Fuzzy Grid Encoded Independent Modeling for Class Analogies (FIMCA)

摘要

A novel representation of chemical measurements has been devised for which the data are encoded as fuzzy grids instead of the standard convention as a vector. The fuzzy grid encoded data and data in the standard format were evaluated with soft independent modeling for class analogies (SIMCA). The fuzzy version of SIMCA is referred to as FIMCA. These two methods were compared with simulated and real data to characterize the advantages of the fuzzy grid encoding. For complex data, the FIMCA approach often achieves better results, and for simpler data sets the similar prediction results are obtained. The benefits of this approach are its simplicity, increase in rank of overdetermined data, and prevention of coincidental correlations with underdetermined data. This paper introduces the use of FIMCA as a method for untargeted (one-class classification) authentication of complex chemical profiles.