Aplicação de redes neurais artificiais na identificação de gasolinas adulteradas comercializadas na região de Londrina - Paraná

The multilayer perceptron network was used to classify the gasoline. The main parameters used in the classification were established by the Ordinance nº 309 of the Agência Nacional do Petróleo, but without informing the network the legal limits of these parameters. The network used had 10 neurons in a single hidden layer, learning rate of 0.04 and 250 training epochs. The application of artificial neural network served classify 100% of the commercialized gas in the region of Londrina-PR and to identify the tampered gasoline even those suspected of tampering.

Influence of distillation time and sample mass on sulfur dioxide analysis in passion fruit juice through Monier-Williams method

AbstractThis study aimed to evaluate the effect of the distillation time and the sample mass on the total SO2 content in integral passion fruit juice (Passiflora sp). For the SO2 analysis, a modified version of the Monier-Williams method was used. In this experiment, the distillation time and the sample mass were reduced to half of the values proposed in the original method. The analyses were performed in triplicate for each distilling time x sample mass binomial, making a total of 12 tests, which were performed on the same day. The significance of the effects of the different distillation times and sample mass were evaluated by applying one-factor analysis of variance (ANOVA). For a 95% confidence limit, it was found that the proposed amendments to the distillation time, sample mass, and the interaction between distilling time x sample mass were not significant (p > 0.05) in determining the SO2 content in passion fruit juice. In view of the results that were obtained it was concluded that for integral passion fruit juice it was possible to reduce the distillation time and the sample mass in determining the SO2 content by the Monier-Williams method without affecting the result.

Flower scent analysis of Encyclia Vespa (vell.) Dressler & G. E. Pollard and E. Fragrans (Sw.) Lemée

The volatile constituents obtained from the pentane extract, using simultaneous distillation-extraction of the flowers of Encyclia vespa and E. fragrans were analysed by GC/ MS. The main volatile components identified in the flowers of E. vespa were terpinen-4-ol (20.3%), verbenone (14.8%), trans-verbenol (13.6%) and x-pinene (11.8%). The major volatiles of the flowers of E. fragrans were terpinen-4-ol (18.3%), (2Z,6E)-farnesol (15.4%) and trans-verbenol (10.2%).