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Differential detection of potentially hazardous Fusarium species in wheat grains by an electronic nose.

Eifler J, Martinelli E, Santonico M, Capuano R, Schild D, Di Natale C - PLoS ONE (2011)

Bottom Line: Fusarium species are especially harmful pathogens due to their toxic metabolites.By using an electronic nose we were able to differentiate between infected and non-infected wheat grains in the post-harvest chain.Our electronic nose was capable of distinguishing between four wheat Fusaria species with an accuracy higher than 80%.

View Article: PubMed Central - PubMed

Affiliation: Department of Crop Sciences, Georg-August-Universität Göttingen, Göttingen, Germany.

ABSTRACT
Fungal infestation on wheat is an increasingly grave nutritional problem in many countries worldwide. Fusarium species are especially harmful pathogens due to their toxic metabolites. In this work we studied volatile compounds released by F. cerealis, F. graminearum, F. culmorum and F. redolens using SPME-GC/MS. By using an electronic nose we were able to differentiate between infected and non-infected wheat grains in the post-harvest chain. Our electronic nose was capable of distinguishing between four wheat Fusaria species with an accuracy higher than 80%.

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Related in: MedlinePlus

Enose results indicating different Fusarium species and infection levels.A: Scores plot of the first two latent variables, showing groups of F. culmorum, F. graminearum, F. redolens and F. cerealis. B: Discrimination between high- (15 days incubation) and low -infected (5 to 10 days incubation) samples of Fusarium. The diagram shows the scores of the first two out of 3 latent variables.
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pone-0021026-g001: Enose results indicating different Fusarium species and infection levels.A: Scores plot of the first two latent variables, showing groups of F. culmorum, F. graminearum, F. redolens and F. cerealis. B: Discrimination between high- (15 days incubation) and low -infected (5 to 10 days incubation) samples of Fusarium. The diagram shows the scores of the first two out of 3 latent variables.

Mentions: As a first step we investigated the capability of the Enose to differentiate between Fusarium strains. The PLS-DA model used for this discrimination in the infected samples covered five latent variables, the first two of which are shown in Figure 1A. The smallest variance within a group occured in the samples of F. cerealis. Samples of this group were overlapping with the highly contaminated samples of F. redolens and F. culmorum. The widest distribution was found in the samples of F. graminearum showing a partial overlap with F. culmorum and F. cerealis. It can be noticed that the gas chromatographic profiles from F. redolens and F. culmorum are similar. However, there was a large interclass variance detectable. Thus, the model has a satisfactory capability of differentiating different classes, as confirmed by the confusion matrix (Table 1). In the cross-validation F. culmorum and F. cerealis were perfectly recognized (100% correct). The classification rates of F. graminearum and F. redolens were 83% and 89%, respectively. The correct classification rate across all fungi was 94%.


Differential detection of potentially hazardous Fusarium species in wheat grains by an electronic nose.

Eifler J, Martinelli E, Santonico M, Capuano R, Schild D, Di Natale C - PLoS ONE (2011)

Enose results indicating different Fusarium species and infection levels.A: Scores plot of the first two latent variables, showing groups of F. culmorum, F. graminearum, F. redolens and F. cerealis. B: Discrimination between high- (15 days incubation) and low -infected (5 to 10 days incubation) samples of Fusarium. The diagram shows the scores of the first two out of 3 latent variables.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3111488&req=5

pone-0021026-g001: Enose results indicating different Fusarium species and infection levels.A: Scores plot of the first two latent variables, showing groups of F. culmorum, F. graminearum, F. redolens and F. cerealis. B: Discrimination between high- (15 days incubation) and low -infected (5 to 10 days incubation) samples of Fusarium. The diagram shows the scores of the first two out of 3 latent variables.
Mentions: As a first step we investigated the capability of the Enose to differentiate between Fusarium strains. The PLS-DA model used for this discrimination in the infected samples covered five latent variables, the first two of which are shown in Figure 1A. The smallest variance within a group occured in the samples of F. cerealis. Samples of this group were overlapping with the highly contaminated samples of F. redolens and F. culmorum. The widest distribution was found in the samples of F. graminearum showing a partial overlap with F. culmorum and F. cerealis. It can be noticed that the gas chromatographic profiles from F. redolens and F. culmorum are similar. However, there was a large interclass variance detectable. Thus, the model has a satisfactory capability of differentiating different classes, as confirmed by the confusion matrix (Table 1). In the cross-validation F. culmorum and F. cerealis were perfectly recognized (100% correct). The classification rates of F. graminearum and F. redolens were 83% and 89%, respectively. The correct classification rate across all fungi was 94%.

Bottom Line: Fusarium species are especially harmful pathogens due to their toxic metabolites.By using an electronic nose we were able to differentiate between infected and non-infected wheat grains in the post-harvest chain.Our electronic nose was capable of distinguishing between four wheat Fusaria species with an accuracy higher than 80%.

View Article: PubMed Central - PubMed

Affiliation: Department of Crop Sciences, Georg-August-Universität Göttingen, Göttingen, Germany.

ABSTRACT
Fungal infestation on wheat is an increasingly grave nutritional problem in many countries worldwide. Fusarium species are especially harmful pathogens due to their toxic metabolites. In this work we studied volatile compounds released by F. cerealis, F. graminearum, F. culmorum and F. redolens using SPME-GC/MS. By using an electronic nose we were able to differentiate between infected and non-infected wheat grains in the post-harvest chain. Our electronic nose was capable of distinguishing between four wheat Fusaria species with an accuracy higher than 80%.

Show MeSH
Related in: MedlinePlus