Quantitative evaluation of multivariate analysis methods for excitation-emission spectroscopy

Multivariable Analysis Methods have been used widely in Spectroscopy Analysis. Partial least square (PLS) and principal component analysis (PCA) are the two most popular methods due to their excellent ability in data components analysis and results prediction. This work derived 1D/2D PLS and 1D/2D PCA based on the viewpoint of three-layer artificial neural networks, and uses theoretical proving to figure out the essences of these two methods. Two 2D experimental dataset was used to verify the calibration and prediction ability, furthermore, the similarity and dissimilarity of PLS and PCA. The finding showed that both the 1D/2D PLS and 1D/2D PCA methods use maximum covariance and minimum sum of square error to figure out the relationship between independent and dependent variables. The dissimilarity is that, weight vectors calibration of PLS is cross-correlation, and that of PCA is autocorrelation. The difference causes that the PCA method would keep more principal characters than PLS under insufficient sample among and provides better calibration ability. PLS would provide higher performance in prediction under sufficient sample among. For the needs of system implementation of spectroscopic measurement and analysis, this study designed Multivariate Analysis Toolkits for LabVIEW for the convenient implementation in automatic measurement system integration.