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 DRYING BEHAVIOR AND SOME QUALITY PARAMETERS OF POTATO FINGER CHIPS USING INFRARED RADIATION

2021 ◽  
Vol 51 (4) ◽  
pp. 261-267
Author(s):  
Ibrahim Doymaz
Keyword(s):  
Type Equation ◽  
Moisture Diffusivity ◽  
Quality Parameters ◽  
Potato Chips ◽  
Coefficient Of Determination ◽  
Slice Thickness ◽  
Chi Square ◽  
Effective Moisture ◽  
Thin Layer Drying ◽  
Drying Behavior

The effect of different infrared (IR) powers on drying of potato finger chips was investigated in infrared dryer. The potato finger chips dried at 48, 50, 62, 74 and 88 W infrared powers and constant slice thickness of 7 mm. Results showed that drying characteristics of potato chips were greatly influenced by infrared power. The experimental drying data were fitted to seven thin-layer drying models. The models were compared on coefficient of determination (R2), reduced chi-square (c2) and root mean square error (RMSE) values between experimental and predicted moisture ratios.  The Midilli & Kucuk model was found to the best in predicting the drying kinetics of potato chips during infrared drying. The effective moisture diffusivity using the Fick’s diffusion model varied from 1.82×10-9 to 3.06×10-9m2/s. It was found that the effective moisture diffusivitiy increased with increasing IR power. Activation energy was estimated by a modified Arrhenius type equation as 1.35 kW/kg. The results also showed that with increasing infrared power, rehydration capacity was decreased.

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.

scholarly journals Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1207
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Wind Speed ◽  
Bias Correction ◽  
Goodness Of Fit ◽  
Mediterranean Climate ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Weather Variables ◽  
Weather Stations

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

scholarly journals Space-Time Sea Surface pCO2 Estimation in the North Atlantic Based on CatBoost

2021 ◽  
Vol 13 (14) ◽  
pp. 2805
Author(s):  
Hongwei Sun ◽  
Junyu He ◽  
Yihui Chen ◽  
Boyu Zhao
Keyword(s):  
North Atlantic ◽  
Function Analysis ◽  
Biological Activities ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
The North ◽  
The North Atlantic

Sea surface partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air–sea CO2 flux, which plays an important role in calculating the global carbon budget and ocean acidification. In this study, we used chlorophyll-a concentration (Chla), sea surface temperature (SST), dissolved and particulate detrital matter absorption coefficient (Adg), the diffuse attenuation coefficient of downwelling irradiance at 490 nm (Kd) and mixed layer depth (MLD) as input data for retrieving the sea surface pCO2 in the North Atlantic based on a remote sensing empirical approach with the Categorical Boosting (CatBoost) algorithm. The results showed that the root mean square error (RMSE) is 8.25 μatm, the mean bias error (MAE) is 4.92 μatm and the coefficient of determination (R2) can reach 0.946 in the validation set. Subsequently, the proposed algorithm was applied to the sea surface pCO2 in the North Atlantic Ocean during 2003–2020. It can be found that the North Atlantic sea surface pCO2 has a clear trend with latitude variations and have strong seasonal changes. Furthermore, through variance analysis and EOF (empirical orthogonal function) analysis, the sea surface pCO2 in this area is mainly affected by sea temperature and salinity, while it can also be influenced by biological activities in some sub-regions.

scholarly journals Sorption isotherms and their fitted equations for dried chips of cassava roots (Manihot Esculenta Crantz; Tme-7 variety) and the resulting isosteric heats of sorption

Food Research ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 703-711
Author(s):  
A.S. Ajala ◽  
P.O. Ngoddy ◽  
J.O. Olajide
Keyword(s):  
Relative Humidity ◽  
Shelf Life ◽  
Gravimetric Method ◽  
Sorption Isotherms ◽  
Isosteric Heat ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Isotherm Models ◽  
Bias Error ◽  
Manihot Esculenta Crantz

Cassava roots are susceptible to deterioration with 24 hrs of harvest; it needs processing into a more stable material such as dried cassava chips to extend its shelf life for long storage. However, improper knowledge of the effect of atmospheric relative humidity on these dried chips during storage makes it mouldy and unacceptable. This work aimed at studying the effect of sorption isotherms on the dried cassava chips. In this study, adsorption and desorption isotherm were carried out using static gravimetric method and data for equilibrium moisture content (EMC) were generated at five (5) temperatures (53, 60, 70, 80, 86oC). These were fitted into four (4) isotherm-models [Oswin, Peleg, the Modified Oswin and GAB]. The statistical criteria to test the models were coefficient of determination (R2 ), reduced chi-square (χ 2 ), root mean square error (RMSE) and mean bias error (MBE). The values of EMC ranged from 7.21-12.44% wb. The values of R2 ranged from 0.95-0.99; χ 2 ranged from 0.008-0.14; RMSE values ranged from 0.06-0.254 while MBE values ranged from -0.0004-1.1E-5. The values of isosteric heat of sorption calculated from the isosteres recorded a range from 6.579 to 67.829 kJ/mole. The Pelegmodel gave the best fit in the relative humidity range of 10 to 80%. The values of EMC show that the chips can have a stable shelf life without spoilage.

scholarly journals Estimating daily evapotranspiration based on a model of evaporative fraction (EF) for mixed pixels

2019 ◽  
Vol 23 (2) ◽  
pp. 949-969
Author(s):  
Fugen Li ◽  
Xiaozhou Xin ◽  
Zhiqing Peng ◽  
Qinhuo Liu
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Heihe River ◽  
Natural Ecosystems ◽  
Evaporative Fraction ◽  
Mixed Pixel ◽  
Mixed Pixels

Abstract. Currently, applications of remote sensing evapotranspiration (ET) products are limited by the coarse resolution of satellite remote sensing data caused by land surface heterogeneities and the temporal-scale extrapolation of the instantaneous latent heat flux (LE) based on satellite overpass time. This study proposes a simple but efficient model (EFAF) for estimating the daily ET of remotely sensed mixed pixels using a model of the evaporative fraction (EF) and area fraction (AF) to increase the accuracy of ET estimate over heterogeneous land surfaces. To accomplish this goal, we derive an equation for calculating the EF of mixed pixels based on two key hypotheses. Hypothesis 1 states that the available energy (AE) of each sub-pixel is approximately equal to that of any other sub-pixels in the same mixed pixel within an acceptable margin of error and is equivalent to the AE of the mixed pixel. This approach simplifies the equation, and uncertainties and errors related to the estimated ET values are minor. Hypothesis 2 states that the EF of each sub-pixel is equal to that of the nearest pure pixel(s) of the same land cover type. This equation is designed to correct spatial-scale errors for the EF of mixed pixels; it can be used to calculate daily ET from daily AE data. The model was applied to an artificial oasis located in the midstream area of the Heihe River using HJ-1B satellite data with a 300 m resolution. The results generated before and after making corrections were compared and validated using site data from eddy covariance systems. The results show that the new model can significantly improve the accuracy of daily ET estimates relative to the lumped method; the coefficient of determination (R2) increased to 0.82 from 0.62, the root mean square error (RMSE) decreased to 1.60 from 2.47 MJ m−2(decreased approximately to 0.64 from 0.99 mm) and the mean bias error (MBE) decreased from 1.92 to 1.18 MJ m−2 (decreased from approximately 0.77 to 0.47 mm). It is concluded that EFAF can reproduce daily ET with reasonable accuracy; can be used to produce the ET product; and can be applied to hydrology research, precision agricultural management and monitoring natural ecosystems in the future.

Influence of Drying Conditions and Mathematical Models on the Thin-Layer Drying of Mushrooms

2014 ◽  
Author(s):  
A. Stegou-Sagia ◽  
D. V. Fragkou
Keyword(s):  
Mass Transfer ◽  
Thin Layer ◽  
Mathematical Models ◽  
Drying Methods ◽  
Suitable Model ◽  
Drying Time ◽  
Thin Layer Drying ◽  
Drying Behavior ◽  
Drying Models ◽  
Drying Rates

In the present research, experimental data from several studies about drying behavior of mushrooms have been selected and used to compare different drying methods and different mathematical thin layer drying models to simulate mushroom drying rates. The white button (Agaricus Bisporus), the oyster (Pleurotus Ostreatus) and the milky mushroom slices have been considered for drying in different dryers such as hot air cabinet dryer and fluidized bed dryer with different slice thicknesses, drying air temperatures (45 °C to 90 °C) and drying air velocities (0.2 m/s to 5 m/s). The entire drying process has taken place in the falling rate period, assuming that internal mass transfer occurred by diffusion in mushroom slices. The study shows that the drying air temperature and the drying air velocity have an effect on the moisture removal from mushrooms and also on the drying time. Mathematical models have been proved to be useful for design and analysis of heat and mass transfer during drying processes. All the drying models considered in this study could adequately represent the thin layer drying behavior of mushrooms. Furthermore, as it is obvious, any type of mushrooms has its own most suitable model.

scholarly journals Drying of MalaysianCapsicum annuumL. (Red Chili) Dried by Open and Solar Drying

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Ahmad Fudholi ◽  
Mohd Yusof Othman ◽  
Mohd Hafidz Ruslan ◽  
Kamaruzzaman Sopian
Keyword(s):  
Root Mean Square ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Solar Drying ◽  
Mean Square ◽  
Drying Time ◽  
Sun Drying ◽  
65 H ◽  
Mean Square Errors

This study evaluated the performance of solar drying in the Malaysian red chili (Capsicum annuumL.). Red chilies were dried down from approximately 80% (wb) to 10% (wb) moisture content within 33 h. The drying process was conducted during the day, and it was compared with 65 h of open sun drying. Solar drying yielded a 49% saving in drying time compared with open sun drying. At the average solar radiation of 420 W/m2and air flow rate of 0.07 kg/s, the collector, drying system, and pickup demonstrated efficiency rates of approximately 28%, 13%, and 45%, respectively. Evaporative capacity ranged from 0.13 to 2.36 kg/h, with an average of 0.97 kg/h. The specific moisture extraction rate (SMER) of 0.19 kg/kWh was obtained. Moreover, the drying kinetics ofC. annuumL. were investigated. A nonlinear regression procedure was used to fit three drying models. These models were compared with experimental data on red chilies dried by open sun drying and those dried by solar drying. The fit quality of the models was evaluated using their coefficient of determination (R2), mean bias error, and root-mean-square error values. The Page model resulted in the highestR2and the lowest mean bias and root-mean-square errors.

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).

scholarly journals Kinetika Pengeringan Lapisan Tipis Daun Jati Belanda (Thin Layer Drying Kinetics of Guazuma Ulmifolia Leaves)

2021 ◽  
Vol 8 (2) ◽  
pp. 53-62
Author(s):  
Hendri Syah ◽  
Armansyah Halomoan Tambunan ◽  
Edy Hartulistiyoso ◽  
Lamhot Parulian Manalu
Keyword(s):  
Mass Transfer ◽  
Thin Layer ◽  
Moisture Diffusivity ◽  
Drying Kinetics ◽  
Coefficient Of Determination ◽  
Convective Mass Transfer ◽  
Thin Layer Drying ◽  
Drying Models ◽  
Guazuma Ulmifolia ◽  
Kinetics Of

The objectives of this study were to determine a suitable thin layer drying model to describe the drying kinetics of Guazuma ulmifolia leaves and determine the mass transfer parameters of Guazuma ulmifolia leaves. The drying of Guazuma ulmifolia leaves was conducted in a laboratory scale dryer with various temperature (40oC, 50oC, and 60oC) and relative humidity (30%, 40%, 50% and 60%). Five drying models, namely, Newton, Henderson and Pabis, Page, Midilli-Kucuk, and Verma et al. were fitted to the drying data. The drying curve of guazuma leaves did not show a constant drying period during the drying period. The models suitability were compared base on coefficient of determination (R2), root square mean errors (RSME), and reduced mean square of deviation (X2). It was found that, among the models evaluated, the Midilli and Kucuk model is the best to describe the drying kinetics of Guazuma ulmifolia leaves. The effective moisture diffusivity was found to be in the range of 10-13 – 10-12 m2/s and the convective mass transfer coefficient was in the range of 10-9 – 10-10 m/s. The activation energy value was found to be 89.21 kJ/mol.

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