TY - JOUR AB - In this work, we have implemented a method which uses structural information from H-1 NMR spectra of fatty esters and biodiesels to infer the corresponding Cetane Number (CN). The method consists of the successive application of Principal Component Analysis (PCA), Fuzzy Clustering and a feed-forward Artificial Neural Network (ANN) to the data set. PCA recognized redundant information, and determined the number of clusters for subsequent Fuzzy Clustering classification. At the final stage ANN used membership values from the Fuzzy Clustering process as inputs to predict the cetane number of different types of biodiesel (complex mixtures) from data of pure substances (esters). Root-mean-square deviations were in the range of 0.2-2.4. (C) 2012 Elsevier Ltd. All rights reserved. AD - State Univ Campinas UNICAMP, Inst Chem, BR-13083970 Campinas, SP, Brazil AN - WOS:000311935400032 AU - Nadai, D. V. AU - Simoes, J. B. AU - Gatts, C. E. N. AU - Miranda, P. C. M. L. DA - Mar DO - 10.1016/j.fuel.2012.06.018 J2 - Fuel KW - neural network LA - English N1 - 048th PY - 2013 SN - 0016-2361 SP - 325-330 ST - Inference of the biodiesel cetane number by multivariate techniques T2 - Fuel TI - Inference of the biodiesel cetane number by multivariate techniques UR - ://WOS:000311935400032 VL - 105 ID - 9075 ER -