DisplayTitle

Prediction of Triticale Grain Quality Properties, Based on Both Chemical and Indirectly Measured Reference Methods, Using Near-Infrared Spectroscopy

¹Department of Food Science, Stellenbosch University, Private Bag X1, Matieland (Stellenbosch), 7602, South Africa. ²Corresponding author. Phone: +27 21 808 3511. Fax: +27 21 808 3510. E-mail: mman@sun.ac.za ³Current address: Department of Bioengineering, Temple University, Philadelphia, PA 19122, U.S.A. ⁴Department of Genetics, Stellenbosch University, Private Bag X1, Matieland (Stellenbosch), 7602, South Africa. ⁵Current address: Queensland Alliance for Agriculture and Food Innovation Institute, The University of Queensland, St. Lucia, Queensland 4072, Australia.

The 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 r² of 0.95. Whole grain protein calibrations were less accurate, with optimum RPD_test of 3.54, SEP of 0.67% (w/w), and r² 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.