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

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 = 10 3 / 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.