Fourier Transform Infrared Spectroscopic Studies Of Wheat In The Mid Infrared

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

ABSTRACTVarious whole‐kernel, milling, flour, dough, and breadmaking quality parameters were compared between hard red winter (HRW) and hard red spring (HRS) wheat. From the 50 quality parameters evaluated, values of only nine quality characteristics were found to be similar for both classes. These were test weight, grain moisture content, kernel size, polyphenol oxidase content, average gluten index, insoluble polymeric protein (%), free nonpolar lipids, loaf volume potential, and mixograph tolerance. Some of the quality characteristics that had significantly higher levels in HRS than in HRW wheat samples included grain protein content, grain hardness, most milling and flour quality measurements, most dough physicochemical properties, and most baking characteristics. When HRW and HRS wheat samples were grouped to be within the same wheat protein content range (11.4–15.8%), the average value of many grain and breadmaking quality characteristics were similar for both wheat classes but significant differences still existed. Values that were higher for HRW wheat flour were color b*, free polar lipids content, falling number, and farinograph tolerance. Values that were higher for HRS wheat flour were geometric mean diameter, quantity of insoluble polymeric proteins and gliadins, mixograph mix time, alveograph configuration ratio, dough weight, crumb grain score, and SDS sedimentation volume. This research showed that the grain and flour quality of HRS wheat generally exceeds that of HRW wheat whether or not samples are grouped to include a similar protein content range.

To investigate an accurate, rapid, and nondestructive method for wheat classification in inspection terminals, backpropagation neural network models were developed, based on single wheat kernel near-infrared transmittance spectra. Six classes of wheat were studied. Neural network models were optimized for two-class and six-class classification. The wavelength range of the spectra was 850 to 1049 nm. For two-class models with 200 input nodes, the average classification accuracy was 97% to 100%. For the six-class model with 200 input nodes, the average accuracy was 94.7%. The classification between hard red winter (HRW) and hard red spring (HRS) was least accurate among the six classes. For rapid classification, a narrower wavelength range, 899 to 1049 nm, with an interval of 2 nm, was proposed and shown to have little loss in accuracy. The most time-consuming two-class (HRW-HRS) model could be calibrated and validated in less than 7 mm. Prediction for new data was nearly instantaneous. A backpropagation neural network model with a learning coefficient of 0.6 to 0.65 and momentum of 0.4 to 0.45, without a hidden layer, was effective for wheat classification.

ABSTRACTA single wheat class or blended wheats from two wheat classes are usually milled in a flour mill. A near‐infrared (NIR) reflectance spectrometer, previously evaluated as granulation sensor for first‐break ground wheat from six wheat classes, was evaluated for a single wheat class, hard red winter (HRW) wheat, using offline methods. The HRW wheats represented seven cultivars ground by an experimental roller mill at five roll gap settings (0.38, 0.51, 0.63, 0.75, and 0.88 mm) which yielded 35 ground wheat samples each for the calibration and validation sets. Granulation models based on partial least squares regression were developed with cumulative mass of size fractions as a reference value. Combinations of four data pretreatments (log 1/R, baseline correction, unit area normalization, and derivatives) and subregions of the 400–1,700 nm wavelength range were evaluated. Models that used pathlength correction (unit area normalization) predicted well each of the four size fractions of first‐break ground wheat. The best model, unit area normalization and first derivative, predicted all the validation spectra with standard errors of performance of 3.80, 1.29, 0.43, and 0.68 for the >1041, >375, >240, and >136 μm size fractions, respectively. Ground HRW wheats have narrower particle size distribution and better sieving properties than ground wheat from six wheat classes. Thus, HRW wheat granulation models performed better than the previously reported models for six wheat classes.

Superior bread‐making quality is a primary objective for most hard red wheat (Triticum aestivum L.) breeding programs. Milled white flour is traditionally used to measure quality. There is increasing demand in the domestic market for whole grain bread products. The objective of this research was to determine the relationship of bread quality parameters based on white flour versus whole wheat flour in a set of hard red spring and hard red winter wheat genotypes. Quality evaluations were conducted on sets of genotypes grown in four environments for both wheat classes. Correlations of genotype performance for white flour versus whole meal varied with the traits. Protein levels were highly correlated, but correlations tended to decrease as dough was processed into a final product. This was especially true for hard red spring wheat genotypes. Mixograph tolerance of white flour versus whole meal was correlated highly for both spring and winter wheat, while water absorption was correlated poorly for both classes of wheat. Correlations for final loaf volume of whole wheat versus white flour was significant in two environments, and ranged from 0.08 to 0.72 for spring wheat genotypes. Correlations for loaf volume were significant in all environments, and ranged from 0.76 to 0.92 for winter wheat genotypes. In general, our data indicated that quality measured on white flour could be used as an estimate of whole wheat performance, but that identification of the best genotypes for whole wheat performance may require separate quality analysis.

Hard red winter (HRW) wheat (Triticum aestivum L. era. Thell.) cultivars are genetically distinct from soft red winter (SRW) wheat cultivars and thus provide new sources of germplasm for SRW wheat breeding programs. Previous research has shown that HRW ✕ SRW wheat crosses can generate segregates with acceptably low grain protein concentration (GPC). This study was conducted evaluate genetic variation of traits related to N uptake, partitioning, and utilization in HRW ✕ SRW wheat populations. The F3 and F4 generations of three HRW ✕ SRW crosses were grown in 1985 and 1986, respectively, on a Maury silt loam soil (fine, mixed, mesic Typic Paleudalf) near Lexington, KY. Significant differences among F4 families were observed for N harvest index (NHI), GPC and use efficiency for yield (NUEY) and protein (NUEP) in all populations. The correlation between N partitioning (NHI) and GPC varied among crosses, ranging from r = −0.57 (P < 0.01) to near zero. Highly significant differences for NUEY and NUEP were observed among F4 families of each cross. A path analysis indicated that the direct effect of N uptake efficiency accounted for as much as 91% of the variation in NUEY and 88% in NUEP. Indirect effects were of little consequence. The data demonstrate that N‐use traits are strongly influenced by the environment, and would be difficult to modify in a breeding program.

This research took from the work of Kohli and Morey in the economics literature to apply a quality derived market share demand function to the international wheat market. Specifically, a Japanese wheat import demand market share model was derived using data for Hard Red Winter, Hard Red Spring, Canadian Western Red Spring, and Australian Standard White wheat. Results indicate that the four wheat classes analyzed here are relatively good substitutes for each other, the own-price elasticity for each wheat class is elastic, and the own-characteristic Protein elasticity for Hard Red Winter 13% protein wheat increased over the period analyzed.

Effect of salt solutions applied during wheat conditioning on lipase activity and lipid stability of whole wheat flour

Multicollinearity refers to the presence of collinearity between multiple variables and renders the results of statistical inference erroneous (Type II error). This is particularly important in environmental health research where multicollinearity can hinder inference. To address this, correlated variables are often excluded from the analysis, limiting the discovery of new associations. An alternative approach to address this problem is the use of principal component analysis. This method, combines and projects a group of correlated variables onto a new orthogonal space. While this resolves the multicollinearity problem, it poses another challenge in relation to interpretability of results. Standard hypothesis testing methods can be used to evaluate the association of projected predictors, called principal components, with the outcomes of interest, however, there is no established way to trace the significance of principal components back to individual variables. To address this problem, we investigated the use of sparse principal component analysis which enforces a parsimonious projection. We hypothesise that this parsimony could facilitate the interpretability of findings. To this end, we investigated the association of 20 environmental predictors with all-cause mortality adjusting for demographic, socioeconomic, physiological, and behavioural factors. The study was conducted in a cohort of 379,690 individuals in the UK. During an average follow-up of 8.05 years (3,055,166 total person-years), 14,996 deaths were observed. We used Cox regression models to estimate the hazard ratio (HR) and 95% confidence intervals (CI). The Cox models were fitted to the standardised environmental predictors (a) without any transformation (b) transformed with PCA, and (c) transformed with SPCA. The comparison of findings underlined the potential of SPCA for conducting inference in scenarios where multicollinearity can increase the risk of Type II error. Our analysis unravelled a significant association between average noise pollution and increased risk of all-cause mortality. Specifically, those in the upper deciles of noise exposure have between 5 and 10% increased risk of all-cause mortality compared to the lowest decile.

Stagonospora nodorum blotch, caused by Phaeosphaeria nodorum, is considered one of the most destructive foliar diseases of wheat in the United States. However, relatively little is known about the population biology of this fungus in the major wheat-growing regions of the central United States. To rectify this situation, 308 single-spore isolates of P. nodorum were analyzed from 12 populations, five from hard red spring wheat cultivars in Minnesota and North Dakota and seven from soft red winter wheat in Indiana and Ohio. The genetic structure of the sampled populations was determined by analyzing polymorphisms at five microsatellite or simple-sequence repeat (SSR) loci and the mating type locus. Although a few clones were identified, most P. nodorum populations had high levels of gene (H(S) = 0.175 to 0.519) and genotype (D = 0.600 to 0.972) diversity. Gene diversity was higher among isolates collected from spring wheat cultivars in North Dakota and Minnesota (mean H(S) = 0.503) than in those from winter wheat cultivars in Indiana and Ohio (H(S) = 0.269). Analyses of clone-corrected data sets showed equal frequencies of both mating types in both regional and local populations, indicating that sexual recombination may occur regularly. However, significant gametic disequilibrium occurred in three of the four populations from North Dakota, and there was genetic differentiation both within and among locations. Genetic differentiation between the hard red spring and soft red winter wheat production regions was moderate (F(ST) = 0.168), but whether this is due to differences in wheat production or to geographical variation cannot be determined. These results suggest that sexual reproduction occurs in P. nodorum populations in the major wheat-growing regions of the central United States, and that geographically separated populations can be genetically differentiated, reflecting either restrictions on gene flow or selection.

A collaborative study was performed to assess accuracy, repeatability, and reproducibility of a nearinfrared (near-IR) method for determining crude protein content (PC) of whole-grain wheat. Four types of commercially available near-IR instruments, representing various combinations of wavelength region, mode of energy capture, method of energy dispersion, and treatment of spectral data, were used. Eight, 9,10, and 11 collaborators were involved, the exact number depending on instrument type. All collaborators received 22 samples of whole-grain hard red winter (HRW) wheat. They were furnished reference PCs (i.e., protein concentrations, w/w) corrected to a 12% moisture basis for instrument standardization. AOAC Method 990.03— combustion analysis—was the reference procedure. Standardization consisted of performing one of the following treatments to the instrument manufacturer's (or federal agency's) PC equation: (1) bias correction, (2) slope and intercept correction, or (3) recalibration with inclusion of standardization sample spectra. Standardized equations were then applied to a test set of 12 unknown HRW wheat sample spectra, with 2 samples blindly duplicated. The PCs of test samples ranged from 9 to 16%. Near-IR predictions were compared with reference measurements. Averaged within instrument type, root mean square of differences were 0.22, 0.24, 0.25, and 0.26% PC, depending on instrument. Corrected for bias within the test set, standard errors became 0.22, 0.18, 0.21, and 0.24% PC, respectively. These values were approximately twice the estimated lower limit for error (representing sample inhomogeneity). Overall repeatability relative standard deviation (RSD,) values were 0.92, 0.36, 0.42, and 0.74%, respectively. Overall reproducibility relative standard deviation (RSDR) values were 1.15, 0.61,1.53, and 1.38%. Such values for within-laboratory and between-laboratory variations of the near- IR methods were equivalent to values reported for the combustion method (990.03) for wheat. An inhouse study that examined all 6 U.S. wheat classes with one of the 4 instrument types produced repeatability and reproducibility values similar to those of the collaborative study, suggesting that the near-IR technique may be applied to red, white, hard, soft, and durum wheats. The near-IR method for determination of PC of whole-grain wheat has been adopted First Action (997.06) by AOAC INTERNATIONAL.

Starch granule size distribution of hard red winter and hard red spring wheat: Its effects on mixing and breadmaking quality

The paper presents a review of wheat classification accepted in various countries — producers of wheat and products of its processing (USA, Canada, Italy, France, Germany, China and others). Much attention is given to differences in the classification systems. Criteria for assigning wheat to certain classes based on physical and chemical properties of grain are examined. Differences in wheat classification by types, including morphological features and merits of grain such as size, color and gluten content, are highlighted. The authors discuss specific requirements and norms of wheat quality dictated by the regulatory authorities and requirements of the market of each country such as the mass fraction of moisture, content of impurities, density of grain and others. Based on the analysis of wheat classification in various world countries, it has been concluded that it is necessary to establish unified and generally accepted systems of wheat classification. This will allow simplifying international grain trade, make the market more transparent, strengthen trust between exporting and importing countries and will also facilitate the development of the international cooperation in the sphere of production and processing of wheat flour. Despite the wide assortment of flour confectionery products on the Russian market, baker’s flour of general purpose is mainly produced in the country. Its specific feature is a high content of flour from hard wheat varieties that are distinguished by higher protein content, strong gluten and large particle size. By its properties, this flour is not suitable for production of most flour confectionery products. Flour confectionery products produced from it are significantly inferior to import analogues in terms of their quality characteristics, which reduces their competitiveness. This dictates the need for the development of a standard for flour intended for production of confectionery products, which will include physico-chemical indicators for various flour types with consideration for their purpose.

Response to Cavalli-Sforza Interview: [Human Biology 82(3):245-266 (June 2010)

Aims: We show the use of principal component analysis (PCA) and Fourier decomposition (FD) method as tools for variable star diagnostics and compare their relative performance in studying the changes in the light curve structures of pulsating Cepheids and in the classification of variable stars. Methods: We have calculated the Fourier parameters of 17,606 light curves of a variety of variables, e.g., RR Lyraes, Cepheids, Mira Variables and extrinsic variables for our analysis. We have also performed PCA on the same database of light curves. The inputs to the PCA are the 100 values of the magnitudes for each of these 17,606 light curves in the database interpolated between phase 0 to 1. Unlike some previous studies, Fourier coefficients are not used as input to the PCA. Results: We show that in general, the first few principal components (PCs) are enough to reconstruct the original light curves compared to FD method where 2 to 3 times more number of parameters are required to satisfactorily reconstruct the light curves. The computation of required number of Fourier parameters on the average needs 20 times more CPU time than the computation of required number of PCs. Therefore, PCA does have some advantage over the FD method in analysing the variable stars in a larger database. However in some cases, particularly in finding the resonances in Fundamental mode (FU) Cepheids, the PCA results show no distinct advantages over the FD method. We also demonstrate that the PCA technique can be used to classify variables into different variability classes in an automated, unsupervised way, a feature that has immense potential for larger databases of the future.