Effect of gamma and electron beam irradiation on the physico-chemical and nutritional properties of mushrooms: a review

The short shelf-life of mushrooms is an obstacle to the distribution and marketing of the fresh product. Thus, prolonging postharvest storage, while preserving their quality, would benefit the mushroom industry as well as consumers. There has been extensive research on finding the most appropriate technology for mushrooms preservation. Gamma, electron-beam and UV irradiation have been shown to be potential tools in extending the postharvest shelf-life of fresh mushrooms. Studies evaluating the effects of ionizing radiation are available mainly in cultivated species such as Agaricus bisporus, Lentinus edodes and Pleurotus ostreatus. This review comprises a comprehensive study of the effects of irradiation on physico-chemical parameters (weight, colour, texture and pH), chemical compounds including nutrients (proteins, sugars and vitamins) and non-nutrients (phenolics, flavonoids and flavour compounds), and on biochemical parameters such as enzymatic activity of mushrooms for different species and from different regions of the world.     

Spectroscopic study of carbaryl sorption on smectite from aqueous suspension

Sorption of carbaryl (1-naphthyl-N-methyl-carbamate) from aqueous suspension to smectite was studied using Fourier transform infrared (FTIR), high-performance liquid chromatography (HPLC) (for batch sorption), and quantum chemical methods. The amount of carbaryl sorbed was strongly dependent on the surface-charge density of the smectite with more sorption occurring on the two "low" surface-charge density smectites (SHCa-1 and SWy-2) compared to that of the high surface-charge SAz-1 smectite. In addition, the amount of carbaryl sorbed was strongly dependent on the nature of the exchangeable cation and followed the order of Ba approximately Cs approximately Ca > Mg approximately K > Na approximately Li for SWy-2. A similartrend was found for hectorite (SHCa-1) of Cs > Ba > Ca > K approximately Mg > Na approximately Li. Using the shift of the carbonyl stretching band as an indicator of the strength of interaction between carbaryl and the exchangeable cation, the observed order was Mg > Ca > Ba approximately K > Na > Cs. The position of the carbonyl stretching band shifted to lower wavenumbers with increasing ionic potential of the exchangeable cation. Density functional theory predicted a cation-induced lengthening of the C=O bond, resulting from the carbonyl group interacting directly with the exchangeable cation in support of the spectroscopic observations. Further evidence was provided by a concomitant shift in the opposite direction by several vibrational bands in the 1355-1375 cm(-1) region assigned to stretching bands of the carbamate N-Ccarbonyl and Oether-Ccarbonyl bonds. These data indicate that carbaryl sorption is due, in part, to site-specific interactions between the carbamate functional group and exchangeable cations, as evidenced by the FTIR data. However, these data suggest that hydrophobic interactions also contribute to the overall amount of carbaryl sorbed. For example, the FTIR data indicated thatthe weakest interaction occurred when Cs+ was the exchangeable cation. In contrast, the highest amount of carbaryl sorption was observed on Cs-exchanged smectite. Of all the cations studied, Cs has the lowest enthalpy of hydration. It is suggested that this low hydration energy provides the carbaryl with greater access to the hydrophobic regions of the siloxane surface.     

CTAB-templated formation of CuCo2O4/CuO nanorods and nanosheets for high-performance supercapacitor applications

Journal of Materials Science: Materials in Electronics - In the present work, Copper cobaltites (CuCo2O4) were efficiently developed by the CTAB-assisted sonochemical process succeeded by...     

A systems analysis of the energy environment in buildings

The author has derived a flow diagram of fire development in order to point out areas where current technology can be applied to the fire problem as well as areas where further research is required.     

Uses of fats,fatty oils,and derivatives in the petroleum industry

Conclusion The use of fats, fatty oils, and derivatives is extensive in the petroleum industry to improve its lubricants in their wetting ability or oiliness. Fats and their derivatives serve to carry active elements such as chlorine, sulfur, and phosphorus for the improvement of load-carrying capacity, resistance to oxidation, inhibiting corrosiveness, and preventing wear during use. Greases are compounded from whole fats and fatty acids which serve under suitable conditions to gel lubricating oil and thus fulfill lubricating requirements which the liquid oils cannot themselves satisfy. Greases and their components may be tested for skin hardening in storage by means of an oxygen absorption test which distinguishes between satisfactory and unsatisfactory ingredients and products. Presented in a symposium on the Industrial Applications of Fats & Fatty Acids at the Fall Convention of the American Oil Chemists Society, Chicago, Oct. 1939.     

Predicting Forest Age Classes from High Spatial Resolution Remotely Sensed Imagery Using Voronoi Polygon Aggregation

Efficient identification of forest age is useful for forest management and ecological applications. Here we propose a user-assisted method for determining forest age using high spatial resolution remotely sensed imagery. This method requires individual trees to be extracted from imagery and represented as points. We use a local maximum filter to generate points that are converted to Voronoi polygons. Properties of the Voronoi polygons are correlated with forest age and used to aggregate points (trees) into areas (stands) based on three forest age classes. Accuracy of the aggregation ranges from approximately 68% to 78% and identification of the mature class is more consistent and accurate than the younger classes.     

Time Series Prediction With Genetic-Algorithm Designed Neural Networks: An Empirical Comparison With Modern Statistical Models

Neural networks whose architecture is determined by genetic algorithms outperform autoregressive integrated moving average forecasting models in six different time series examples. Refinements to the autoregressive integrated moving average model improve forecasting performance over standard ordinary least squares estimation by 8% to 13%. In contrast, neural networks achieve dramatic improvements of 10% to 40%. Additionally, neural networks give evidence of detecting patterns in data which remain hidden to the autoregression and moving average models. The consequent forecasting potential of neural networks makes them a very promising addition to the variety of techniques and methodologies used to anticipate future movements in time series.