Simplified method for the screening of technological maturity of red grape and total phenolic compounds of red grape skin: application of the characteristic vector method to near-infrared spectra.

Characteristic vector analysis has been applied to near-infrared spectra to extract the main spectral information from hyperspectral images. For this purpose, 3, 6, 9, and 12 characteristic vectors have been used to reconstruct the spectra, and root-mean-square errors (RMSEs) have been calculated to measure the differences between characteristic vector reconstructed spectra (CVRS) and hyperspectral imaging spectra (HIS). RMSE values obtained were 0.

Use of three tristimulus values from surface reflectance spectra to calculate the principal components for reconstructing these spectra by using only three eigenvectors.

The division of Color Space into ten zones, corresponding to the ten Munsell hues, allows a good reconstruction of surface reflectance spectra using just three eigenvectors, obtained by applying principal components analysis to the reflectance spectra of the Munsell Atlas specimens (model group), although the basis vectors obtained are different for each subspace. The use of the tristimulus values from each measured spectrum, calculated with the Illuminant D65 and the Standard Observer CIE64 to obtain the principal components necessary to reconstruct the spectrum, allows a very high degree of metamerism to be attained between the two spectra (measured and reconstructed). Furthermore, this method of calculating the principal components allows reconstruction of the spectra of specimens from other sample sets that differ from the model group used in the PCA.