect of adsorption and absorption of the stationary phase of the
DVB/CAR/PDMS fibre creates a higher potential of retention
capacity and, consequently, higher sensitivity for complex
matrices, than fibres based on absorption only, namely the PA
fibre. Therefore, according to the manufacturer guidance, this
fibre is only recommended for polar compounds, while DVB/
CAR/PDMS presents a wide range capacity of sorbing
compounds with different physicochemical properties within a
molecular weight ranging from 40 to 275.
Approach for Assessment of Microbial Volatile
In order to obtain detailed information about potential
microbial volatiles, different types of cereal grains and coffee
beans were analyzed by GC6GCToFMS, after the preliminary
step of optimization of SPME experimental parameters: grains of
rough, brown and, white rice, unprocessed raw oat, unprocessed
raw wheat and green and roasted coffee beans. These products are
not always transported and stored under the most adequate
conditions which may promote the development of microbial
From the several hundred detected compounds, only a set of 54
compounds were tentatively identified in the matrices under study
(available on the supplementary data - Table S1). This set of 54
compounds was selected because 46 of them were previously
reported in the literature as potential markers of microbial
contamination [3,8,9,12,4651], whereas the other 8 compounds
(peak numbers of Table S1:2, 5, 15, 18, 27, 34, 40, 46) have a
chemical structure that may be related to microbial metabolism.
Thus, the following type of compounds were also considered: i)
short chain (# C10) alcohols, aldehydes and ketones, resultant
from enzymatic breakdown of lipids and subsequent oxidations
, ii) 2-enals, linked to food spoilage or degradation , and
iii) terpenes, reported as taxonomic fungi markers or indicative of
mycotoxin formation [52,53]. Moreover, a heatmap was
performed and illustrated on Fig. 2, for straight trough and rapid
interpretation of the relative abundance of each chemical family
(maximum normalization of the GC peak area) from the different
analyzed samples (with three independent assays). Where for
example, as rough and brown coffee present abundant potential
microbial markers (.0.2 of relative abundance), it should be
expected that these samples will present higher microbial load, as
it can be seen on Table 1 and it will be discussed further.
The most reliable way to confirm the identification of each
compound is based on authentic standard co-injection, which in
several cases is economically prohibitive, and often unachievable
in the time available for analysis, or because standards are not
commercially available. Full data matrix is provided as
Supplementary Data (Table S1), which include a list of the 54 selected
metabolites, and the corresponding retention times in both
dimensions, the retention index (RI) obtained through the
modulated chromatogram and the RI reported in the literature
for one dimensional GC with a 5%-Phenyl-methylpolysiloxane
GC column or equivalent and for a comprehensive GC6GC
system with HP-5 for the first dimension. These chromatographic
data is crucial for identification purposes. Furthermore, GC6GC
is an ideal technique for the analysis of complex mixtures where
compounds of similar chemical structure are grouped into distinct
patterns in the 2D chromatographic plane providing useful
information on both their boiling point and polarity (if NP/P set
of columns is used), and relationships of structured retentions have
proved especially useful for compound identification (Fig. 3). This
unique peculiarity of the GC6GC Chromatograms is a powerful
tool in the identification step.
For example, Fig. 3 (AC) shows the total ion chromatogram
contour plots obtained from rough rice (Fig. 3A), brown rice
(Fig. 3B) and white rice (Fig. 3C). This figure is displayed as an
Figure 5. PC16PC2 scores scatter plot (A) and loadings profiles (B) plots of six selected compounds. The selected compounds are
related to the higher contamination levels: methylbenzene, 2-hexanone, 3-octanone, 2-nonanone, 2-methyl-3-pentanol, and 1-octen-3-ol.
example of structured 2D contour plots observed as a result of
differences in volatility inherent to the 1D, and the polarity on the
2D. Through Table S1 and Fig. 3, it is possible to identify the
different chemical groups, i.e., aliphatic hydrocarbons have the
lower polarity, therefore have the lower retention time on the 2D
(2tR 0.4400.450 s), followed by aromatic hydrocarbons (2tR
0.5600.620 s), and the increasing number of carbons on the
carbon chain increases the 1tR. The differences in polarity are also
observable for aldehyde, ketone and alcohol groups, because they
have higher 2tR (0.5301.80 s, 0.4501630 s, and 0.5603.330 s,
respectively), i.e., higher polarity than hydrocarbons. Within these
chemical families, the aromatic components presented higher 2tR
than the aliphatic ones, which is in accordance with the results
previously reported in literature . Based on the functional
groups of the chemical families under study, the 2tR values
increased in this order, alkyl,aryl,aldehydes < ketones #
alcohols, as it was previously observed .
The 54 compounds were tentatively identified by comparison of
their mass spectra to reference database (MS) and chemical
standards, when available, and by comparison of the RIs
calculated (RIcalc) with the values reported in the literature (RIlit)
for 5% phenylpolysilphenylene-siloxane (or equivalent) column
(Table S1). A range between 0 and 26 (|RIcalc-RIlit|) was obtained
for RIcal compared to the RIlit reported in the literature for one
dimensional GC with 5%-phenyl-methylpolysiloxane GC column
or equivalent. This difference in RIs (|RIcalc-RIlit|) is considered
reasonable (,4%) if one takes into account that RIlit values were
determined in a 1D chromatographic separation system, and the
modulation causes some inaccuracy in 1D retention time
(comparison with RIcalc). In addition, comparative literature data
are obtained from a large range of GC stationary phases (several
commercial GC columns are composed of 5% phenyl
polysilphenylene-siloxane or equivalent stationary phases) , which have a
slight different separation selectivity than DB-FFAP, i.e., the
second column of the GC6GC system.
Aldehydes presented the higher chromatographic areas in white
rice, brown rice and wheat, alcohols prevailed in rough rice and
green coffee, aldehydes/alcohols predominate in oat grains and
alcohols/miscellaneous (namely, butyrolactone) in roasted coffee,
as can easily be seen on Fig. 2, and with more detail on Table S1.
This is in accordance with the literature . The reproducibility,
expressed in RSD, of the different identified volatile compounds
ranged from 0.2% to 55.8%, which is common for natural
products, since they exhibit a great intrinsic variability in
composition, and possibly due to the heterogeneity of the
microbial spoilage, even in the same lot. The highest variability
was usually observed for the trace components.
From the five tested standards (3-octanone, 1-octanol,
1-octen3-ol, 3-methyl-1-butanol, and geosmin) only geosmin was not
detected in the cereal grains and coffee beans under study.
Otherwise, the other standards were identified in all food matrices.
Geosmin is produced by soil Actimomycetes  and is related to
unpleasant earthy/musty notes in various types of foods .
Furthermore, from the most common volatile metabolite
microbial potential markers referred in literature (c.f. introduction),
2methyl-1-propanol, 3-methyl-1-butanol, 1-octanol, 1-octen-3-ol,
2-butanone, 3-octanone, 2-hexanone, 2-heptanone, limonene,
dimethyl disulfide, 2-methylisoborneol, geosmin, and
3-methylfuran, only the last three were not detected in any of the matrices
Volatile Microbial Metabolites as Potential Markers for
The results of the enumeration of yeasts, moulds and total
aerobic microorganisms presented in Table 1 showed a wide range
of contamination levels in the different food matrices under study.
Rough rice showed the highest degree of contamination, and the
roasted coffee, the lowest one, without any detectable microbial
contaminants. In fact, for the case of coffee beans, the most
commonly reported health problems associated with the intake of
roasted coffee products are not directly related to the spoilage
itself, but rather associated to contamination of the raw material,
green coffee beans (according Table 1, green coffee beans
exhibited some degree of microbial contamination), from which
hazardous thermoresistant mycotoxins may result, commonly
ochratoxins, aflatoxins, sterigmatocystin and/or patulin [56,57].
These toxins can remain unaltered or only slightly altered (but sill
with toxic activity) after the thermal treating . These toxins
usually present health concerns, namely potential carcinogenic,
immunosuppressive, teratogenic and mutagenic activities . As
a systematic preventive approach, a rapid screening method for
the evaluation of the contamination level of the green coffee beans
raw material during storage and/or transportation is imperative.
The approach used in the present work was to perform PCA on
the merged data from the potential volatile markers of each matrix
and from microbial contaminants (TAM, yeasts and moulds).
Fig. 4A shows the scores scatter plot of the second (PC2) and the
fourth component (PC4), which contains 33% of the total
variability of the data set. A high variability, which is commonly
found using the firsts principal components, as PC1 versus PC2 or
PC3, was not expected because that type of variability is mainly
driven by the natural volatile profile of the studied samples and not
from the microbial spoilage itself.
The samples are distributed along the PC2 axis, according to
their overall degree of contamination (Table 1 - total aerobic
microorganisms (TAM), moulds and yeasts), from the less
contaminated sample, roasted coffee, corresponding to negative
scores on PC2, to the most contaminated sample, rough rice,
corresponding to positive scores on PC2 positive. Unprocessed oat
grains were an exception. This sample was not located on PC2
accordingly to its microbial contamination level, but rather on the
quadrant corresponding to positive scores for both, PC2 and PC4.
This result was related to the high abundance of
6-methyl-5hepten-2-one (peak number 23), 2-methyl-1-propanol (peak
number 31), and especially 2-hydroxy-6-methylbenzaldehyde
(peak number 18) in the oat sample compared to other matrices,
which positioned the unprocessed oat at positive PC2/PC4. From
the three detected compounds in this sample,
2-hydroxy-6methylbenzaldehyde is intrinsically linked to mite contamination
(see section below, 2-Hydroxy-6-methylbenzaldehyde for Mite Detection
for further discussion). Fig. 4B presented the results of the
respective PCA loading plots, where the peaks number 3, 21, 24,
26, 34, 38 correspond to methylbenzene, 2-hexanone, 3-octanone,
2-nonanone, 2-methyl-3-pentanol, 1-octen-3-ol, respectively.
These compounds characterise the samples with highest microbial
contamination level, especially rough rice.
A second PCA was performed only with these six compounds
for the fully distinction between higher and lower contamination
level. Fig. 5A represents the scores scatter plot of the first (PC1)
and the second component (PC2), which contains 90% of the total
variability. PC1 axis seems related to the microbial contamination
level: samples with lower contamination (roasted coffee, white rice,
green coffee, wheat and oat) were located at PC1 negative,
otherwise, samples with higher microbial level (brown and rough
rice) were located at PC1 positive. It was expected a wide range of
contamination levels due to the type of samples under study. For
instance, thermal processed samples as roasted coffee revealed no
contamination level as they suffer rough thermal treatment, while
raw cereals as rough rice presented a higher contamination level.
Moreover, a distinction along PC2 was observed within the
samples with highest contamination levels: brown and rough rice.
This distinction was based on yeasts, moulds and TAM data.
Rough rice, characterized by a high concentration of TAM
(1780.0 CFU g21), moulds (1246.7 CFU g21) and yeasts
(335.7 CFU g21) is positioned in negative PC2, and brown rice,
characterized by a lower level of TAM (383.3 CFU g21) and
moulds (1036.7 CFU g21) and yeasts (undetected), is located at on
the positive side of PC2 (Fig. 5A). Consequently, 3-octanone and
2-methyl-3-pentanol might be associated to TAM and yeasts
contamination by the fact that they had ruled the position of rough
rice in the negative side of PC2 axis. On the other hand, as moulds
contributed positively to PC2, as well as 1-octen-3-ol, 2-nonanone
and 2-hexanone, these compounds may be related to moulds. The
observed distinction, based on the GC peak area of the sub-set of
six compounds, allows to infer that the discrimination between
samples was associated to microbial related-metabolites rather
than to the volatile profile of the cereals/coffee per se. These
observations are consistent with literature [3,9], as these
compounds (except 2-methyl-3-pentanol) were already linked to
Finally, it may be pointed out that with the application of a PCA
within the selected set of the proposed markers, it was possible to
cluster the different types of matrices under study, based on the
level microbial of contamination (Fig. 5). Moreover, with this
selection approach, a better distinction and characterization
accordingly to contamination degree was achieved.
2-Hydroxy-6-methylbenzaldehyde for Mite Detection
2-Hydroxy-6-methylbenzaldehyde was a trace compound for all
matrices under study, with the exception of the oat grains where a
GC peak area was 1.856107 (Table S1, peak number 18 and
pointed in Fig. 2). This compound has been reported as biological
intermediate from several adult mite species that plays a role in
alarm pheromone and sexual behaviour mediator [60,61]. In
order to relate the GC peak area of
2-hydroxy-6-methylbenzaldehyde with the presence of mites, mite counting was performed in
all samples using an optical microscope (Fig. 6), and the results are
presented on Fig. 7, in which presents the relation between the
adult mite counting (AMC) and the GC peak area of
2-hydroxy-6methylbenzaldehyde for the samples under study.
Microscopically, the morphological characteristics of the life
stages corresponded to the larvae, and male/female adults can be
distinguished (Fig. 6). As this pheromone is produced only in adult
stage , only adult specimens were considered for the
correlation of the GC peak area of the concerned compound,
that represent ca. 70% of all mite population detected. Fig. 7
revealed that the samples under study varied in a range of 0 to 190
adult mites per 100 g of sample (AMC 100 g21). Oat grains
exhibited the highest content of adult mites (190 AMC 100 g21),
which was related to the highest GC peak area of
2-hydroxy-6methylbenzaldehyde, while for the other matrices the low content
of adult mite per 100 g (040) led to a lower GC peak area.
The values obtained for adult mite counting (Fig. 7) are within
the values reported in literature for cereal-based foods, wherein a
wide range of mites can be present . For example, after
domestic storage of cereal-based foods, 62% of the samples did not
shown mite contamination, and only 5% of the samples presented
more than 100 mites 100 g21, where the maximum of detected
mites was 1875 100 g21 . Also, in wheat flour consumed by
humans the level of mites was fairly higher, reached the 5200 mites
100 g21 . These tremendous high level of mites in foods, could
lead to problems related to mite allergens, that in the reported
study, provoked anaphylactic reactions, which is especially
expressed by patients that suffer respiratory allergies to mites
. U.S. Food and Drug Administration, recognizes the risk of
ingestion mites in foods and states the need to focus the research
on the health associated problems, by the fact, that those can easily
induce allergic reactions in sensitized individuals . Moreover,
despite the dose/response of inhaled allergens of mites is well
defined, until now, no consensus of dose/response of ingested
allergenic mites has been achieved to scientifically determine what
levels might provoke an allergic reaction . Assuming that mite
contamination is more than aesthetic problem, and considering
the high level of sensitized individuals to mites in developed
countries, the implementation of an easy and rapid approach for
screening mite contamination level in foods is an actual and
HS-SPME/GC6GCToFMS is proposed as a potential tool for
the parallel assessment of microbiological and mite contamination,
directly in cereals grains and coffee beans, achieving a significant
reduction in time of analysis, compared to standard microbial and
mite count methods. Specifically, 70 min are required for the
complete HS-SPME/GC6GCToFMS analysis (extraction plus
GC analysis) which is substantially lower than the time requires by
the conventional plate-count approach (about 72 to 120 h for
bacteria and fungi, respectively), and mite isolation (ca 24 h).
HSSPME/GC6GCToFMS methodology was used to analyse five
types of cereal grains as well as green and roasted coffee. As result,
54 potential microbial volatile metabolites reported in literature or
compounds structurally associated to those. Due to its orthogonal
properties, GC6GCToFMS reduced co-elution and improved
the quality of the selection of volatile compounds potentially
related to the microbial contamination. The application of PCA to
analyse results obtained by different methodological approaches
(GC areas and microbial counts) confirmed that the level of
microbiological contamination can be inferred from the profile of
volatile metabolites. Furthermore, the sub-set of six compounds
(methylbenzene, 3-octanone, 2-nonanone, 2-methyl-3-pentanol,
1octen-3-ol, and 2-hexanone) considered as microbial
relatedmetabolites contributed for few advantages of the proposed
approach: i) may increase the specificity of the methodology, as
the selected sub-set is associated to microbiological contamination,
rather than to the intrinsic volatile profile of the cereal grains and
coffee beans; and ii) reduce the complexity of the analysis, allowing
a rapid access of information about microbial contamination.
However, the application of HS-SPME/GC6GCToFMS to the
assessment of mite contamination is the major novelty. This new
approach can be regarded as the basis for the development of a
rapid and versatile, method that can be applied in industry to the
simultaneous assessment the level of microbiological
contamination and for detection of mites in cereals grains and coffee beans,
and may be extended to other solid matrices.
Samples supplying, which not includes any financial support: The samples
were supplied through the Project QREN Nu 1561 (Development of an
equipment to determine the level of fungal contamination in food matrices
and related materials, supported by Sistema de Incentivos a` Investigacao e
Desenvolvimento Tecnologico, Portugal).
Conceived and designed the experiments: AC AA SMR. Performed the
experiments: ACS IB. Analyzed the data: ACS IB ASB NG AC AA SMR.
Contributed reagents/materials/analysis tools: ASB NG AC AA SMR.
Wrote the paper: ACS IB ASB NG AC AA SMR.
1. Mottram DS ( 1998 ) Chemical tainting of foods . Int J Food Sci Technol 33 : 19 - 29 .
2. Borjesson T , Stollman U , Adamek P , Kaspersson A ( 1989 ) Analysis of volatile compounds for detection of moulds in stored cereals . Cereal Chem 66 : 300 - 304 .
3. Kim JL , Elfman L , Mi Y , Wieslander G , Smedje G , et al. ( 2007 ) Indoor molds, bacteria, microbial volatile organic compounds and plasticizers in schools - associations with asthma and respiratory symptoms in pupils . Indoor Air 17 : 153 - 163 .
4. Whitfield FB ( 1998 ) Microbiology of food taints . Int J Food Sci Technol 33 : 31 - 51 .
5. Sunesson A , Vaes W , Nilsson C , Blomquist G , Andersson B , et al. ( 1995 ) Identification of Volatile Metabolites from Five Fungal Species Cultivated on Two Media . Appl Environ Microbiol 61 : 2911 - 2918 .
6. Pasanen AL , Lappalainen S , Pasanen P ( 1996 ) Volatile organic metabolites associated with some toxic fungi and their mycotoxins . Analyst 121 : 1949 - 1953 .
7. Schuchardt S , Kruse H ( 2009 ) Quantitative volatile metabolite profiling of common indoor fungi: relevancy for indoor air analysis . J Basic Microbiol 49 : 350 - 362 .
8. Jelen H , Wasowicz E ( 1998 ) Volatile fungal metabolites and their relation to the spoilage of agricultural commodities . Food Rev Int 14 : 391 - 426 .
9. Korpi A , Jarnberg J , Pasanen AL ( 2009 ) Microbial Volatile Organic Compounds . Crit Rev Toxicol 39 : 139 - 193 .
10. Schnu rer J , Olsson J , Borjesson T ( 1999 ) Fungal Volatiles as Indicators of Food and Feeds Spoilage . Fungal Genet Biol 27 : 209 - 217 .
11. Nieminen T , Neubauer P , Sivela S, Vatamo S , Silfverberg P , et al. ( 2008 ) Volatile compounds produced by fungi grown in strawberry jam . LWT - Food Sci Technol 41 : 2051 - 2056 .
12. Magan N , Evans P ( 2000 ) Volatiles as an indicator of fungal activity and differentiation between species, and the potential use of electronic nose technology for early detection of grain spoilage . J Stored Prod Res 36 : 319 - 340 .
13. Curtis R , Hobson-Frohock A , Fenwick G , Berreen J ( 1981 ) Volatile compounds from the mite Acarus siro L . in food. J Stored Prod Res 17 : 197 - 203 .
14. Tan W , Shelef LA ( 1999 ) Automated detection of Salmonella spp . in foods. J Microbiol Methods 37 : 87 - 91 .
15. Kataoka H , Lord HL , Pawliszyn J ( 2000 ) Applications of solid-phase microextraction in food analysis . J Chromatogr A 880 : 35 - 62 .
16. Adahchour M , Beens J , Vreuls RJJ , Brinkman UAT ( 2006 ) Recent developments in comprehensive two-dimensional gas chromatography (GC6GC) I. Introduction and instrumental set-up . TrAC-Trend Anal Chem 25 : 438 - 454 .
17. Adahchour M , Beens J , Vreuls RJJ , Brinkman UAT ( 2006 ) Recent developments in comprehensive two-dimensional gas chromatography (GC6GC) II. Modulation and detection . TrAC-Trend Anal Chem 25 : 540 - 553 .
18. Perestrelo R , Petronilho S , Camara JS , Rocha SM ( 2010 ) Comprehensive twodimensional gas chromatography with time-of-flight mass spectrometry combined with solid phase microextraction as a powerful tool for quantification of ethyl carbamate in fortified wines . The case study of Madeira wine . J Chromatogr A 1217 : 3441 - 3445 .
19. Rocha SM , Coelho E , Zrostlkova J, Delgadillo I , Coimbra MA ( 2007 ) Comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry of monoterpenoids as a powerful tool for grape origin traceability . J Chromatogr A 1161 : 292 - 299 .
20. Silva I , Rocha SM , Coimbra MA , Marriott PJ ( 2010 ) Headspace solid-phase microextraction combined with comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry for the determination of volatile compounds from marine salt . J Chromatogr A 1217 : 5511 - 5521 .
21. Humston EM , Knowles JD , McShea A , Synovec RE ( 2010 ) Quantitative assessment of moisture damage for cacao bean quality using two-dimensional gas chromatography combined with time-of-flight mass spectrometry and chemometrics . J Chromatogr A 1217 : 1963 - 1970 .
22. Johanningsmeier SD , McFeeters RF ( 2011 ) Detection of Volatile Spoilage Metabolites in Fermented Cucumbers Using Nontargeted, Comprehensive 2- Dimensional Gas Chromatography-Time-of-Flight Mass Spectrometry (GCxGC-TOFMS) . J Food Sci 76 : C168 - C177 .
23. van den Dool H , Kratz PD ( 1963 ) Generalization of the retention index system including linear temperature programmed gas-liquid partition chromatography . J Chromatogr A 11 : 463 - 471 .
24. Adams RP ( 1995 ) Identification of Essential Oil Components by Gas Chromatography/Mass Spectrometry . Carol Stream, IL: Allured Publishing Corp.
25. Machiels D , Van Ruth SM , Posthumus MA , Istasse L ( 2003 ) Gas chromatography-olfactometry analysis of the volatile compounds of two commercial Irish beef meats . Talanta 60 : 755 - 764 .
26. Triqui R , Bouchriti N ( 2003 ) Freshness assessments of Moroccan sardine (Sardina pilchardus): comparison of overall sensory changes to instrumentally determined volatiles . J Agric Food Chem 51 : 7540 - 7546 .
27. Sotomayor JA , Martinez RM , Garcia AJ , Jordan MJ ( 2004 ) Thymus zygis subsp. gracilis: watering level effect on phytomass production and essential oil quality . J Agric Food Chem 52 : 5418 - 5424 .
28. Carrapiso AI , Jurado A , Timon ML , Garcia C ( 2002 ) Odor-active compounds of Iberian hams with different aroma characteristics . J Agric Food Chem 50 : 6453 - 6458 .
29. Le Quere J-L , Latrasse A ( 1990 ) Composition of the essential oils of blackcurrant buds ( Ribes nigrum L.). J Agric Food Chem 38 : 3 - 10 .
30. Leffingwell JC , Alford ED ( 2005 ) Volatile constituents of perique tobacco . J Environ Agric Food Chem 4 : 899 - 915 .
31. Holldobler B , David Morgan E, Oldham N , Liebig J , Liu Y ( 2004 ) Dufour gland secretion in the harvester ant genus Pogonomyrmex . Chemoecology 14 : 101 - 106 .
32. Priestap HA , Van Baren CM , Di Leo Lira P, Coussio JD , Bandoni AL ( 2003 ) Volatile constituents of Aristolochia argentina . Phytochem 63 : 221 - 225 .
33. Campeol E , Flamini G , Cioni PL , Morelli I ( 2003 ) Volatile fractions from three cultivars of Olea europaea L. collected in two different seasons . J Agri Food Chem 51 : 1994 - 1999 .
34. Jordan MJ , Margaria CA , Shaw PE , Goodner KL ( 2002 ) Aroma active components in aqueous kiwi fruit essence and kiwi fruit puree by GC-MS and multidimensional GC/GC-O . J Agri Food Chem 50 : 5386 - 5390 .
35. Engel E , Baty C , LeCorre D , Souchon I , Martin N ( 2002 ) Flavor-active compounds potentially implicated in cooked cauliflower acceptance . J Agric Food Chem 50 : 6459 - 6467 .
36. Rychlik M , Schieberle P , Grosch W ( 1998 ) In compilation of odour thresholds, odour qualities and retention indices of key food odorants . Munich: Technischen Universitat Munchen.
37. Pino JA , Mesa J , Munoz Y , Marti MP , Marbot R ( 2005 ) Volatile Components from Mango (Mangifera indica L.) Cultivars. J Agric Food Chemy 53 : 2213 - 2223 .
38. Chevance FFV , Farmer LJ ( 1999 ) Identification of Major Volatile Odor Compounds in Frankfurters . J Agric Food Chem 47 : 5151 - 5160 .
39. Dhanda JS , Pegg RB , Shand PJ ( 2003 ) Saskatchewan specialty livestock valueadded program - Saskatchewan agri-food innovation fund . Available: http:// www.agr.gov.sk.ca/afif/Projects/19980016.pdf. Acessed 2010 Oct 1.
40. Eyres G , Dufour JP , Hallifax G , Sotheeswaran S , Marriott PJ ( 2005 ) Identification of character-impact odorants in coriander and wild coriander leaves using gas chromatography-olfactometry (GCO) and comprehensive twodimensional gas chromatography-time-of-flight mass spectrometry (GC6GCTOFMS) . J Sep Sci 28 : 1061 - 1074 .
41. ISO 4833: 2003 (2003) Microbiology of food and animal feeding stuffs - Horizontal method for enumeration of microorganisms. Colony counts technique at 30uC . International Organization for Standardization. Geneva, Switzerland: International Organization for Standardization.
42. NP 3277-1: 1987 (1987) Microbiologia Alimentar - Contagem de bolores e leveduras (Food Microbiology - Moulds and yeasts count. 1st Part: incubation at 25uC) . Parte 1: Incubacao a 25uC, Instituto Portugues da Qualidade , Lisbon, Portugal.
43. Irfan-ul-Haq , Afzal M ( 2007 ) Mites associated with stored grains and their products in Faisalabad district . African Crop Science Conference Proceedings 8 : 2205 - 2207 .
44. Ruppert EE , Barnes RD ( 1994 ) Invertebrate Zoology . Orlando, FL: Harcourt College Publishers. 661 - 670 p.
45. Jolliffe IT ( 2002 ) Principal Component Analysis . New York : Springer.
46. Zhou M , Robards K , Glennie-Holmes M , Helliwell S ( 1999 ) Analysis of Volatile Compounds and Their Contribution to Flavor in Cereals . J Agri Food Chem 47 : 3941 - 3953 .
47. Wihlborg R , Pippitt D , Marsili R ( 2008 ) Headspace sorptive extraction and GCTOFMS for the identification of volatile fungal metabolites . J Microbiol Methods 75 : 244 - 250 .
48. Semwal AD , Sharma GK , Arya SS ( 1996 ) Flavour degradation in dehydrated convenience foods: changes in carbonyls in quick-cooking rice and Bengalgram dhal . Food Chem 57 : 233 - 239 .
49. Cantergiani , Cantergiani E , Brevard, Brevard H , Krebs Y , et al. ( 2001 ) Characterisation of the aroma of green Mexican coffee and identification of mouldy/earthy defect . Eur Food Res Technol 212 : 648 - 657 .
50. Kai M , Haustein M , Molina F , Petri A , Scholz B , et al. ( 2009 ) Bacterial volatiles and their action potential . Appl Microb Biot 81 : 1001 - 1012 .
51. Rasanen R-M, Hakansson M , Viljanen M ( 2010 ) Differentiation of air samples with and without microbial volatile organic compounds by aspiration ion mobility spectrometry and semiconductor sensors . Buil Environ 45 : 2184 - 2191 .
52. Borjesson T , Stollman U , Schnurer J ( 1992 ) Volatile metabolites produced by six fungal species compared with other indicators of fungal growth on cereal grains . Appl Environ Microbiol 58 : 2599 - 2605 .
53. Perkowski J , Busko M , Chmielewski J , Goral T, Tyrakowska B ( 2008 ) Content of trichodiene and analysis of fungal volatiles (electronic nose) in wheat and triticale grain naturally infected and inoculated with Fusarium culmorum . Int J Food Microbiol 126 : 127 - 134 .
54. Perestrelo R , Barros AS , Camara JS , Rocha SM ( 2011 ) In-Depth Search Focused on Furans, Lactones, Volatile Phenols , and Acetals As Potential Age Markers of Madeira Wines by Comprehensive Two-Dimensional Gas Chromatography with Time-of-Flight Mass Spectrometry Combined with Solid Phase Microextraction . J Agric Food Chem 59 : 3186 - 3204 .
55. Wenke K , Kai M , Piechulla B ( 2010 ) Belowground volatiles facilitate interactions between plant roots and soil organisms . Planta 231 : 499 - 506 .
56. Noba S , Uyama A , Mochizuki N ( 2009 ) Determination of Ochratoxin A in Ready-To-Drink Coffee by Immunoaffinity Cleanup and Liquid Chromatography-Tandem Mass Spectrometry . J Agric Food Chem 57 : 6036 - 6040 .
57. Bokhari FM , Aly MM ( 2009 ) Evolution of Traditional Means of Roasting and Mycotoxins Contaminated Coffee Beans in Saudi Arabia . Adv Biol Res 3 : 71 - 78 .
58. Tsubouchi H , Yamamoto K , Hisada K , Sakabe Y , Udagawa S ( 1987 ) Effect of roasting on ochratoxin A level in green coffee beans inoculated with Aspergillus ochraceus . Mycopathologia 97 : 111 - 115 .
59. Peshin SS , Lall SB , Gupta SK ( 2002 ) Potential food contaminants and associated health risks . Acta Pharmacol Sin 23 : 193 - 202 .
60. Ruther J , Steidle JLM ( 2000 ) Mites as Matchmakers: Semiochemicals from Host-associated Mites Attract Both Sexes of the Parasitoid Lariophagus distinguendus . J Chem Ecol 26 : 1205 - 1217 .
61. Kuwahara Y , Sato M , Koshii T , Suzuki T ( 1992 ) Chemical ecology of astigmatid mites XXXII. 2-Hydroxy-6-methyl-benzaldehyde, the sex-pheromone of the brown-legged grain mite aleuroglyphus-ovatus (Troupeau) (Acarina, acaridea) . Appl Entomol Zoolog 27 : 253 - 260 .
62. Oconnor BM ( 2009 ) Astigmatid mites (Acari: Sarcoptiformes) of forensic interest . Exp Appl Acarol 49 : 125 - 133 .
63. Thind BB , Clarke PG ( 2001 ) The Occurrence of Mites in Cereal-Based Foods Destined for Human Consumption and Possible Consequences of Infestation . Exp Appl Acarol 25 : 203 - 215 .
64. Blanco C , Quiralte J , Castillo R , Delgado J , Arteaga C , et al. ( 1997 ) Anaphylaxis after ingestion of wheat flour contaminated with mites . J Allergy Clin Immunol 99 : 308 - 312 .
65. Olsen AR , Gecan JS , Ziobro GC , Bryce JR ( 2001 ) Regulatory action criteria for filth and other extraneous materials V. Strategy for evaluating hazardous and nonhazardous filth . Regul Toxicol Pharmacol 33 : 363 - 392 .