Selecting and Sorting Waxy Wheat Kernels Using Near‐Infrared Spectroscopy

An automated single kernel near‐infrared (NIR) sorting system was used to separate single wheat (Triticum aestivum L.) kernels with amylose‐free (waxy) starch from reduced‐amylose (partial waxy) or wild‐type wheat kernels. Waxy kernels of hexaploid wheat are null for the granule‐bound starch synthase alleles at all three Wx gene loci; partial waxy kernels have at least one null and one functional allele. Wild‐type kernels have three functional alleles. Our results demonstrate that automated single kernel NIR technology can be used to select waxy kernels from segregating breeding lines or to purify advanced breeding lines for the low‐amylose kernel trait. Calibrations based on either amylose content or the waxy trait performed similarly. Also, a calibration developed using the amylose content of waxy, partial waxy, and wild‐type durum (T. turgidum L. var durum) wheat enabled adequate sorting for hard red winter and hard red spring wheat with no modifications. Regression coefficients indicated that absorption by starch in the NIR region contributed to the classification models. Single kernel NIR technology offers significant benefits to breeding programs that are developing wheat with amylose‐free starches.     

Protein Content of Single Kernels of Wheat by Near-Infrared Reflectance Spectroscopy

Protein content is well known to affect the functional properties of processed wheat products. Traditionally performed on aliquots (0·25–2·2 g) from samples ranging in size from 30–40 g (for combustion and Kjeldahl analyses) to several hundred grams (for whole-grain near-infrared analysis), these methods inherently do not provide information on single-kernel protein variability. Inspection procedures by the United States Department of Agriculture for grading and classification of wheat are undergoing change to provide the processor or end user with information on the variability of several single-kernel properties including hardness, moisture, weight, and wheat class. The present study has focused on demonstrating the feasibility of measuring crude protein content of single wheat kernels by near-infrared reflectance. More than 300 commercial wheat samples from the 1992 U.S. harvest, representing five (hard red winter, hard red spring, soft red winter, hard white, and soft white) of the six (durum excluded) market classes were chosen, from which 10 kernels were randomly selected and handled on a single-kernel basis. Handling consisted of reflectance scanning (1100–2498 nm), drying (for moisture compensation), and combustion (for reference protein-content determination). Partial least squares and multiple linear regression models, when applied to samples excluded from calibration, demonstrated standard errors of performance ranging from 0·462 to 0·720% protein depending on the modeling technique, number of classes used to develop the model, and the wheat class tested. The pooling of wheat classes to produce a general model did not diminish model accuracy. Best results were achieved with an 1100–1400-nm region. Model performance worsened as the wavelength region widened or as the minimum wavelength shifted from 1100 nm to higher values.     

Detection of Scab‐Damaged Hard Red Spring Wheat Kernels by Near‐Infrared Reflectance

Scab (Fusarium head blight) is a fungal disease that has become increasingly prevalent in North American wheat during the past 15 years. It is of concern to growers, processors, and the consumers because of depressed yields, poor flour quality, and the potential for elevated concentrations of the mycotoxin, deoxynivalenol (DON). Both wheat breeder and wheat inspector must currently deal with the assessment of scab in harvested wheat by manual human inspection. The study described herein examined the accuracy of a semi‐automated wheat scab inspection system that is based on near‐infrared (NIR) reflectance (1,000–1,700 nm) of individual kernels. Using statistical classification techniques such as linear discriminant analysis and nonparametric (k‐nearest‐neighbor) classification, upper limits of accuracy for NIR‐based classification schemes of ≈88% (cross‐validation) and 97% (test) were determined. An exhaustive search of the most suitable wavelength pairs for the spectral difference, [log(1/R)_λ1 ‐ log(1/R)_λ2], revealed that the slope of the low‐wavelength side of a broad carbohydrate absorption band (centered at ≈1,200 nm) was very effective at discriminating between healthy and scab‐damaged kernels with test set accuracies of 95%. The achieved accuracy levels demonstrate the potential for the use of NIR spectroscopy in commercial sorting and inspection operations for wheat scab.     

Measurement of inorganic phosphorus in soybeans with near-infrared spectroscopy

This study explored the feasibility of near-infrared (NIR) quantitative and qualitative models for soybean inorganic phosphorus (Pi), which is complementary to phytic acid, a component of nutritional and environmental importance. Spectra, consisting of diffuse reflectance (1100-2500 nm) of ground meal and single-bean transmittance (600-1900 nm) of whole seed, were collected on 191 recombinant inbred soybean lines. Partial least-squares regression models were individually developed for soy meal diffuse reflectance, single-bean transmittance, and averaged (24 beans/line) whole seed transmittance data. The best performance was obtained with diffuse reflectance data, in which the standard errors (rmsd) were 263 and 248 mg/kg for cross-validation and validation sets, respectively. Model accuracy was lower for the 24-bean average transmittance spectra and still lower for single beans. Despite the overall poorer modeling ability of Pi with respect to the common macronutrient NIR regressions, such as those for protein and oil, this technique holds promise for use in breeding programs.     

Fertigation techniques for use with multiple hydrozones in simultaneous operation

Site-specific delivery of fertilizer is a useful tool to address differences in crop need. Modern systems with wirelessly networked sensors and valves allow multiple hydrozones to be created more easily than traditional wired systems. This allows irrigation and fertigation rates to be varied across small portions of a field. However, fertigation to multiple hydrozones with different fertilizer requirements may be complex if each zone cannot be fertigated in an independent set. Instead, it might be necessary to operate several fertigation zones simultaneously. This raises a concern over the ability to deliver fertilizer uniformly within each zone. Four fertigation strategies were tested. The conventional method was to fertigate multiple hydrozones at different times. Three site-specific strategies were considered, involving overlapping fertigation phases in multiple hydrozones. Fertilizer distribution uniformity tests were conducted with a 64-emitter drip line to determine which strategy gave the most uniform distribution of fertilizer within a hydrozone. All fertigation techniques performed well, with fertilizer distribution uniformities between 0.88 and 0.96. Selection of the optimum site-specific fertigation strategy will depend on crop needs, scheduling limitations, and system design parameters such as emitter type, fluid travel time, and slope. Similar to conventional fertigation, the main factor in fertilizer distribution uniformity for this study was drip emitter variability. In the presence of sloped terrain, the site-specific control strategy that involved a delay between fertilizer injection and flushing had the least uniform fertilizer application.     

Environmental Effects on Developing Wheat as Sensed by Near‐Infrared Reflectance of Mature Grains

For 30 years, near‐infrared (NIR) spectroscopy has routinely been applied to the cereal grains for the purpose of rapidly measuring concentrations of constituents such as protein and moisture. The research described herein examined the ability of NIR reflectance spectroscopy on harvested wheat to determine weather‐related, quality‐determining properties that occurred during plant development. Twenty commercial cultivars or advanced breeding lines of hard red winter and hard white wheat (Triticum aestivum L.) were grown in 10 geographical locations under prevailing natural conditions of the U.S. Great Plains. Diffuse reflectance spectra (1,100–2,498 nm) of ground wheat from these samples were modeled by partial least squares one (PLS1) and multiple linear regression algorithms for the following properties: SDS sedimentation volume, amount of time during grain fill in which the temperature or relative humidity exceeded or was less than a threshold level (i.e., >30, >32, >35, <24°C; >80%, <40% rh). Rainfall values associated with four pre‐ and post‐planting stages also were examined heuristically by PLS2 analysis. Partial correlation analysis was used to statistically remove the contribution of protein content from the quantitative NIR models. PLS1 models of 9–11 factors on scatter‐corrected and (second order) derivatized spectra produced models whose dimensionless error (RPD, ratio of standard deviation of the property in a test set to the model standard error for that property) ranged from 2.0 to 3.3. Multiple linear regression models, involving the sum of four second‐derivative terms with coefficients, produced models of slightly higher error compared with PLS models. For both modeling approaches, partial correlation analysis demonstrated that model success extends beyond an intercorrelation between property and protein content, a constituent that is well‐modeled by NIR spectroscopy. With refinement, these types of NIR models may have the potential to provide grain handlers, millers, and bakers a tool for identifying the cultural environment under which the purchased grain was produced.     

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.