Detection of fungal contamination of cereal grain samples by an electronic nose

Sensors and Actuators B 119 (2006) 425–430

Detection of fungal contamination of cereal grain samples by an electronic nose Roberto Paolesse a,c,∗ , Adriano Alimelli a , Eugenio Martinelli b , Corrado Di Natale b,c , Arnaldo D’Amico b,c , Maria Grazia D’Egidio d , Gabriella Aureli d , Alessandra Ricelli e , Corrado Fanelli e a

Department of Chemical Science and Technologies, University of Rome “Tor Vergata”, via della Ricerca Scientifica 1, 00133 Roma, Italy b Department of Electronic Engineering, University of Rome “Tor Vergata”, via di Tor Vergata 110, 00133 Roma, Italy c IMM-CNR, via del Fosso del Cavaliere, 00133 Roma, Italy d C.R.A. Istituto Sperimentale per la Cerealicoltura, 00191 Roma, Italy e Universit` a degli Studi di Roma “La Sapienza”, Dipartimento di Biologia Vegetale, 00165 Roma, Italy Received 19 September 2005; accepted 21 December 2005 Available online 7 February 2006

Abstract Fungal growth on cereal grains decreases their nutritional value and constitutes health hazards, probably, because of the production of toxic metabolites (mycotoxins). Therefore, attempts are coming out to detect and quantify the degree of fungal infection at the early stage of mold infection. One of the most promising techniques is the analysis of volatile compounds in the headspace gas surrounding the samples. The aim of this work was to study the possibility of the application of electronic nose for an early detection of volatile compounds in infected samples and to discriminate between non-infected and infected samples with two different species of fungi (Penicillium chrysogenum and Fusarium verticillioides). Moreover, GC–MS analysis of the headspaces of the same samples confirmed that electronic nose as a powerful tool is able to provide satisfactory indications about the rate of contamination. © 2006 Elsevier B.V. All rights reserved. Keywords: Electronic nose; Cereal grains; Fungal contamination; Mycotoxins

1. Introduction Food and feeds can be often contaminated with spoilage or pathogenic microorganisms making many products no more edible or affecting their taste by the production of undesirable flavours. In some cases, these microorganisms can produce toxic substances causing a serious hazard for human and animal health besides a very high economic loss. Food poisoning can arise either through the ingestion of food containing toxigenic microorganisms or by the ingestion of food containing only toxins, which have been formed by the microorganisms. In particular, during the post-harvest period many toxin-producing microorganisms can grow heavily on several food products.

∗

Corresponding author. Tel.: +39 06 7259 4752; fax: +39 06 7259 4328. E-mail address: [email protected] (R. Paolesse).

0925-4005/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.snb.2005.12.047

There is a need for rapid development of methods to prevent contaminated food reaching and being utilized by the consumer. In the past, the control of food safety has been carried out by product testing of both raw materials and processed products, without considering that the damage can occur during the production process [1]. In the recent years, the hazard analysis critical control point (HACCP) system is generally considered the method of choice for ensuring the safety of food [1,2]. By applying HACCP, it is important to identify the step of the production processes where hazards could occur in order to implement monitoring procedures in place to prevent these hazards from occurring. The traditional cultural detection methodology is performed by growing the microorganisms on selective media. This method requires several days from isolation to identification and is time consuming and expensive [1]. For these reasons, there is currently a strong demand for a faster and suitable sensitive microorganism detection method that can reduce the time

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taken to achieve results from days to a few hours or even minutes. Today many rapid methods are commercially available including DNA probes [3], the polymerase chain reaction (PCR) [3,4], latex agglutination tests [3a,5], direct epifluorescent filter techniques [6], enzyme linked immunosorbent assay (ELISA) [5], conductance, impedance, bioluminescence [7], immunomagnetic beads and biochemical assay, such as API 20E and Micro ID. Other currently available methods for measuring mold contamination in food include: microscopic examination, culture on agar, electrical measurements of conductance and other changes in electrical properties of the contaminated food substratum and detection of fungal metabolites, such as chitin, ergosterol or ATP [5]. However, all these methods are not always applicable both for the cost and labor time to analyze one sample, and new rapid methods are always being researched and developed for application for detection of microorganisms. Recently some biosensors have been developed [8] and in particular, Grow et al. [9] reported a new method called ␮SERS, a new biochip technology for pathogens and their toxins. Another proposed methodology is the detection of the production of volatiles and odors by microorganisms. It is well known that microorganisms produce a wide range of volatiles, such as alcohols, ketones, aldehydes, esters, carboxylic acids, lactones, terpenes, sulphur and nitrogen compounds [10]. The volatiles produced arise in foods due to decomposition caused by many endogenous enzymes, microbial contamination or chemical oxidation [11] and many factors such as substrate temperature, pH, oxygen concentration, age of culture and microbial species can affect the composition of volatiles. Moreover, previous studies have shown the positive correlation between fungal volatile organic metabolites with some parameters of micelial development on cereal grains, such as ergosterol, CO2 production [12] and mycotoxin production [13]. The volatile organic patterns results are particularly useful not only to detect the early stages of grain spoilage but also to distinguish between presence of toxigenic and non-toxigenic strains of fungi such as Fusarium verticillioides [14]. Many works have also reported bacterial [15] and fungal [12,16] volatiles and a large number of components of naturally occurring odors. It is well known that flavours are constituted by a large number of components that are perceived as integrated response of the olfactory system to the complex mixture [17]. The human olfactory system can discriminate aromas without separating mixtures into individual compounds [18]. From this point of view electronic nose parallels the human olfactory system [17]. Olfactory receptors are represented by a group of chemical sensors, which produce a time-dependant electrical signal in response to an odor. Signal processing techniques can be used to reduce any noise and sensor drift. One of the most important uses of the electronic nose regarding the employment of this technology is to obtain an early and rapid detection of fungal and bacterial activity, and thus, it is a useful tool to distinguish between good and poor quality grain [19]. In the present work, we have studied the volatile chemical pattern produced by Penicillium chrysogenum or F. verticillioides,

two widely spread fungi on wheat seeds, at different water activities in order to correlate a chemically volatile profile with the presence of a specific fungal specie and with a specific water activity value with the aim to improve rapid methodologies for the detection of food spoilage microorganisms. 2. Experimental 2.1. Samples Soft wheat seeds (cv Pandas) were supplied by “Istituto Sperimentale per la Cerealicoltura”. The seeds have a 0.60 water activity value (aw ) and were rehydrated to 0.85, 0.90 or 0.95 aw by the addition of sterile distilled water. After water addition the seeds were maintained at 6 ◦ C for 24 h in Erlenmeyer’s flasks and were periodically shaken to allow water adsorption and equilibration. The moisture adsorption curve used has been obtained by a PBI aw analyzer and it is shown in Fig. 1. The experiments have been performed both on sterilized and nonsterilized seeds, the sterilization has been made by autoclaving the rehydrated wheat seeds for 20 min at 120 ◦ C. aw value was also checked after autoclaving. The used fungi were P. chrysogenum (Thom) and F. verticillioides Saccardo (Nirenberg) from the collection of the Department of Plant Biology, University of Rome “La Sapienza”. Fungal inocula were performed using a conidial water suspension (1 × 106 conidia/g seeds) obtained from 15 days old fungal cultures grown on potato dextrose agar (PDA). 2.2. Mycological analysis In order to investigate the fungal genera and their relative abundance on the assayed seed samples, 100 wheat seeds were moistened up to 0.85, 0.90 or 0.95 aw and then incubated for 7 days at 25 ◦ C. After this period different fungal species have been isolated from each seed (from 1 to 4), and they have been inoculated on PDA added with 0.02% (w/w) of streptomycin sulfate salt. After 2–3 days of incubation at 25 ◦ C, fungal determination by microscopic observation has been performed.

Fig. 1. Correlation curve between water activity (aw ) and moisture content of soft wheat seeds (cv Pandas). Each result is the mean ± S.E. of five determinations.

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2.3. Electronic nose

2.6. GC–MS analysis

The last prototype (Libra nose) of a series of electronic noses designed and produced, since 1995, at the University of Rome was utilised in the experiment described here. The instrument contains eight thickness shear mode resonators coated with various films of metalloporphyrins. Seven different metal complexes (Mn, Fe, Cu, Ni, Sn, Ru and Co) and a free-base of Tetrakis(4-butyloxyphenyl) porphyrin (H2 TBPP) were used. The porphyrin molecule was functionalized with butyloxy substituents at the meso-phenyl groups in order to ensure the porosity of the solid-state film, necessary for optimal analyte diffusion through the absorbing film [20]. Metalloporphyrins have been deposited onto AT-cut quartz surfaces, oscillating at the fundamental frequency of 20 MHz. Film deposition was done with the spray casting method. Molecules were dissolved in chloroform and sprayed with a nitrogen carrier onto the quartz surface in order to deposit a fixed amount of sensing coating. More details about the electronic nose can be found elsewhere [21].

The analyses were performed with a gas chromatograph model HRGC 5160 (Carlo Erba, Italy) coupled to a mass spectrometer model Quattro (VG Micromass, UK). A SPB-5 capillary column (30 m by 0.25 mm, i.d. 1.4 ␮m), from Supelco (USA) was used in split mode (100:1 ratio) at temperature programmed from 40 ◦ C (for 3 min) to 270 ◦ C at 20 ◦ C/min, using helium (P = 100 kPa) as carrier and with the injector maintained at 260 ◦ C. The mass spectrometer worked at 70 eV ionization energy. The headspaces of grain samples were collected by solid phase micro-extraction (SPME) technique. Different fibers were tested and the best results were obtained with the polydimethylsiloxane/divinylbenzene (PDMS/DVB) 65 ␮m fiber (Supelco), which was then used for all the measurements. The fiber was inserted for 30 min into the grain-sealed bottle and then inserted into the GC injector for 3 min to desorb the volatile compounds. 2.7. Data analysis

For electronic nose, grain samples (50 g) were closed in sealed bottles endowed with inlet and outlet. In order to obtain a reproducible headspace composition, each sample was held at 30 ◦ C for the whole experiment. Headspace was sampled with the internal pumping system of the instrument. The micro-pump introduced volatiles in the stainless steel measurement chamber at 0.2 l/min flow.

Data have been analyzed with chemometric techniques. Partial least square discriminant analysis (PLS-DA) and principal component analysis (PCA) have been used to discriminate the time evolution of the same class or different kind of fungi classes [22]. For the PLS-DA, the obtained models have been crossvalidated with leave-one-out technique. The frequency shifts between the steady frequency in the cleaning and in the measuring phase have been used as the feature characterizing the electronic nose sensor responses. All calculations were performed in Matlab® software platform.

2.5. Grain sample measurements

3. Results and discussion

In the first experiment the grain samples (50 g) were closed into sealed vials at controlled temperature and at the following different water activities: blank-aw base (non-moistened grain samples), 0.85, 0.90 and 0.95 aw . The headspace was fluxed into the sensors chamber of the Libra nose and then re-injected into the vials by a closed circuit, using the Libra-nose pump to generate the flux. This sampling approach was adopted in order to minimize the variation of headspace composition after each measurement. The measurements were daily performed for the first 5 days after the vials closure and replicates were prepared and measured for each sample. On the basis of the analysis of the grain samples of the first experiment, in the second experiment the grain samples were stored at two different water activities (aw = 0.85 and 0.95). Four samples at 0.85 aw were inoculated with P. chrysogenum, while four samples at 0.95 aw were inoculated with F. verticillioides. Four blank samples for each water content (aw = 0.85 and 0.95) and four non-moistened blanks were also included in the measurement protocol. In the third experiment the seed samples were sterilized before the inoculums as reported in Section 2.1 to avoid accidental fungal contamination. Also, in this case, the headspace evolution has been measured in order to have some indications about fungi metabolism productions.

The results obtained in the first experiment are presented in Fig. 2 that shows the scores plot of the first two latent variables of the PLS-DA model obtained considering a day’s samples with different water activities or samples measured on different days

2.4. Headspace analysis

Fig. 2. The first experiment: score plot showing a partial overlapping between classes in the intermediate period (second and third day).

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as belonging to different classes. It is possible to note that each sample is well discriminated for each day of measurement and that the evolution of the different samples has followed a complicated pattern, probably due to a non-linear variation of the chemical composition of the sample headspaces. To obtain information about this aspect, GC–MS measurements were carried out, using SPME technique to collect the volatile compounds present in the grain headspaces. The chemical patterns of the analyzed samples were very complex, with several peaks present in the gas-chromatogram. This result demonstrated the good performance of the fiber in the extraction of the volatiles produced during the grain fungal spoilage. This complex chemical pattern prevented a complete characterization of the different volatile organic compounds present in the grain headspaces, although some analytes were confirmed by comparison with standard samples. In particular we identified the presence of alcohols (such as 3-methyl-1-butanol and 1-hexanol), ketones (3-pentanone and 2-hexanone) and hydrocarbons (1-hexene and toluene). However, it is important to note that none of these compounds can be indicated as marker for the presence of particular specie of fungi, because they were present in several samples although with different concentrations. This result seems to indicate that the discrimination among the different headspaces cannot be related to the presence of a single analyte but to the overall chemical composition of the sample. In the second experiment, the regression model obtained is aimed to discriminate between the five different classes of grain samples: blank non-moistened, blank at two different water activity values (aw = 0.85 and 0.95), samples inoculated with P. chrysogenum (aw = 0.85) and with F. verticillioides (aw = 0.95). The scores plot of the first two latent variables of this PLS-DA model shows a partial overlapping between the classes that have the same water activity (Fig. 3). The same remark can be done observing Table 1, where the confusion matrix of the above model is shown. From this table

Table 1 Matrix concerning the second experiment

A B C D E

A

B

C

D

E

29 7 0 1 0

0 10 0 0 1

0 1 12 0 6

0 8 3 28 0

0 0 15 0 23

(A) Non-moistened blank; (B) blank with aw = 0.85; (C) blank with aw = 0.95; (D) sample inoculated with P. chrysogenum and aw = 0.85; (E) sample inoculated with F. verticillioides and aw = 0.95. Table 2 Mycological analysis of 100 whole-wheat seeds moistened up to the indicated aw and incubated for 7 days at 25 ◦ C Fungal genera

aw

Penicillium Aspergillus Alternaria Fusarium Rhyzopus Cladosporium Not identified

0.85

0.90

0.95

96 12 22 6 4 3 20

102 11 12 22 3 6 22

38 12 8 94 1 3 18

The isolation of fungal genera has been performed on PDA and the identification has been made after 2–3 days of incubation at 25 ◦ C.

it is clear how the samples with inoculated fungi are well discriminated between them (classes D and E). The biggest errors are produced by the samples of the class B classified as belonging to class D and C samples classified as E. These two couples of classes, B–D and C–E are characterized by the same water activity. To study the reason of the low classification obtained in the case of inoculated and blank samples, the blank batches were analyzed to determine the presence of fungi, and also in this case it was revealed that the presence of Penicillium and Fusarium species depend on different water activity values. In particular the mycological analysis of the assayed grain samples, represented in Table 2, showed that the fungi developed at 0.85 and 0.90 aw were mostly of Penicillium species, while Fusarium predominated at the highest (0.95) water activity. In the second experiment seed samples at 0.85 or 0.95 aw were used. The samTable 3 Matrix concerning the third experiment

1 2 3 4 5 6 Fig. 3. Second experiment: the class (A) related to the blank (non-moistened); (B) related to blank samples with aw = 0.85; (C) related to blank samples with aw = 0.95; (D) related to samples with aw = 0.85 inoculated with P. chrysogenum; (E) related to blank samples with aw = 0.95 inoculated with F. verticillioides.

1

2

3

4

5

6

12 0 0 0 0 0

0 8 1 0 0 0

0 1 8 2 1 0

0 2 1 8 0 0

0 0 1 1 10 0

0 0 0 0 0 12

(1) Non-moistened blank; (2) blank with aw = 0.85; (3) blank with aw = 0.95; (4) sample inoculated with P. chrysogenum and aw = 0.85; (5) sample inoculated with P. chrysogenum and aw = 0.95; (6) sample inoculated with F. verticillioides and aw = 0.95.

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ples at 0.85 aw were inoculated with P. chrysogenum, while the samples at 0.95 aw were inoculated with F. verticillioides. The low discrimination observed in the case of class B–D and C–E was probably due to the presence of the same fungal species in the grain samples and consequently a similar headspace composition. To further confirm this hypothesis, the samples were analyzed by GC–MS technique, and the results obtained indicated a similar chemical profile for samples B and D and for samples C and E. The overlap observed in the analysis of these samples is due to the presence of the same water activity and the same species of fungi, resulting in similar chemical compositions of the related headspaces. In the third experiment, the seed samples were previously sterilized by autoclaving to avoid accidental fungal contamina-

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tion, then inoculated with P. chrysogenum or F. verticillioides to study the performance of the Libra nose in the discrimination of the two different fungal species. A better discrimination between the classes has been obtained as shown in Table 3, where the confusion matrix of PLS-DA model is plotted and a classification rate of 85.3% is reached. It is also interesting to remark that the recognition rate for all the classes is much greater than 71% despite the second experiment, where the classes B and C had percentage of correct classification lower than 45% (Figs. 4 and 5). Then the results support the indication that the discrimination among the different samples depends on the volatile chemical pattern produced by the fungi metabolism. 4. Conclusions

Fig. 4. Third experiment: scores plot of two principal components concerning the samples inoculated with P. chrysogenum (aw = 0.85) without blank correction.

The chromatograms obtained by GC–MS showed several peaks, and each sample gave a different profile supporting the fact that the discrimination operated by the electronic nose is related to the different chemical composition of the grain headspaces. The results shown in Fig. 3 suggest that water activity has a crucial importance in the configuration of a chemical profile since the different classes with the same water activities presented a significant overlapping. The seed samples tested in this experiment were not sterilized before fungal inocula, allowing the development of the fungal mycoflora, which is naturally present on seeds. Since the fungal species, which are able to grow on a specific substrate, are strongly influenced by water content, the fungal species grown on the seed samples with the same water activity have a significant overlapping, whether they have been inoculated with P. chrysogenum (aw = 0.85) and F. verticillioides (aw = 0.95) or not. This hypothesis is confirmed by the mycological analysis performed on the assayed seed samples (Table 2) that shows the influence of water activity on the development of different fungal species. In conclusion, the electronic nose demonstrated its ability to follow the variation of grain sample headspaces due to the fungal contamination. This feature is particularly promising for the future exploitation of this instrument as a rapid and noninvasive method for the detection of fungal contamination of grain seeds. References

Fig. 5. Third experiment: score plot of two principal components concerning the samples inoculated with P. chrysogenum (aw = 0.85) with blank correction.

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