Quantification of fructan concentration in grasses using NIR spectroscopy and PLSR

Near-infrared reflectance (NIR) spectroscopy combined with chemometrics was used to quantify fructan concentration in samples from seven grass species. Savitzky–Golay first derivative with filter width 7 and polynomial order 2 with mean centering was applied as a spectral pre-treatment method to remove unimportant baseline signals. In order to model the NIR spectroscopy data the partial least squares regression (PLSR) approach was used on the full spectra. Variable selection based on PLSR by jack-knifing within a cross-model validation (CMV) framework was applied in order to remove non-relevant spectral regions. PLSR was also used to model fructan concentrations from an augmented matrix [X|G], where X is spectra and G is correlation matrix of band specific information and X, in order to integrate the chemical band information in regression models. The present analysis showed that rapid quantification of fructans by NIR spectroscopy is possible and that jack-knifing PLSR within a CMV framework is an effective way to eliminate the wavelengths of no interest. Jack-knifing PLSR did not improve the predictive ability because the root mean square error of prediction (RMSEP) increased (1.37) compared to the full model (1.26). This was possibly due to signals from carbohydrates, which could act as cofactor in the prediction of fructans. However, jack-knifing PLSR within a CMV framework simplified the interpretation of the regression model with r² = 0.90 and RMSEP = 1.37.