Evaluation of aflatoxin decontaminating by two strains of Saccharomyces cerevisiae and Lactobacillus rhamnosus strain GG in pistachio nuts

In this study, the surface binding ability of Saccharomayces cerevisiae and Lactobacillus rhamnosus GG (LBGG) to aflatoxin in pistachio nuts was compared. Results showed that Saccharomayces cerevisiae and Lactobacillus rhamnosus strains had aflatoxin binding ability of 40% and 35% with initial concentration of 10 ppb and 70% and 60% with initial concentration of 20 ppb aflatoxin, respectively. Acid treatment increased this ability for yeast and bacterium to 60% and 85% in first concentration and 73% and 90% for second concentration of aflatoxin, respectively. Also, heat treatment could raise surface binding of yeast to 55% and 75% for two concentrations. In addition, heat condition for Lactobacillus improved binding to 85% and 90% for two concentrations of aflatoxin. Experiments showed that microorganism’s immobilisation on contaminated pistachio had no effect on qualitative characteristics of pistachio such as colour, texture and peroxide value.     

Sizing of rock fragmentation modeling due to bench blasting using adaptive neuro-fuzzy inference system and radial basis function

One of the most important characters of blasting, a basic step of surface mining, is rock fragmentation. It directly effects on the costs of drilling and economics of the subsequent operations of loading, hauling and crushing in mines. Adaptive neuro-fuzzy inference system (ANFIS) and radial basis function (RBF) show potentials for modeling the behavior of complex nonlinear processes such as those involved in fragmentation due to blasting of rocks. In this paper we developed ANFIS and RBF methods for modeling of sizing of rock fragmentation due to bench blasting by estimation of 80% passing size (K₈₀) of Golgohar iron ore mine of Sirjan, Iran. Comparing the results of ANFIS and RBF models shows that although the statistical parameters RBF model is acceptable but the ANFIS proposed model is superior and also simpler because the ANFIS model is constructed using only two input parameters while seven input parameters used for construction of the RBF model.     

Selective dispersive liquid–liquid microextraction and preconcentration of Ni(II) into a micro droplet followed by ETAAS determination using a yellow Schiff's base bisazanyl derivative

A simple, rapid and sensitive method was developed for the selective separation and preconcentration of Ni(II) using dispersive liquid–liquid microextraction, by a yellow Schiff's base bisazanyl derivative, as a selective complexing agent. In this method, a mixture of 45 μL chloroform (extraction solvent) and 450 μL tetrahydrofuran (dispersive solvent) is rapidly injected by syringe into a 5 mL aqueous sample containing 3% (w/v) sodium chloride and an appropriate amount of the Schiff's base. As a result, a cloudy solution is formed by entire dispersion of the extraction solvent into the aqueous phase. After centrifuging for 5 min at 5000 rpm, the sedimented phase is directly injected into the electrothermal atomic absorption spectrometry for Ni(II) determination. Some important parameters, such as kind and volume of extraction and dispersive solvents, extraction time, salt effect, pH and concentration of the chelating agent have been optimized. Under the optimum conditions, the enrichment factor for the presented method is 138. The calibration curve was linear over a nickel concentration range of 10–50 ng mL^? 1. The detection limit and relative standard deviation were 0.04 ng mL^? 1 and 2.1%, respectively. The method was successfully applied to the extraction and determination of Ni(II) in different water samples.     

Abnormal event detection in crowded scenes using one-class SVM

In this paper, a new method for detecting abnormal events in public surveillance systems is proposed. In the first step of the proposed method, candidate regions are extracted, and the redundant information is eliminated. To describe appearance and motion of the extracted regions, HOG-LBP and HOF are calculated for each region. Finally, abnormal events are detected using two distinct one-class SVM models. To achieve more accurate anomaly localization, the large regions are divided into non-overlapping cells, and the abnormality of each cell is examined separately. Experimental results show that the proposed method outperforms existing methods based on the UCSD anomaly detection video datasets.     

Sizing of rock fragmentation modeling due to bench blasting using adaptive neuro-fuzzy inference system (ANFIS)

One of the most important characters of blasting, a basic step of surface mining, is rock fragmentation because it directly effects on the costs of drilling and economics of the subsequent operations of loading, hauling and crushing in mines. Adaptive neuro-fuzzy inference system (ANFIS) and radial basis function (RBF) show potentials for modeling the behavior of complex nonlinear processes such as those involved in fragmentation due to blasting of rocks. We developed ANFIS and RBF methods for modeling of sizing of rock fragmentation due to bench blasting by estimation of 80% passing size (K₈₀) of Golgohar iron mine of Sirjan, Iran. Comparing the results of ANFIS and RBF models shows that although the statistical parameters RBF model is acceptable but ANFIS proposed model is superior and also simpler because ANFIS model is constructed using only two input parameters while seven input parameters used for construction of RBF model.     

The use of biodiesel as a green polymerization solvent at elevated temperatures

BACKGROUND: Many important polymers are produced via solution polymerization. The solvent maintains a low viscosity, which provides many practical advantages related to heat transfer, mixing and material handling. Despite these advantages, commonly used solvents often present health and environmental problems. In an effort to replace these toxic solvents, a ‘green’ polymerization solvent, namely canola‐based FAME (fatty acid methyl ester or biodiesel), was used for solution polymerizations at an elevated temperature. RESULTS: Homopolymerizations of methyl methacrylate, styrene, butyl acrylate and vinyl acetate in FAME were studied at different solvent concentrations at 120 °C. Chain transfer to solvent rate constants (C_fs) were obtained for each polymer system and Arrhenius parameters for C_fs, i.e. E_a and A, were also calculated. These new solvent data were employed in a polymerization simulator to predict rate of polymerization and number‐ and weight‐average molecular weights for these commercially important systems. Model predictions showed reasonable agreement with experimental data. CONCLUSION: FAME fulfills the demands as a polymerization solvent. From an ecological perspective, FAME provides an environmentally friendly alternative to common solvents. From an industrial perspective, using FAME as a high‐boiling polymerization solvent can increase productivity by enabling polymerizations at elevated temperatures. Copyright © 2008 Society of Chemical Industry     

Solution polymerization of styrene using biodiesel as a solvent: Effect of biodiesel feedstock

Increasing environmental concerns regarding solvents commonly used in solution polymerization has led to a search for alternatives, such as biodiesel (a.k.a. fatty acid methyl esters or FAME). The effect of the feedstock used in biodiesel production when FAME is used as a polymerization solvent was studied for styrene. A series of homopolymerizations was carried out at different solvent concentrations for two biodiesel feedstocks: soybean oil and 50% yellow grease–50% canola oil. Results were compared to FAME produced from canola oil. Considerable differences in the rate of polymerization were observed, while chain transfer to solvent constants had comparable values. The collected kinetic experimental data were modelled using a polymerization simulator.