Gluten free pasta with natural ingredient of color and carotene source

The gluten-free pasta (GFP) has a higher cost and is often brittle, pale, and poorly balanced in macro and micronutrients when compared to wheat-based pasta. This study aimed to use the biofortified sweet potato flour, a natural source of color and provitamin A ingredient, with rice flour, hydrolyzed soy protein concentrate, and carboxymethyl cellulose gum to produce GFP, using a central composite rotatable design with three factors (23) and the central point. The GFP were evaluated by instrumental color analysis, and visual analysis of technological characteristics (presence of cracks, defect, and shape aspect), used for selection. Three GFP were selected: E1, E2 and E12, which showed absence of breaks or defects and an intermediate aspect after cooking, which were submitted to nutritional, functional and sensory evaluations. These GFPs presented levels of β-carotene of 135.22, 292.04 and 147.54 µg/g (dry basis), respectively, and showed sensory overall liking means in the acceptance region (>4.5) with no significant differences among them. According to the penalty analysis, the parameters color and consistency penalized these averages. Therefore, optimizing the ingredients’ concentrations improved the nutritional value and the sensory acceptability of GFP.

Uncertainty Evaluation in Hydrological Frequency Analysis Based on Confidence Interval and Prediction Interval

The shortage of extreme rainfall data gives substantial uncertainty to design rainfalls and causes predictions for torrential rainfall to deviate strongly from adopted probability distributions used in river planning. These torrential rainfalls are treated as outliers which existing studies do not evaluate. However, probability limit method test which its acceptance region expresses with high accuracy the range where observed ith order statistics could realize. Confidence interval which quantifies uncertainty of adopted distributions can be constructed by assuming that these critical values in both sides of the adopted region follow the same function form applied to actual observed data. Furthermore, its validity is proved through comparison of confidence interval derived from ensemble downscaling calculations. In addition, these critical values are almost in accordance with outliers in samples from the ensemble downscaling calculations. Therefore, prediction interval which expresses the range that an unknown observed datum can take is constructed by extrapolating the critical values for limit estimation of a future datum. In this paper, quantification method of uncertainty of design rainfall and occurrence risk of outliers in the traditional framework, using the proposed confidence interval and prediction interval, is shown. Moreover, their application to future climate by using Bayesian statistics is explained.

A Test for Normality in the Presence of Outliers

The Jarque-Bera test is a test based on the coefficients of skewness (S) and kurtosis (K) for testing whether the given random sample is from a normal population. When the random sample of size n contains m outliers, we use the remaining n-m observations to compute two statistics S* and K* which mimic the statistics S and K. The statistics S* and K* are next transformed to z1 and z2 which are uncorrelated and having standard normal distributions when the original population is normal. We show that the acceptance region given by a circle in the (z1, z2) plane is suitable for testing the normality assumption.