Simultaneous rice bran oil stabilization and extraction using sub-critical water medium

Sub-critical water technique was used for simultaneous inactivation of lipase enzyme existing in rice bran and extraction of its oil in order to obtain the stabilized edible rice bran oil. Sub-critical water treatment was carried out in the temperature range between 120 and 240 °C for 10 and/or 20 min residence time in a batch reactor. The quality of the extracted oil was evaluated with respect to its total free fatty acids concentration over a 12 week period, and compared with the oil obtained by conventional extraction methods. Without sub-critical water treatment, the concentration of total free fatty acids in the rice bran significantly increased from 5.6% to 36.0%. In contrast, no increase was observed in the total free fatty acids concentration in the samples treated by sub-critical water. Experimental evidence showed that total free fatty acids concentration increased somewhat in the oils treated by conventional methods. Considering no change was observed in total free fatty acid concentration in the treated oils by sub-critical water, it was found that sub-critical water not only could efficiently extract oil from rice bran in a short residence time but also completely stabilized the extracted oil. Oil extraction yields generally increased with increases in sub-critical water treatment temperature and residence time. The highest extraction yield of oil was 249 (mg/g dry matter) obtained at 240 °C and 10 min residence time. Oil extraction by sub-critical water could be conducted in a very short residence time (10 and/or 20 min). Also, the kinetics of free fatty acids formation in untreated rice bran was investigated and developed which successfully describes the concentration of total free fatty acids in the course of rice bran storage.     

Effect of copper nanoparticles on permeability, physical, and mechanical properties of particleboard

Effects of a 200 ppm nanocopper suspension, with size range from 10 to 80 nm, on the physical and mechanical properties of particleboard made on an industrial scale at the Iran-Choob Factory were studied. Nanocopper suspension was added to the mat at two levels of 100 and 150 ml/kg dry weight wood particles and compared with control boards. Results showed that hot-pressing time was reduced by 5.7 and 3.4 % when 100 and 150 ml of nanocopper were used, respectively. Also, both levels of nanocopper consumption had improving effects on physical and mechanical properties, although in some cases not significant. Permeability was significantly decreased to its lowest value in 150 ml/kg treatment. It can be concluded that 150 ml of nanocopper/kg may be used to improve the physical and mechanical properties, to reduce press time, and to decrease permeability in particleboards. For industrially accepted outcome, 150 ml/kg of nanocopper is recommended.     

An expert egg grading system based on machine vision and artificial intelligence techniques

The main purpose of this research was design and development of an intelligent system based on combined fuzzy logic and machine vision techniques for grading of egg using parameters such as defects and size of eggs. The detected defects were internal blood spots, cracks and breakages of eggshell. The Hue-Saturation-Value (HSV) color space was found useful in obtaining visual features during Image Processing (IP) stage. The fuzzy inference system (FIS) was designed based on triangular and trapezoidal membership functions, fuzzy rules with logical operator of AND inference system of Mamdani and method of center average for defuzzifier. The evaluation results of IP algorithms showed that use of IP technique has good performance for defects and size detection. The Correct Classification rate (CCR) was 95% for size detection, 94.5% for crack detection and 98% for breakage detection. The overall accuracy FIS model in grading of the eggs was 95.4.     

Soluble soybean polysaccharide/TiO2 nanocomposites: Biological activity,release behavior,biodegradability, and biosafety

This study aimed to produce and characterize eco-friendly SSPS nanocomposites incorporated with various concentrations of TiO₂ nanoparticles (NPs) (1%, 3%, and 7%). The antimicrobial activity of the nanocomposite films against five strains of pathogenic bacteria was examined. Salmonella typhi PTCC 1609 was the most sensitive species to TiO₂ NPs at concentrations equal to the synthetic antibiotic. The migration of TiO₂ to ethanol and acetic acid, as two food simulants increased when the initial nano-TiO₂ content increased. The release profiles for TiO₂ in two simulants of ethanol and acetic acid indicated a non-Fickian release, and the release kinetics were concentration-dependent. SSPS/TiO₂ nanocomposites degraded easily and thus have the potential to be applied as an eco-friendly packaging system. Oral administration of doses of 1, 12.5, 25, 50, and 75 mg/kg TiO₂ revealed that the dose of 50 and 75 mg/kg increased malondialdehyde (p < .001) concentration in the liver tissue. In addition, it decreased glutathione (p < .001) concentration in the liver tissue.     

Sub-critical water treatment of rice bran to produce valuable materials

The target of this study is application of sub-critical water as a green solvent for hydrolysis of rice bran and extraction of its oil in order to obtain value-added materials. Experiments were carried out at temperatures ranging from 100 to 360 °C with 5 min residence time in a batch reactor. Four phases were isolated after reaction: hexane-soluble, acetone-soluble, water-soluble, and solid residue phases. Rice bran oil was successfully and efficiently obtained by sub-critical water extraction. Significant increases of total organic carbon (TOC) and total nitrogen (TN) in the water phase were also observed, because polysaccharides and proteins (generally bio-macro polymers) in rice bran were hydrolysed by sub-critical water. The highest yields of TOC and TN were 140 and 13 mg/g dry matter, respectively. As a result, varieties of compounds, such as amino acids, organic acids, and water-soluble saccharides, were identified in the water phase. In particular, significant amounts of water-soluble sugars (maximum yield of total sugars was nearly 190 mg/g dry matter) proved that sub-critical water was a promising medium for dissolution of biomass in water. Acetone-soluble contents were attributed to tar, carbonized biomass, and in general, water- and hexane-insoluble compounds. Solid residue consisted of mainly un-reacted rice bran and insoluble inorganic compounds.     

Application of Organic Acid Based Artificial Neural Network Modeling for Assessment of Commercial Vinegar Authenticity

Food Analytical Methods - Vinegar as a nutraceutical substance is classified to various types related to the different substances applied in production process. Therefore, identity of the source...     

Learning curve analysis of concentrated photovoltaic systems

Price declines and volume growth of concentrated photovoltaic (CPV) systems are analysed using the learning curve methodology and compared with other forms of solar electricity generation. Logarithmic regression analysis determines a learning rate of 18% for CPV systems with 90% confidence of that rate being between 14 and 22%, which is higher than the learning rates of other solar generation systems (11% for CSP and 12 to 14% for PV). Current CPV system prices are competitive with PV and CSP, which, when combined with the higher learning rate, indicates that CPV is likely to further improve its marketability. A target price of 1 $/W in 2020 could be achieved with a compound growth rate of 67% for the total deployed volume between 2014 and 2020, which would realize a cumulative deployed volume of 7900 MW. Other projections of deployment volumes from commercial sources are converted using the learning rate into future price scenarios, resulting in predicted prices in the range of 1.1 to 1.3 $/W in 2020. © 2014 The Authors. Progress in Photovoltaics: Research and Applications published by John Wiley & Sons Ltd.     

A parsimonious SVM model selection criterion for classification of real-world data sets via an adaptive population-based algorithm

This paper proposes and optimizes a two-term cost function consisting of a sparseness term and a generalized v-fold cross-validation term by a new adaptive particle swarm optimization (APSO). APSO updates its parameters adaptively based on a dynamic feedback from the success rate of the each particle’s personal best. Since the proposed cost function is based on the choosing fewer numbers of support vectors, the complexity of SVM model is decreased while the accuracy remains in an acceptable range. Therefore, the testing time decreases and makes SVM more applicable for practical applications in real data sets. A comparative study on data sets of UCI database is performed between the proposed cost function and conventional cost function to demonstrate the effectiveness of the proposed cost function.

     

Application of a modified VIKOR method for decision-making problems in lean tool selection

Group decision making with multiple criteria is the most popular method for ranking a set of alternatives. In this regard, all alternatives are compared based on a common criteria set. Meanwhile, decision makers sometimes encounter special situations, for example, having to select from among a set of alternatives without a set of criteria or with a set of criteria that are grouped/related to various alternatives. Thus, it may be impossible to select from among a set of alternatives using typical methods. Hence, in this study, a new, modified VIKOR method is proposed to address the lean tool selection problems in manufacturing systems. In this study, a model was developed to help practitioners improve their ability to solve problems when the possible solutions have their own individual criteria. In fact the modified VIKOR method can be applied to rank alternatives in threefold: alternatives with common criteria, without common criteria, with integrated common, and uncommon criteria. This paper offers numerical examples of the model, using a case study to illustrate an application of the proposed model and properly assess the validity of this new method. The results demonstrate the usefulness and effectiveness of the modified new method. The model covers the lack existing in the current literature to assess effectiveness of applying lean tools.