Comparing and predicting sensory profiles from NIRS data: use of the GOMCIA and GOMCIA-PLS multiblock methods
Sequential 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 (C) 2005 John Wiley & Sons, Ltd.
prediction sensory profiles nirs gomcia pls multiblock