No abstract

No abstract

Abstract. The pH value in bioethanol is a quality control parameter related to its acidity and to the corrosiveness of vehicle engines when it is used as fuel. In order to verify the comparability and reliability of the measurement of pH in bioethanol matrix among some experienced chemical laboratories, reference material (RM) of bioethanol developed by Inmetro -the Brazilian National Metrology Institute -was used in a proficiency testing (PT) scheme. There was a difference of more than one unit in the value of the pH measured due to the type of internal filling electrolytic solutions (potassium chloride, KCl or lithium chloride, LiCl) from the commercial pH combination electrodes used by the participant laboratories. Therefore, bimodal distribution has occurred from the data of this PT scheme. This work aims to present the possibilities that a PT scheme provider can use to overcome the bimodality problem. Data from the PT of pH in bioethanol were treated by two different statistical approaches: kernel density model and the mixture of distributions. Application of these statistical treatments improved the initial diagnoses of PT provider, by solving bimodality problem and contributing for a better performance evaluation in measuring pH of bioethanol.

By Brazilian law, biodiesel has to satisfy certain quality requirements and measurements established by standardized procedures, as is the case for kinematic viscosity and density. In this respect, information on the profile of methyl esters in biodiesel is very important because they are directly related to both these parameters. The objective of this study was to determine the profile of methyl esters present in a biodiesel sample by electrospray ionization mass spectrometry and evaluate its reliability in predicting their kinematic viscosity and density. Two multivariate statistical models were used for this purpose, the multiple multivariate linear regression (MMLR) and the partial least square regression (PLSR). The input variables used in the models were the relative intensities of the main methyl ester peaks, and the models were compared by their predictive behavior. Samples were randomly divided into two parts: 87% in the training or calibration set, used for the estimation of MMLR and PLSR models, and the remaining 13% in the test or validation set, which was used to evaluate the predictive power of each model that was estimated. Although the root mean squared error and R 2 for the MMLR model were slightly better than those of the PLSR model (R 2 PLSR = 0.9232 and R 2 MMLR = 0.9908 for kinematic viscosity and R 2 PLSR = 0.8721 and R 2 MMLR = 0.9415 for density), both showed a similarity with respect to predicted values for the training and validation sets, and thus for the performance statistics, attesting to the quality of these models in predicting kinematic viscosity and density. Furthermore, the prediction of kinematic viscosity showed better performance compared to the density.

Fatty acid methyl ester (FAME) intensities, by ESI-MS, used to their quantification in biodiesel.