New Method for Spectral Data Classification: Two-Way Moving Window Principal Component Analysis

Abstract

Two-way moving window principal component analysis (TMWPCA), which considers all possible variable regions by using variable and sample moving windows, is proposed as a new spectral data classification method. In TMWPCA, the similarity between model function and the index obtained by variable and sample moving windows is defined as “fitness”. For each variable region selected by a variable moving window, the fitness is obtained through the use of a model function. By maximizing the fitness, an optimal variable region can be searched. A remarkable advantage of TMWPCA is that it offers an optimal variable region for the classification. To demonstrate the potential of TMWPCA, it has been applied to the classification of visible–near-infrared (Vis-NIR) spectra of mastitic and healthy udder quarters of cows measured in a nondestructive manner. The misclassification rate of TMWPCA has been compared with those of other chemometric methods, such as principal component analysis (PCA), soft independent modeling of class analogies (SIMCA), and principal discriminant variate (PDV). TMWPCA has yielded the lowest misclassification rate. The result indicates that TMWPCA is a powerful tool for the classification of spectral data.