Steviol glycosides in purified stevia leaf extract sharing the same metabolic fate.

The safety of steviol glycosides is based on data available on several individual steviol glycosides and on the terminal absorbed metabolite, steviol. Many more steviol glycosides have been identified, but are not yet included in regulatory assessments. Demonstration that these glycosides share the same metabolic fate would indicate applicability of the same regulatory paradigm. In vitro incubation assays with pooled human fecal homogenates, using rebaudiosides A, B, C, D, E, F and M, as well as steviolbioside and dulcoside A, at two concentrations over 24-48 h, were conducted to assess the metabolic fate of various steviol glycoside classes and to demonstrate that likely all steviol glycosides are metabolized to steviol. The data show that glycosidic side chains containing glucose, rhamnose, xylose, fructose and deoxy-glucose, including combinations of α(1-2), ß-1, ß(1-2), ß(1-3), and ß(1-6) linkages, were degraded to steviol mostly within 24 h. Given a common metabolite structure and a shared metabolic fate, safety data available for individual steviol glycosides can be used to support safety of purified steviol glycosides in general. Therefore, steviol glycosides specifications adopted by the regulatory authorities should include all steviol glycosides belonging to the five groups of steviol glycosides and a group acceptable daily intake established.

Structural characterization of the degradation products of a minor natural sweet diterpene glycoside Rebaudioside M under acidic conditions.

Degradation of rebaudioside M, a minor sweet component of Stevia rebaudiana Bertoni, under conditions that simulated extreme pH and temperature conditions has been studied. Thus, rebaudioside M was treated with 0.1 M phosphoric acid solution (pH 2.0) and 80 °C temperature for 24 h. Experimental results indicated that rebaudioside M under low pH and higher temperature yielded three minor degradation compounds, whose structural characterization was performed on the basis of 1D (1H-, 13C-) & 2D (COSY, HSQC, HMBC) NMR, HRMS, MS/MS spectral data as well as enzymatic and acid hydrolysis studies.

Structures of some novel α-glucosyl diterpene glycosides from the glycosylation of steviol glycosides.

Four new minor diterpene glycosides with a rare α-glucosyl linkage were isolated from a cyclodextrin glycosyltransferase glucosylated stevia extract containing more than 98% steviol glycosides. The new compounds were identified as 13-[(2-O-ß-D-glucopyranosyl-3-O-(4-O-α-D-glucopyranosyl)-ß-D-glucopyranosyl-ß-D-glucopyranosyl)oxy] ent-kaur-16-en-19-oic acid-[(4-O-α-D-glucopyranosyl-ß-D-glucopyranosyl) ester] (1), 13-[(2-O-ß-D-glucopyranosyl-ß-D-glucopyranosyl)oxy] ent-kaur-16-en-19-oic acid-[(4-O-(4-O-(4-O-α-D-glucopyranosyl)-α-D-glucopyranosyl)-α-D-glucopyranosyl)-ß-D-glucopyranosyl ester] (2), 13-[(2-O-ß-D-glucopyranosyl-3-O-(4-O-(4-O-(4-O-α-D-glucopyranosyl)-α-D-glucopyranosyl)-α-D-glucopyranosyl)-ß-D-glucopyranosyl-ß-D-glucopyranosyl)oxy] ent-kaur-16-en-19-oic acid ß-D-glucopyranosyl ester (3), and 13-[(2-O-ß-D-glucopyranosyl-3-O-(4-O-(4-O-(4-O-α-D-glucopyranosyl)-α-D-glucopyranosyl)-α-D-glucopyranosyl)-ß-D-glucopyranosyl- ß-D-glucopyranosyl)oxy] ent-kaur-16-en-19-oic acid-[(4-O-α-D-glucopyranosyl-ß-D-glucopyranosyl) ester] (4) on the basis of extensive NMR and mass spectral (MS) data as well as hydrolysis studies.

Additional new minor cucurbitane glycosides from Siraitia grosvenorii.

Continuous phytochemical studies of the crude extract of Luo Han Guo (Siraitia grosvenorii) furnished three additional new cucurbitane triterpene glycosides, namely 11-deoxymogroside V, 11-deoxyisomogroside V, and 11-deoxymogroside VI. The structures of all the isolated compounds were characterized on the basis of extensive NMR and mass spectral data as well as hydrolysis studies. The complete ¹H- and ¹³C-NMR spectral assignments of the three unknown compounds are reported for the first time based on COSY, TOCSY, HSQC, and HMBC spectroscopic data.

Bioconversion of rebaudioside I from rebaudioside A.

To supply the increasing demand of natural high potency sweeteners to reduce the calories in food and beverages, we have looked to steviol glycosides. In this work we report the bioconversion of rebaudioside A to rebaudioside I using a glucosyltransferase enzyme. This bioconversion reaction adds one sugar unit with a 1→3 linkage. We utilized 1D and 2D NMR spectroscopy (1H, 13C, COSY, HSQC-DEPT, HMBC, 1D TOCSY and NOESY) and mass spectral data to fully characterize rebaudioside I.

Quantification of diffusion coefficients of commonly used high-intensity sweeteners through mucin.

Low- and no-calorie sweeteners reduce the amount of carbohydrates in foods and beverages. However, concerns about taste perception surrounding the role of non-nutritive sweeteners in the oral cavity remain unanswered. One of the parameters that influences taste perception is the diffusion coefficient of the sweetener molecules inside the mucin layer lining the mouth. This study investigated the impact of diffusion coefficients of common high-intensity sweeteners on taste perception focusing on the sweeteners' diffusion through mucin. Transwell Permeable Support well plates were used to measure diffusion coefficients of samples that were collected at specific intervals to estimate the coefficients based on concentration measurements. The diffusion coefficients of acesulfame-K, aspartame, rebaudioside M, sucralose, and sucrose with and without NaCl were compared. We found that different sweeteners show different diffusion behavior through mucin and that the presence of salt enhances the diffusion. These findings contribute insights into the diffusion of high-intensity sweeteners, offer a way to evaluate diffusion coefficients in real-time, and inform the development of products with improved taste profiles.

Hydrogenation of the exocyclic olefinic bond at C-16/C-17 position of ent-kaurane diterpene glycosides of Stevia rebaudiana using various catalysts.

Catalytic hydrogenation of the exocyclic double bond present between C16 and C17 carbons of the four ent-kaurane diterpene glycosides namely rebaudioside A, rebaudioside B, rebaudioside C, and rebaudioside D isolated from Stevia rebaudiana has been carried out using Pt/C, Pd(OH)2, Rh/C, Raney Ni, PtO2, and 5% Pd/BaCO3 to their corresponding dihydro derivatives with 17α and 17ß methyl group isomers. Reactions were performed using the above-mentioned catalysts with the solvents methanol, water, and ethanol/water (8:2) under various conditions. Synthesis of reduced steviol glycosides was performed using straightforward chemistry and their structures were characterized on the basis of 1D and 2D NMR spectral data, including a comparison with reported spectral data.

Additional minor diterpene glycosides from Stevia rebaudiana Bertoni.

Two additional novel minor diterpene glycosides were isolated from the commercial extract of the leaves of Stevia rebaudiana Bertoni. The structures of the new compounds were identified as 13-{ß-D-glucopyranosyl-(1 → 2)-O-[ß-D-glucopyranosyl-(1 → 3)-ß-D-glucopyranosyl-oxy} ent-kaur-16-en-19-oic acid {ß-D-xylopyranosyl-(1 → 2)-O-[ß-D-glucopyranosyl-(1 → 3)]-O-ß-D-glucupyranosyl-ester} (1), and 13-{ß-D-6-deoxy-glucopyranosyl-(1 → 2)-O-[ß-D-glucopyranosyl-(1 → 3)-ß-D-glucopyranosyl-oxy} ent-kaur-16-en-19-oic acid {ß-D-glucopyranosyl-(1 → 2)-O-[ß-D-glucopyranosyl-(1 → 3)-ß-D-gluco-pyranosyl-ester} (2), on the basis of extensive 1D (1H- and 13C-) 2D NMR (COSY, HSQC and HMBC) and MS spectroscopic data as well as chemical studies.

Ensemble models based on radial basis function network for landslide susceptibility mapping.

Ensemble learning techniques have shown promise in improving the accuracy of landslide models by combining multiple models to achieve better predictive performance. In this study, several ensemble methods (Dagging, Bagging, and Decorate) and a radial basis function classifier (RBFC) were combined to predict landslide susceptibility in the Trung Khanh district of the Cao Bang Province, Vietnam. The ensemble models were developed using a geospatial database containing 45 historical landslides (1074 points) and thirteen influencing variables characterizing the topography, geology, land use/cover, and human activities of the study area. The performance of the models was evaluated based on the area under the receiver operating characteristic curve (AUC) and several other performance metrics, including positive predictive value (PPV), negative predictive value (NPV), sensitivity (SST), specificity (SPF), accuracy (ACC), and root mean square error (RMSE). The Bagging-RBFC model with PPV = 86%, NPV = 95%, SST = 95%, SPF = 87%, ACC = 91%, RMSE = 0.297, and AUC = 98% was found to be the most accurate model for the prediction of landslide susceptibility, followed by the Dagging-RBFC, Decorate-RBFC, and single RBFC models. The study demonstrates the efficacy of ensemble learning techniques in developing reliable landslide predictive models, which can ultimately save lives and reduce infrastructure damage in landslide-prone regions worldwide.

Catalytic hydrogenation of the sweet principles of Stevia rebaudiana, Rebaudioside B, Rebaudioside C, and Rebaudioside D and sensory evaluation of their reduced derivatives.

Catalytic hydrogenation of rebaudioside B, rebaudioside C, and rebaudioside D; the three ent-kaurane diterpene glycosides isolated from Stevia rebaudiana was carried out using Pd(OH)(2). Reduction of steviol glycosides was performed using straightforward synthetic chemistry with the catalyst Pd(OH)(2) and structures of the corresponding dihydro derivatives were characterized on the basis of 1D and 2D nuclear magnetic resonance (NMR) spectral data indicating that all are novel compounds being reported for the first time. Also, the taste properties of all reduced compounds were evaluated against their corresponding original steviol glycosides and sucrose.

Synthetic study on the relationship between structure and sweet taste properties of steviol glycosides.

The structure activity relationship between the C16-C17 methylene double bond on the aglycone of steviol glycosides and the corresponding impact on their sweet taste has been reported here for the first time. It has been observed that converting stevioside and rebaudioside A to their corresponding ketones by switching the doubly bonded methylene on C-17 for a ketone group actually removes the sweet taste properties of these molecules completely. Regenerating the original molecules tends to restore the sweet taste of both the steviol glycosides. Thus this C16-C17 methylene double bond in rebaudioside A and stevioside can be regarded as a pharmacophore essential for the sweetness property of these molecules.

Synthesis and sensory evaluation of ent-kaurane diterpene glycosides.

Catalytic hydrogenation of the three ent-kaurane diterpene glycosides isolated from Stevia rebaudiana, namely rubusoside, stevioside, and rebaudioside-A has been carried out using Pd(OH)2 and their corresponding dihydro derivatives have been isolated as the products. Synthesis of reduced steviol glycosides was performed using straightforward chemistry and their structures were characterized on the basis of 1D and 2D NMR spectral data and chemical studies. Also, we report herewith the sensory evaluation of all the reduced compounds against their corresponding original steviol glycosides and sucrose for the sweetness property of these molecules.

Solubility enhancement of steviol glycosides and characterization of their inclusion complexes with gamma-cyclodextrin.

Steviol glycosidesrebaudioside (reb) A, C and D have low aqueous solubilities. To improve their aqueous solubilities, inclusion complex of steviol glycosides, reb A, C and D and gamma cyclodextrin were prepared by freeze drying method and further characterized by means of differential scanning calorimetry, Fourier transform infrared spectroscopy and Raman spectroscopy. The effect of gamma cyclodextrin on chemical shifts of the steviol glycosides was also studied in proton NMR experiments as well as in solid state (13)C CP/MAS NMR experiments. These results indicated that the steviol glycosides were clearly in inclusion complex formation with the gamma cyclodextrin which also results in solubility enhancement of these steviol glycosides. Phase solubility studies showed that amounts of soluble reb A, C and D increased with increasing amounts of gamma cyclodextrin indicating formation of 1:1 stoichiometric and higher order inclusion complexes.

Diterpene glycosides from Stevia rebaudiana.

Three novel diterpene glycosides were isolated for the first time from the commercial extract of the leaves of Stevia rebaudiana, along with several known steviol glycosides, namely stevioside, rebaudiosides A-F, rubusoside and dulcoside A. The new compounds were identified as 13-[(2-O-ß-D-glucopyranosyl-3-O-ß-D-glucopyranosyl-ß-D-glucopyranosyl)oxy] ent-kaur-15-en-19-oic acid, 13-[(2-O-ß-D-glucopyranosyl-3-O-ß-D-glucopyranosyl-ß-D-glucopyranosyl)oxy]-16ß-hydroxy-ent-kauran-19-oic acid and 13-methyl-16-oxo-17-nor-ent-kauran-19-oic acid-ß-D-glucopyranosyl ester on the basis of extensive 2D NMR and MS spectroscopic data as well as chemical studies.

A new diterpene glycoside from Stevia rebaudiana.

From the commercial extract of the leaves of Stevia rebaudiana, a new diterpene glycoside was isolated besides the known steviol glycosides including stevioside, rebaudiosides A-F, rubusoside and dulcoside A. The new compound was identified as 13-[(2-O-ß-D-glucopyranosyl-3-O-ß-D-glucopyranosyl-ß-D-glucopyranosyl)oxy] ent-kaur-16-en-19-oic acid-(2-O-α-L-rhamnopyranosyl-ß-D-glucopyranosyl) ester (1) on the basis of extensive spectroscopic (NMR and MS) and chemical studies.

Synthesis of ent-kaurane diterpene monoglycosides.

Synthesis of two ent-kaurane diterpene glycosides, steviol 19-O-ß-D-glucopyranosiduronic acid (steviol glucuronide, 5), and 13-hydroxy ent-kaur-16-en-19-oic acid-ß-D-glucopyranosyl ester (7) has been achieved from a common starting material, steviol, using phase transfer catalyst. Also, synthesis of an additional 17-nor-ent-kaurane glycoside, namely 13-methyl-16-oxo-17-nor-ent-kauran-19-oic acid-ß-D-glucopyranosyl ester (10) was performed using the starting material isosteviol and similar synthetic methodology. Synthesis of all three steviol glycosides was performed using straightforward chemistry and their structures were characterized on the basis of 1D and 2D NMR as well as mass spectral (MS) data.

Groundwater Potential Mapping Using GIS-Based Hybrid Artificial Intelligence Methods.

Groundwater is one of the major valuable water resources for the use of communities, agriculture, and industries. In the present study, we have developed three novel hybrid artificial intelligence (AI) models which is a combination of modified RealAdaBoost (MRAB), bagging (BA), and rotation forest (RF) ensembles with functional tree (FT) base classifier for the groundwater potential mapping (GPM) in the basaltic terrain at DakLak province, Highland Centre, Vietnam. Based on the literature survey, these proposed hybrid AI models are new and have not been used in the GPM of an area. Geospatial techniques were used and geo-hydrological data of 130 groundwater wells and 12 topographical and geo-environmental factors were used in the model studies. One-R Attribute Evaluation feature selection method was used for the selection of relevant input parameters for the development of AI models. The performance of these models was evaluated using various statistical measures including area under the receiver operation curve (AUC). Results indicated that though all the hybrid models developed in this study enhanced the goodness-of-fit and prediction accuracy, but MRAB-FT (AUC = 0.742) model outperformed RF-FT (AUC = 0.736), BA-FT (AUC = 0.714), and single FT (AUC = 0.674) models. Therefore, the MRAB-FT model can be considered as a promising AI hybrid technique for the accurate GPM. Accurate mapping of the groundwater potential zones will help in adequately recharging the aquifer for optimum use of groundwater resources by maintaining the balance between consumption and exploitation.

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam.

: The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.

Gas-phase intramolecular elimination reaction studies of steviol glycosides in positive electrospray and tandem mass spectrometry.

This paper reports the first study of the gas-phase intramolecular elimination reaction of steviol glycosides in positive electrospray mass spectrometry. The observed glycosylated product ions are proposed to be formed via an intramolecular elimination of sugar units from the parent molecule ion. It was further proven by MS/MS studies and deuterium labeling experiments with one of the steviol glycosides, rebaudioside A. These mass spectrometric results confirmed that the new glycosylated product ions observed are most likely formed by the combination of glucose moieties (Glu) II-IV and Glu I via a gas-phase intramolecular elimination reaction.

Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis.

In this study, we developed Different Artificial Intelligence (AI) models namely Artificial Neural Network (ANN), Adaptive Network based Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) for the prediction of Compression Coefficient of soil (Cc) which is one of the most important geotechnical parameters. A Monte Carlo approach was used for the sensitivity analysis of the AI models and input parameters. For the construction and validation of the models, 189 soft clayey soil samples were analyzed. In the models study, 13 input parameters: depth of sample, bulk density, plasticity index, moisture content, clay content, specific gravity, void ratio, liquid limit, dry density, porosity, plastic limit, degree of saturation, and liquidity index were used to obtain one output parameter "Cc". Validation of the models was done using statistical methods such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of determination (R2). Results of the model validation indicate that though performance of all the three models is good but SVM model is the best in the prediction of Cc. The Monte Carlo method based sensitivity analysis results show that out of the 13 input parameters considered for the models study, four parameters namely clay, degree of saturation, specific gravity and depth of sample are the most relevant in the prediction of Cc, and other parameters (bulk density, dry density, void ratio and porosity) are the most insignificant parameters for the prediction of Cc. Removal of these insignificant parameters helped to reduce the dimension of the input space and also model running time, and improved significantly the performance of the AI models. The results of this study might help in selecting the suitable AI models and input parameters for better and quick prediction of the Cc of soil.