https://www.selleckchem.com/MEK.html
Multivariate statistical models using hyperspectral data accurately estimated (R 2 0.7 in ∼34% of the metabolites) and predicted (Q 2 0.5 in 15-25% of the metabolites) many leaf metabolites. Predicted values of metabolites could efficiently discriminate stressed and non-stressed samples and reveal which regions of the reflectance spectrum were the most informative for predictions. Combined together, these findings suggest that hyperspectral sensors can be used for the rapid, non-destructive estimation of leaf metabolites, which can summari