Neural Networks and Self-Referent Acoustic-Wave Sensor Signaling
Lan Nang Bui, Michael Thompson · ACS symposium series · 1998
This paper presents the application of artificial neural networks (ANN) to the self-referent calibration of thickness-shear mode (TSM) acoustic wave chemical sensors. Spectrum analysis of impedance measurements affords complete characterization of the TSM sensor, which includes the use of the Butterworth - Van Dyke (BVD) equivalent circuit to quantify the electrical responses. The multidimensional nature of this method and a novel weight-adjustment procedure, applied to ANN calculations, are utilised to effect a method of calibration in the presence of interferents. A network is trained, using exemplary I/O data acquired for a potassium chloride (KCl) system, to predict unknown outputs ie. concentration, given four sets of measured inputs ie . series resonant frequency (Fs), parallel resonant frequency (Fp), motional capacitance (Cm) and motional resistance (Rm). The trained and tested network achieved a high predictive efficiency with errors in the range of 2%-6%. An interferent, ethanethiol, is added to test the robustness of the trained network and was found to adversely affect the predictive ability of the network. The magnitudes of the weights, which are associated with the set of inputs deemed to be most affected by the interferent (Fs) are adjusted to minimise this deterioration. The resultant network, calibrated for the interferent, achieved the same predictive efficiency for adulterated samples as that achieved by the original network for unadulterated samples.