scholarly journals Mild Pretreatments to Increase Fructose Consumption in Saccharomyces cerevisiae Wine Yeast Strains

Foods ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1129
Author(s):  
Hatice Aybuke Karaoglan ◽  
Filiz Ozcelik ◽  
Alida Musatti ◽  
Manuela Rollini
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Wine Yeast ◽  
Temperature Treatment ◽  
Mean Bias Error ◽  
Bias Error ◽  
Model Parameters ◽  
Suitable Model ◽  
Mild Stress ◽  
Chi Square ◽  
Sigmoidal Model

The present research investigates the effect of different pretreatments on glucose and fructose consumption and ethanol production by four Saccharomyces cerevisiae wine strains, three isolated and identified from different wine regions in Turkey and one reference strain. A mild stress temperature (45 °C, 1 h) and the presence of ethanol (14% v/v) were selected as pretreatments applied to cell cultures prior to the fermentation step in synthetic must. The goodness fit of the mathematical models was estimated: linear, exponential decay function and sigmoidal model were evaluated with the model parameters R2 (regression coefficient), RMSE (root mean square error), MBE (mean bias error) and χ2 (reduced Chi-square). Sigmoidal function was determined as the most suitable model with the highest R2 and lower RMSE values. Temperature pretreatment allowed for an increase in fructose consumption rate by two strains, evidenced by a t90 value 10% lower than the control. One of the indigenous strains showed particular promise for mild temperature treatment (45 °C, 1 h) prior to the fermentation step to reduce residual glucose and fructose in wine. The described procedure may be effective for indigenous yeasts in preventing undesirable sweetness in wines.

scholarly journals Evaluation of models for the dew point temperature determination

2017 ◽  
Vol 3 (20) ◽  
pp. 241-257
Author(s):  
Krzysztof Górnicki ◽  
Radosław Winiczenko ◽  
Agnieszka Kaleta ◽  
Aneta Choińska
Keyword(s):  
Dew Point ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square ◽  
Ann Model ◽  
Chi Square ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Temperature Determination ◽  
The Mean

The accuracy of the available from the literature models for the dew point temperature determination was compared. The proposal of the modelling using artificial neural networks was also given. The experimental data were taken from the psychrometric tables. The accuracies of the models were measured using the mean bias error MBE, root mean square error RMSE, correlation coefficient R, and reduced chi-square χ2. Model M3, especially with constants A=237, B=7.5, gave the best results in determining the dew point temperature (MBE: -0.0229 – 0.0038 K, RMSE: 0.1259 – 0.1286 K, R=0.9999, χ2: 0.0159 – 0.0166 K2). Model M1 with constants A=243.5, B=17.67 and A=243.3, B=17.269 can be also considered as appropriate (MBE=-0.0062 and -0.0078 K, RMSE=0.1277 and 0.1261 K, R=0.9999, χ2=0.0163 and 0.0159 K2). Proposed ANN model gave the good results in determining the dew point temperature (MBE=-0.0038 K, RMSE=0.1373 K, R=0.9999, χ2=0.0189 K2).

Air Drying Characteristics of Chili Pepper

2010 ◽  
Vol 6 (1) ◽  
Author(s):  
Toyosi Y Tunde-Akintunde ◽  
Adeladun Ajala
Keyword(s):  
Correlation Coefficients ◽  
Chili Pepper ◽  
Mean Bias Error ◽  
Bias Error ◽  
Chi Square ◽  
Drying Characteristics ◽  
Page Model ◽  
Thin Layer Drying ◽  
Pre Treatment ◽  
Drying Behaviour

The effect of pretreatments (water and steam blanching and by soaking in osmotic solutions of 60 and 70° brix) on drying behaviour of chili pepper dried at 60°C were investigated. During the experiments, the chili pepper was dried until there was no more water loss. The pre-treatment affected the course and rate of drying since the pretreated pepper dried faster than the untreated pepper and hence had a higher drying rate. The drying of the pepper occurred in the falling rate drying period. Four mathematical models were studied for the description of thin layer drying characteristics of the chili pepper. The models considered were the Newton, Henderson and Pabis, Logarithmic and Page model. Comparing the correlation coefficients (R2), chi-square (?2), mean bias error (MBE) and root mean square error (RMSE) values of the four models, it was concluded that the Page model represents the drying characteristics better than the other models.

scholarly journals Estimation of Room-Level Cooling Energy in Hot/Arid Climate by Machine Learning-Based Approaches

2021 ◽  
Author(s):  
Bingyan Jia ◽  
Danlin Hou ◽  
Liangzhu (Leon) Wang ◽  
Ibrahim Galal Hassan
Keyword(s):  
Machine Learning ◽  
Power Density ◽  
Mean Squared Error ◽  
Building Energy ◽  
Energy Performance ◽  
Mean Bias Error ◽  
Support Vector ◽  
Bias Error ◽  
Model Parameters ◽  
Meta Model

Abstract Building energy models (BEM) are developed for understanding a building’s energy performance. A meta-model of the whole building energy analysis is often used for the BEM calibration and energy prediction. The literature review shows that studies with a focus on the development of room-level meta-models are missing. This study aims to address this research gap through a case study of a residential building with 138 apartments in Doha, Qatar. Five parameters, including cooling setpoint, number of occupants, lighting power density, equipment power density, and interior solar reflectance, are selected as input parameters to create ninety-six different scenarios. Three machine-learning models are used as meta-models to generalize the relationship between cooling energy and the model parameters, including Multiple Linear Regression, Support Vector Regression, and Artificial Neural Networks. The three meta-models’ prediction accuracies are evaluated by the Normalized Mean Bias Error (NMBE), Coefficient of Variation of the Root Mean Squared Error CV (RMSE), and R square (R2). The results show that the ANN model performs best. A new generic BEM is then established to validate the meta-model. The results indicate that the proposed meta-model is accurate and efficient in predicting the cooling energy in summer and transitional months for a building with a similar floor configuration.

scholarly journals Estimation of the Daily Global Solar Radiation using RadEst 3.00 software-A Case Study at Low land Plain Region of Nepal

2013 ◽  
Vol 29 ◽  
pp. 48-57
Author(s):  
Khem N. Poudyal ◽  
Binod K. Bhattarai ◽  
Balkrishna Sapkota ◽  
Berit Kjeldstad
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Model Parameters ◽  
Plain Area ◽  
Radiation Distribution ◽  
Iterative Procedures ◽  
Atmospheric Transmissivity

The RadEst 3.00 verson software estimates daily global solar radiation at low altitude plain area using meteorological parameters precipitation, maximum and minimum temperatures and solar radiation of Simara (Lat.27.15°N, Lon.84.98°E, and Alt.137m). Radiation is calculated as the product of the atmospheric transmissivity of radiation times radiation outside the earth atmosphere. The model parameters are fitted in two years data by iterative procedures. An accurate knowledge of solar radiation distribution in each particular geographysical location is crucial for the promotion of solar energy technology. The values estimated by the models are compared with measured radiation data. The performance of the model was evaluated using the statistical tools such as root mean square error (RMSE), mean bias error (MBE), Coefficient of Residual Mass (CRM) and coefficient of determination (r2). The empirical solar radiation models that showed better results using BC, CD, and DB and among them Modular DCBB is the best model for this location The finding coefficients of different models can be utilized for the estimation of solar radiation at the similar meteorological sites of Nepal. DOI: http://dx.doi.org/10.3126/jncs.v29i0.9237Journal of Nepal Chemical SocietyVol. 29, 2012Page: 48-57Uploaded date : 12/3/2013

Modeling of Thin Layer Drying of Banana (Nendran Spp) under Microwave, Convective and Combined Microwave-Convective Processes

2011 ◽  
Vol 6 (1) ◽  
Author(s):  
Magesh Ganesapillai ◽  
I. Regupathi ◽  
Thanapalan Murugesan
Keyword(s):  
Thin Layer ◽  
Microwave Drying ◽  
Convective Drying ◽  
Mean Bias Error ◽  
Bias Error ◽  
Slice Thickness ◽  
Drying Process ◽  
Chi Square ◽  
Drying Time ◽  
Thin Layer Drying

Drying kinetics of microwave, convective and microwave assisted convective drying of thin layer Nendran banana was investigated on a modified microwave oven. The drying characteristics through the operating parameters of the drying process, such as power output, air temperature, slice thickness and sample mass in terms of drying rate equation, were analyzed. An appropriate empirical model to represent the drying process was established by analyzing the available literature models with current experimental data. The statistical analysis for the selected model was performed, parameters like Mean Bias Error, Root Mean Square Error, reduced chi square and t-stat were estimated to examine the consistency of the model to represent the present experimental results. Higher rates and shorter drying times were achieved at a higher temperature and microwave heating power and lesser sample thickness and load. Microwave drying resulted in a substantial decrease in the drying time with better quality product when dried at higher power (300 W) level compared to other processes.

scholarly journals Effect of Different Drying Methods on the Drying Kinetics of Fermented Cardaba Banana Peels

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Kunle O Oni ◽  
A S Ajala ◽  
Abraham O Oloye
Keyword(s):  
Moisture Diffusivity ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Drying Methods ◽  
Chi Square ◽  
Tunnel Dryer ◽  
Model Sample ◽  
Drying Behavior ◽  
Drying Models

Cardaba banana peels (Musa acuminata) were fermented for three days and dried using solar dryer, open sun and tunnel dryer. Nonlinear regression analysis was used to fit in the experimental data.  Moisture drying was investigated using Fick’s second law. Statistical tools such as coefficient of determination (R2), reduced chi square (χ2), Mean Bias Error (MBE) and Root Mean Square Error (RMSE) were used to test the reliability of the model. Sample dried in sun had single falling rate pattern whereas samples in solar and tunnel dryer exhibited a second falling rate pattern. The values of R2 ranged from 0.872 - 0.989, χ2(1.4E-34 - 0.0624), MBE (-0.0067 - 0.0491) and RMSE (1.1E-17 - 0.2247). Effective moisture diffusivity for samples dried in solar, tunnel and sun were 2.92 E-11m2/s, 1.98 E-11m2/s and 1.09 E-11m2/s, respectively. The energy of activation in the process was 64.9kJ/mol. Page model best described drying behavior of the samples.Keywords: Fermentation, banana peels, drying, models, diffusivity, activation energy

scholarly journals Estimation of the Daily Global Solar Radiation using RadEst 3.00 Software at Nepalgunj, Nepal

2017 ◽  
Vol 12 (1) ◽  
pp. 199-209
Author(s):  
Bed Raj KC ◽  
Shekhar Gurung
Keyword(s):  
Solar Radiation ◽  
Geographical Location ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Model Parameters ◽  
Total Solar Radiation ◽  
Radiation Distribution ◽  
Atmospheric Transmissivity

The RadEst 3.00 version software estimates daily total solar radiation at low land area using meteorological parameters such as precipitation, temperatures and solar radiation of Nepalgunj (Lat.28.05°N, Lon.81.62°E, and Alt.150m). Radiation is calculated as the product of the atmospheric transmissivity of radiation and radiation outside earth atmosphere. The model parameters are fitted in two years data. An accurate knowledge of solar radiation distribution in each particular geographical location is crucial for the promotion of solar active and passive energy technology. The values estimated by the models are compared with measured solar radiation data. The performance of the model was evaluated using root mean square error (RMSE), mean bias error (MBE), Coefficient of Residual Mass (CRM) and coefficient of determination (R2). The RadEst 3.0 software which showed the better results using BC, CD, DB and DCBB, among them the DCBB model is the best model for this site. The values of RMSE, MBE, CRM and R2are 5.20, 3.98, 0.00 and 0.47 respectively. The finding coefficients of different models can be utilized for the estimation of solar radiation at the similar meteorological sites of Nepal.Journal of the Institute of Engineering, 2016, 12(1): 199-209

scholarly journals Correction of Eddy Covariance Based Crop ET Considering the Heat Flux Source Area

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 281
Author(s):  
Stuart L. Joy ◽  
José L. Chávez
Keyword(s):  
Heat Flux ◽  
Eddy Covariance ◽  
Agricultural Land ◽  
Flux Measurement ◽  
Source Area ◽  
Mean Bias Error ◽  
Bias Error ◽  
Crop Water ◽  
The Past ◽  
Measurement Results

Eddy covariance (EC) systems are being used to measure sensible heat (H) and latent heat (LE) fluxes in order to determine crop water use or evapotranspiration (ET). The reliability of EC measurements depends on meeting certain meteorological assumptions; the most important of such are horizontal homogeneity, stationarity, and non-advective conditions. Over heterogeneous surfaces, the spatial context of the measurement must be known in order to properly interpret the magnitude of the heat flux measurement results. Over the past decades, there has been a proliferation of ‘heat flux source area’ (i.e., footprint) modeling studies, but only a few have explored the accuracy of the models over heterogeneous agricultural land. A composite ET estimate was created by using the estimated footprint weights for an EC system in the upwind corner of four fields and separate ET estimates from each of these fields. Three analytical footprint models were evaluated by comparing the composite ET to the measured ET. All three models performed consistently well, with an average mean bias error (MBE) of about −0.03 mm h−1 (−4.4%) and root mean square error (RMSE) of 0.09 mm h−1 (10.9%). The same three footprint models were then used to adjust the EC-measured ET to account for the fraction of the footprint that extended beyond the field of interest. The effectiveness of the footprint adjustment was determined by comparing the adjusted ET estimates with the lysimetric ET measurements from within the same field. This correction decreased the absolute hourly ET MBE by 8%, and the RMSE by 1%.

scholarly journals CO2 Flux over the Contiguous United States in 2016 Inverted by WRF-Chem/DART from OCO-2 XCO2 Retrievals

2021 ◽  
Vol 13 (15) ◽  
pp. 2996
Author(s):  
Qinwei Zhang ◽  
Mingqi Li ◽  
Maohua Wang ◽  
Arthur Paul Mizzi ◽  
Yongjian Huang ◽  
...  
Keyword(s):  
United States ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Co2 Flux ◽  
Total Carbon ◽  
Mean Bias Error ◽  
Bias Error ◽  
Parallel Experiment ◽  
Concentration Measurements ◽  
Global Stocktake

High spatial resolution carbon dioxide (CO2) flux inversion systems are needed to support the global stocktake required by the Paris Agreement and to complement the bottom-up emission inventories. Based on the work of Zhang, a regional CO2 flux inversion system capable of assimilating the column-averaged dry air mole fractions of CO2 (XCO2) retrieved from Orbiting Carbon Observatory-2 (OCO-2) observations had been developed. To evaluate the system, under the constraints of the initial state and boundary conditions extracted from the CarbonTracker 2017 product (CT2017), the annual CO2 flux over the contiguous United States in 2016 was inverted (1.08 Pg C yr−1) and compared with the corresponding posterior CO2 fluxes extracted from OCO-2 model intercomparison project (OCO-2 MIP) (mean: 0.76 Pg C yr−1, standard deviation: 0.29 Pg C yr−1, 9 models in total) and CT2017 (1.19 Pg C yr−1). The uncertainty of the inverted CO2 flux was reduced by 14.71% compared to the prior flux. The annual mean XCO2 estimated by the inversion system was 403.67 ppm, which was 0.11 ppm smaller than the result (403.78 ppm) simulated by a parallel experiment without assimilating the OCO-2 retrievals and closer to the result of CT2017 (403.29 ppm). Independent CO2 flux and concentration measurements from towers, aircraft, and Total Carbon Column Observing Network (TCCON) were used to evaluate the results. Mean bias error (MBE) between the inverted CO2 flux and flux measurements was 0.73 g C m−2 d−1, was reduced by 22.34% and 28.43% compared to those of the prior flux and CT2017, respectively. MBEs between the CO2 concentrations estimated by the inversion system and concentration measurements from TCCON, towers, and aircraft were reduced by 52.78%, 96.45%, and 75%, respectively, compared to those of the parallel experiment. The experiment proved that CO2 emission hotspots indicated by the inverted annual CO2 flux with a relatively high spatial resolution of 50 km consisted well with the locations of most major metropolitan/urban areas in the contiguous United States, which demonstrated the potential of combing satellite observations with high spatial resolution CO2 flux inversion system in supporting the global stocktake.

scholarly journals Hourly Ground-Level PM2.5 Estimation Using Geostationary Satellite and Reanalysis Data via Deep Learning

2021 ◽  
Vol 13 (11) ◽  
pp. 2121
Author(s):  
Changsuk Lee ◽  
Kyunghwa Lee ◽  
Sangmin Kim ◽  
Jinhyeok Yu ◽  
Seungtaek Jeong ◽  
...  
Keyword(s):  
Cross Validation ◽  
Ground Level ◽  
Reanalysis Data ◽  
Training Dataset ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Air Quality Model ◽  
Quality Model ◽  
Near Surface

This study proposes an improved approach for monitoring the spatial concentrations of hourly particulate matter less than 2.5 μm in diameter (PM2.5) via a deep neural network (DNN) using geostationary ocean color imager (GOCI) images and unified model (UM) reanalysis data over the Korean Peninsula. The DNN performance was optimized to determine the appropriate training model structures, incorporating hyperparameter tuning, regularization, early stopping, and input and output variable normalization to prevent training dataset overfitting. Near-surface atmospheric information from the UM was also used as an input variable to spatially generalize the DNN model. The retrieved PM2.5 from the DNN was compared with estimates from random forest, multiple linear regression, and the Community Multiscale Air Quality model. The DNN demonstrated the highest accuracy compared to that of the conventional methods for the hold-out validation (root mean square error (RMSE) = 7.042 μg/m3, mean bias error (MBE) = −0.340 μg/m3, and coefficient of determination (R2) = 0.698) and the cross-validation (RMSE = 9.166 μg/m3, MBE = 0.293 μg/m3, and R2 = 0.49). Although the R2 was low due to underestimated high PM2.5 concentration patterns, the RMSE and MBE demonstrated reliable accuracy values (<10 μg/m3 and 1 μg/m3, respectively) for the hold-out validation and cross-validation.

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