Syneresis and rheological behaviors of set yogurt containing green tea and green coffee powders

This study aimed to investigate the effect of added green coffee powder (GCP) and green tea powder (GTP) on syneresis behavior and consistency of set yogurts. Adding GCP (1 or 2%) decreased syneresis rate. The effect of GTP on the syneresis rate was concentration dependent. In comparison to the control, GTP decreased syneresis rate when it was added at 0.02%, but it caused an increase when added at 2%. No significant difference was observed in the syneresis rates when GTP was added at 1 and 0.01%, until 14 and 7 d of storage, respectively. The Herschel-Bulkley model parameters indicated that the consistency of control was considerably lower than that of GCP yogurts during 14 d, whereas it was higher at the end of storage. The GTP yogurt results showed that the consistency coefficients of GTP yogurts were different from those of control samples until 14 d of storage. In conclusion, GTP and GCP behaved differently in acidified gel networks of set yogurt, modifying its rheological behavior, as they have different profiles and concentrations of polyphenols.     

Localization of Groundwater Vulnerability Assessment Using Catastrophe Theory

Groundwater vulnerability is assessed by the DRASTIC method, which is more popular among the alternatives available. The challenge taken on board is to treat its inherent subjectivity in estimating the values of the embedded weights as recommended by USEPA for all aquifers. This paper uses a catastrophe based multi-objective evaluation system and applies it to the DRASTIC method to avoid undue preferences by decision maker. Thus, catastrophe fuzzy membership functions are used to treat the dependency of state variables on control parameters and thereby to determine the weights embedded in DRASTIC parameters. The proposed method estimates the weights of DRASTIC parameters with respect to local condition of study area. The Maragheh-Bonab aquifer in Iran is under threat by agricultural and industrial activities and therefore its vulnerability is assessed by the proposed method as well as by the general and pesticide DRASTIC methods. Comparison of results with the special distribution of nitrate-N (NO3-N) shows that the improved DRASTIC has a higher correlation index with respect to the general and pesticide DRASTIC methods. Also in seeking more reliability, the addition of new parameters is possible in the proposed method.     

Supervised Intelligent Committee Machine Method for Hydraulic Conductivity Estimation

Hydraulic conductivity is the essential parameter for groundwater modeling and management. Yet estimation of hydraulic conductivity in a heterogeneous aquifer is expensive and time consuming. In this study; artificial intelligence (AI) models of Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Multilayer Perceptron Neural Network associated with Levenberg–Marquardt (ANN), and Neuro-Fuzzy (NF) were applied to estimate hydraulic conductivity using hydrogeological and geoelectrical survey data obtained from Tasuj Plain Aquifer, Northwest of Iran. The results revealed that SFL and NF produced acceptable performance while ANN and MFL had poor prediciton. A supervised intelligent committee machine (SICM), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of the hydraulic conductivity in Tasuj plain. The performance of SICM was also compared to those of the simple averaging and weighted averaging intelligent committee machine (ICM) methods. The SICM model produced reliable estimates of hydraulic conductivity in heterogeneous aquifers.