The incident solar radiation on soil is an important variable used in agricultural applications; it is also relevant in hydrology, meteorology and soil physics, among others. To estimate this variable, empirical models have been developed using several parameters and, recently, prognostic and prediction models based on artificial intelligence techniques such as neural networks. The aim of this work was to develop linear models and neural networks, multilayer perceptron, to estimate daily global solar radiation and compare their efficiency in its application to a region of the Province of Salta, Argentina. Relative sunshine duration, maximum and minimum temperature, rainfall, binary rainfall and extraterrestrial solar radiation data for the period 1996-2002, were used. All data were supplied by Experimental Station Salta, Instituto Nacional de Tecnología Agropecuaria (INTA), Argentina. For both, neural networks models and linear regressions, three alternative combinations of meteorological parameters were considered. Good results with both prediction methods were obtained, with root mean square error (RMSE) values between 1.99 and 1.66 MJ m -2 d -1 for linear regressions and neural networks, and coefficients of correlation (r 2 ) between 0.88 and 0.92, respectively. Even though neural networks and linear regression models can be used to predict the daily global solar radiation appropriately, neural networks produced better estimates.

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Fatty alcohol ethoxylates (FAE) (a mixture of nonionic surfactants) have been characterized using NACE with UV detection. Phenyl polyurethane derivatives of these compounds were previously obtained by reaction with phenyl isocyanate. The derivatization reaction only requires microwave irradiation for 30 s (600 W). Phenyl polyurethanes were separated and characterized using a BGE containing a mixture of ammonium nitrate (15 mM), acetic acid (1.5%) and 9:1 v/v methanol/ACN. After optimization of the instrumental conditions for the separation, phenyl polyurethane compounds (formed from the corresponding FAE) with ethylene oxide numbers (EON) of 6 (certified standard and industrial samples), 7 and 10 (both as industrial samples), and 5.5 (microemulsion phase) were successfully separated and characterized. The properties of these FAE nonionic surfactants are very important in the petroleum industry, which requires characterization of the quality of the purchased materials as well as the final products in the microemulsion-oil-water stream process. This analytical objective has been achieved by the proposed NACE methods, allowing FAE to be distinguished from 5.5 to 10 EON with errors below 4%, and shows advantages against to HPLC methods.