Comparative Study of Diesel Analysis by Ftir, Ftnir and Ft-Raman Spectroscopy Using Pls and Artificial Neural Network Analysis
Analytica Chimica Acta 547 (2005) 188–196
Acomparative study of diesel analysis by FTIR, FTNIR and FT-Raman spectroscopy using PLS and artificial neural network analysis
Vianney O. Santos Jr., Flavia C.C. Oliveira, Daniella G. Lima, Andrea C. Petry, Edgardo Garcia, Paulo A.Z. Suarez, Joel C. Rubim ∗
Laborat´ rio de Materiais e Combust´veis (LMC), Instituto de Qu´mica da Universidade de Brasilia, C.P. 04478, 70904-970 Bras´lia, DF, Brazil o ı ı ı Received 10 January 2005; received in revised form 28 April 2005; accepted 17 May 2005 Available online 24 June 2005
Abstract Diesel properties determined by ASTM reference methods as cetane index, density, viscosity, distillation temperatures at 50% (T50) and 85% (T85) recovery, and the total sulfur content (%, w/w) were modeled by FTIR-ATR, FTNIR, and FT-Raman spectroscopy using partial last square regression (PLS) and artificial neural network (ANN) spectral analysis. In the PLS models, 45 diesel samples were used in the training group and the other 45 samples were used in the validation. In the ANN analysis a modular feedforward network was used. Sixty diesel samples were used in the neural network training and other 30 samples were used in the validation. Two different ATR configurations were compared in the FTIR, a conventional (ATR1) and an immersion (ATR2) cell. The ATR1 cell presented the best results, with smaller prediction errors (root mean square error of prediction, RMSEP). The comparison of the three PLS models (FTIR-ATR1, FTNIR, and FT-Raman) shows that reasonable values of R2 and RMSEP were obtained by the FTIR-ATR1 and FTNIR models in the evaluation of density, viscosity, and T50. The PLS/FT-Raman models presented reasonable results only for the T50 property. None of the techniques was able to generate suitable PLS calibration models for the determination of sulfur content. The ANN/FT-Raman models presented the best performances, with all models presenting R2 -values above 85% some of them with RMSEP
Acomparative study of diesel analysis by FTIR, FTNIR and FT-Raman spectroscopy using PLS and artificial neural network analysis
Vianney O. Santos Jr., Flavia C.C. Oliveira, Daniella G. Lima, Andrea C. Petry, Edgardo Garcia, Paulo A.Z. Suarez, Joel C. Rubim ∗
Laborat´ rio de Materiais e Combust´veis (LMC), Instituto de Qu´mica da Universidade de Brasilia, C.P. 04478, 70904-970 Bras´lia, DF, Brazil o ı ı ı Received 10 January 2005; received in revised form 28 April 2005; accepted 17 May 2005 Available online 24 June 2005
Abstract Diesel properties determined by ASTM reference methods as cetane index, density, viscosity, distillation temperatures at 50% (T50) and 85% (T85) recovery, and the total sulfur content (%, w/w) were modeled by FTIR-ATR, FTNIR, and FT-Raman spectroscopy using partial last square regression (PLS) and artificial neural network (ANN) spectral analysis. In the PLS models, 45 diesel samples were used in the training group and the other 45 samples were used in the validation. In the ANN analysis a modular feedforward network was used. Sixty diesel samples were used in the neural network training and other 30 samples were used in the validation. Two different ATR configurations were compared in the FTIR, a conventional (ATR1) and an immersion (ATR2) cell. The ATR1 cell presented the best results, with smaller prediction errors (root mean square error of prediction, RMSEP). The comparison of the three PLS models (FTIR-ATR1, FTNIR, and FT-Raman) shows that reasonable values of R2 and RMSEP were obtained by the FTIR-ATR1 and FTNIR models in the evaluation of density, viscosity, and T50. The PLS/FT-Raman models presented reasonable results only for the T50 property. None of the techniques was able to generate suitable PLS calibration models for the determination of sulfur content. The ANN/FT-Raman models presented the best performances, with all models presenting R2 -values above 85% some of them with RMSEP