Metal ion solutions identification using acoustic plate mode sensors and principal component analysis

The parameters used to rate acoustic wave-based chemical sensors are sensitivity and selectivity. While sensitivity can be improved by selecting the appropriate sensor device, selectivity still remains a major concern. This is primarily because the analytes under consideration often belong to the same group/class of chemicals. In such cases, the sensor output does not provide enough information to reliably identify, estimate and/or classify the analytes being investigated. As a result, data analysis techniques are used to extract selectivity from the sensor. An approach to analyze sensor signal data using statistical pattern recognition techniques such as principal component analysis and nearest neighbor algorithm is presented. Sensor signals namely frequency and attenuation data were collected for single component and binary dilute solutions of alkali metal ions (cesium, lithium, potassium, and sodium) using acoustic plate mode (APM) sensor devices. Principal component analysis of the attenuation data serves to identify an unknown sample of single component metal ion solution, and the nearest neighbor algorithm is used to estimate the concentration of the identified solution. The identification and estimation of an unknown sample of a dilute metal ion solution were performed with very high accuracy and very few errors and exhibit little sensitivity to the sampling rate of the measured data. For the binary mixtures, the proposed algorithm is based on the distance comparison between frequency and attenuation data of the binary solutions and similar data measured for the respective single component solutions.