Starch waxiness in hexaploid wheat (Triticum aestivum L.) by NIR reflectance spectroscopy

Wheat (Triticum aestivum L.) breeding programs are currently developing varieties that are free of amylose (waxy wheat), as well as genetically intermediate (partial waxy) types. Successful introduction of waxy wheat varieties into commerce is predicated on a rapid methodology at the commodity point of sale that can test for the waxy condition. Near-infrared (NIR) reflectance spectroscopy, one such technology, was applied to a diverse set of hard winter (hexaploid) wheat breeders' lines representing all eight genotypic combinations of alleles at the wx-A1, wx-B1, and wx-D1 loci. These loci encode granule-bound starch synthase, the enzyme responsible for amylose synthesis. Linear discriminant analysis of principal components scores 1-4 was successful in identifying the fully waxy samples at typically greater than 90% accuracy; however, accuracy was reduced for partial and wild-type genotypes. It is suggested that the spectral sensitivity to waxiness is due to (1) the lipid-amylose complex which diminishes with waxiness, (2) physical differences in endosperm that affect light scatter, or (3) changes in starch crystallinity.     

Control of individual microsprinklers and fault detection strategies

Based on yield variability in orchards, it is evident that many trees receive too much or too little water and fertilizer under uniform management. Optimizing water and nutrient management based on the demand of individual trees could result in improved yield and environmental quality. A microsprinkler sensor and control system was developed to provide spatially variable delivery of water and fertilizer, and a prototype was installed in a nectarine orchard. Fifty individually addressable microsprinkler nodes, one located at every tree, each contained control circuitry and a valve. A drip line controller stored the irrigation schedule and issued commands to each node. Pressure sensors connected to some of the nodes provided lateral line pressure feedback. The system was programmed to irrigate individual trees for specific durations or to apply a specific volume of water at each tree. Time scheduled irrigation demonstrated the ability to provide microsprinkler control at individual trees, but also showed variation in discharge because of pressure differences between laterals. Volume scheduled irrigation used water pressure feedback to control the volume applied by individual microsprinklers more precisely, and the average error in application volume was 3.7%. Fault detection was used to check for damaged drip lines and clogged or damaged emitters. A pressure monitoring routine automatically logged errors and turned off the microsprinklers when drip line breaks and perforations caused pressure loss. Emitter diagnosis routines correctly identified clogged and damaged microsprinkler emitters in 359 of 366 observations. Irrigation control at the individual tree level has many useful features and should be explored further to characterize fully the benefits or disadvantages for orchard management.     

Measurement of Blend Concentrations of Conventional and Waxy Hard Wheats Using NIR Spectroscopy

Breeding development of waxy (amylose‐free) hard wheat lines adapted to the North American climate has been underway for more than a decade, with releases of competitive varieties imminent. Because of required identity preservation and a possible premium value placed on waxy lots, a rapid and accurate method is desired to identify and quantify the mixing of conventional wheat with waxy wheat, a condition that might occur at harvest or any point downstream. Our previous work demonstrated that lines pure with waxy starch can be identified from nonwaxy lines by use of near‐infrared (NIR) spectroscopy applied either on a whole kernel or ground meal basis. However, mixture quantification by NIR techniques has not been examined until now. Using hard winter wheat grown in two seasons (2011 and 2012) and at two locations (Nebraska and Arizona), a series of mixtures ranging in proportion (conventional/waxy) percentage by weight, from 0:100 to 100:0, were formed from nine pairs of waxy and nonwaxy varieties or lines, with year and location being consistent within a pair. Twenty‐nine mixtures (0, 1, 2, 3, 4, 5, 10, 15, …, 85, 90, 95, 96, 97, 98, 99, and 100%) were formed for each pair. Partial least squares regression models were developed by using eight of the nine pairs, with model validation accomplished by using the pair excluded. This procedure was repeated for each pair. The results indicate that, regardless of sample format or spectral pretreatment, the optimal models typically produce coefficients of determination in excess of 0.98, with standard errors of 4–7%, thus demonstrating the feasibility of the use of the NIR technique to predict the mixture level to within 10% by weight.     

Collaborative Analysis of Wheat Endosperm Compressive Material Properties

The objective measurement of cereal endosperm texture, for wheat (Triticum spp. L.) in particular, is relevant to the milling, processing, and utilization of grain. The objective of this study was to evaluate the interlaboratory results of compression failure testing of wheat endosperm specimens of defined geometry. Parallelepipeds (bricks) and cylinders were prepared from individual soft and hard near‐isogenic wheat kernels and compressed in two orientations (parallel and perpendicular to the long brush‐to‐germ axis). Compression curves were used to derive failure stress, failure strain, work density (area under the curve), and Young's modulus. In all five laboratories, the ability to delineate hard from soft wheat endosperm material properties was quite high. Four laboratories compressed endosperm bricks in the same orientation, on edge; texture class (soft vs. hard) was consistently the greatest source of variation in analysis of variance models (F‐values from 417 to 1401, Young's modulus and failure stress, respectively). Failure stress was found to be the best overall means of measuring the difference in what is known in the vernacular as wheat hardness. Across laboratories, the absolute measures of all four material properties ranged on the order of about two‐ to threefold from low to high, although within a laboratory, results were highly consistent. Laboratory by texture class interaction was deemed to be of minor importance. Brick size and moisture content within the ranges tested were not major sources of variation, and cylinders prepared from endosperm produced results similar to those obtained from bricks. The results suggested that wheat endosperm might express some level of anisotropic behavior, as specimens compressed in the kernel orientation parallel to the long axis failed at lower strain and stress values, with lower work density, when compared with kernel orientation perpendicular to the long axis. A key feature of interlaboratory variation was identified as being instrument rigidity, a subject of ongoing research. In conclusion, the preparation of endosperm specimens of defined size and shape, in combination with compression failure testing at low moisture content (<18%), is useful for objectively delineating the phenomenon known as hardness. The study presented here will advance our ability to objectively measure cereal grain texture and the material properties of endosperm.     

Chemometric Localization Approach to NIR Measurement of Apparent Amylose Content of Ground Wheat

The development of new wheat cultivars that target specific end‐uses, such as low or zero amylose contents of partially waxy and waxy wheats, has become a modern focus of wheat breeding. But for efficient and cost‐effective breeding, inexpensive and high‐throughput quality testing procedures, such as near infrared (NIR) spectroscopy, are required. The genetic nature of a set of wheat lines, which included waxy to nonwaxy cultivars, results in a bimodal distribution of amylose contents that presents some special challenges for the formulation of stable NIR calibrations for this property. The obvious and intuitive solution lies in the use of some form of localization procedure and we explored three localization algorithms in comparison with the default partial least squares. Localization with respect to the waxy (zero amylose) cultivars resulted in a modified partial least squares calibration with a standard error of prediction of 0.16%. The results establish unambiguously that there are advantages in performing a suitable localization to achieve a reliable NIR calibration and prediction. The accuracy of the method can also be enhanced by application of an appropriate resampling strategy. In addition, there are advantages in performing a suitable localization to achieve a reliable NIR calibration‐prediction. It resolves the issue of how to utilize the bimodal distribution of apparent amylose values. The best results are obtained when the localization is performed simultaneously with respect to the sample property under investigation and the NIR spectra. The key problem with the measurement of amylose is the laboratory reference method which, in reality, only measures the apparent amylose content of the wheat. As a direct consequence, the measurements of amylose have such a large error that traditional calibration‐prediction procedures generate unacceptable results. To resolve this difficulty, a statistically based resampling strategy is proposed as a method of identifying samples where there is a large error in the reference measurement.     

Identification of Wheat Lines Possessing the 1AL.1RS or 1BL.1RS Wheat-Rye Translocation by Near-Infrared Reflectance Spectroscopy

Wheat-rye chromosomal translocations, particularly those involving the short arm of rye chromosome 1R, have been used during the past 25 years to instill resistance to plant pathogens and insects and improve the hardiness, adaptation, and yield of wheat. Unfortunately, the presence of the 1AL.1RS or 1BL.1RS rye translocations in wheat has been shown to impart inferior dough handling and baking characteristics. Although numerous analytical techniques (e.g., HPLC, monoclonal antibody tests, high-performance capillary electrophoresis) have been developed for detecting these translocations, the complexity of the analytical procedures restricts their use to research and analytical laboratories. The purpose of this study was to examine the potential of diffuse reflectance near-infrared spectroscopy, a well-accepted technique in the grain industry, for detecting 1RS-containing genotypes. This research used three independent groups of wheat samples, ranging in genetic diversity from sister lines derived from 1RS breeding populations to commercial cultivars. Based on the diffuse reflectance spectra (1,100–2,500 nm) of flour, partial least squares (PLS) models, through cross-validation, exhibited misclassification rates as low as 0%, particularly for commercial cultivars. Misclassification rates for corresponding, but separate, test sets were as low as 1%. When the same modeling procedure was applied to samples of more closely related genetic backgrounds, cross-validation misclassification rates rose to 15–20%. Most problematic were samples that were heterogeneous for 1RS such as the cultivar Rawhide. Incorporating heterogeneous samples into a calibration equation improved the classification accuracy of these samples but diminished the prediction accuracy of nonheterogeneous samples.