METHODS FOR ANALYTE DETECTION

• US 20120235690A1
• Filed: 05/31/2012
• Published: 09/20/2012
• Est. Priority Date: 04/15/2009
• Status: Active Grant

• Current Assignee: GE Infrastructure Technology, LLC (General Electric Company)
• Original Assignee: General Electric Company
• Inventors: Potyrailo, Radislav Alexandrovich, Pietrzykowski, Matthew Damian, Lee, Yongjae
• Granted Patent: US 9,052,263 B2
• Time in Patent Office: Days
• US Class Current: 324/652
• CPC Class Codes: G01N 27/026 Dielectric impedance spectroscopy electrochemical impedance spectroscopy for measuring corrosion G01N17/02
• CPC Class Codes: G01N 27/028 Circuits therefor measuring impedance per se G01R27/02

Abstract

A method is provided for selectively detecting the presence and concentration of at least four analytes in a mixture. In certain embodiments, the method comprises contacting a single sensor with the mixture of analytes, wherein the sensor comprises at least one resonant sensor circuit comprising a sensing material that comprises at least two material properties that change in the presence of four or more analytes in their mixtures, and generating a multivariate sensor response pattern. The methods disclosed herein further optionally comprise performing analyte classification and analyte quantitation. Methods for selectively detecting the concentration of at least one analyte in a mixture further comprising at least one interference are also described in the instant application.

Claims

1. A method for selectively detecting concentrations of at least four analytes in a mixture of the analytes, wherein the method comprises the steps of:

contacting a single sensor with the mixture, wherein the sensor comprises at least one resonant sensor circuit comprising a sensing material responsive to the mixture; and

generating a multivariate sensor response pattern, and thereby detecting the concentrations of the at least four analytes in the mixture.

2. The method of claim 1, wherein the sensing material has at least two material properties that change at different ratios in the presence of at least four analytes in the mixture.

3. The method of claim 1 further comprising acquiring the complex impedance response of the sensor.

4. The method of claim 1, wherein generating the multivariate sensor response pattern comprises performing a multivariate analysis on the complex impedance response of the sensor.

5. The method of claim 1, wherein the multivariate sensor response pattern identifiably separates patterns associated with the different analytes in the mixture in a manner dependent on the concentration of a particular analyte present in the mixture.

6. The method of claim 1, wherein the sensing material comprises an organic electronic material, a conjugated polymer, a ligand-protected metal nanoparticles film, a nanotube, a nanoparticle, a nanosheet, a carbon nanotube, or a graphene sheet.

7. The method of claim 6, wherein the sensing material is formulated with an additive.

8. The method of claim 1, wherein the at least four analytes are selected from the group consisting of inorganic gases, volatile organic compounds (VOC), oxidizing and reducing gases, dissolved gases, biological species in water or in air, and any combination thereof.

9. The method of claim 1 further comprising acquiring the complex impedance response of the sensor, wherein the imaginary portion of the complex impedance contains positive and negative impedance values.

10. The method of claim 1, where sensor is a sensing node and is a part of the sensor network.

11. A method for selectively detecting concentrations of at least four analytes in a mixture of the analytes, wherein the method comprises the steps of:

contacting a single sensor with the mixture, wherein the sensor comprises at least one resonant sensor circuit comprising a sensing material that comprises at least two material properties that change at different ratios in the presence of at least four analytes in the mixture; and

generating a multivariate sensor response pattern;

performing analyte classification; and

performing analyte quantitation and thereby detecting the concentrations of the at least four analytes in the mixture.

12. The method of claim 11, wherein methods for performing analyte classification are selected from the group consisting of Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA), and Support Vector Machines (SVM) algorithm.

13. The method of claim 11, wherein methods for performing analyte quantitation are selected from Principal Component Regression (PCR) and Independent Component Regression (ICR).

14. The method of claim 11, wherein the analyte classification is performed by an SVM algorithm and the analyte quantitation is performed by PCR.

15. A method for selectively detecting concentrations of at least one analyte in a mixture further comprising at least one unknown interference, wherein the method comprises the steps of:

comprising contacting a sensor with the mixture, wherein the sensor comprises at least one resonant sensor circuit comprising a sensing material that comprises at least two material properties that change at different ratios in the presence of at least one analyte in the mixture; and

generating a multivariate sensor response pattern, and thereby detecting the concentration of the at least one analyte in the mixture.

16. The method of claim 15 further comprising the steps of:

performing analyte classification; and

performing analyte quantitation and thereby detecting the concentration of the at least one analyte in the mixture.

17. The method of claim 16, wherein the analyte classification is performed by an SVM algorithm and the analyte quantitation is performed by PCR.

18. The method of claim 16 further comprising acquiring the complex impedance response of the sensor.

19. The method of claim 15, wherein generating the multivariate sensor response pattern comprises performing a multivariate analysis on the complex impedance response of the sensor.

20. The method of claim 15, wherein the multivariate sensor response pattern identifiably separates patterns associated with the at least one analyte in the mixture in a manner dependent on the concentration of a particular analyte present in the mixture.

21. The method of claim 15, wherein the at least one analyte is selected from the group consisting of inorganic gases, volatile organic compounds (VOC), oxidizing and reducing gases, dissolved gases, biological species in water or in air, and any combination thereof.

22. The method of claim 15, wherein the interference has a dielectric constant that is different than the dielectric constant of the analyte.

23. The method of claim 15, wherein the interference has complex permittivity different from the complex permittivity of analyte.

24. The method of claim 15, wherein the interference is an environmental, industrial, food spoilage, or breath volatile interference.

25. A method for selective detection of at least two analytes with one wireless, multivariate, battery-free sensor, the method comprises the steps of:

contacting at least two analytes with a sensor circuit, wherein the sensor circuit comprises at least one integrated circuit chip that is configured to accept inputs from at least two sensors, where the sensors are positioned in proximity to the chip or are integrated into the chip;

detecting more than one analyte using at least two inputs into an integrated circuit chip of the sensor circuit where the inputs into the integrated circuit chip are from the same or from different sensing materials;

determining a multivariate response pattern of the sensor system upon sensor exposure to different analytes by performing a multivariate analysis of the response of at least two sensors; and

identifying the analytes and concentrations of at least two analytes from this pattern, wherein one of the analytes is an analyte of interest to an end-user and the second analyte is an interference or another analyte of interest to the end-user.

26. The method of claim 25, wherein the sensor is a sensing node and is a part of the sensor network.