Application of recursive partial least square regression for prediction of apple juice sensory attributes from NMR spectra
N. Iaccarino,a C. Varming,b M.A. Petersen,b F. Savorani,b,c A. Randazzo,a B. Schütz,d T.B. Toldam-Andersene and S.B. Engelsenb* aDepartment of Pharmacy, University of Naples “Federico II”, Via D. Montesano 49, 80131 Naples, Italy. E-mail: [email protected] bDepartment of Food Science, University of Copenhagen, Rolighedsvej 26, 1958 Frederiksberg C, Denmark. Corresponding Author: [email protected] cDepartment of Applied Science and Technology, Polytechnic University of Turin, Corso Duca degli Abruzzi 24, 10129 Turin, Italy dBruker BioSpin, Silberstreifen 4, 76287 Rheinstetten, Germany. eDepartment of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark
This study demonstrates the application of a novel variable selection method here employed for the prediction of sweet and sour taste of apple juice from Nuclear Magnetic Resonance (NMR) spectra. The method is called recursive weighted Partial Least Square (rPLS). It operates by iteratively re-weighting the spectral variables using the regression coefficients calculated by PLS. The only parameter to be estimated by the operator is the number of latent factors to be used in the model. This approach provides an easier model interpretation than a regular PLS model, since it converges towards a very limited number of variables and therefore the assignment effort is drastically reduced. These properties suggest a profitable use of the rPLS for the prediction of even more complex sensory features from different types of spectroscopic data.