1. A process for creating a calibration data set to predict the amount of deliberately added constituents in an electrolyte solution at various temperatures, the process comprising: (a) obtaining a sample set, wherein each sample in the set comprises an electrolyte solution of known composition; (b) obtaining an electroanalytical response for each sample in the sample set to produce an electroanalytical response data set at various temperatures in a preselected range of low to high temperature limits; (c) obtaining a training set that comprises the sample set and the corresponding electroanalytical response data set at various temperatures in the preselected range of low to high temperature limits including the target temperature; (d) preprocessing the training set; (e) determining the calibration range; (f) detecting and eliminating outliers from the response data set; (g) determining the optimal number of factors; (h) detecting and eliminating outliers within training set; (i) analyzing the training set using multivariate regression to produce a regression set with embedded temperature compensation of temperature variation in the temperature of the electrolyte solution; (j) cross validating a subset of the training set using multivariate regression to compensate for the effect of temperature on electroanalytical response, which produces a regression set with temperature compensation; and (k) validating the regression set with temperature compensation to produce a predictive set for a predictive calibration model for various temperatures in the preselected range low to high limits.