Elements of Pattern Recognition Theory in the Analysis of Biosensor Signals

1. Elements of Pattern Recognition Theory in the Analysis of Biosensor Signals Reshetilov A. N., Lobanov A.V., *Ponamoreva O. N., Reshetilova T. A., *Alferov V. A. G. K. Skryabin Institute of Biochemistry and Physiology of Microorganisms RAS *Tula State University, Tula, Russia anatol@ibpm.pushchino.ru

2. Biosensor – scheme. Attention on TRANSDUCER and BIOMATERIALS Transducers: electrochemical detectors like Clark type electrodes, Screen-printed electrodes, pH-sensitive field-effect transistors – small pH electrodes

3. Transducer Transducer Biomaterial Biomaterial Analyzedcompound Analyzedcompound Purpose Purpose Amperometric electrodes Clark type Amperometric electrodes Clark type Microorganisms of the genera Nitrobacter,Gluconobacter, Comamonas, Pseudomonas, Rhodococcus. Yeast cells of the genera Arxula, Pichia. Carbo-hydrates, xenobiotics, alcohols Environmentalmonitoring Monitoring of fermentation processes Environmentalmonitoring Monitoring of fermentation processes Clark type electrodes Enzymes Ethylalcohol Monitoring of fermentation processes Monitoring of fermentation processes Basic trends of research at our laboratory

4. pH-sensitive field-effect transistors (FET) Transducer Microbial cells Biomaterial Sugars Analyzed compound Purpose Monitoring of fermentationprocesses pH-sensitive FETs Enzymes Cholineste-rase Detection of cholinesteraseinhibitors pH-sensitive FETs Immunosensors, enzymes – labels Horse-radishperoxidase Urease Glucoseoxidase Pesticides Microbialcells Basic trends of research at our laboratory

5. From: K.Riedel et al., 1997, Antonie Van Leeuwenhoek, V. 71. The scheme of measurement of cell respiration - the intensity of oxidation process. A cell is fixed on electrode surface. Oxygen consumption is insignificant in the absence of substrate and increases when substrate is added. Electrode current is proportional to oxidation rate.

6. The presented picture is called a substrate portrait. 1 - ethanol 2 - glucose 3 - xylose 4 - xylitol 5 - arabinose 6 - arabitol 7 - glycerol 8 – pyruvate 9- citrate 7 strains. High difference in sensitivity. Sensor signals corresponding to GLUCOSE are taken as 100%. Clark –type electrode registration.

7. The substrate (metabolic) portrait With many substrates, one can obtain a unique characteristic describing the given type of cells – that is “metabolic fingerprint” (the substrate (metabolic) portrait).

8. Suppose there is an objective to detect three substances, which are simultaneously present in a sample. Let us consider the case when the substance is either present or absent in a sample. The substances are ethanol, glucose, and xylose. This objective is important for the monitoring of some biotechnological processes (for example – alcohol production): xylose (sugar) and glucose (sugar) are substrates and ethanol is a product. The questions are: (1) Is it possible to use biosensors for this purpose? (2) Is it possible to solve this problem using the elements of cluster analysis?

9. The plan of solution of the above problem – a scheme The measuring system was represented by three amperometric microbial sensors with immobilized cells of Gluconobacter oxydans (no 6), Hansenula polymorpha (no 3), and Escherichia coli (no 8). 10 strains were tested altogether; 3 strains were selected.

10. Michaelis constants – biochemical assessment

11. Gluconobacter oxydans, Hansenula polymorpha и Escherichia coli.

12. An appearance of the 4-channel set-upin the process of an experiment

13. Types of clusters – analyzed substances and their combinations made 8 types of clusters 1 - Glucose, 1 mM 2 – Xylose, 1 mM 3 – Ethanol, 1 mM 4 - Glucose + xylose, 1 + 1 mM 5 - Glucose + ethanol, 1+ 1 mM 6 - Ethanol + glucose, 1 + 1 mM 7 - Glucose + xylose + ethanol, 1 + 1 + 1 mM 8 - Glucose = xylose = ethanol = 0 mM

14. Normalization of sensor signals –for the possibility of comparing the signals from different sensors in response to different substances Where Sai is the response of sensor a to sample i. The responses of sensors S1, S2, and S3 make it possible to obtain a single vector Siin the space of “Sensor response 1” – “Sensor response 2” – “Sensor response 3”.

15. The space of clusters – glucose-xylose-ethanol.The clusters are located on the surface of a single sphere.

16. Identification of a sample by microbial sensors – the scheme For measurement, sensor readings are normalized in accordance with the given formula. The formula allows obtaining of sensor responses S1, S2, and S3, the sum of which gives vector S. i = 1, 2, 3, 4, 5, 6, 7, 8 clusters. 39 samples were used for clusters formation – 7 centers Then, the distance from the point specified by vector Si to the centers of clusters is calculated. The sample is considered to correspond to the closest cluster (the Figure shows an example of a sample belonging to the “ethanol” cluster).

17. The sample is considered to correspond to the closest cluster (the Figure shows an example of a sample belonging to the “ethanol” cluster).

18. The example of recognition of two compositions – distance to the clusters is presented. (A) glucose + xylose + ethanol and (B) ethanol + xylose= ethanol a б А) B)

19. Task Conclusion (1) Is it possible to use biosensors for recognition? (2) Is it possible to solve this problem using the elements of cluster analysis, i.e. pattern recognition theory? --------------------------------------------------------------------------------------------------------- It has been shown possible to identify the components of a mixture containing glucose, xylose, and ethanol by sensors based on microbial cells.

20. Conclusion At the analysis of 39 control samples, 37 samples were recognized correctly and two samples were recognized falsely (the ethanol + xylose mixture was identified twice as ethanol only). What was new ? Difficult type of analysis – LC ? Microbial cells – not enzymes (GOD, AO)

21. Conclusion The possibility of combining the efforts of specialists • in chemometrics(pattern recognition) and • developers of biosensors for joint solution of the problems of detection quality improvement is welcome. For example – detection of false strong drinks.

22. Laboratory of biosensors

23. Thank you! Reshetilov Anatoly N. E-mail: anatol@ibpm.pushchino.ru