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

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.

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rachida Ouaabou ◽  
Said Ennahli ◽  
Chira Di Lorenzo ◽  
Hafida Hanine ◽  
Aadil Bajoub ◽  
...  

Understanding sorption isotherms is crucial in food science for optimizing the drying processes, enhancing the shelf-life of food, and maintaining food quality during storage. This study investigated the isotherms of sweet cherry powder (SCP) using the static gravimetric method. The experimental water sorption curves of lyophilized sweet cherry powder were determined at 30°C, 40°C, and 50°C. The curves were then fitted to six isotherm models: Modified GAB, Halsey, Smith, Oswin, Caurie, and Kühn models. To define the energy associated with the sorption process, the isosteric sorption heat, differential entropy, and spreading pressure were derived from the isotherms. Among the six models, the Smith model is the most reliable in predicting the sorption of the cherry powder with a determination coefficient (R2) of 0.9978 and a mean relative error (MRE) ≤1.61. The values of the net isosteric heat and differential entropy for the cherry increased exponentially as the moisture content decreased. The net isosteric heat values varied from 10.63 to 90.97 kJ mol−1, while the differential entropy values varied from 27.94 to 273.39 J. mol−1K−1. Overall, the enthalpy-entropy compensation theory showed that enthalpy-controlled mechanisms could be used to regulate water adsorption in cherry powders.


Author(s):  
Elys Nayanne Vieira Matos ◽  
Romildo Martins Sampaio ◽  
Maria Do Livramento de Paula

O babaçu (Orbignyamartiana), palmeira abundante no Nordeste brasileiro, apresenta grande potencial econômico nas regiões em que é produzida. Seu fruto é dividido em quatro partes: epicarpo, mesocarpo, endocarpo e amêndoa. Todas estas partes possuem aplicações industriais. Sua farinha, obti- da a partir do mesocarpo, tem tido crescente aceitação no setor alimentício, sem, no entanto, apresentar na literatura muitos estudos a respeito de suas isotermas de sorção, importantes para predizer a estabili- dade química e física em função do conteúdo de água e o teor de umidade que impedem o crescimento dos microrganismos. Assim, os objetivos deste trabalho foram estudar o comportamento das isotermas de adsorção da farinha de babaçu comercial às temperaturas de 25 e 40°C, selecionar o modelo que melhor se ajusta aos dados experimentais e estimar o calor isostérico de sorção. As isotermas foram construídas pelo método gravimétrico, que consiste na medição do ganho de massa da amostra quando colocada em contato com soluções salinas que proporcionaram diferentes umidades relativas nas temperaturas de 25 e 40°C. Para os ensaios de adsorção, foram empregadas amostras de farinhas de babaçu previamente secas. A seleção do melhor modelo foi feita com base no coeficiente de determinação e no qui-quadrado. Todos os modelos testados apresentaram bons ajustes nas temperaturas estudadas, sendo que a 25 °C os modelos de Henderson e Peleg apresentaram os melhores ajustes, enquanto que a 40°C, o de GAB foi o mais adequado Os calores isostéricos de adsorção apresentaram comportamento inversamente proporcional à umidade de equilíbrio. Palavras-chave: Farinha de Babaçu. Isoterma de sorção. Calor isostérico. DETERMINATION OF THE ADSORPTION ISOTHERM AND SORPTION HEAT OF COMMERCIAL BABASSU FLOURABSTRACT: The babassu (Orbignyamartiana), abundant palm in the Brazilian Northeast, has great social, economic and cultural potential in the regions where it is produced. Its fruit is divided into four parts: epi- carp, mesocarp, endocarp, and almond. All these parties have industrial applications. His flour, obtained from the mesocarp, has had increasing acceptance in the food industry, without however, presenting many studies in the literature about their sorption isotherms that are important for predicting the chemical and physical stability as a function of water content and moisture content that prevents the growth of microor- ganisms. The objectives of this work were to study the behavior of the adsorption isotherms of commercial babassu flour at temperatures of 25 and 40 ° C, select the model that best fits the experimental data and estimate the isosteric heat of sorption. The isotherms were constructed by the gravimetric method, which consists in measuring the mass gain of the sample when placed in contact with saline solutions that pro- vided different relative humidities at 25 and 40 ° C. For the adsorption experiments, samples of babassu flour were dried previously. The selection of the best model was based on the coefficient of determination and chi -square test. All tested models showed good fits in the temperatures studied. For the adsorption at 25 ° C Henderson and Peleg models showed a better fit, while at 40 ° C the GAB was the better model. The isosteric heats of adsorption presented inversely proportional to the equilibrium moisture behavior. KEYWORDS: Babassu flour. Sorption isotherm. Isosteric heat.  DETERMINACIÓN DE LAS ISOTERMAS DE ADSORCIÓN Y DEL CALOR DE SORCIÓN DE HARINA DE “BABAÇU” COMERCIALRESUMEN: El “babaçú” (Orbignyamartiana), una “palmera” abundante en el nordeste del Brasil, tiene un gran potencial social, económico y cultural en las regiones donde se produce. Su fruto se divide en cuatro partes: epicarpio, mesocarpio, endocarpio y almendras. Todas estas partes tienen aplicaciones industria- les. Su harina de mesocarpio ha tenido creciente aceptación en la industria alimenticia, pero no hay mu- chos estudios en la literatura acerca de sus isotermas de sorción que son importantes para la predicción de la estabilidad química y física como una función del contenido de agua y contenido de humedad que evita el crecimiento de microorganismos. Los objetivos de este trabajo fue estudiar el comportamiento de las isotermas de adsorción de harina de “babaçú” comercial a temperaturas de 25 y 40 °C, seleccionar el modelo que mejor se ajusta a los datos experimentales y estimar el calor isostérico de sorción. Las isoter- mas fueron construidas por el método gravimétrico, que consiste en medir la gaño de masa de la muestra cuando está en contacto con soluciones salinas que proporcionaron diferentes humedades relativas a 25 y 40 °C. Para los experimentos de adsorción se utilizaron muestras de harina de “babaçú” previamente seca. La selección del mejor modelo se basa en el coeficiente de determinación y de “qui-quadrado”. To- dos los modelos utilizados muestran buenos ajustes en las temperaturas evaluadas. Para la adsorción a25 °C modelos de Henderson y Peleg, presentaron los mejores ajustes, mientras que a 40 °C modelo de GAB fue el más adecuado. Los calores isostéricos de adsorción mostraron inversamente proporcional al comportamiento de la humedad de equilíbrio.PALABRAS CLAVE: Harina de “babaçú”. Isoterma de sorción. Calor isotérico.


2019 ◽  
Vol 13 (1) ◽  
pp. 43-55
Author(s):  
D. O. Akpootu ◽  
A. M. Rabiu

Background:Estimation of tropospheric radio refractivity is significant in the planning and design of terrestrial communication links.Methods:In this study, the monthly average daily atmospheric pressure, relative humidity and temperature data obtained from the National Aeronautics and Space Administration (NASA) during the period of twenty two years (July 1983 - June 2005) for Osogbo (Latitude 7.470N, Longitude 4.290E, and 302.0 m above sea level) were used to estimate the monthly tropospheric radio refractivity. The monthly average daily global solar radiation with other meteorological parameters was used to developed one, two, three and four variable correlation(s) tropospheric radio refractivity models for the location. The accuracy of the proposed models are validated using statistical indicator of coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), Nash - Sutcliffe Equation (NSE) and Index of Agreement (IA).Results:In each case one empirical model was recommended based on their exceptional performances after ranking, except for the two variation correlations with two empirical models. The recommended models were further subjected to ranking from which the three variable correlations model that relates the radio refractivity with the absolute temperature, relative humidity and global solar radiation was found more suitable for estimating tropospheric radio refractivity for Osogbo with R2= 100.0%, MBE = -0.2913 N-units, RMSE = 0.3869 N-units, MPE = 0.0811%, NSE = 99.9999% and IA = 100.00%.Conclusion:The newly developed recommended models (Equations 16c, 17d, 17f, 18d and 19) can be used for estimating daily and monthly values of tropospheric radio refractivity with higher accuracy and has good compliance to highly varying climatic conditions for Osogbo and regions of similar climatic information.


2021 ◽  
Vol 7 (2) ◽  
pp. 42-48
Author(s):  
U. Joshi ◽  
P. M. Shrestha ◽  
S. Maharjan ◽  
B. Maharjan ◽  
N. P. Chapagain ◽  
...  

Accurate knowledge of global solar radiation distribution is essential for designing, sizing, and performing an evaluation of solar energy system in any part of the world. However, it is not available in many sites of Nepal due to the high expense of the technical process. This study is focused on the performance of different models based on daily global solar radiation, sunshine hour, temperature, and relative humidity at mid-hill region Lumle, (lat. 28.29650N, long. 83.8179oE, and Alt. 1740.0 m.a.s.l.). This study is carried for the year 2018 to 2020. The performance of different models based on sunshine hour, temperature, and relative humidity were analyzed using the regression technique and statistical tools such as Root Mean Square Error (RMSE), Mean Bias Error (MBE), Mean Percentage Error (MPE), and Coefficient of determination (R2). After the analysis, the modified Angstrom model (M-9) based on temperature difference and relative humidity was found to be the best in terms of accuracy of least RMSE value and highest coefficient of determination. Finally, the empirical constants for model m-9 are a = 0.003, b = 0.523, c = 0.118 and, d = 0.002 obtained. The calculated empirical constants can be utilized for the prediction of GSR at similar geographical locations of Nepal.


2019 ◽  
Vol 13 (1) ◽  
pp. 43-55
Author(s):  
D. O. Akpootu ◽  
A. M. Rabiu

Background:Estimation of tropospheric radio refractivity is significant in the planning and design of terrestrial communication links.Methods:In this study, the monthly average daily atmospheric pressure, relative humidity and temperature data obtained from the National Aeronautics and Space Administration (NASA) during the period of twenty two years (July 1983 - June 2005) for Osogbo (Latitude 7.470N, Longitude 4.290E, and 302.0 m above sea level) were used to estimate the monthly tropospheric radio refractivity. The monthly average daily global solar radiation with other meteorological parameters was used to developed one, two, three and four variable correlation(s) tropospheric radio refractivity models for the location. The accuracy of the proposed models are validated using statistical indicator of coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), Nash - Sutcliffe Equation (NSE) and Index of Agreement (IA).Results:In each case one empirical model was recommended based on their exceptional performances after ranking, except for the two variation correlations with two empirical models. The recommended models were further subjected to ranking from which the three variable correlations model that relates the radio refractivity with the absolute temperature, relative humidity and global solar radiation was found more suitable for estimating tropospheric radio refractivity for Osogbo with R2= 100.0%, MBE = -0.2913 N-units, RMSE = 0.3869 N-units, MPE = 0.0811%, NSE = 99.9999% and IA = 100.00%.Conclusion:The newly developed recommended models (Equations 16c, 17d, 17f, 18d and 19) can be used for estimating daily and monthly values of tropospheric radio refractivity with higher accuracy and has good compliance to highly varying climatic conditions for Osogbo and regions of similar climatic information.


2021 ◽  
Vol 13 (11) ◽  
pp. 2121
Author(s):  
Changsuk Lee ◽  
Kyunghwa Lee ◽  
Sangmin Kim ◽  
Jinhyeok Yu ◽  
Seungtaek Jeong ◽  
...  

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.


Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1207
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga

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.


2021 ◽  
Vol 13 (14) ◽  
pp. 2805
Author(s):  
Hongwei Sun ◽  
Junyu He ◽  
Yihui Chen ◽  
Boyu Zhao

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.


2019 ◽  
Vol 23 (2) ◽  
pp. 949-969
Author(s):  
Fugen Li ◽  
Xiaozhou Xin ◽  
Zhiqing Peng ◽  
Qinhuo Liu

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.


2018 ◽  
Vol 33 (1) ◽  
pp. 301-315 ◽  
Author(s):  
Wesley G. Page ◽  
Natalie S. Wagenbrenner ◽  
Bret W. Butler ◽  
Jason M. Forthofer ◽  
Chris Gibson

Abstract Wildland fire managers in the United States currently utilize the gridded forecasts from the National Digital Forecast Database (NDFD) to make fire behavior predictions across complex landscapes during large wildfires. However, little is known about the NDFDs performance in remote locations with complex topography for weather variables important for fire behavior prediction, including air temperature, relative humidity, and wind speed. In this study NDFD forecasts for calendar year 2015 were evaluated in fire-prone locations across the conterminous United States during periods with the potential for active fire spread using the model performance statistics of root-mean-square error (RMSE), mean fractional bias (MFB), and mean bias error (MBE). Results indicated that NDFD forecasts of air temperature and relative humidity performed well with RMSEs of about 2°C and 10%–11%, respectively. However, wind speed was increasingly underpredicted when observed wind speeds exceeded about 4 m s−1, with MFB and MBE values of approximately −15% and −0.5 m s−1, respectively. The importance of accurate wind speed forecasts in terms of fire behavior prediction was confirmed, and the forecast accuracies needed to achieve “good” surface head fire rate-of-spread predictions were estimated as ±20%–30% of the observed wind speed. Weather station location, the specific forecast office, and terrain complexity had the largest impacts on wind speed forecast error, although the relatively low variance explained by the model (~37%) suggests that other variables are likely to be important. Based on these results it is suggested that wildland fire managers should use caution when utilizing the NDFD wind speed forecasts if high wind speed events are anticipated.


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