Biosensors use a combination of biological receptor compounds and the physical or physical-chemical transducer directing real-time monitoring of specific biological events. Surface plasmon resonance (SPR) technique is a powerful optical biosensing technique for non-label affinity interaction analysis. Lectins as biological receptor molecules and Biacore 3000 as optical transducer were employed for bacteria differentiation. The lectins were covalently immobilized on Biacore CM5 chips and the solution containing only endotoxin (lipopolysaccharide) or whole bacterial cells was flowed over the surface. The obtained lectin binding patterns well correlated with theoretical pattern derived from carbohydrate structure of endotoxin. The experiment with whole bacteria showed possibility to distinguish bacteria with cross-reactivity in immunoassay (Citrobacter freundii and Escherichia coli O157) by using of the lectin panel. The expression of carbohydrate structures on the outer side of the bacteria was required for increase the sensitivity of measurement.

In an earlier study [Anal. Chim. Acta, 459 (2002) 25] a similar set of lectins was used to discriminate between healthy and infected serum samples. Fine tuning of this technique is under investigation. This study was also carried out with a Biacore instrument. Faster measurements can be made with scanning or imaging ellipsometers. Sera from human, pig, sheep and guinea pig were applied to a panel of eight different lectins immobilized on a gold wafer. The 2-D matrices obtained by scanning ellipsometry were evaluated with image analysis and multivariate data analysis. There was a clear difference in protein binding pattern between the different species and dendrograms indicated that human and pig sera are the most related of the four different sera [Anal. Chim. Acta, 530 (2005) 167]. Similarly, simple micro contact-printed gold-wafers were used to make a lectin panel for investigation and discrimination of different meat juices from fresh meat of cattle, chicken, pig, cod, turkey and lamb. Using a simple model based on an artificial neuronal net, it was also possible to classify meat juices from the mammals investigated [Anal. Chim. Acta, 547 (2005) 229].

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