Triticale (× Triticosecale sp. Wittmack ex A. Camus 1927) is an anthropogenic cereal designed to incorporate the functionality and high yield of wheat (Triticum spp. Linnaeus 1753) and durability of rye (Secale cereale Linnaeus 1753). The potential of triticale has remained largely unrealised, and in the 135 years since A. Stephen Wilson first crossed wheat and rye, triticale has mostly been used as animal feed. Growing demand for food resources has led to an increased interest in triticale development. Efforts to breed cultivars appropriate for baking have met with difficulty, although relatively new approaches to triticale end-use propose greater applicability for human consumption. Further, environmental awareness has generated interest in the use of triticale within biofuel production. We review environmental and genetic effects on triticale yield with a view towards increased demand on a hardy and useful cereal crop. We find triticale could satisfy many of the hopes originally placed upon it, and may be useful in foodstuffs and fuel, but only when growth environment is carefully considered.

Cereal Chem. 90(6):540-545The increasing demand for triticale as food, feed, and fuel has resulted in the availability of cultivars with different grain quality characteristics. Analyses of triticale composition can ensure that the most appropriate cultivars are obtained and used for the most suitable applications. Near-infrared (NIR) spectroscopy is often used for rapid measurements during quality control and has consequently been investigated as a method for the measurement of protein, moisture, and ash contents, as well as kernel hardness (particle size index [PSI]) and sodium dodecyl sulfate (SDS) sedimentation from both whole grain and ground triticale samples. NIR spectroscopy prediction models calculated using ground samples were generally superior to whole grain models. Protein content was the most effectively modeled quality property; the best ground grain calibration had a ratio of the standard error of test set validation to the standard deviation of the reference data of the test set (RPD test ) of 4.81, standard error of prediction (SEP) of 0.52% (w/w), and r 2 of 0.95. Whole grain protein calibrations were less accurate, with optimum RPD test of 3.54, SEP of 0.67% (w/w), and r 2 of 0.92. NIR spectroscopy calibrations based on direct chemical reference measurements (protein and moisture contents) were better than those based on indirect measurements (PSI, ash content, and SDS sedimentation). Calibrations based on indirect measurements would, however, still be useful to identify extreme samples. 2 Corresponding