Evaluation of the Single Kernel Characterization System (SKCS) for Measurement of Sorghum Grain Attributes

Cereal Chem. 83(1):108–113 The single kernel characterization system (SKCS) has been widely used in the wheat industry, and SKCS parameters have been linked to end-use quality in wheat. The SKCS has promise as a tool for evaluating sorghum grain quality. However, the SKCS was designed to analyze wheat, which has a different kernel structure from sorghum. To gain a better understanding of the meaning of SKCS predictions for grain sorghum, individual sorghum grains were measured for length, width, thickness (diameter), and weight by laboratory methods and by the SKCS. SKCS predictions for kernel weight and thickness were highly correlated to laboratory measurements. However, SKCS predictions for kernel thickness were underestimated by ≈20%. The SKCS moisture prediction for sorghum was evaluated by tempering seven samples with varying hardness values to four moisture levels. The moisture contents predicted by SKCS were compared with a standard oven method and, while correlated, SKCS moisture predictions were less than moisture measured by air oven, especially at low moisture content. Finally, SKCS hardness values were compared with hardness measured by abrasive decortication. A moderate (r = 0.67, P < 0.001) correlation was observed between the hardness measurements. The SKCS predictions of kernel weight and diameter were highly correlated with laboratory measurement. Moisture prediction, however, was substantially lower by the SKCS than as measured by an air oven method. The SKCS should be suitable for measuring sorghum grain attributes. Further research is needed to determine how SKCS hardness predictions are correlated to milling properties of sorghum grain. Grain hardness or endosperm texture (grain strength) is an important grain quality attribute that plays a role in the processing of cereal grains and in the end-use quality of cereal grain products such as breads and snack foods (Cagampang and Kirleis 1984; Bettge and Morris 2000). Grain hardness also plays a role in plant defense against molds and possibly from insect attack (Chandrashekar and Mazhar 1999). For sorghum, grain hardness has been linked to a number of specific end-use quality traits. Cagampang and Kirleis (1984) reported that sorghum cooking quality parameters such as adhesion, cooked grain texture, alkali gel stiffness, and amylograph viscosities were significantly related to grain hardness. Rooney et al (1986) reported that sorghum grain hardness was the most important component related to porridge quality. Grain hardness was also related to the production of high-quality couscous granules from sorghum (Aboubacar and Hamaker 1999). Within a given grain lot, large sorghum kernels were harder than small kernels and related to higher quality grain (Lee et al 2002). Milling quality of sorghum grain has been related to grain hardness as well (Maxson et al 1971; Munck et al 1981; Munck 1995; Rooney and Waniska 2000). Grain hardness has also been linked to mold and weathering resistance in sorghum (Jambunathan et al 1992; Kumari and Chandrashekar 1994; Audilakshimi et al 1999; Waniska 2000). To measure grain hardness in sorghum, a number of different methods have been used. Pomeranz (1986) used the Brabender hardness tester, Stenvert micro-hammermill test, particle size index, and near-infrared reflectance to determine sorghum grain hardness. Lawton and Faubion (1989) reported the sorghum milling data using the tangential abrasive dehulling device (TADD) followed the first-order rate loss function. They used these data to categorize 13 sorghum hybrids into seven hardness groups. Perhaps the most widely used method for measuring grain hardness and relating it to milling performance in sorghum is the tangential abrasive dehulling device (Rooney and Waniska 2000). The single kernel characterization system (SKCS) has also been used to measure grain hardness in sorghum (Pedersen et al 1996). These authors analyzed grains from 64 sorghum genotypes using a prototype SKCS instrument and compared the hardness values with seed vitreousness. SKCS hardness values were correlated (r = 0.75) to percent vitreousness in sorghum. Also, SKCS diameter and weight predictions were compared with laboratory measurements as well as with density measurements. The SKCS predictions for diameter and weight were highly correlated to laboratory measurements, though SKCS underestimated kernel diameter. The SKCS, which was designed to analyze wheat, works by crushing individual grains between a serrated rotor and a crescent (Martin et al 1993; Martin and Steele 1996). Typically 300 kernels are analyzed per sample. Both the average and the standard deviations for the 300 kernels are reported. As noted by Pedersen et al (1996), the standard deviations reported by the SKCS for a given sample may also be useful information for sorghum by providing a measure of sample uniformity. Kernel hardness is determined from formulas developed by Martin et al (1993) using various instrument parameters and moisture and diameter. It is important to note that the four parameters predicted by SKCS are all indirect and must be calibrated against standard methods (Osborne and Anderssen 2003). An excellent overview on the operation and principles of the SKCS is presented in Osborne and Anderssen (2003). These authors also review the applications of SKCS in wheat research and the wheat industry as well as the use of SKCS for nonwheat cereals. Pedersen et al (1996) demonstrated that SKCS could successfully analyze sorghum grain. However, the sorghum kernel is substantially different in shape than wheat, the grain for which the instrument was originally designed, which could cause uncertainty in relating SKCS predictions to sorghum grain. For example, Pedersen et al (1996) reported that SKCS underestimated kernel diameter in sorghum. Thus the goals of this study were to evaluate SKCS predictions of hardness, weight, moisture, and diameter of sorghum kernels and compare them with traditional methods for obtaining these measurements and, if necessary, to develop new slope and bias adjustments to improve their accuracy. 1 USDA-ARS, Grain Marketing and Production Research Center, Manhattan, KS 66502. Names are necessary to report factually on available data; however, the USDA neither guarantees nor warrants the standard of the product, and the use of the name by the USDA implies no approval of the product to the exclusion of others that may also be suitable. 2 Corresponding author. Phone: 785-776-2725. Fax: 785-537-5534. E-mail: [email protected] 3 Kansas State University, Dept. of Agronomy, Manhattan, KS 66506. 4 USDA-ARS, Wheat, Sorghum, and Forage Research, Lincoln, NE 68583-0937. 5 USDA-ARS Tropical Agriculture Research Station, Mayaguez, Puerto Rico. DOI: 10.1094 / CC-83-0108 This article is in the public domain and not copyrightable. It may be freely reprinted with customary crediting of the source. AACC International, Inc., 2006.