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* Dipartimento di Scienze Molecolari Agroalimentari, and
Dipartimento di Scienze e Tecnologie Alimentari, Università di Milano, 20133 Milano, Italy
CRA-ISLC, Via Lombardo 11, 26900 Lodi, Italy
1 Corresponding author: stefania.iametti{at}unimi.it
| ABSTRACT |
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Key Words: aflatoxin M1 goat and ewe milk protein dairy by-product electronic nose
| INTRODUCTION |
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Aflatoxins in cheese have 3 main sources: the presence of AFM1 in milk, as a consequence of feed contaminated with AFB1 eaten by dairy cattle; synthesis of aflatoxin (B1, B2, G1, and G2) by fungi that grow on cheese (although the low level of carbohydrate does not make it a very suitable substrate; Zerfiridis, 1985); and the use of powdered milk contaminated with AFM1 for cheese production (Blanco et al., 1998).
Many studies on bovine milk have demonstrated that AFM1 binds to milk proteins, and in particular to CN, and that it is concentrated in the curd during manufacture of cheese (Brackett and Marth, 1982; Lopez et al., 2001). For these reasons, AFM1 concentrations are 3-to 4-fold higher in cheese than in milk (Kiermeier and Buchner, 1977). Investigations on the affinity of AFM1 toward different proteins in milk from other species are very limited. Information on the binding of AFM1 to proteins in ovine and caprine milk is particularly important because most ovine and caprine milk is processed into cheese (Battacone et al., 2003, 2005; Bognanno et al., 2006). The presence of AFM1 in commercial ovine cheese was reported by Minervini et al. (2000); those authors reported AFM1 amounts between 50 and 210 ng/kg; that is, at levels well above the current European limits.
In this study, we investigated AFM1 distribution in the different protein fractions in milk and in protein fractions obtained during production of ovine and caprine cheese, with the aim of identifying a possible relationship between the physical status of the various proteins and AFM1 binding. For this purpose, the AFM1 content was measured by an ELISA procedure. The same samples were analyzed by a commercial electronic nose that in previous work was used as an innovative screening methodology for simple and rapid detection of AFM1 in a large number of ovine milk samples (Benedetti et al., 2005).
| MATERIALS AND METHODS |
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An ELISA-format Ridascreen aflatoxin M1 kit (R-Biopharm GmbH, Darmstadt, Germany) was used to detect AFM1 immunochemically.
Samples
Twenty-four raw milk samples were collected from several different groups of ewes and goats fed a formulated feed containing increasing amounts of aflatoxin B1. Samples were immediately frozen after milking and stored at 20°C until analysis. Individual milks were then pooled according to their AFM1 content as determined immunochemically. This gave 3 distinct pools of each milk with AFM1 contents of 50, 100, and 150 ng/kg. A reference milk sample had an AFM1 content lower than the detection limit of the ELISA assay.
The experiments on the animals followed the guidelines of the Council Directive of EC (European Community, 1986). The health of animals was monitored continuously before and during the experimental period.
Milk Treatment
Unless otherwise specified, the same procedures were used for caprine and ovine milk. Ultrafiltration was performed in an Amicon apparatus using a 10,000-Da cut-off membrane for milk samples and a 5,000-Da cutoff membrane for whey samples.
Separation of native CN micelles was obtained by ultracentrifugation at 80,000 x g at 4°C for 45 min, using an SW 28 rotor in a Beckman L575 ultracentrifuge as described by Iametti et al. (1993). The precipitated CN fraction was resuspended in 50 mM phosphate buffer, pH 6.8, to a final volume identical to that of the starting milk. The supernatant was used as such for all determinations.
Acid coagulation of milks was performed by addition of 88% lactic acid to lower the milk pH to 4.3. Flocculated CN was separated by centrifugation at 3,000 x g for 10 min. Enzymatic coagulation was performed on milk heated at 60°C for 15 min. After this treatment, milk was refrigerated to 35°C, and a 0.8% (wt/vol) solution of chymosin in water was added at a ratio of 1:25. After 30 min at 35°C, the curd was separated by using a suitable metallic mesh. Ricotta cheese was produced starting from the residual whey fraction from either enzymatic or acid coagulation. Whey samples from enzymatic coagulation were acidified to pH 5.6 with 2 N HCl before the heat coagulation step, which was carried out at 90°C for 15 min under stirring. After resting for 15 min, the resulting ricotta was separated by draining.
ELISA Procedure
Quantification of AFM1 in all samples was carried out with a commercial competitive ELISA kit (Ridascreen aflatoxin M1, R-Biopharm). A suitable sample aliquot was centrifuged at 2,800 x g at 4°C for 10 min, and 0.1 mL of the supernatant was used for the ELISA determination, which was carried out as recommended by the kit supplier. Final absorbance was measured in a BioRad model 3550 Microplate Reader (BioRad, Richmond, VA).
Electronic Nose
Analyses were performed with a commercial electronic nose (model 3320, Applied Sensor Lab Emission Analyser; Applied Sensor Co., Linkoping, Sweden). The instrument consisted of 3 parts: an automatic sampling apparatus, a detector unit containing the arrays of sensors and the controls for keeping them at the appropriate working temperature, and the hardware and software required for pattern recognition (Gardner and Bartlett, 1994; Shaller et al., 1999). The automatic sampling system allowed sample loading under controlled temperature.
The sensor array in the instrument used here was composed of 22 different sensors, namely, 10 metal oxide semiconductor field effect transistors (MOSFET), and 12 Taguchi-type sensors (metal oxide semiconductors, MOS; Benedetti et al., 2005). The MOSFET sensors were divided into 2 arrays of 5 sensors each, one operating at 140°C and the other at 170°C, whereas the 12 MOS sensors, mounted in a separate chamber, were kept at 400 to 500°C.
One milliliter of each sample was placed in a 40-mL Pyrex vial fitted with a silicon/Teflon septum. After a 20-min equilibration at 20°C, the measurement sequence was started. Operating conditions were flow rate: 1 mL/min; sampling time: 60 s; standby temperature: 20°C. The total cycle time for each measurement was 5 min. No sensor drift was experienced during the measurement period. Each sample was evaluated 3 times and the average of the results was used for subsequent analysis.
Data Analysis
The data obtained from the electronic nose for the ovine and caprine milk samples were analyzed by principal component analysis (PCA) performed with the SCAN software (v. 1.1; Minitab Inc., State College, PA).
Principal component analysis was used for explorative data analysis on the autoscaled data because it identifies orthogonal directions of maximum variance in the original data, in decreasing order, and projects the data into a lower-dimensionality space formed of a subset of the highest variance components. The orthogonal directions are linear combinations (the principal components) of the original variables and each component explains in turn a part of the total variance of the data; in particular, the first significant component explains the largest percentage of the total variance, the second one, the second largest percentage, and so forth.
Cluster analysis performs agglomerative hierarchical clustering of objects based on distance measures of dissimilarity or similarity. The hierarchy of clusters can be represented by a binary tree called a dendrogram. A final partition (i.e., the cluster assignment of each object) may be obtained by cutting the tree at a specified level (Gardner and Bartlett, 1992).
| RESULTS AND DISCUSSION |
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We first explored whether laboratory-scale ultrafiltration was able to remove AFM1 from ewe or goat milk, because this physical technique has been used for the selective separation of some toxic compounds from milk (Kosikowski and Jimenez-Flores, 1985). The membranes used for this study had a nominal cutoff of 10,000 Da, thus retaining all proteins in milk, as confirmed by SDS-PAGE analysis (not shown).
The distribution of AFM1 between the retentate and permeate fractions of ewe and goat milk samples is shown in Figure 1
as a function of the amount of toxin in the starting milk. Figure 1
shows that more than 80% of the AFM1 is retained in the protein-containing phase. Almost identical trends are observed for ewe and goat milks. From a practical standpoint, these results indicate that ultrafiltration does not represent a suitable method for removal of AFM1 from contaminated ovine and caprine milks.
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The partition of AFM1 between the 2 protein fractions separated by ultracentrifugation from ovine milk is shown in Figure 2
. Almost identical results were obtained with caprine milk, indicating a change in the distribution of the toxin when the toxin content in the starting milk was higher. Binding of AFM1 to native CN shows saturation-like behavior. In view of the modest increase in free AFM1 even at the highest concentrations used here (as indicated by ultrafiltration data), it seems that nonprecipitable proteins bind AFM1 only after the toxin saturates the binding sites on the native, precipitable CN.
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Electronic Nose
As previously demonstrated, the electronic nose is well suited to the routine detection of AFM1-contaminated milk samples and can be successfully applied for rapid screening of AFM1 contamination in ovine milk samples (Benedetti et al., 2005). The electronic nose is only sensitive to molecules in the headspace, which implies that AFM1 must be present in the headspace even in conditions where significant protein binding occurs, and that binding of AFM1 to milk proteins is characterized by relatively high dissociation constants. In previous studies, the presence of AFM1 in the headspace of naturally contaminated and AFM1-spiked ovine milk samples was confirmed by mass spectrometry analysis (Benedetti et al., 2005). As pointed out by those authors, this observation does not rule out the possibility that the electronic nose senses other AFM1-derived molecules (including other AFB1 catabolites) present in the headspace along with unmodified AFM1. In the frame of this study, the electronic nose was used to gather further information on the stability of protein-AFM1 complexes in the various samples considered in the first part of this report, including the effects of the various treatments considered above on the interaction between AFM1 and proteins.
Figure 4
shows typical sensor patterns for milk samples containing increasing amounts of AFM1, and includes data on curd, whey, ricotta, and scotta obtained by enzymatic or acidic curding of ewe and goat milk. The individual bars within each sample column (e.g., milk 50) represent the signals of the 23 different sensors of the electronic nose. Each bar represents the mean value obtained from the analysis of 3 different samples. The differences in the signal patterns are clearly visible, as is their dependence on the presence of AFM1. When comparing the data presented in Figure 4
with the distribution data in the previous section, it appears that the results obtained with the sensor array closely parallel those obtained with the more laborious ELISA assays. Of particular relevance is the fact that the electronic nose supports immunochemical evidence for the absence of AFM1 in all the ricotta samples, regardless of the origin of the whey used for their production.
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| CONCLUSIONS |
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This study also indicates that the physical status of the various protein components of ewe and goat milks does not influence the capability of sensor arrays (electronic nose) to detect the presence of AFM1. Although the sensor response in the electronic nose does not need and does not provide information on the nature of the compound under investigation (only its digital finger-print), changes in the digital fingerprint provide reliable information regarding the presence of AFM1 regardless of the physical status of the sample. In spite of previous observations about the presence of AFM1 in the headspace of AFM1-contaminated samples, the nature of other possible species present in these samples to which the sensor array may respond needs clarification through the application of suitable analytical tools such as mass spectrometry (alone or in combination with other separation techniques).
The classical performance descriptors used in analytical chemistry (specificity, accuracy, sensitivity, and the possible occurrence of false positives and false negatives) do not apply to the electronic nose, because sensor arrays only sense electrical signals on a hot semiconductor surface, as noted above. Use of the electronic nose makes sense only in combination with suitable statistical tools, and this combination may represent a very practical and quick tool for screening purposes. This work confirms that cluster analysis of the electronic nose data may offer substantial help in creating clusters that allow recognition of samples at different contamination levels.
Cluster analysis may be used for selecting those samples in a given lot that require accurate quantitative analysis by chemical or immunochemical methods (Manetta et al., 2005; Micheli et al., 2005; Sørensen and Elbæk, 2005; Cavaliere et al., 2006). These methods are expensive and time consuming, but are capable of offering accurate information at the molecular level.
| ACKNOWLEDGEMENTS |
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Received for publication July 21, 2006. Accepted for publication August 24, 2006.
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