Quantitative Detection of Complex Mixtures using a Single Chemical Sensor: Analysis of Response Transients using Multi-Stage Estimation.

Most chemical sensors are only partially selective to any specific target analyte(s), making identification and quantification of analyte mixtures challenging, a problem often addressed using arrays of partially selective sensors. This work presents and experimentally verifies a signal-processing technique based on estimation theory for online identification and quantification of multiple analytes using only the response data collected from a single polymer-coated sensor device. The demonstrated technique, based on multiple stages of exponentially weighted recursive least-squares estimation (EW-RLSE), first determines which of the analytes included in the sensor response model are absent from the mixture being analyzed; these are then eliminated from the model prior to executing the final stage of EW-RLSE, in which the sample's constituent analytes are more accurately quantified. The overall method is based on a sensor response model with specific parameters describing each coating-analyte pair and requires no initial assumptions regarding the concentrations of the analytes in a given sample. The technique was tested using the measured responses of polymer-coated shear-horizontal surface acoustic wave devices to multi-analyte mixtures of benzene, toluene, ethylbenzene, xylenes, and 1,2,4-trimethylbenzene in water. The results demonstrate how this method accurately identifies and quantifies the analytes present in a sample using the measured response of just a single sensor device. This effective, simple, lower-cost alternative to sensor arrays needs no arduous training protocol, just measurement of the response characteristics of each individual target analyte and the likely interferents and/or classes thereof.

Obtaining Chemical Selectivity from a Single, Nonselective Sensing Film: Two-Stage Adaptive Estimation Scheme with Multiparameter Measurement to Quantify Mixture Components and Interferents.

A new approach is reported to detect and quantify the members of a group of small-aromatic-molecule target analytes: benzene, toluene, ethylbenzene, and xylenes (BTEX), dissolved in water, in the presence of interferents, using only the data collected from a single polymer-coated SH-SAW (shear horizontal surface acoustic wave) device and a two-stage adaptive estimation scheme. This technique is composed of exponentially weighted recursive least-squares estimation (EW-RLSE) and a bank of Kalman filters (BKFs) and does not require any prior knowledge of the initial concentration range of the target analytes. The proposed approach utilizes the transient sensor response to sorption and/or desorption of the analytes as well as the error range associated with the response time constants to provide more information about the analyte-specific interactions with the polymer film. The approach assumes that the sensor response to contaminated groundwater is a linear combination of the responses to the single target analytes, the interferents that interact with the selected polymer sensor coatings, and measurement noise. The proposed technique was tested using actual sensor responses to contaminated groundwater samples containing multiple BTEX compounds with concentrations ranging from 10 to 2000 parts per billion, as well as common interferents including ethanol, 1,2,4-trimethylbenzene, naphthalene, n-heptane, and MTBE (methyl tert-butyl ether). Estimated concentration values, accurate to ±10% for benzene/toluene and ±15% for ethylbenzene/xylenes, are obtained in near-real time. The utilization of sorption and/or desorption data enables detection and quantification of BTEX compounds with improved accuracy, high tolerance to measurement noise, and improved chemical selectivity.