Whole grain quality

C. K. Black, J. F. Panozzo. Whole grain quality evaluation in wheat based on near infrared spectroscopy. Proceedings of 49th Australian Cereal Chemistry Conference, J. F. Panozzo, M. Ratcliffe, M. Wootton, C. V. Wrigley, eds. RACI, Melbourne, 1999, p. 309-313. 

Bach Knudsen, K. E., Kirleis, A. W., Eggum, B. O., and Munck, L. (1988b). Carbohydrate composition and nutritional quality for rats of sorghum to prepared from decorticated white and whole grain red flour. /. Nutr. 118, 588-597. 

Neural networks have been used, most effectively, as a way of establishing robust NIR calibrations. It is the calibration method of choice for a whole cereal grain, transmission instrument, which is being used worldwide for many of the analyses required for estimating grain quality. An artificial neural network program performs many iterations in order to establish the optimum solution required from large, complex datasets. 

Among soft wheats, the best quality is the white colored because they are the most suitable for whole grain products, breakfast cereals, cookies, and cakes. Club wheats yield whiter flours compared with their red colored counterparts. In addition, white soft wheats are preferred for manufacturing whole wheat or high-fiber products. 

The breakfast cereal industry relies on the quality control parameters previously discussed for whole grains, dry- and wet-milled products, and wheat quality tests. 

In 1995 potatoes and rice were stored in an abandoned road tunnel in Japan. At the end of the winter the tunnel was filled with snow that was covered with aluminum coated tarpaulins. The products were stored both beside the snow piles and under the tarpaulins. Rice from the same harvest was stored in a grain magazine and at a research centre for comparison. The rice in the tunnel had best quality after two month. The potatoes under the tarpaulins kept its quality longest and it was found that the storage method was feasible over the whole year (Suzuki et al., 1997). 

---