Pendimethalin determination in natural water, baby food and river sediment samples using electroanalytical methods

This work describes the electroanalytical determination of pendimethalin herbicide levels in natural waters, river sediment and baby food samples, based on the electro-reduction of herbicide on the hanging mercury drop electrode using square wave voltammetry (SWV). A number of experimental and voltammetric conditions were evaluated and the best responses were achieved in Britton-Robinson buffer solutions at pH 8.0, using a frequency of 500 s(-1). a scan increment of 10 mV and a square wave amplitude of 50 mV. Under these conditions, the pendimethalin is reduced in an irreversible process, with two reduction peaks at -0.60 V and -0.71 V. using a Ag/AgCl reference system. Analytical curves were constructed and the detection limit values were calculated to be 7.79 mu g L(-1) and 4.88 mu g L(-1), for peak 1 and peak 2, respectively. The precision and accuracy were determinate as a function of experimental repeatability and reproducibility, which showed standard relative deviation values that were lower than 2% for both voltammetric peaks. The applicability of the proposed methodology was evaluated in natural water, river sediments and baby food samples. The calculated recovery efficiencies demonstrate that the proposed methodology is suitable for determining any contamination by pendimethalin in these samples. Additionally, adsorption isotherms were used to evaluate information about the behavior of pendimethalin in river sediment samples. (C) 2010 Elsevier B.V. All rights reserved.

Use of a predictive model for food insecurity estimates in Brazil

In 2004 the National Household Survey (Pesquisa Nacional par Amostras de Domicilios - PNAD) estimated the prevalence of food and nutrition insecurity in Brazil. However, PNAD data cannot be disaggregated at the municipal level. The objective of this study was to build a statistical model to predict severe food insecurity for Brazilian municipalities based on the PNAD dataset. Exclusion criteria were: incomplete food security data (19.30%); informants younger than 18 years old (0.07%); collective households (0.05%); households headed by indigenous persons (0.19%). The modeling was carried out in three stages, beginning with the selection of variables related to food insecurity using univariate logistic regression. The variables chosen to construct the municipal estimates were selected from those included in PNAD as well as the 2000 Census. Multivariate logistic regression was then initiated, removing the non-significant variables with odds ratios adjusted by multiple logistic regression. The Wald Test was applied to check the significance of the coefficients in the logistic equation. The final model included the variables: per capita income; years of schooling; race and gender of the household head; urban or rural residence; access to public water supply; presence of children; total number of household inhabitants and state of residence. The adequacy of the model was tested using the Hosmer-Lemeshow test (p=0.561) and ROC curve (area=0.823). Tests indicated that the model has strong predictive power and can be used to determine household food insecurity in Brazilian municipalities, suggesting that similar predictive models may be useful tools in other Latin American countries.