Rapid Assessment and Prediction of Wheat and Gluten Baking Quality With the 2‐g Direct Drive Mixograph Using Multivariate Statistical Analysis

A computerized 2‐g direct drive mixograph was used to study the mixing characteristics of flours milled from a range of breadmaking cultivars obtained from five separate locations around the UK, providing 54 flour samples. Fifteen parameters were extracted from each mixograph trace using the Mixsmart software program and correlated with baking volume using partial least squares multiple regression statistical analysis to give a prediction of baking volume. Location had a considerable influence on the prediction of baking volume. Excellent predictions of baking volume were obtained from flours from individual locations (R² = 0.805–0.995), but predictions based on all cultivars without discriminating locations were poor. When mixograph and baking volume data for each cultivar were averaged over all five locations, a very high correlation was obtained (R² = 0.999). Preparation of flour samples using rapid, small‐scale milling procedures (Brabender Quadrumat Jr. mill and Perten 3100 hammer mill) did not have any adverse effect on prediction of baking volume. Mixograph parameters obtained from six commercial glutens of varying quality gave good correlations with test baking volumes, based on 6% gluten addition to a control flour.     

Effects of Different Salts on Mixing and Extension Parameters on a Diverse Group of Wheat Cultivars Using 2‐g Mixograph and Extensigraph Methods

Cations of differing chaotropic capacities (LiCl, NaCl, and KCl) were used in small‐scale mixing and extensigraph studies to assess functional changes in dough behavior of wheat cultivars varying in total protein content and HMW glutenin composition. Salt addition, regardless of cationic type, caused an increase in dough strength and stability. The smaller (hydrated) and least chaotrophic cations (Li⁺<Na⁺<K⁺) effected the greatest increase in mixing time (MT) and resistance to extension (R_max) and produced the most stable resistance breakdown (RBD). The effects of different cations on mixing and extensions indicated strong intercultivar variation; differential responses to salt addition were further shown when the cultivars were grouped according to protein content and Glu‐1D or Glu‐1B genome composition. Increases in dough strength parameters due to the addition of salt were consistently more significant for cultivars showing an overexpression of Bx7 (>12% protein). In the absence of genotypic variation, a significant interactive effect of cultivar type, protein amount, and salt addition was found for all functional dough parameters except extensibility. During mixing, there was a decrease in the amount of apparent unextractable polymeric protein (%UPP) in the dough. This phenomenon was ameliorated by the presence of salt in doughs formed from weaker flours and was most pronounced early on in the mixing process (t = 100–200 sec). Results show the importance of refining 2‐g mixograph studies to include salt in the “flour and water” dough formula.     

Dough Microextensibility Method Using a 2‐g Mixograph and a Texture Analyzer

Development of a small‐scale method to measure dough extensibility, using a 2‐g mixograph and the TA.XT2 texture analyzer (TA) equipped with Kieffer rig, suitable for early‐generation wheat quality screening is presented. Three hook speeds 3.3, 7.0, and 10.0 mm/sec were tested on the TA. Only at the lower hook speed of 3.3 mm/sec were wheats, varying in quality, clearly differentiated. The ability to differentiate between wheats using the TA was compared with the Brabender Extensigraph. The sample ranking based on the resistance to extension (R_max) from the TA at a hook speed of 10.0 mm/sec correlated highly (r = 0.99) to the ranking obtained on the extensigraph. Dough extensibility data from the extensigraph and the TA, using hook speed 10.0 mm/sec, was correlated (r = 0.90) to loaf volume. Similarly, dough extensibility on the TA, using hook speed 3.3 mm/sec, was correlated to loaf volume (r = 0.96). The effect of three dough water contents (farinograph absorption, farinograph absorption + 6%, and 2‐g mixograph water absorption) on physical properties of dough were evaluated by mixing the dough in a 2‐g mixograph and testing the extensibility on the TA. Dough prepared at farinograph absorption + 6% and at mixograph absorption allowed differentiation between wheats based on the resistance to extension (R_max).     

Assessment of Deep Groundwater Quality in Kathmandu Valley Using Multivariate Statistical Techniques

This study was carried out to assess the overall water quality and identify major variables affecting the deep groundwater quality in Kathmandu Valley, Nepal. Forty-two deep wells were sampled during premonsoon and monsoon seasons in 2007 and analyzed for the major physicochemical variables. The water quality variables such as NH ₄ ⁺ -N, Fe, Pb, As, and Cd at most of the sampling locations exceeded the World Health Organization guideline levels for drinking water. Multivariate statistical techniques such as factor analysis and cluster analysis were applied to identify the major factors (variables) corresponding to the different source of variation in deep groundwater quality. Factor analysis indentified six major factors explaining 74.77% of the total variance in water quality; and the major variations are related with the degree of groundwater mineralization, decomposition of organic matter, and reduction of groundwater environment. The water quality of deep groundwater is influenced by the natural hydrogeochemical environment. The wells are broadly divided into two major groups based on the similar groundwater characteristics using cluster analysis. Results show that water quality of deep groundwater does not vary significantly as a function of season.     

Surface Water Quality Assessment of Lis River Using Multivariate Statistical Methods

This study presents the application of multivariate statistical tools for the evaluation of spatial variations and the interpretation of water quality data obtained in a monitoring program of Lis river basin surface water, Portugal. Twenty-seven physicochemical and microbiological parameters were determined in six water sampling campaigns at 16 monitoring sites during the period from September 2003 to November 2006. Correlation analysis, principal component analysis, and cluster analysis were performed to evaluate the main water pollution sources and to characterize the spatial distribution of water pollution profiles in river basin. The results achieved with the statistical methodologies led to distinguish natural and anthropogenic pollution sources. Additionally, monitoring sites with similar water pollution profile were identified, indicating that some monitoring locations can be changed to improve the spatial characterization of water quality in the river basin. CBO, CQO, P, and N were identified as significant variables affecting spatial variations, namely in the Lis river middle reach. Besides the identification of main pollution sources, the applied statistical tools were able to identify spatial patterns of water pollution in Lis river basin, which further helps in the reassessment of the number and location of monitoring sites.     

基于多元统计分析的黄河水质评价方法

为探讨一种适用于大尺度、多断面和长时间的水质评价方法,用系统聚类分析将2000—2002年黄河6个监测断面的90个水质样本分为7组,并用判别分析验证了结果的可靠性。其主要程序为：利用多元方差分析对各断面多年水质监测样本进行空间尺度上的显著差异性分析,识别出具有显著差异的样本,然后通过系统聚类分析把上述样本进行聚类分组,最后应用判别分析方法对各组样本进行水质评价,此方法的特点为在不损失信息的前提下能大大减轻水质评价工作量,且客观可信、分辨率高,并能综合反映总体与个别特征。结果表明,黄河流域干流从上游到下游水质总体状况呈逐渐下降趋势,上游水质一般为Ⅰ-Ⅲ类,而中游和下游水质基本为Ⅳ-Ⅴ类和超Ⅴ类。     

Prediction of Baking Characteristics of Hard Winter Wheat Flours Using Computer‐Analyzed Mixograph Parameters

The objective of this research was to determine whether computer‐analyzed (objective) mixograph parameters could replace conventional mixograph parameters in the evaluation of flour quality. The 642 hard winter wheat flours, collected from federal regional performance nurseries in 1995 and 1996, were analyzed by a conventional and computerized mixograph. Mixograph bandwidths at 6 min (BW6) showed the most significant linear correlation with subjective mixing tolerance scores (r = 0.81, P < 0.1%, n = 642). Prediction models of conventional and experimental baking parameters were developed by continuum regression using computer‐analyzed mixograph parameters of a calibration set (n = 282). The developed models could estimate conventional mixograph mixing time and tolerance scores, baking water absorption and mixing time, and bread loaf volume, showing R² values of 0.86, 0.74, 0.68, 0.80, and 0.51, respectively, for a validation set (n = 380). These results indicated that computer‐analyzed mixograph parameters could be applied to develop prediction models to be used for flour quality evaluation in wheat breeding programs.     

四川小麦品种贮藏蛋白位点及SSR分析*

利用种子贮藏蛋白和SSR标记研究了四川近20多年育成的小麦(Triticum aestivum)品种第1和第6同源群染色体的蛋白基因位点及遗传多样性。结果表明：供试的47个小麦品种共检测出47条醇溶蛋白条带，其中39条具有多态性，占82．98％；高分子量(HMW)谷蛋白亚基共出现7种亚基和11种亚基组合，表明四川主栽小麦品种在醇溶蛋白位点上存在广泛的变异，而HMW谷蛋白亚基遗传基础较狭窄。SSR结果表明，在21个位点中有8个(38．1％)位点具多态性，共检测到19个等位变异，供试品种在：DNA水平上存在一定的变异。聚类分析表明：两种标记均可将供试品种(包括中国春)分为三大类。通过Mantel检测，两种标记的分析结果间有显著的相关性，反映了在蛋白质和DNA水平上的结果可以互相补充，增加可靠性。     

Fermentability of Oat and Wheat Fractions Enriched in β‐Glucan Using Human Fecal Inoculation

Fermentation by human fecal bacteria of fractions of wheat bran prepared by preprocessing technology were examined and compared with a β‐glucan‐rich oat bran and a purified β‐glucan (OG). The wheat fractions were essentially a beeswing bran (WBA), mainly insoluble dietary fiber, and an aleurone‐rich fraction (WBB) containing more soluble fiber and some β‐glucan (2.7%). The oat bran (OB) had more endosperm and was very rich in β‐glucan (21.8%). Predigestion of WBB and OB to mimic the upper gastrointestinal (GI) tract gave digested wheat bran fraction B (WBBD) and digested oat bran (OBD), respectively. These predigested fractions were fermented in a batch technique using fresh human feces under anaerobic conditions. Changes in pH, total gas and hydrogen production, short chain fatty acids (SCFA), and both soluble and insoluble β‐glucan and other polysaccharide components, as determined from analysis of monosaccharide residues, were monitored. Fractions showed increasing fermentation in the order WBA < WBBD < OBD < OG. Variations in SCFA production indicated that microbial growth and metabolism were different for each substrate. Polysaccharide present in the supernatant of the digests had disappeared after 4 hr of fermentation. Fermentability of oat and wheat β‐glucan reflected solubility differences, and both sources of β‐glucan were completely fermented in 24 hr. Although the overall patterns of fermentation indicated the relative amounts of soluble and insoluble fiber, the anatomical origin of the tissues played a major role, presumably related to the degree of lignification and other association with noncarbohydrate components.     

Predicting Wheat Quality Characteristics and Functionality Using Near‐Infrared Spectroscopy

The accuracy of using near‐infrared spectroscopy (NIRS) for predicting 186 grain, milling, flour, dough, and breadmaking quality parameters of 100 hard red winter (HRW) and 98 hard red spring (HRS) wheat and flour samples was evaluated. NIRS shows the potential for predicting protein content, moisture content, and flour color b* values with accuracies suitable for process control (R² > 0.97). Many other parameters were predicted with accuracies suitable for rough screening including test weight, average single kernel diameter and moisture content, SDS sedimentation volume, color a* values, total gluten content, mixograph, farinograph, and alveograph parameters, loaf volume, specific loaf volume, baking water absorption and mix time, gliadin and glutenin content, flour particle size, and the percentage of dark hard and vitreous kernels. Similar results were seen when analyzing data from either HRW or HRS wheat, and when predicting quality using spectra from either grain or flour. However, many attributes were correlated to protein content and this relationship influenced classification accuracies. When the influence of protein content was removed from the analyses, the only factors that could be predicted by NIRS with R² > 0.70 were moisture content, test weight, flour color, free lipids, flour particle size, and the percentage of dark hard and vitreous kernels. Thus, NIRS can be used to predict many grain quality and functionality traits, but mainly because of the high correlations of these traits to protein content.     

Multivariate Statistical Techniques for the Assessment of Surface Water Quality at the Mid-Black Sea Coast of Turkey

The aim of this study was to investigate the seasonal and spatial variations in surface water quality at the mid-Black Sea coast of Turkey. The samples were collected from ten monitoring stations including rivers and sea water during the years from 2007 to 2008. The samples were analyzed for 25 parameters: total carbon, total inorganic carbon, total organic carbon, chromium, cadmium, copper, lead, iron, nickel, manganese, phenol, surfactants, ammonium, nitrite and nitrate-nitrogen, total phosphorus, adsorbable organic halogen, sulfate, hardness, dissolved oxygen, pH, temperature, total dissolved solids, electrical conductivity, and salinity. Multivariate statistical techniques, cluster analysis (CA) and factor analysis/principal component analysis (FA/PCA), were applied to analyze the similarities among the sampling sites to identify the source apportionment of pollution parameters in surface waters. The results indicate that seven factors for river water explained 82.24% of the variance. In seawater, seven factors account for 89.65% of the total variance. Varifactors obtained from factor analysis indicate that the parameters responsible for water quality variations are mainly related to organic pollution (municipal effluents), inorganic pollution (industrial effluents and waste disposal areas), nutrients (agricultural runoff), and dissolved salts (soil leaching and runoff process).     

Association Between % SDS‐Unextractable Polymeric Protein (%UPP) and End‐Use Quality in Chinese Bread Wheat Cultivars

Trial I, with 33 spring cultivars, and trial II, with 21 winter cultivars, sown in four environments in the northwestern China spring wheat region and northern winter wheat region, respectively, were used to study the effect of genotype and environment on the size distribution of polymeric proteins. Association between quantity and size distribution of polymeric protein and dough properties (both trials) and northern‐style Chinese steamed‐bread (CSB) (trial I) and pan bread (trial II) qualities were also investigated. In trial I, all protein attributes, such as flour protein content, SDS‐extractable polymeric protein in the flour (EPP), SDS‐unextractable polymeric protein in the flour (UPP), and percent UPP in total polymeric protein (%UPP), were largely determined by environment, whereas variation in dough strength resulted from variation in UPP and %UPP across environments. In trial II, EPP was largely determined by environment, and UPP and %UPP were largely determined by genotype. These differences might result from different levels of protein content and dough strength in the two trials. The EPP was positively correlated with dough extensibility and was generally negatively correlated with dough stability and maximum resistance in both trials. However, %UPP was significantly positively correlated with dough stability and maximum resistance and end‐use quality in both trials. In trial I, correlation coefficients between %UPP and maximum resistance and CSB score were r = 0.90 and 0.71, respectively, whereas in trial II, the correlation coefficients between %UPP and maximum resistance and pan bread score were 0.96 and 0.87, respectively. Therefore, selection for high %UPP together with high‐quality glutenin subunits should lead to improved dough strength and end‐use quality in Chinese wheats.     

22个冬小麦品种主要农艺和品质性状遗传多样性分析

为了解甘肃天水地区育成冬小麦品种的遗传多样性,提高种质资源的利用效率,以22份冬小麦品种为试验材料,研究了其主要农艺性状与品质性状的变异情况、遗传多样性指数,并进行了主成分分析和聚类分析。结果表明,农艺性状中,穗粒数的变异系数最大(11.5%),千粒重的遗传多样性指数最高(1.75);品质性状中,沉降值的变异系数最大(31.3%),容重的遗传多样性指数最高(1.87)。说明供试材料的遗传多样性较丰富。8个性状的主成分分析结果表明,前4个主成分的累计贡献率为82.636%,用类平均法(UPGMA)将22个品种聚为5类,其中第Ⅰ类群可作为提高品质的材料供育种选择,第Ⅴ类群可作为增产材料供育种选择。     

Extraction of β‐Glucan from Oats for Soluble Dietary Fiber Quality Analysis

Extraction protocols for β‐glucan from oat flour were tested to determine optimal conditions for β‐glucan quality testing, which included extractability and molecular weight. We found mass yields of β‐glucan were constant at all temperatures, pH values, and flour‐to‐water ratios, as long as sufficient time and enough repeat extractions were performed and no hydrolytic enzymes were present. Extracts contained about 30–60% β‐glucan, with lower proportions associated with higher extraction temperatures in which more starch and protein were extracted. All commercial starch hydrolytic enzymes tested, even those that are considered homogenous, degraded β‐glucan apparent molecular weight as evaluated by size‐exclusion chromatography. Higher concentration β‐glucan solutions could be prepared by controlling the flour‐to‐water ratio in extractions. Eight grams of flour per 50 mL of water generated the highest native β‐glucan concentrations. Routine extractions contained 2 g of enzyme‐inactivated flour in 50 mL of water with 5mM sodium azide (as an antimicrobial), which were stirred overnight, centrifuged, and the supernatant boiled for 10 min. The polymer extracted had a molecular weight of about 2 million and was stable at room temperature for at least a month.     

Mixograph Responses of Gluten and Gluten‐Fortified Flour for Gluten Produced by Cold‐Ethanol or Water Displacement of Starch from Wheat Flour

The total protein of gluten obtained by the cold‐ethanol displacement of starch from developed wheat flour dough matches that made by water displacement, but functional properties revealed by mixing are altered. This report characterizes mixing properties in a 10‐g mixograph for cold‐ethanol‐processed wheat gluten concentrates (CE‐gluten) and those for the water‐process concentrates (W‐gluten). Gluten concentrates were produced at a laboratory scale using batter‐like technology: development with water as a batter, dispersion with the displacement fluid, and screening. The displacing fluid was water for W‐gluten and cold ethanol (≥70% vol, ‐12°C) for CE‐gluten. Both gluten types were freeze‐dried at ‐10°C and then milled. Mixograms were obtained for 1) straight gluten concentrates hydrated to absorptions of 123–234%, or 2) gluten blended with a low protein (9.2% protein) soft wheat flour to obtain up to 16.2% total protein. The mixograms for gluten or gluten‐fortified flour were qualitatively and quantitatively distinguishable. We found differences in the mixogram parameters that would lead to the conclusion of greater stability and strength for CE‐gluten than for W‐Gluten. Differences between the mixograms for these gluten types could be markedly exaggerated by increasing the amount of water to the 167–234% range. Mixograms for evaluation of gluten have not been previously reported in this hydration range. Mixograms for fortification suggest that less CE‐gluten than W‐gluten would be required for the same effect.     

Using Multivariate Techniques to Predict Wheat Flour Dough and Noodle Characteristics from Size‐Exclusion HPLC and RVA Data

Flour proteins of hard and soft winter wheats grown in Oregon were characterized by size‐exclusion HPLC (SE‐HPLC). Flour pasting characteristics were assessed by a Rapid Visco Analyser (RVA). Principle component scores (PCS) were calculated from both RVA data and from absorbance area and % absorbance values from SE‐HPLC. The PCS and cross‐products, ratios, and squares were used to derive wheat classification and quality prediction models. A classification model calculated from PCS of SE‐HPLC data could reliably separate these hard and soft wheats. The prediction models for mixing and noodle characteristics showed better performance when calculated from PCS values of both SEHPLC and RVA data than from SE‐HPLC data only. The R² values of prediction models for mixograph absorption, peak time, and tolerance were 0.827, 0.813, and 0.851, respectively. Prediction models for noodle hardness, cohesiveness, chewiness, and resilience immediately after cooking had R² values of 0.928, 0.928, 0.896, and 0.855, respectively. These results suggest that multivariate methods could be used to develop reliable prediction models for dough mixing and noodle characteristics using just SE‐HPLC and RVA data.     

Detection,Localization, and Variability of Endogenous β‐Glucanase in Wheat Kernels

Clinical studies with isolates of β‐glucan have shown that the health benefits are regulated not only by the polysaccharide concentration but also by the molecular weight and concentration in solution, because these health benefits are controlled, inter alia, by viscosity in the gut. The degradation of β‐glucan in baked products is likely caused by baking ingredients or processes, or by endogenous enzymes in wheat flour. The objectives of the present study were to quantify β‐glucanase in wheat kernels and to determine factors that influence the levels of this enzyme. A modified protocol to quantify β‐glucanase was developed and then confirmed through high‐performance size‐exclusion chromatography (HPSEC) with Calcofluor detection. Under this protocol, it was shown that the concentration of β‐glucanase activity was the highest in the bran fraction of the kernel in ungerminated wheats, whereas it was distributed throughout the entire kernel following germination. Furthermore, investigation on different wheat cultivars planted in the same and different locations showed that genotype, environment, and agronomic practice all can have an effect on β‐glucanase activity level in wheat kernels.     

Effect of Processing on Viscosity and Molecular Weight of (1,3)(1,4)‐β‐Glucan in Western Australian Oat Cultivars

Six Australian milling oat cultivars grown over two growing seasons were characterized for differences in (1,3)(1,4)‐β‐glucan (β‐glucan) viscosity, solubility, molecular weight (M_w), and the effect of processing. Oat cultivars grown in 2012 had significantly higher extracted β‐glucan viscosity from oat flour than the same oat cultivar grown in 2011 (P < 0.05, mean 137 and 165 cP, respectively). Noodle β‐glucan mean viscosity for 2012 (147 cP) was significantly higher than for 2011 (128 cP). β‐Glucan from ‘Williams’ and ‘Mitika’ oats had the highest viscosity (P < 0.05) in flour (5.92 and 5.25%, respectively) and noodles (1.64 and 1.47%, respectively) for both years, compared with the other oat cultivars. β‐Glucan (M_w) of Williams for 2012 and ‘Kojonup’ for both years were the least affected by processing, with an average drop of 33% compared with a maximum of 63% for other cultivars. Therefore, Williams showed superior β‐glucan properties to other oat cultivars studied, and can potentially provide improved health benefits. High and low β‐glucan M_w populations were found in the same elution peak after processing. Oat cultivars chosen for processing should be those with β‐glucans that are more resistant to processing, and that maintain their physiochemical properties and, therefore, bioactivity.     

Rapid Size‐Exclusion Chromatography Analysis of Molecular Size Distribution for Wheat Endosperm Protein

A quick method for the separation of the main size classes of wheat endosperm proteins using size‐exclusion HPLC is presented. Separations achieved in a 10‐min run showed high correlation with the reference method of our laboratory (35‐min) using the same type of column. The clear separation obtained allows a quick analytical determination of important parameters such as the proportion of the main classes of wheat endosperm protein, the glutenin‐to‐gliadin ratio, and the percentage of unextractable (SDS‐soluble with sonication) polymeric protein, all of which have been closely correlated with breadmaking quality parameters. The improved method offers the advantages of better utilization of valuable resources such as HPLC equipment, quicker analysis of large sample sets, and collection of eluted fractions.