A process for creating a predictive data set predicting the amount of target constituents are in an electrolyte solution at varying temperatures is provided. The process includes (a) obtaining a sample set in which each sample comprises an electrolyte solution of known composition; (b) obtaining an electroanalytical response for each sample in the sample set to produce a electroanalytical response data set at a pre-determined, chosen target temperature; (c) obtaining a plurality of training sets, each training set being measured at a different pre-selected temperature in a range of low to high temperatures that comprises the sample set and corresponds to the electroanalytical response data set; (d) analyzing each of the training sets individually using decomposition and multivariate regression methods to produce a plurality of regression data sets, each at different temperature; and (e) validating the plurality of training data sets to produce a predictive data set for predictive calibration models, each at different temperatures in the pre-selected range of low to high temperatures.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A process for creating a predictive data set for use in predicting the amount of target constituent in an electrolyte solution at varying temperatures, said 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 a pre-selected target temperature; (c) obtaining a plurality of training sets, each training set being measured at a different pre-selected temperature in a range of low to high temperatures that comprises the sample set and corresponds to the electroanalytical response data set; (d) analyzing each of the training sets individually using decomposition and multivariate regression methods to produce a plurality of regression data sets, each at different temperature; (e) validating the plurality of training data sets to produce a predictive data set for predictive calibration models, each at different temperatures in the pre-selected range of low to high temperatures. 2. The process according to claim 1 wherein the electrolyte solution is an electroplating bath. 3. The process according to claim 2 wherein the electroplating bath comprises a plating bath of one or more metals selected from the group consisting of Cu, Sn, Pb, Zn, Ni, Ag, Cd, Co, Cr, and/or their alloys. 4. The process according to claim 1 wherein the electrolyte solution is an electroless plating bath. 5. The process of claim 4 wherein the electroless plating bath comprises an (a) an autocatalytic plating bath of one or more metals selected from the group consisting of Cu, Sn, Pb, Ni, Ag, Au, and/or their alloys or (b) an immersion plating bath of one or more metals selected from the group consisting of Cu, Sn, Pb, Ni, Ag, Au and/or their alloys. 6. The process according to claim 1 wherein the electroanalytical response of step (b) is obtained by a method selected from the group consisting of DC Voltammetry, Normal Pulse Voltammetry, Reverse Pulse Voltammetry, Differential Pulse Voltammetry, Square Wave Voltammetry, AC Voltammetry, Multi-Frequency AC Voltammetry, Chronoamperometry, Chronopotentiometry, Electrochemical Impedance Spectroscopy, Dynamic Electrochemical Impedance Spectroscopy, a Polarographic technique, and a combination of any two or more of the foregoing techniques. 7. The process of claim 6 wherein the DC Voltammetry is selected from the group consisting of DC Cyclic Voltammetry, DC Linear Scan Voltammetry, DC Anodic Stripping Voltammetry, DC Cathodic Stripping Voltammetry, DC Adsorptive Stripping Voltammetry, DC Cyclic Voltammetric Stripping, and DC Staircase Voltammetry. 8. The process according to claim 1 wherein the electroanalytical response of step (b) comprises a plurality of data points. 9. The process according to claim 1 wherein the electroanalytical response of step (b) is a combination of one or more portions of a complete electroanalytical response. 10. The process according to claim 1 wherein the electroanalytical response of step (b) comprises a combination of one or more portions of independent electroanalytical responses. 11. The process according to claim 1 wherein the regression data set of step (d) is obtained by sequential decomposition followed by a multivariate regression selected from PCR, HPCR, CPCR and PARAFAC/ILS. 12. The process according to claim 1 wherein the regression data set of step (d) is obtained by simultaneous decomposition and regression selected from PLS, HPLS, MBPLS and N-PLS. 13. A process for creating a calibration data set to predict the amount of a target constituent 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 a target temperature and at various temperatures in a pre-selected 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 pre-selected 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 at the target temperature; (j) analyzing a subset of the training using multivariate regression to compensate for the temperature effect to produce 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 pre-selected range low to high limits. 14. The process according to claim 13 wherein the electrolyte solution is an electroplating bath. 15. The process of claim 14 wherein the electroplating bath comprises a plating bath of one or more metals selected from the group consisting of Cu, Sn, Pb, Zn, Ni, Ag, Cd, Co, Cr, and/or their alloys. 16. The process according to claim 13 wherein the electrolyte solution is an electroless plating bath. 17. The process of claim 16 wherein the electroless plating bath comprises an autocatalytic plating bath of one or more metals selected from the group consisting of Cu, Sn, Pb, Ni, Ag, Au, and/or their alloys or an immersion plating bath of one or more metals selected from the group consisting of Cu, Sn, Pb, Ni, Ag, Au and/or their alloys. 18. The process according to claim 13 wherein the electroanalytical response of step (b) is obtained by a method selected from the group consisting of DC Voltammetry, Normal Pulse Voltammetry, Reverse Pulse Voltammetry, Differential Pulse Voltammetry, Square Wave Voltammetry, AC Voltammetry, Multi-Frequency AC Voltammetry, Chronoamperometry, Chronopotentiometry, Electrochemical Impedance Spectroscopy, Dynamic Electrochemical Impedance Spectroscopy, a Polarographic technique, and a combination of any two or more of the foregoing techniques. 19. The process of claim 18 wherein the DC Voltammetry technique is selected from the group consisting of DC Cyclic Voltammetry, DC Linear Scan Voltammetry, DC Anodic Stripping Voltammetry, DC Cathodic Stripping Voltammetry, DC Adsorptive Stripping Voltammetry, and DC Cyclic Voltammetric Stripping, and DC Staircase Voltammetry. 20. The process according to claim 13 wherein the electroanalytical response of step (b) comprises a plurality of data points. 21. The process according to claim 13 wherein the electroanalytical response of step (b) is a combination of one or more portions of a complete electroanalytical response. 22. The process according to claim 13 wherein the electroanalytical response of step (b) comprises a combination of one or more portions of independent electroanalytical responses. 23. The process according to claim 13 wherein step (d) comprises autoscaling the data to unit variance. 24. The process according to claim 13 wherein step (e) comprises the steps of: a) analyzing the data using correlation coefficient calculations based on the least squares regression; b) analyzing the data using SIMCA based calculations of modeling power; c) analyzing the data using a product of the correlation coefficient and the modeling power. 25. The process according to claim 13 wherein step (f) comprises analyzing the data using a techniques selected from the group consisting of principle component analysis, Mahalanobis distance, Mahalanobis distance coupled with principal component analysis, Mahalanobis distance coupled with said principal component analysis with Q residuals, SIMCA, and PRESS. 26. The process according to claim 25 wherein the PRESS analysis is based on PCR, HPCR, CPCR, PARAFAC/ILS calculations or on PLS, HPLS, MBPLS, N-PLS calculations. 27. The process according to claim 13 wherein step (h) comprises analyzing the data using a technique selected from the group consisting of F sup. C-ratio analysis, Studentized concentration residuals analysis, leverages analysis, and Studentized concentration residuals analysis coupled with leverages analysis. 28. The process according to claim 13 wherein step (i) comprises analyzing the data using PLS, HPLS, MBPLS, N-PLS or using PCR, HPCR, CPCR, PARAFAC/ILS. 29. A process for predicting the concentration of target constituent in an electrolyte solution comprising: (a) generating a predictive data set by: (1) obtaining a sample set, wherein each sample in the sample set comprises an electrolyte solution of known composition; (2) obtaining an electroanalytical response for each sample in the sample set to produce an electroanalytical response data set for various temperatures in a pre-selected low to high limit range; (3) obtaining a training set that comprises the sample set and the corresponding electroanalytical response data set for various temperatures in the pre-selected low to high limit range; (4) analyzing the training set using decomposition and multivariate regression method to produce a first regression data set; (5) analyzing the training set using multivariate regression method for temperature compensation to produce a second regression data set; (6) validating the training data set to produce the predictive data set for a predictive calibration model. (b) using the predictive data set produced in (a) to predict the concentration of target constituent at various temperature levels in a pre-selected low to high limit range by: (1) obtaining an unknown sample set, wherein each unknown sample in said unknown sample set contains an electrolyte solution; (2) obtaining an electroanalytical response at various temperature levels in a pre-selected low to high limit range for each unknown sample to produce an electroanalytical response data set; (3) preprocessing the electroanalytical response data set; (4) applying the predictive calibration model to predict the concentration of target component in each sample; and (5) applying the predictive calibration model with compensation for the temperature effect on concentration of target component in each sample.