Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Chemical analysis
Reexamination Certificate
2001-03-01
2003-08-12
Hoff, Marc S. (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Chemical analysis
C702S090000
Reexamination Certificate
active
06606567
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates generally to a method for characterizing, classifying, and identifying unknown chemicals. Specifically, the present invention is a method for taking the data generated from an array of responses from a multichannel instrument, and determining the characteristics of a chemical in the sample without the necessity of calibrating or training the instrument with known samples containing the same chemical. The characteristics determined by the method are then used to classify and identify the chemical in the sample. The method can also be used to quantify the concentration of the chemical in the sample.
BACKGROUND OF THE INVENTION
The characterization and identification of unknown chemical is a common requirement throughout an enormous variety of scientific inquiry, running across disciplines as diverse as biochemistry and environmental science. Unsurprisingly, there exist an equally enormous variety of techniques for determining the characteristics and identity of a chemical in a sample. Liquid and gas chromatography, mass spectroscopy, absorption spectroscopy, emission spectroscopy, and chemical sensors are but a few of the myriad of techniques scientists have devised in their efforts to characterize, classify, and identify unknown chemicals in samples.
Typically, these methods rely on inferences drawn from the information that is the output of a particular instrument. For example, methods that identify chemicals through absorption spectroscopy rely on the absorption of light at certain wavelengths when the sample containing the chemical is exposed to a light. By understanding the properties of a given chemical which give rise to absorption at certain wavelengths, scientists are able to infer some of a sample's characteristics and perhaps identity the chemical(s) in the sample for example, by comparing the absorption spectra of a sample with a library of spectra taken from known chemicals. As such, these techniques often rely on determining the output signals of an instrument in response to chemicals whose identity and characteristics are known. Additionally, samples of chemicals whose concentrations are unknown may present problems for characterizing, classifying, identifying or quantifying unknowns using these types of instruments. Quantification often relies on rigorous calibration of the instrument in response to known samples of the chemical to be determined in the unknown samples. To overcome these and other difficulties, scientists have developed methods wherein a sample with an unknown chemical is interrogated with an array of channels from a particular instrument, for example, wherein the differences in the interactions between the various channels across the array with different chemicals is known from prior training and calibration on samples containing the same chemical as the unknown sample.
For example, a great many studies have described the use of arrays of chemical sensors to classify, identify, and quantify chemicals in a sample. Typically in these methods, the sensor array must be trained on samples containing chemicals of known identity and concentration in order to develop pattern recognition algorithms and calibration models that are used to classify, identify and quantify chemicals in unknown samples.[B. M. Wise, N. B. Gallagher, and M. W. A. U. S. A. Eigenvector Research, The process chemometrics approach to process monitoring and fault detection,
J. Process Control,
6 (1996) 329-348. K. R. Beebe, R. J. Pell, and M. B. Seasholtz,
Chemometrics: A Practical Guide
, John Wiley and Sons, Inc., New York, 1998.] The only chemicals that can be classified, identified and quantified by this technique are chemicals to which the array has been previously exposed to generate output data that have been incorporated into the development of the pattern recognition algorithms and calibration models.
For example, acoustic wave sensors coated with layers of sorbent materials, such as polymers, have been investigated as array detectors by many groups.[J. W. Grate, S. J. Martin, and R. M. White, Acoustic Wave Microsensors, Part I,
Anal Chem.,
65 (1993) 940A-948A. J. W. Grate, S. J. Martin, and R. M. White, Acoustic Wave Microsensors, Part II,
Anal. Chem.,
65 (1993) 987A-996A. J. W. Grate, and G. C. Frye, “Acoustic Wave Sensors,” in
Sensors Update
, VSH, Weinheim, 1996, pp. 37-83.] Polymer-coated acoustic wave sensors are well understood in terms of the sensors' transduction mechanisms and the interactions of analyte species with the polymeric sensing layers. A great variety of acoustic wave devices have been developed and demonstrated for chemical sensing applications in the gas and liquid phases. These include thickness shear mode (TSM) devices (also known as the quartz crystal microbalance or QCM), surface acoustic wave (SAW) devices, Leaky SAW devices, surface transverse wave (STW) devices, Love wave devices, shear-horizontal acoustic plate mode (SH-APM) devices, flexural plate wave (FPW) devices, thin film resonators, and thin rod flexural devices. Acoustic wave vapor sensors respond to any vapor that is sorbed at the sensing surface with a response that is proportional to the amount of vapor sorbed. The transduction mechanism of these sensors, which always involves a mass-loading contribution and often involves a polymer modulus change contribution, does not discriminate among sorbed species. Discrimination is dependent largely on the extent to which the applied polymer layer interacts with and sorbs particular chemical species. In addition, other sensor devices exist that are also sensitive to added mass, such as microbar, microbeam, and microcantilever devices.
The interactions between vapor molecules and polymeric sorbent phases are solubility interactions, which have been modeled and systematically investigated using linear solvation energy relationships (LSERs).[J. W. Grate, M. H. Abraham, and R. A. McGill, “Sorbent Polymer Coatings for Chemical Sensors and Arrays,” in
Handbook of Biosensors: Medicine, Food, and the Environment
, CRC Press, Boca Raton, Fla., USA, 1996, pp. 593-612.]
In this approach, vapor solubility properties are characterized and quantified by solvation parameters related to polarizability, dipolarity, hydrogen bond acidity, hydrogen bond basicity, and dispersion interactions. The solvation parameters are the descriptors for vapor characteristics. LSER equations correlate the log of the partition coefficient of a vapor in a polymer with the vapor solvation parameters using a series of LSER coefficients related to the polymer solubility properties
LSERs are linear multivariate correlations with solvation parameters that have been applied to many systems, including water/air partition coefficients, the sorption of vapors by blood and tissue, toxicity of gases and vapors, adsorption on solid sorbents, adsorption on fullerene, and partitioning into gas-liquid chromatographic stationary phases. In addition, LSERs have been used to correlate various sensory measures with solvation parameters, including retention across frog olfactory mucosa, respiratory tract irritation, potency, nasal pungency thresholds and odor thresholds. The partitioning of vapors into sorbent polymers at 298K has been investigated with LSERs (correlation coefficients were typically 0.99), and these LSER equations have been used to estimate the responses of polymer-coated surface acoustic wave (SAW) vapor sensors. In addition, LSERs have been developed that correlate the responses of polymer-coated SAW devices to vapor solvation parameters. These yield LSER coefficients related to partitioning and detection of vapors with polymer films on SAW device surfaces.
When a polymer-coated acoustic wave vapor sensor is exposed to a vapor, the equilibrium distribution of the vapor between the gas phase and a polymeric sorbent phase on the sensor surface is given by the partition coefficient, K. This partition coefficient is the ratio of the concentration of the vapor in the sorbent polymer,
Grate Jay W.
Wise Barry M.
Battelle (Memorial Institute)
Hoff Marc S.
Miller Craig Steven
Woodard Emhardt Moriarty McNett & Henry LLP
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