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.

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.

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.

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.

A comparison study of DRASTIC methods with various objective methods for groundwater vulnerability assessment.

Groundwater vulnerability assessment is a measure of potential groundwater contamination for areas of interest. The main objective of this study is to modify original DRASTIC model using four objective methods, Weights-of-Evidence (WOE), Shannon Entropy (SE), Logistic Model Tree (LMT), and Bootstrap Aggregating (BA) to create a map of groundwater vulnerability for the Sari-Behshahr plain, Iran. The study also investigated impact of addition of eight additional factors (distance to fault, fault density, distance to river, river density, land-use, soil order, geological time scale, and altitude) to improve groundwater vulnerability assessment. A total of 109 nitrate concentration data points were used for modeling and validation purposes. The efficacy of the four methods was evaluated quantitatively using the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). AUC value for original DRASTIC model without any modification of weights and rates was 0.50. Modification of weights and rates resulted in better performance with AUC values of 0.64, 0.65, 0.75, and 0.81 for BA, SE, LMT, and WOE methods, respectively. This indicates that performance of WOE is the best in assessing groundwater vulnerability for DRASTIC model with 7 factors. The results also show more improvement in predictability of the WOE model by introducing 8 additional factors to the DRASTIC as AUC value increased to 0.91. The most effective contributing factor for ground water vulnerability in the study area is the net recharge. The least effective factors are the impact of vadose zone and hydraulic conductivity.

A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran.

Floods are one of the most damaging natural hazards causing huge loss of property, infrastructure and lives. Prediction of occurrence of flash flood locations is very difficult due to sudden change in climatic condition and manmade factors. However, prior identification of flood susceptible areas can be done with the help of machine learning techniques for proper timely management of flood hazards. In this study, we tested four decision trees based machine learning models namely Logistic Model Trees (LMT), Reduced Error Pruning Trees (REPT), Naïve Bayes Trees (NBT), and Alternating Decision Trees (ADT) for flash flood susceptibility mapping at the Haraz Watershed in the northern part of Iran. For this, a spatial database was constructed with 201 present and past flood locations and eleven flood-influencing factors namely ground slope, altitude, curvature, Stream Power Index (SPI), Topographic Wetness Index (TWI), land use, rainfall, river density, distance from river, lithology, and Normalized Difference Vegetation Index (NDVI). Statistical evaluation measures, the Receiver Operating Characteristic (ROC) curve, and Freidman and Wilcoxon signed-rank tests were used to validate and compare the prediction capability of the models. Results show that the ADT model has the highest prediction capability for flash flood susceptibility assessment, followed by the NBT, the LMT, and the REPT, respectively. These techniques have proven successful in quickly determining flood susceptible areas.

A Novel Diterpene Glycoside with Nine Glucose Units from Stevia rebaudiana Bertoni.

Following our interest in new diterpene glycosides with better taste profiles than that of Rebaudioside M, we have recently isolated and characterized Rebaudioside IX-a novel steviol glycoside-from a commercially-supplied extract of Stevia rebaudiana Bertoni. This molecule contains a hexasaccharide group attached at C-13 of the central diterpene core, and contains three additional glucose units when compared with Rebaudioside M. Here we report the complete structure elucidation-based on extensive Nuclear Magnetic Resonance (NMR) analysis (1H, 13C, Correlation Spectroscopy (COSY), Heteronuclear Single Quantum Coherence-Distortionless Enhancement Polarization Transfer (HSQC-DEPT), Heteronuclear Multiple Bond Correlation (HMBC), 1D Total Correlation Spectroscopy (TOCSY), Nuclear Overhauser Effect Spectroscopy (NOESY)) and mass spectral data-of this novel diterpene glycoside with nine sugar moieties and containing a relatively rare 16 α-linked glycoside. A steviol glycoside bearing nine glucose units is unprecedented in the literature, and could have an impact on the natural sweetener catalog.

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.

A New Diterpene Glycoside: 15α-Hydroxy-Rebaudioside M Isolated from Stevia rebaudiana.

In a continued search for novel diterpenoid glycosides, we recently isolated and characterized a Rebaudioside M derivative with a hydroxyl group at position 15 in the central diterpene core from an extract of Stevia rebaudiana Bertoni. Here we report the complete structure elucidation of 15α-hydroxy-Rebaudioside M (2) on the basis of NMR (1H, 13C, COSY, HSQC-DEPT, HMBC, 1D TOCSY, NOESY) and mass spectral data. Steviol glycoside with a hydroxyl group at C-15 in the central diterpene core has not been previously reported.

Degradation products of rubusoside under acidic conditions.

A natural sweetener, Rubusoside (1), subjected to extreme pH and temperature conditions, resulted in the isolation and structural elucidation of one novel rubusoside degradant (7), together with seven known degradants (2-6 and 8-9). ID and 2D NMR spectroscopy (1H, 13C, COSY, HSQC-DEPT, HMBC, and NOESY) and mass spectral data were used to fully characterize the degradant 7.

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.

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.

Isolation and structure elucidation of rebaudioside D2 from bioconversion reaction of rebaudioside A to rebaudioside D.

We report the isolation and complete structure of an isomer of rebaudioside D, known as rebaudioside D2. This novel steviol glycoside was isolated from a bioconversion reaction of rebaudioside A to rebaudioside D. Rebaudioside D2 possesses a relatively rare 1 --> 6 sugar linkage, which was discovered by extensive analysis of NMR (1H, 13C, COSY, HSQC-DEPT, HMBC, 1D TOCSY and NOESY) and mass spectral data.

Isolation and characterization of a novel rebaudioside M isomer from a bioconversion reaction of rebaudioside A and NMR comparison studies of rebaudioside M isolated from Stevia rebaudiana Bertoni and Stevia rebaudiana Morita.

A minor product, rebaudioside M2 (2), from the bioconversion reaction of rebaudioside A (4) to rebaudioside D (3), was isolated and the complete structure of the novel steviol glycoside was determined. Rebaudioside M2 (2) is considered an isomer of rebaudioside M (1) and contains a relatively rare 1→6 sugar linkage. It was isolated and characterized with NMR (1H, 13C, COSY, HSQC-DEPT, HMBC, 1D-TOCSY, and NOESY) and mass spectral data. Additionally, we emphasize the importance of 1D and 2D NMR techniques when identifying complex steviol glycosides. Numerous NMR spectroscopy studies of rebaudioside M (1), rebaudioside D (3), and mixture of 1 and 3 led to the discovery that SG17 which was previously reported in literature, is a mixture of rebaudioside D (3), rebaudioside M (1), and possibly other related steviol glycosides.

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.

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.

Development of Next Generation Stevia Sweetener: Rebaudioside M.

This work aims to review and showcase the unique properties of rebaudioside M as a natural non-caloric potential sweetener in food and beverage products. To determine the potential of rebaudioside M, isolated from Stevia rebaudiana Bertoni, as a high potency sweetener, we examined it with the Beidler Model. This model estimated that rebaudioside M is 200-350 times more potent than sucrose. Numerous sensory evaluations of rebaudioside M's taste attributes illustrated that this steviol glycoside possesses a clean, sweet taste with a slightly bitter or licorice aftertaste. The major reaction pathways in aqueous solutions (pH 2-8) for rebaudioside M are similar to rebaudioside A. Herein we demonstrate that rebaudioside M could be of great interest to the global food industry because it is well-suited for blending and is functional in a wide variety of food and beverage products.

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.

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.