Potential of Amaranthus cruentus BRS Alegria in the production of flour, starch and protein concentrate: chemical, thermal and rheological characterization

BACKGROUND: Amaranth is a little-known culture in Brazilian agriculture. Amaranthus cruentus BRS Alegria was the first cultivar recommended by Embrapa for the soil of the Brazilian scrubland. In order to evaluate the potential of this species in the production of flour, starch and protein concentrates, the latter products were obtained from A. cruentus BRS Alegria seeds, characterized and compared with the products obtained from the A. caudatus species cultivated in its soil of origin. RESULTS: The seeds of A. cruentus BRS Alegria furnished high-purity starch and flour with significant content of starch, proteins, and lipids. The starch and flour of this species presented higher gelatinization temperatures and formed stronger gels upon cooling compared with those obtained from the A. caudatus species. This is due to their greater amylose content and a difference in the composition of the more important fatty acids, such as stearic, oleic and linoleic acids, which indicates that they have greater heat stability. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis and differential scanning calorimetry revealed the presence of albumins, globulins, glutelins and prolamins in the protein concentrate, which was obtained as a byproduct of starch production. CONCLUSION: Amaranthus cruentus BRS Alegria has potential application in the production of flour, starch and protein concentrates, with interesting characteristics for use as food ingredients. (C) 2010 Society of Chemical Industry

Dietary trends: Estimates from food supply and survey data

Objective: To illustrate methodological issues involved in estimating dietary trends in populations using data obtained from various sources in Australia in the 1980s and 1990s. Methods: Estimates of absolute and relative change in consumption of selected food items were calculated using national data published annually on the national food supply for 1982-83 to 1992-93 and responses to food frequency questions in two population based risk factor surveys in 1983 and 1994 in the Hunter Region of New South Wales, Australia. The validity of estimated food quantities obtained from these inexpensive sources at the beginning of the period was assessed by comparison with data from a national dietary survey conducted in 1983 using 24 h recall. Results: Trend estimates from the food supply data and risk factor survey data were in good agreement for increases in consumption of fresh fruit, vegetables and breakfast food and decreases in butter, margarine, sugar and alcohol. Estimates for trends in milk, eggs and bread consumption, however, were inconsistent. Conclusions: Both data sources can be used for monitoring progress towards national nutrition goals based on selected food items provided that some limitations are recognized. While data collection methods should be consistent over time they also need to allow for changes in the food supply (for example the introduction of new varieties such as low-fat dairy products). From time to time the trends derived from these inexpensive data sources should be compared with data derived from more detailed and quantitative estimates of dietary intake.

Forecasting regional crop production using SOI phases: an example for the Australian peanut industry

Using peanuts as an example, a generic methodology is presented to forward-estimate regional crop production and associated climatic risks based on phases of the Southern Oscillation Index (SOI). Yield fluctuations caused by a highly variable rainfall environment are of concern to peanut processing and marketing bodies. The industry could profitably use forecasts of likely production to adjust their operations strategically. Significant, physically based lag-relationships exist between an index of ocean/atmosphere El Nino/Southern Oscillation phenomenon and future rainfall in Australia and elsewhere. Combining knowledge of SOI phases in November and December with output from a dynamic simulation model allows the derivation of yield probability distributions based on historic rainfall data. This information is available shortly after planting a crop and at least 3-5 months prior to harvest. The study shows that in years when the November-December SOI phase is positive there is an 80% chance of exceeding average district yields. Conversely, in years when the November-December SOI phase is either negative or rapidly falling there is only a 5% chance of exceeding average district yields, but a 95% chance of below average yields. This information allows the industry to adjust strategically for the expected volume of production. The study shows that simulation models can enhance SOI signals contained in rainfall distributions by discriminating between useful and damaging rainfall events. The methodology can be applied to other industries and regions.