Extraction of rutin from buckwheat (Fagopyrum esculentumMoench) seeds and determination by capillary electrophoresis.

The content of the flavonoid rutin was determined in different milling fractions of buckwheat seeds and in buckwheat stems, leaves, and flowers. The extraction was performed by using a solvent containing 60% of ethanol and 5% of ammonia in water. The extracts were analyzed by capillary electrophoresis (running buffer of 50 mM borate (pH 9.3), 100 mM sodium dodecyl sulfate; determination at 380 nm). In bran fractions the concentration of rutin was 131-476 ppm, and in flour fractions 19-168 ppm. On average, about 300, 1000, and 46000 ppm of rutin were found in leaves, stems, and flowers, respectively. The results indicate that buckwheat could be an important nutritional source of flavonoids, especially in countries with a low mean daily flavonoid intake.

Nutritional properties of starch in buckwheat products: studies in vitro and in vivo.

The nutritional characteristics of buckwheat starch were studied to identify the possibility for reduced postmeal metabolic responses to various buckwheat products. The in vitro rate of starch hydrolysis and resistant starch (RS) formation in boiled buckwheat groats and in a series of breads, baked with 30-70% of buckwheat flour (BWF) or groats (BWG), respectively, were evaluated in vitro. In parallel, postprandial glucose and insulin responses and also the satiety score to BWG and wheat bread with 50% BWG as compared with the reference white wheat bread (WWB) were studied in healthy humans. The highest concentration of RS was found in boiled BWG (6% total starch basis). The RS level in bread products based on different proportions of BWF or BWG (30-70%) varied from 0.9 to 4.4%. The rate of in vitro amylolysis was significantly lower (P < 0.05) in all buckwheat products in comparison with the reference WWB. The calculated hydrolysis indices (HI) were lowest in boiled BWG (HI = 50) and in bread with 70% BWG (HI = 54). Consumption of boiled BWG or bread based on wheat flour and 50% BWG induced significantly lower postprandial blood glucose and insulin responses compared with the WWB. The calculated glycemic and insulinemic indices (GI and II) for boiled BWG were 61 and 53 and for the buckwheat bread, 66 and 74, respectively. The highest satiety score was found with boiled BWG. It is concluded that buckwheat has potential use in the design of foods with lower GI properties.

Effect of spelt wheat flour and kernel on bread composition and nutritional characteristics.

Spelt wheat seeds (Triticum aestivum subsp. spelta cv. Ostro) were used to obtain white spelt flour (64.5% yield), wholemeal spelt flour (100% yield), and scalded spelt wheat kernels. From these materials, white spelt wheat bread (WSB), wholemeal spelt wheat bread (WMSB), and spelt wheat bread with scalded spelt wheat kernels (SSKB) were made and were compared to the reference white wheat bread (WWB). The spelt wheat flours and breads contained more proteins in comparison to wheat flour and bread. Among the samples the highest rate of starch hydrolysis was noticed in WSB. During the first 30 min of incubation this particular bread was shown to have significantly more (P < 0.05) rapidly digestible starch than the WMSB and later on also more starch than in WWB and SSKB, respectively. The WMSB had the lowest hydrolysis index (HI = 95.7). However, the result did not differ significantly from that in the reference common wheat bread. On the other hand, the most refined spelt wheat flour resulted in a bread product (WSB) that was statistically withdrawn (P < 0.05) as one with the highest HI (112.6).

Influence of genotype and processing on the in vitro rate of starch hydrolysis and resistant starch formation in peas (Pisum sativum L.).

The formation of resistant starch (RS) and the rate of starch hydrolysis were evaluated in vitro in a wild type of green-seeded pea genotype RRRbRb BC3 (33-Am) with 32.7% amylose content and in two mutants RRrbrb BC3 (23-Am) and rrRbRb BC3 (65-Am) with amylose contents of 23.3 and 65.1%, respectively. Pea samples were intact or homogenized and subjected either to autoclaving or to boiling at atmospheric pressure. The amount of RS (total starch basis) varied from 6.2 to 12.9% in the 23-Am products and from 31.2 to 33.4% in the 65-Am products. The RS level of the 33-Am product with a regular amylose content was 11.0%. Both the 23-Am and the 65-Am products were abundant sources of dietary fiber (39 and 34%, dry matter basis, respectively) versus 23% in the regular pea product. The amylose/amylopectin ratio was an important determinant of the rate of starch hydrolysis. The hydrolysis indices (HI) and predicted glycemic indices were lowest in the 65-Am peas (HI range = 42-59) as compared to the 23-Am peas (HI range = 53-84). It is concluded that the pea genotypes covered a wide range in starch availability, which is likely to affect nutritional parameters such as glycemic responses and colonic delivery of starch.

Endogenous factors responsible for the textural characteristics of buckwheat products.

Endogenous factors responsible for the textural characteristics of buckwheat products were studied. An analysis with various buckwheat samples showed that there were variations in protein, starch, amylose and amylopectin contents among the various buckwheat flours examined. The protein contents of the buckwheat flours were significantly negatively-correlated to the starch, amylose and amylopectin contents of buckwheat. A texturometric analysis showed that the springiness of heated-dough made from buckwheat flour was positively correlated to its starch content and amylopectin content, and the springiness and chewiness was negatively correlated to the protein content. Experiments adding isolated buckwheat protein or starch to buckwheat dough confirmed the above-mentioned correlation between texture and components. This study suggests that both the protein and starch present in buckwheat flour may be important factors responsible for the textural characteristics of buckwheat products.

An illustration of item homogeneity scaling and multilevel analysis techniques in the evaluation of drug prevention programs.

Many reports of successful school-based intervention programs can be criticized for their choice of a unit of analysis and for the neglect of measurement errors. This article is an illustration of how different conclusions can be reached from different choices of units of analyses and/or of different treatment of the data. This is done by a reanalysis of a well-reported data set. The data is thoroughly taken apart, using different statistical techniques. The result of the analyses shows that earlier reported effects of a normative school-based drug prevention program were not found. The subsequent search for moderator effects of the same program, such as a lowering effect on the relationship between the pre- and posttest or on the relationship between respondents' use and the use of their friends, was not successful either. It is concluded that the null hypothesis of zero effects should be retained. More successful was a search for individual characteristics that show significant relationships with respondents' alcohol use. Among them was the abuse of alcohol by adults in respondents' direct social environment and the use of friends.

Multilevel models for hierarchically nested data: potential applications in substance abuse prevention research.

This chapter reports on an application of a multilevel analysis. A multilevel analysis is a data analysis that uses variables that are measured at different levels of the hierarchy. A hierarchy can have many levels, such as student level, class level, school level, and State or country level, where students are nested within classes, classes are nested within schools or school districts, and school districts can be nested within towns, States, or countries. As soon as one pays attention, hierarchies are present in all data. In large-scale prevention research, researchers usually have information about two or more levels involved, for instance, variables describing individuals (such as achievement, drug use, gender, and measures of socioeconomic status or home environment); variables describing schools (such as school environment, urban versus rural, and type of treatment administered); and perhaps variables describing districts, States, or countries. It is well known that the analysis of variables (i.e., measures at different levels of the hierarchy) on any of these levels separately can be misleading, as will be shown in this chapter. It is more satisfactory to construct a model and technique that simultaneously take information on all levels into account. This chapter introduces such a multilevel model for hierarchically nested data by evaluating the effect of a drug prevention program, Normative Education (NORM), wherein data are collected on students nested within schools. The model is a linear regression model. The difference between this model and the traditional linear regression model is that it takes the intraclass correlation into account and treats variables measured at different levels of the hierarchy in a more appropriate way.

Protein-polyphenol interactions and in vivo digestibility of buckwheat groat proteins.

A rat model system, based on a diet with (Nb+) or without (Nb-) the inclusion of 0.7% of a 2:1 (w/w) mixture of Bacitracin and Neomycin sulfate (Nebacitin) was used to elucidate interactions of polyphenols with proteins in buckwheat. The treatment with the antibiotic Nebacitin suppresses the microflora in the large intestine and is used to obtain an estimate of the digestion occurring in the small intestine. Experimental buckwheat samples were obtained in order to contain different levels of polyphenol and were subjected to different hydrothermal treatments. It was found that a considerable interaction between polyphenols and proteins appeared during the hydrothermal treatment, and this interaction reduces the digestion of proteins through the small and large intestine. Microbial processes in the colon enhance the digestibility of protein, blocked by polyphenols in hydrothermally processed buckwheat.

The Effect of Different Forms of Centering in Hierarchical Linear Models.

Multilevel models are becoming increasingly used in applied educational social and economic research for the analysis of hierarchically nested data. In these random coefficient regression models the parameters are allowed to differ over the groups in which the observations are nested. For computational ease in deriving parameter estimates, predictors are often centered around the mean. In nested or grouped data, the option of centering around the grand mean is extended with an option to center within groups or contexts. Both are statistically sound ways to improve parameter estimation. In this article we study the effects of these two different ways of centering, in comparison to the use of raw scores, on the parameter estimates in random coefficient models. The conclusion is that centering around the group mean amounts to fitting a different model from that obtained by centering around the grand mean or by using raw scores. The choice between the two options for centering can only be made on a theoretical basis. Based on this study, we conclude that centering rules valid for simple models, such as the fixed coefficients regression model. are no longer applicable to more complicated models, such as the random coefficient model. We think researchers should be made aware of the consequences of the choice of particular centering options.