Nonlinear Regression Algorithm for Processing Signals from Semiconductor Chemical Sensors to Provide Selective Detection of Impurities in Artificial Air

Abstract

A new method has been developed for processing the signal of changes in electrical conductivity Δσ under temperature (T) modulation of a chemical sensor for the selective determination of trace concentrations of ammonia, acetone, n-hexane, propane, toluene, and other impurities in air. The method consists in the fact that, in the range of precisely set concentrations C of each of impurities Y, the signal Δσ as a function of reciprocal temperature z = 10³/T is interpolated using nonlinear regression by a set of parameterized functions F_i (z, A_i, b_i, c_i, …), i = 1−4, and the dependences for principal (concentration) parameters A_iY (C) are plotted, which determine the so-called “selectivity portrait” of Y. Fitting into it, similar values ​​for detected impurity X confirm its identity with Y, and the common abscissa of all intersection points A_iX level lines with A_iY (C) defines the numerical value and unit of measurement for the C_X concentration.