Patent Document ID: 5435309
Application ID: 08104857
Patent Status: 1

Claim One:
1. In a method for use with optical instrumentation for determining one or more unknown values of at least one known characteristic by an optical measurement, said method including the steps of: (a) irradiating said material having said unknown values of said known characteristic with electromagnetic energy including at least several wavelengths so that there is differential absorption of at least some of said wavelengths by said material as a function of said wavelengths and said characteristic, said differential absorption causing intensity variations of said wavelengths incident from said material as a function of said wavelengths and said unknown values of said known characteristic; (b) measuring said intensity variations from said material; and (c) calculating said unknown values of said known characteristic in said material from said measured intensity variations utilizing an algorithm and a model, said algorithm being capable of using all independent sources of intensity variations v. wavelengths information obtained from irradiating a set of samples with a range of wavelengths in which said values of said known characteristic are known, said algorithm also being capable of using more wavelengths than samples in said set of samples, said model constructed from said set of samples and being a function of said known values of said characteristic and said intensity variations v. wavelengths information obtained from irradiating said set of samples, the improvement comprising selecting multiple variable subsets for generation and use in an improved model, each of said subsets containing one or more variables, said model being improved by selecting said multiple variable subsets from the set of instrument variables and wherein said algorithm with said improved model improves the fitness for said determination of said unknown values of said known characteristic, said selection process utilizing multivariate search methods that select both predictive and synergistic variables.