Independent component analysis in information extraction from visible/near-infrared hyperspectral imaging data of cucumber leaves
Hyperspectral imaging at visible and short near infrared (VIS/SNIR) region has been used to estimate the pigment content of leaves. A complicating feature of measurements with any hyperspectral imaging methodology is the large amount of information generated during the measurement process. In this paper we discuss the identification of the desirable information using independent component analysis (ICA). After hyperspectral image acquisition and pre-processing, the average spectra obtained from the region of interest (ROI) in cucumber leaves were used for model development. Additionally a multi-linear regression model was developed for the prediction of cucumber leaf chlorophyll content. When compared with normal principal component analysis (PCA), the ICA multi-linear regression model provided improved estimates. When the calibration models were applied to an independent validation set, chlorophyll content was reasonably well predicted with a high correlation (r(2) = 0.774). Depending on the sample, the technique enabled the identification and characterization of the relative content of various chlorophyll types that were distributed within the cucumber leaves. Typically low levels of chlorophyll at leaf margins and higher levels along main vein regions were identified. Our results indicate that hyperspectral imaging exhibits considerable promise for predicting pigments within cucumber leaves and furthermore can be applied non-destructively and in situ to living plant samples. (C) 2010 Elsevier B.V. All rights reserved.
independent component analysis hyperspectral imaging cucumber leaves multi-linear regression chlorophyll content principal component analysis