Vitamin E contents of processed meats blended with palm oils

The vitamin E contents of beef burgers and chicken frankfurters blended with palm oil (PO) were determined. PO and red PO cooked beef burgers resulted in a significant (P � 0.05) loss of vitamin E from 427.5 to 178.0 mg/g and from 367.0 to 271.0 mg/g, respectively, after 6 months of storage. The concentration of alpha-tocopherol (a-tocopherol) for all retorted chicken frankfurters was reduced (P � 0.05) by 66.0–91.50 (16–46%) mg/g while the alpha-tocotrienol (a-tocotrienol) in all retorted chicken frankfurters significantly decreased (P � 0.05) by 63.0–95.5 mg/g (28–48%) after 6 months of storage. Both a-tocopherol and a-tocotrienol decreased at a faster rate (62– 64% and 53–61% loss, respectively) and was less stable than the gammatocotrienol (12–59%) and the delta-tocotrienol (4–28%) in beef burgers. The effect of processing, cooking, frozen storage and the type of fats used could influence vitamin E stability and content in meat products

Long-term wind resource assessment for small and medium-scale turbines using operational forecast data and measure-correlate-predict

Output from a state-of-the-art, 4 km resolution, operational forecast model (UK4) was investigated as a source of long-term historical reference data for wind resource assessment. The data were used to implement measure-correlate-predict (MCP) approaches at 37 sites throughout the United Kingdom (UK). The monthly and hourly linear correlation between the UK4-predicted and observed wind speeds indicates that UK4 is capable of representing the wind climate better than the nearby meteorological stations considered. Linear MCP algorithms were implemented at the same sites using reference data from UK4 and nearby meteorological stations to predict the long-term (10-year) wind resource. To obtain robust error statistics, MCP algorithms were applied using onsite measurement periods of 1-12 months initiated at 120 different starting months throughout an 11 year data record. Using linear regression MCP over 12 months, the average percentage errors in the long-term predicted mean wind speed and power density were 3.0% and 7.6% respectively, using UK4, and 2.8% and 7.9% respectively, using nearby meteorological stations. The results indicate that UK4 is highly competitive with nearby meteorological observations as an MCP reference data source. UK4 was also shown to systematically improve MCP predictions at coastal sites due to better representation of local diurnal effects

Comparison between the bivariate Weibull probability approach and linear regression for assessment of the long-term wind energy resource using MCP

A detailed investigation of a measure-correlate-predict (MCP) approach based on the bivariate Weibull (BW) probability distribution of wind speeds at pairs of correlated sites has been conducted. Since wind speeds are typically assumed to follow Weibull distributions, this approach has a stronger theoretical basis than widely used regression MCP techniques. Building on previous work that applied the technique to artificially generated wind data, we have used long-term (11 year) wind observations at 22 pairs of correlated UK sites. Additionally, 22 artificial wind data sets were generated from ideal BW distributions modelled on the observed data at the 22 site pairs. Comparison of the fitting efficiency revealed that significantly longer data periods were required to accurately extract the BW distribution parameters from the observed data, compared to artificial wind data, due to seasonal variations. The overall performance of the BW approach was compared to standard regression MCP techniques for the prediction of the 10 year wind resource using both observed and artificially generated wind data at the 22 site pairs for multiple short-term measurement periods of 1-12 months. Prediction errors were quantified by comparing the predicted and observed values of mean wind speed, mean wind power density, Weibull shape factor and standard deviation of wind speeds at each site. Using the artificial wind data, the BW approach outperformed the regression approaches for all measurement periods. When applied to the real wind speed observations however, the performance of the BW approach was comparable to the regression approaches when using a full 12 month measurement period and generally worse than the regression approaches for shorter data periods. This suggests that real wind observations at correlated sites may differ from ideal BW distributions and hence regression approaches, which require less fitting parameters, may be more appropriate, particularly when using short measurement periods