Abstract
The potential of visible-near infrared spectroscopy to predict glucosinolates and total phenolic content in rocket (Eruca vesicaria) leaves has been evaluated. Accessions of the E. vesicaria species were scanned by NIRS as ground leaf, and their reference values regressed against different spectral transformations by modified partial least squares (MPLS) regression. The coefficients of determination in the external validation (R²VAL) for the different quality components analyzed in rocket ranged from 0.59 to 0.84, which characterize those equations as having from good to excellent quantitative information. These results show that the total glucosinolates, glucosativin and glucoerucin equations obtained, can be used to identify those samples with low and high contents. The glucoraphanin equation obtained can be used for rough predictions of samples and in case of total phenolic content, the equation showed good correlation. The standard deviation (SD) to standard error of prediction ratio (RPD) and SD to range (RER) were variable for the different quality compounds and showed values that were characteristic of equations suitable for screening purposes or to perform accurate analyses. From the study of the MPLS loadings of the first three terms of the different equations, it can be concluded that some major cell components such as protein and cellulose, highly participated in modelling the equations for glucosinolates.