Oligosaccharides ofSaponaria officinalis
ConclusionsIt has been found that the roots ofSaponaria officinalis L. contain three oligosaccharides.Two of them have been isolated: gentiobiose and the pentasaccharide saponarose. It has been shown that the latter is O-β-D-galactopyranosyl-(1 → 3)-O-α-D-galactopyranosyl-(1 → 3)-O-α-D-galactopyranosyl-(1 → 3)-O-α-D-galactopyranosyl-(1 → 1)-α-D-glucopyranoside.
Analysis of carbohydrates in Saponaria officinalis L. using GC/MS method
Saponaria officinalis L. (common soapwort), usually named fuller’s herb, is encountered in most of Europe, in Spain, France, Italy, for example, and also in Syria and North Africa. Saponaria officinalis L. is known in most of the world as an introduced species, often a weed, and sometimes as cultivated decorative plant. Saponaria officinalis contains a large amount of saponins, which foam during extraction with water. In addition to saponins, common soapwort also contains flavonoids, quillaic acid, fatty acids and different phenolic compounds. There is a lack of information about carbohydrates content of common soapwort. Thus the aim of this study was to determine the content of carbohydrates Saponaria officinalis L. herb and roots. The qualitative composition and quantitative content of carbohydrates in herb and roots of Saponaria officinalis L. were determined by using GC/MS method. The studies have shown that Saponaria officinalis L. herb is mainly composed of free carbohydrates such as D-glucose (3.65 mg/g), D-galactose (0.29 mg/g), D-fructose (0.20 mg/g) and D-saccharose (3.72 mg/g). In common soapwort herb, after acidic hydrolysis and derivatization with acetylated aldononitriles, D-arabinose, D-fucose, D-mannose, D-glucose, D-galactose, D-fructose and Myo-inositol were identified. Free carbohydrates in the roots of Saponaria officinalis L., including D-glucose, D-galactose and D-saccharose, were determined with GC/MS method too. D-saccharose was common among free carbohydrates of Saponaria officinalis L. in the largest amounts. Its content in herb and roots of the common soapwort was 3.72 mg/g and 25.39 mg/g respectively.
A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization
This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of this paper.