Image Analysis Approach to Understand the Differences in Flour Particle Surface and Shape Characteristics

The separation efficiency of wheat flour particles based on size, with minimum bran contamination, is important for a flour mill. Separation of flour during fractionation depends on the surface characteristics and shape of flour particles. Wheat flour particle characteristics such as surface lipid content, roughness, and morphology with respect to particle size were studied to better understand the differences between hard and soft wheat flours. Fractal analysis using image analysis was used to ascertain surface roughness. That was in turn verified by atomic force microscopy measurements. Soft wheat flours (soft red winter and soft white) had a higher degree of surface roughness than the hard wheat flours (hard red spring, hard red winter, and hard white). The fractal dimension values ranged from 2.67 to 2.78 and from 2.28 to 2.55 for soft and hard wheat flours, respectively. The surface lipid content increased with particle size in hard wheat but decreased in soft wheat flours. The surface lipid levels ranged from 1.02 to 1.18 and from 2.55 to 2.58% (% of total area) for 45 μm particles in hard wheat flours (hard red spring, hard red winter, and hard white) and soft wheat flours (soft red winter and soft white), respectively. For the 90 μm particles the lipid levels ranged from 1.54 to 1.62 and from 1.70 to 1.83% (% of total area) for flour particles in hard wheat flours (hard red spring, hard red winter, and hard white) and soft wheat flours (soft red winter and soft white), respectively. Surface lipid content and roughness values showed that soft wheat flours will be more cohesive than hard wheat flours. The morphology values revealed the irregularity in flour particles, irrespective of wheat class and particle size, owing to nonuniform fragmentation of protein and starch matrix of the wheat endosperm.     

Factors in Hard Wheat Flour Responsible for Reduced Cookie Spread

Time-lapse photography showed that, during baking, the diameter of sugar-snap cookies increased linearly then suddenly became fixed. Therefore, cookie diameter was a function of spread rate and set time. Cookies made with soft wheat flour were significantly larger in diameter (184 mm) than those made with hard wheat flour (161 mm). Cookies made with soft wheat flour set later (5.8 min) during baking than those made with hard wheat flour (5.1 min). The differences in set time within cookies made with various hard wheat flours or within cookies made with various soft wheat flours appeared to be affected by flour protein content. However, other factors also affected the difference in set time between cookies made with hard wheat and soft wheat flours. Cookies made with soft wheat flour spread at a faster rate (7.8 mm/min) than those made with hard wheat flour (4.6 mm/min). The level of soluble starch in the flour appeared to cause the difference in spread rate between cookies made with hard wheat and soft wheat flour. The higher level of soluble starch in hard wheat flour (0.352 ± 0.008%) than in soft wheat flour (0.152 ± 0.030%) increased dough viscosity, thus the spread rate was slower. However, soluble starch content did not explain the differences in spread rate within cookies made with various hard wheat flours or within cookies made with various soft wheat flours.     

Bayesian Maximum Entropy prediction of soil categories using a traditional soil map as soft information

Bayesian Maximum Entropy was used to estimate the probabilities of occurrence of soil categories in the Netherlands, and to simulate realizations from the associated multi‐point pdf. Besides the hard observations (H) of the categories at 8369 locations, the soil map of the Netherlands 1:50 000 was used as soft information (S). The category with the maximum estimated probability was used as the predicted category. The quality of the resulting BME(HS)‐map was compared with that of the BME(H)‐map obtained by using only the hard data in BME‐estimation, and with the existing soil map. Validation with a probability sample showed that the use of the soft information in BME‐estimation leads to a considerable and significant increase of map purity by 15%. This increase of map purity was due to the high purity of the existing soil map (71.3%). The purity of the BME(HS) was only slightly larger than that of the existing soil map. This was due to the small correlation length of the soil categories. The theoretical purity of the BME‐maps overestimated the actual map purity, which can be partly explained by the biased estimates of the one‐point bivariate probabilities of hard and soft categories of the same label. Part of the hard data is collected to describe characteristic soil profiles of the map units which explains the bias. Therefore, care must be taken when using the purposively selected data in soil information systems for calibrating the probability model. It is concluded that BME is a valuable method for spatial prediction and simulation of soil categories when the number of categories is rather small (say < 10). For larger numbers of categories, the computational burden becomes prohibitive, and large samples are needed for calibration of the probability model.     

Starch-Water Relationships in the Sugar-Snap Cookie Dough System

Prime starch was extracted from soft and hard wheat flours and ballmilled to produce 100% damaged starch. Small amounts of the ball-milled starch or a pregelatinized starch were added to sugar-snap cookie formulations. Other cookie doughs were produced from prime starch only (no flour) with small amounts of the ball-milled starch added. Starch damages of the resulting substituted soft and hard wheat flours and soft and hard wheat prime starches were determined and compared to diameters of sugarsnap cookies produced from the control and treatments. Soft wheat flour and starches produced larger diameter cookies than their hard wheat counterpart at all levels of damaged starch. Both sources of damaged starch (ball-milled or pregelatinized starch) had similar effects on cookie diameter. Cookies produced from all starch (no flour) were similar to their respective flour controls at ≈8% damaged starch. To produce the same size cookie as that produced by soft wheat flour and starch, hard wheat flour and starch cookie formulations required less damaged starch and had lower alkaline water retention than did the soft wheat flour and starch cookie formulations. Other flours were treated with chlorine gas to pH 4.8. Pregelatinized starch (≈5%) was required to reduce the cookie diameter as much as chlorine treatment did. Results suggest unique quality differences between soft and hard wheat starch as they function in sugar-snap cookie baking. The functional results of those differences are not adequately quantified by the estimation of damaged starch level.     

Pedogenesis of plinthite during early Pliocene in the Mediterranean environment: Case study of a buried paleosol at Podere Renieri,central Italy

A complex buried paleosol at Podere Renieri at Montalcino (central Italy) formed during a series of continental episodes within an interval of Pliocene marine sediments from about 4.1 to 4.8 My BP. The aim of this work was to document the kind of pedogenesis which occurred throughout this time and, in particular, the plinthite formation. Plinthite (soft and hard) is poor in organic carbon, neutral or subalkaline, dominated by illite, and enriched in iron and chromium. The main differences between soft and hard plinthite are attributed to soil structure, which is absent in hard plinthite, and to the abundance of iron depleted zones, which are much larger in soft plinthite. Variations in mineralogy, chemistry and genesis of soft and hard plinthite can be ascribed to the specific environment of plinthite formation, close to the Pliocene sea. Pedogenetic evidence indicates that plinthite evolved in a hot and humid paleoclimate, showing a progressive increase in seasonality, and that low-grade plinthite formation occurred in a time span of a few hundred thousand years. Soils with low-grade plinthite have agronomic value in their suitability for the production of high quality Brunello di Montalcino wine.     

Pedogenesis of plinthite during early Pliocene in the Mediterranean environment: Case study of a buried paleosol at Podere Renieri,central Italy

A complex buried paleosol at Podere Renieri at Montalcino (central Italy) formed during a series of continental episodes within an interval of Pliocene marine sediments from about 4.1 to 4.8 My BP. The aim of this work was to document the kind of pedogenesis which occurred throughout this time and, in particular, the plinthite formation. Plinthite (soft and hard) is poor in organic carbon, neutral or subalkaline, dominated by illite, and enriched in iron and chromium. The main differences between soft and hard plinthite are attributed to soil structure, which is absent in hard plinthite, and to the abundance of iron depleted zones, which are much larger in soft plinthite. Variations in mineralogy, chemistry and genesis of soft and hard plinthite can be ascribed to the specific environment of plinthite formation, close to the Pliocene sea. Pedogenetic evidence indicates that plinthite evolved in a hot and humid paleoclimate, showing a progressive increase in seasonality, and that low-grade plinthite formation occurred in a time span of a few hundred thousand years. Soils with low-grade plinthite have agronomic value in their suitability for the production of high quality Brunello di Montalcino wine.     

Pedogenesis of plinthite during early Pliocene in the Mediterranean environment: Case study of a buried paleosol at Podere Renieri, central Italy

A complex buried paleosol at Podere Renieri at Montalcino (central Italy) formed during a series of continental episodes within an interval of Pliocene marine sediments from about 4.1 to 4.8 My BP. The aim of this work was to document the kind of pedogenesis which occurred throughout this time and, in particular, the plinthite formation. Plinthite (soft and hard) is poor in organic carbon, neutral or subalkaline, dominated by illite, and enriched in iron and chromium. The main differences between soft and hard plinthite are attributed to soil structure, which is absent in hard plinthite, and to the abundance of iron depleted zones, which are much larger in soft plinthite. Variations in mineralogy, chemistry and genesis of soft and hard plinthite can be ascribed to the specific environment of plinthite formation, close to the Pliocene sea. Pedogenetic evidence indicates that plinthite evolved in a hot and humid paleoclimate, showing a progressive increase in seasonality, and that low-grade plinthite formation occurred in a time span of a few hundred thousand years. Soils with low-grade plinthite have agronomic value in their suitability for the production of high quality Brunello di Montalcino wine.     

Puroindoline Genotype of the U.S. National Institute of Standards & Technology Reference Material 8441, Wheat Hardness

Grain hardness (kernel texture) is of central importance in the quality and utilization of wheat (Triticum aestivum L.) grain. Two major classes, soft and hard, are delineated in commerce and in the Official U.S. Standards for Grain. However, measures of grain hardness are empirical and require reference materials for instrument standardization. For AACC Approved Methods employing near‐infrared reflectance (NIR) and the Single Kernel Characterization System (39‐70A and 55‐31, respectively), such reference materials were prepared by the U.S. Dept. of Agriculture Federal Grain Inspection Service. The material was comprised of genetically pure commercial grain lots of five soft and five hard wheat cultivars and was made available through the National Institute of Standards and Technology (SRM 8441, Wheat Hardness). However, since their establishment, the molecular‐genetic basis of wheat grain hardness has been shown to result from puroindoline a and b. Consequently, we sought to define the puroindoline genotype of these 10 wheat cultivars and more fully characterize their kernel texture through Particle Size Index (PSI, Method 55‐30) and Quadrumat flour milling. NIR, SKCS, and Quadrumat break flour yield grouped the hard and soft cultivars into discrete texture classes; PSI did not separate completely the two classes. Although all four of these methods of texture measurement were highly intercorrelated, each was variably influenced by some minor, secondary factors. Among the hard wheats, the two hard red spring wheat cultivars that possess the Pina‐D1b (a‐null) hardness allele were harder than the hard red winter wheat cultivars that possess the Pinb‐D1b allele based on NIR, PSI, and break flour yield. Among the soft wheat samples, SKCS grouped the Eastern soft red winter cultivars separate from the Western soft white. A more complete understanding of texture‐related properties of these and future wheat samples is vital to the use and calibration of kernel texture‐measuring instruments.     

A Comprehensive Genotype and Environment Assessment of Wheat Grain Ash Content in Oregon and Washington: Analysis of Variation

A comprehensive analysis of the variation in wheat grain ash content has not been previously conducted. This study assessed the relative contribution of genotype and environment to variation in ash content, with a particular aim of ascertaining the potential for manipulating the trait using contemporary adapted germplasm. A total of 2,240 samples were drawn from four years of multilocation field plots grown in the wheat production areas of Oregon and Washington states. Genotypes included commercial cultivars and advanced breeding lines of soft and hard winter, and soft and hard spring wheats with red and white kernel color, several soft white club wheats, and one soft white spring waxy wheat cultivar. In addition to ash, protein content, test weight, and Single Kernel Characterization System kernel hardness, weight and size were also measured. In total, 20 separate fully balanced ANOVA results were conducted. Whole model R² values were highly significant, 0.62–0.91. Nineteen of the 20 ANOVA results indicated significant genotype effects, but the effects were not large. In contrast, environment effects were always highly significant with F values often one to two orders of magnitude larger than the genotype F values. The grand mean for all samples was 1.368% ash. For individual data sets, genotype means across environments varied ≈0.1–0.3% ash. The genotypes judged noteworthy because they had the highest least squares mean ash content were OR9900553 and ClearFirst soft white winter, NuHills hard red winter, Waxy‐Pen and Cataldo soft white spring, and WA8010 and Lochsa hard spring wheats. Genotypes with lowest least squares mean ash were Edwin (club) soft white winter, OR2040073H hard red winter, WA7952 soft white spring, and WA8038 hard spring wheats. In conclusion, wheat grain ash is more greatly influenced by crop year and location than by genotype. However, sufficient genotype variation is present to plausibly manipulate this grain trait through traditional plant breeding.     

A Study of Maize Endosperm Hardness in Relation to Amylose Content and Susceptibility to Damage

The relative amounts of amylose and amylopectin in maize starch were determined in samples representing hard and soft endosperm. Although differences were small, amylose content differed significantly (P < 0.001 and P < 0.05) between the two types of endosperm, with hard endosperm containing a higher percentage of amylose. Scanning electron microscopy was used to determine that the surface appearance of starch granules from hard and soft endosperm differed. Starch granules from soft endosperm had randomly distributed pores on their surfaces, which had a rough appearance. Few pores were observed on granules from hard endosperm. A fairly common occurrence with starch granules from soft endosperm was the development of wrinkles or fissures upon prolonged exposure to the beam of the electron microscope. Thus, a correlation existed between endosperm hardness, amylose content, and susceptibility to wrinkling and fissures. The granules of the soft endosperm of maize, presumably less mature than the granules of the hard endosperm, have a lower amylose content (20.5 ± 1.9% vs. 23.0 ± 1.0%), exhibit more surface pores, and are more susceptible to wrinkling in an electron beam, compared with granules of the hard endosperm. Results suggested that the composition and internal architecture of the starch granule differ depending on the hardness of the endosperm from which it was obtained.     

Structural Modifications of Gluten Proteins in Strong and Weak Wheat Dough During Mixing

The network‐forming attributes of gluten have been investigated for decades, but no study has comprehensively addressed the differences in gluten network evolution between strong and weak wheat types (hard and soft wheat). This study monitored changes in SDS protein extractability, SDS‐accessible thiols, protein surface hydrophobicity, molecular weight distribution, and secondary structural features of proteins during mixing to bring out the molecular determinants of protein network formation in hard and soft wheat dough. Soft wheat flour and dough exhibited greater protein extractability and more accessible thiols than hard wheat flour and dough. The addition of the thiol‐blocking agent N‐ethylmaleimide (NEM) resulted in similar results for protein extractability and accessible thiols in hard and soft wheat samples. Soft wheat dough had greater protein surface hydrophobicity than hard wheat and exhibited a larger decrease in surface hydrophobicity in the presence of NEM. Formation of high‐molecular‐weight (HMW) protein in soft wheat dough was primarily because of formation of disulfides among low‐molecular‐weight (LMW) proteins, as indicated by the absence of changes in protein distribution when NEM was present, whereas in hard wheat dough the LMW fraction formed disulfide interaction with the HMW fraction. Fourier transform infrared spectroscopy indicated formation of β‐sheets in dough from either wheat type at peak mixing torque. Formation of β‐sheets in soft wheat dough appears to be driven by hydrophobic interactions, whereas disulfide linkages stabilize secondary structure elements in hard wheat dough.     

Acrylamide-induced nerve terminal damage: relevance to neurotoxic and neurodegenerative mechanisms

Acrylamide (ACR) has demonstrable neurotoxic effects in animals and humans that stem from its chemical behavior as a soft electrophilic alpha,beta-unsaturated carbonyl compound. Evidence is presented that the nerve terminal is a primary site of ACR action and that inhibition of neurotransmission mediates the development of neurological deficits. At the mechanistic level, recent proteomic, neurochemical, and kinetic data are considered, which suggest that ACR inhibits neurotransmission by disrupting presynaptic nitric oxide (NO) signaling. Nerve-terminal damage likely mediates the neurological complications that accompany the occupational exposure of humans to ACR. In addition, the proposed molecular mechanism of synaptotoxicity has substantial implications for the pathogenesis of Alzheimer's disease and other neurodegenerative conditions that involve neuronal oxidative stress and the secondary endogenous generation of acrolein and other conjugated carbonyl chemicals.     

基于贝叶斯最大熵和多源数据的作物需水量空间预测

作物需水量是灌溉工程规划、设计和管理的重要基础数据,充分利用多源数据和先验知识,快速经济地获取精度较高的区域作物需水量对于区域水资源的优化配置具有重要意义。为精确预测作物需水量,该文以长系列实际监测和校核作物系数后计算得到的作物需水量为硬数据,利用硬数据确定获得最大熵的约束条件,根据软数据获取渠道的不同(部分年份缺失的站点数据、文献中获得的数据、利用灌溉试验数据库中的作物需水量资料,采用协同克立格方法获得的数据、考虑主要地形因子和主要气象要素的影响,采用主成分分析和地理加权回归(geographically weighted regression,GWR)方法获得作物需水量数据以及遥感数据),提出不同来源软数据的概率密度函数表达方法,采用贝叶斯最大熵(Bayesian maximum entropy,BME)方法对不同来源的作物需水量信息进行有机整合。结果表明:除硬数据+文献软数据外,其他数据整合呈现一致结果。华北地区冬小麦作物需水量在豫南地区较小,中部地区黄河北岸有连片的相对高值区,山东需水量相对较高,冀东北的乐亭、唐山附近有相对低值区。除硬数据+文献软数据比不整合的精度低9.41%外,其他软数据源均可不同程度地提高整合效果,硬数据+克立格软数据、硬数据+GWR软数据和硬数据+除文献数据外的其他软数据分别比不整合的精度提高85.33%、85.75%和91.69%。对考虑地形、气象等要素的多源数据进行整合可更好地反映冬小麦作物需水量空间分布的细节,显著提高估算精度,为稀疏监测站点地区水土资源的精准管理和优化配置提供数据支撑。     

Protein Structural Features in Winter Wheat: Benchmarking Diversity in Ontario Hard and Soft Winter Wheat

A set of 32 winter wheat lines and varieties was selected to benchmark Ontario winter wheat as a first step toward improving quality. Protein secondary structure, total and accessible thiols, rheological properties, gluten aggregation kinetics, and network forming capabilities of different polymers were determined for each wheat line. Results revealed that there were statistically significant differences among the lines selected (P < 0.05). The differences between hard and soft wheat classes were not as large as would be expected, however, despite the range of quality parameters measured. Benchmarks revealed that several soft wheat lines outperformed hard wheat lines in standard breadmaking quality measures. Protein conformation changed significantly as the moisture content of the samples increased to mimic different model product systems: flour, dough, and batter. The conformation of the flour samples exhibited different patterns between hard and soft wheat classes, although these differences became narrower in the dough and batter states. Principal component analysis (PCA) factors included most quality parameters measured, with the notable exceptions of solvent retention capacity tests and total thiols. Protein conformation and accessible thiols were significant PCA factors that tended to override the rheological measures of quality they represented, suggesting that protein secondary structure and disulfide bonding patterns are fundamental aspects of rheological quality measures.     

Quantitative and Qualitative Role of Added Gluten on White Salted Noodles

A commercial gluten and glutens isolated from four soft and four hard wheat flours were incorporated into a hard and a soft white flour by replacement to directly determine the quantitative and qualitative role of gluten proteins in making noodles. Gluten incorporation (6%) decreased water absorption of noodle dough by 3%, shortened the length of the dough sheet by 15 and 18%, and increased the thickness of the dough sheet by 18 and 20% in soft and hard wheat flour, respectively. Noodles imbibed less water and imbibed water more slowly during cooking with gluten incorporation, which resulted in a 3‐min increase in cooking time for both soft and hard wheat noodles. Despite the extended cooking time of 3 min, noodles incorporated with 6% gluten exhibited decreases in cooking loss by 15% in soft wheat. In hard wheat flour, cooking loss of noodles was lowest with 2% incorporation of gluten. Tensile strength of fresh and cooked noodles, as well as hardness of cooked noodles, increased linearly with increase in gluten incorporation, regardless of cooking time and storage time after cooking. While hardness of cooked noodles either increased or showed no changes during storage for 4 hr, tensile strength of noodles decreased. There were large variations in hardness and tensile strength of cooked noodles incorporated with glutens isolated from eight different flours. Noodles incorporated with soft wheat glutens exhibited greater hardness and tensile strength than noodles with hard wheat glutens. Tensile strength of cooked noodles incorporated with eight different glutens negatively correlated with SDS sedimentation volume of wheat flours from which the glutens were isolated.     

Environmental Influences on Flour Composition,Dough Rheology,and Baking Quality of Spring Wheat

The highly variable environmental conditions across the Pacific Northwest (PNW) influence the milling and baking quality of wheat grain produced in this region. This study was conducted to compare the flour composition, dough rheology, and baking quality of soft and hard spring wheat grain produced in diverse environments. Thirteen soft and five hard spring wheat cultivars were grown at Lind, WA (semiarid) and Fairfield, WA (high precipitation) for three years. Grain was evaluated for flour composition, rheology, and experimental baked product quality. Flour composition, rheological properties, and baking qualities were primarily influenced by the environment. Protein contents, microSDS values, and water absorption levels were significantly (P < 0.0001) higher for all cultivars grown at Lind compared with those from Fairfield. Cookie diameters were larger (P < 0.0001) for soft flours from Fairfield, whereas loaf volumes were higher (P < 0.0001) for hard wheat flours from Lind. Results indicate that producing soft or hard wheat outside of its optimal climatic zone reduces experimental baked product quality.     

Kinetics of the stability of broccoli (Brassica oleracea Cv. Italica) myrosinase and isothiocyanates in broccoli juice during pressure/temperature treatments

The Brassicaceae plant family contains high concentrations of glucosinolates, which can be hydrolyzed by myrosinase yielding products having an anticarcinogenic activity. The pressure and temperature stabilities of endogenous broccoli myrosinase, as well as of the synthetic isothiocyanates sulforaphane and phenylethyl isothiocyanate, were studied in broccoli juice on a kinetic basis. At atmospheric pressure, kinetics of thermal (45-60 degrees C) myrosinase inactivation could be described by a consecutive step model. In contrast, only one phase of myrosinase inactivation was observed at elevated pressure (100-600 MPa) combined with temperatures from 10 up to 60 degrees C, indicating inactivation according to first-order kinetics. An antagonistic effect of pressure (up to 200 MPa) on thermal inactivation (50 degrees C and above) of myrosinase was observed indicating that pressure retarded the thermal inactivation. The kinetic parameters of myrosinase inactivation were described as inactivation rate constants (k values), activation energy (Ea values), and activation volume (Va values). On the basis of the kinetic data, a mathematical model describing the pressure and temperature dependence of myrosinase inactivation rate constants was constructed. The stability of isothiocyanates was studied at atmospheric pressure in the temperature range from 60 to 90 degrees C and at elevated pressures in the combined pressure-temperature range from 600 to 800 MPa and from 30 to 60 degrees C. It was found that isothiocyanates were relatively thermolabile and pressure stable. The kinetics of HP/T isothiocyanate degradation could be adequately described by a first-order kinetic model. The obtained kinetic information can be used for process evaluation and optimization to increase the health effect of Brassicaceae.     

Importance of insoluble-bound phenolics to antioxidant properties of wheat

Two commercial samples of soft (70% Canadian Eastern soft red spring and 30% Canadian Eastern soft white winter) and hard (90% Canadian western hard red spring and 10% Canadian Eastern hard red winter) wheats were used to obtain different milling fractions. Phenolics extracted belonged to free, soluble esters and insoluble-bound fractions. Soluble esters of phenolics and insoluble-bound phenolics were extracted into diethyl ether after alkaline hydrolysis of samples. The content of phenolics was determined using Folin-Ciocalteu's reagent and expressed as ferulic acid equivalents (FAE). The antioxidant activity of phenolic fractions was evaluated using Trolox equivalent antioxidant capacity, 2,2-diphenyl-1-picrylhydrazyl radical scavenging, reducing power, oxygen radical absorbance capacity, inhibition of oxidation of human low-density lipoprotein cholesterol and DNA, Rancimat, inhibition of photochemilumenescence, and iron(II) chelation activity. The bound phenolic content in the bran fraction was 11.3 +/- 0.13 and 12.2 +/- 0.15 mg FAE/g defatted material for hard and soft wheats, respectively. The corresponding values for flour were 0.33 +/- 0.01 and 0.46 +/- 0.02 mg FAE/g defatted sample. The bound phenolic content of hard and soft whole wheats was 2.1 (+/-0.004 or +/-0.005) mg FAE/g defatted material. The free phenolic content ranged from 0.14 +/- 0.004 to 0.98 +/- 0.05 mg FAE/g defatted milling fractions of hard and soft wheats examined. The contribution of bound phenolics to the total phenolic content was significantly higher than that of free and esterified fractions. In wheat, phenolic compounds were concentrated mainly in the bran tissues. In the numerous in vitro antioxidant assays carried out, the bound phenolic fraction demonstrated a significantly higher antioxidant capacity than free and esterified phenolics. Thus, inclusion of bound phenolics in studies related to quantification and antioxidant activity evaluation of grains and cereals is essential.     

不同品种小麦籽粒中戊聚糖含量分析

从全国小麦主产区收集弱筋小麦品种19份、强筋品种24份,对小麦籽粒中的戊聚糖含量以及籽粒基本品质特性进行了测定与统计分析。结果显示,不同类型与品种的小麦籽粒中总戊聚糖(TP)和水溶性戊聚糖(WSP)含量存在极显著差异（P<0.01）;强筋小麦的TP和水不溶性戊聚糖（WUP）平均值较弱筋小麦的偏高,但二者的WSP平均值基本一致。对籽粒的戊聚糖含量与其品质特性进行相关性分析发现,强筋小麦籽粒的WSP含量与籽粒蛋白质含量、湿面筋含量、zel沉降值、面团稳定时间存在正相关关系;弱筋小麦籽粒WSP含量与籽粒蛋白质含量、湿面筋含量、硬度、zel沉降值、形成时间存在负相关关系。通径分析结果显示,蛋白质与湿面筋含量对戊聚糖含量的影响更为显著。由本研究得出结论,强筋与弱筋类型小麦的品质指标对戊聚糖含量的影响有明显不同,建议未来戊聚糖的研究按小麦类型区别对待。     

Estimating the local mean for Bayesian maximum entropy by generalized least squares and maximum likelihood, and an application to the spatial analysis of a censored soil variable

The Bayesian maximum entropy (BME) method is a valuable tool, with rigorous theoretical underpinnings, with which to predict with soft (imprecise) data. The methodology uses a general knowledge base to derive a joint prior distribution of the data and the prediction by the criterion of maximum entropy; the hard (precise) and soft data are then processed using this prior distribution to yield a posterior distribution that provides the BME prediction. The general knowledge base commonly consists of the mean and covariance functions, which may be extracted from the data. The common method for extracting the mean function from the data is a generalized least squares (GLS) approach. However, when the soft data take the form of intervals of plausible values, this method can result in errors in the BME predictions. This paper suggests a maximum likelihood (ML) method for fitting the local mean. The two methods are compared in terms of their predictions, firstly on simulated random fields and then on a case study to predict the depth of soil using some censored data. The results show that the ML method can result in more accurate BME predictions; the degree of improvement over the GLS method depends on the parameters of the spatial covariance model.