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The aim of this study was to employ iota-carrageenan (IC) and wheat protein (WP) as an emulsifier alternative to egg yolk in a model mayonnaise system. A solution of 0.1% IC and 4% WP was prepared and used as an emulsifier in five different mayonnaise formulas. All mayonnaise treatments were evaluated and compared based on lightness and yellowness (i.e., L and b values respectively) at 4, 23, and 40°C. In addition, an adaptive neuro-fuzzy inference system (ANFIS) was used to model and identify the properties of the resulted mayonnaise, with the temperature and ratios. Experimental validation runs were conducted to compare the measured values and the predicted ones. The L value of the mayonnaise produced from different emulsifiers decreased at the lower storage temperature. The b-value was significantly the highest for mayonnaise formulated from 100% egg yolk. The comparison showed that the adoption of this neuro-fuzzy modeling technique (i.e., ANFIS) achieved a very satisfactory prediction accuracy of about 98%.

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The aim of this study was to employ whey proteins isolate (WPI) and carboxymethylcelulose (CMC) as stabilizers in beverage production and to evaluate viscosity of the products. Three beverages were formulated using 6% WPI combined with three different concentrations of CMC (0.1, 0.5, and 1%). The combination of 6% WPI/1.0% CMC was selected and added to three different ratios of pure orange juice (50:50 "T1," 40:60 "T2," and 15:85 "T3"). The apparent viscosity decreased as shear rate and temperature increased. Additionally, the apparent viscosity for the same treatment at certain shear rate/same temperature increased after storage. In addition, an adaptive neuro-fuzzy inference system (ANFIS) was used to model and identify the viscosity of the resulted beverages. Experimental validation runs were conducted to compare the measured values and the predicted ones. ANFIS models achieved an average prediction error of viscosity of only 9%. It is believed that this approach can be applied to predict many other parameters and properties in beverage industry.

SUMMARYThis study analyzes changes in industrial aggregate electricity intensity, during the period 1998-2005, and identifies major factors affecting the aggregate electricity intensity change using the refined Laspeyers method decomposition technique. The Jordanian industrial sector was disaggregated into seven groups: mining of chemical and fertilizers, paper, plastics, petrochemical, cement, steel and others industries. Aggregate electricity intensity has decreased from approximately 1.30 to 0.93 kWh US$ À1 in 1998 and 2005, respectively. The analysis showed that the structural and efficiency effects contribute to decreases of around 21 and 79%, respectively, of total aggregate electricity intensity decline in the industrial sector. Such result is considered of high importance for energy and/or electricity analysts and planners, in Jordan and other countries, especially for the purpose of forecasting future demand more logically and without unnecessary exaggerations.