Most conventional sensors for the detection of chemicals and/or fire are electrochemical sensors. Electrochemical sensors operate at a fixed potential. Changes in that fixed potential, caused by the presence of an analyte, signal the analytes presence. Most often these sensors are directed towards the recognition of a single analyte and utilize a single sensor or a simple array of sensors.
Current concerns about homeland security, battlefield protection and terrorist activities have created an interest in sensors that detect a variety of chemicals, such as a variety of blood agents, toxic industrial chemicals, explosives, etc, preferably with specificity for the identification of type and quantity of chemicals detected.
Also, an interest has developed in fire prevention and fire detection. Along with fire detection, early detection of various gases or of precursors of fires, before the presence of flame and smoke, which are necessary for detection using conventional smoke detection/fire detection devices, is also desirable. For example, with increased automation on ships, the Navy has sought fire detection systems capable of improved performance over conventional smoke detectors. To the extent such precursors can be identified, fire prevention can occur in advance of mere fire detection to avoid damage to expensive infrastructure.
Although the use of multiple conventional electrochemical sensors monitoring multiple criteria has been studied for this type of chemical identification with varying degree of success, most of the sensors did not have the specificity desired. For example, in fire detection, multiple criteria electrochemical sensors have difficulty discriminating fire-like nuisance sources, such as welding, grinding steel, and cutting with a torch. Further, multiple criteria electrochemical sensors are typically limited to only a few standardized test sources, without providing the range of detection capability often desired.
Cermet sensors are well known in the art that use various ceramic metallic (cermet) films. See U.S. Pat. No. 6,218,687, which is incorporated herein by reference in its entirety. Rather than operating based on a single potential, some cermet sensors characterize data using cyclic voltammetry, in which current is measured while manipulating the potential in a cyclical fashion. Thus, the data provided by such sensors is provided in a cyclical waveform.
Cermet sensor arrays have been used for more realistic and difficult conditions than prior electrochemical sensors and have been used to identify chemical analytes and fires, particularly distinguishing nuisance sources from harmful events. Such cermet sensors are capable of high temperature operation and can be fabricated using both thick and thin film techniques. Cermet sensors are small, lightweight and low cost alternatives to conventional electrochemical sensors. Plus, cermet sensors have design flexibility since different cermet sensors may respond to the same gas in different ways. Using conventional methods for data analysis of cyclic voltammetry, cermet sensors are capable of detecting very low level concentrations (part per billion) for a variety of analytes using aqueous electrolytes, but only higher levels (parts per million) for gas phase detection.
However, the use of cyclic voltammetry creates complex data waveforms having a great deal of information, such as peak position, magnitude, and shape, all of which can be exploited for analytical purposes. Such waveforms contain data that incorporates variations in the presence, concentration, temperature, accessibility to the sensor, function of the sensor etc. which must be selectively analyzed before any meaningful information about the identity and/or quantity of a certain analyte or fire/non-fire condition can be ascertained.
Previously pattern recognition software has been used to analyze waveform data. However, the shear volume of data generated by cyclic voltammetry has caused concern about accuracy of probable identification of analyte sources, particular in the presence of quite similar nuisance sources. Further, the volume of data required may be more than a microprocessor embedded within a portable device used for field applications is capable of processing in a fast, real-time manner.
Wavelet analysis of analytical data prior to pattern recognition has been used in a variety of different applications. For example, wavelet analysis has been used in medicine to specifically look at identifying conditions from time-dependant data such as EEG and ECG scans and in identifying features of interest in medical imagery. Non-medical applications also included image analysis, as well as optical character recognition, and acoustic pattern recognition.
In sensing applications, wavelet analysis of linear voltammetric data of analytical chemical data, has been demonstrated as a means of de-noising linear voltammetric data. An “electric tongue” system has been developed for monitoring industrial liquid process streams. They utilize a “Wavelet Neural Network” as a pattern recognition technique which incorporates wavelet transformation into a feed forward multilayer neural network architecture. The parameters of the wavelet transform are thus modified by the error propagation structure of the neural network during training. Wavelet packet transforms have also been used for pattern recognition. The wavelet packet transforms have included the use of a Euclidean distance measure to generate the best wavelet basis set of an analytical signal for classification purposes. This approach has been generally applied only to spectroscopic and chromatographic data. Wavelet transformation has also been used for semi-conductor based gas sensors, particularly the use of discrete wavelet transform as a data pretreatment step prior to pattern recognition via a number of different techniques. Wavelet transform followed by classification by a PNN has been done by HPLC-DAD data in the classification of Chilean wines, peak detection in LC-TOF MS data and to capture features in NIR data.
In these various known data analysis techniques, the wavelet-transformed data are not down-selected or narrowed based on any particular classifying ability. As such, the data continues to include random variables and data points which are not necessary for classification purposes. The inclusion of unnecessary data points increases processing time and creates additional variables which can unnecessarily complicate or interfere with pattern recognition. In some cases, the wavelet coefficients may be selected based upon mere visual inspection. However, it is particularly difficult to discern narrow differences by mere visual inspection. In other cases, another “quick and dirty” method has been used to reduces the wavelet data, but not by any particular method particularly suitable for classification purposes. These “quick and dirty” methods do not ensure selection of the most desirable features of a wavelet, which can lead to lack of accuracy in pattern recognition.