Abstract
AbstractSequential multiblock component and regression methods are used to analyse pea sensory data. First the repeatability of sensory profiles is studied with the generalized orthogonal multiple co‐inertia analyis (GOMCIA) method. Then a calibration model is built to predict the averaged sensory profiles from near‐infrared spectroscopy (NIRS) data by means of the GOMCIA partial least squares (GOMCIA‐PLS) method. It is proposed to split the NIRS data into blocks and search the most influential block in the calibration model in order to select spectral wavelengths. This approach is compared with a PLS regression model. It is shown that a PLS regression model can be less efficient than a multiblock regression model in terms of prediction. Copyright © 2005 John Wiley & Sons, Ltd.
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