Optimization of pre-processing variables for hyperspectral analysis of focal plane array Fourier transform infrared images.

摘要

A genetic algorithm was employed to select the optimal combination of preprocessing variables, including data pretreatment, data manipulation and feature extraction procedures, for eventual clustering of a data set consisting of hyperspectral images acquired by a focal plane array Fourier transform infrared (FPA-FTIR) spectrometer. The data set consisted of infrared images of bacterial films, and the classification task investigated was the discrimination between Gram-positive and Gram-negative bacteria. The genetic algorithm evaluated combinations of variables pertaining to bacterial film thickness tolerances, baseline correction, pixel co-addition, outlier removal, smoothing, mean centering, normalization, derivatization, integration and principal component selection. Following numerous iterations of unsupervised processing, the genetic algorithm arrived at a sub-optimal solution yielding a clustering accuracy of 97.8% and a data utilization of 28.6%. The results provided insight into the co-dependencies of the pre-processing variables and their consequential effect on the selected data. The robustness of the classification model was evaluated and reinforced by the successful classification of two distinct validation sets. The overall success of the genetic algorithm suggests that it is an effective time saving resource for the optimization of pre-processing variables that does not require operator intervention.