scholarly journals Statistical prediction of the drying behavior of blanched ginger rhizomes

2021 ◽  
Vol 4 (2) ◽  
pp. 98-107
Author(s):  
A. I. Gbasouzor ◽  
J. E. Dara ◽  
C. O. Mgbemena
Keyword(s):  
Linear Regression ◽  
Moisture Content ◽  
Thin Layer ◽  
Goodness Of Fit ◽  
Drying Time ◽  
Thin Layer Drying ◽  
Non Linear ◽  
Drying Behavior ◽  
Drying Models ◽  
Thin Layer Drying Models

ARS-680 environmental chamber was employed in this study to determine the drying behavior of sliced ginger rhizomes. Blanched and unblanched treated ginger rhizomes were considered at drying temperature of 40 °C for a period of 2 – 24 h. Linear and non-linear regression analyses were employed to establish the correlation that exits between the drying time and the moisture ratio. Correlation analysis, root mean square error (RMSE) and standard error of estimate (SEE) analysis were chosen in selecting the best thin layer drying models. Higher values of determination coefficient (R2) show goodness of fit and lower values of SEE implies better correlation; and RMSE values were also utilized in determining the goodness of fit. The drying data of the variously treated ginger samples were fitted into the twelve thin layer drying models and the data obtained were fitted by multiple non-linear regression technique. Blanched treated sample exhibited a better drying behavior losing about 82.87 % moisture content compared with unbleached sample that lost about 62.03 % of moisture content. Two-term exponential drying model proved to be the most suitable model for predicting the drying behavior of ginger rhizome. The model exhibited high R2 values of 0.9349-0.9792 (which are close to unity) for both blanched and unbleached samples. Also, it recorded relatively low values of RMSE and SEE (3.6865 - 2.0896 and 3.6564-2.7486 respectively) for both treatments.  

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.

Drying Characteristics of Soybean (Glycine Max) Using Continuous Drying and Intermittent Drying

2018 ◽  
Vol 14 (9-10) ◽  
Author(s):  
Hyeon W. Park ◽  
Won Y. Han ◽  
Won B. Yoon
Keyword(s):  
Activation Energy ◽  
Moisture Content ◽  
Analysis Method ◽  
Image Analysis Method ◽  
Drying Time ◽  
Drying Characteristics ◽  
Thin Layer Drying ◽  
Drying Models ◽  
Thin Layer Drying Models ◽  
Best Fit

AbstractThe effects of drying temperature by continuous and intermittent drying on the drying characteristics of soybean were determined in this study. Among the thin-layer drying models, the Midilli–Kucuk model showed the best fit (R2> 0.99) to describe the drying of soybean. At 300 min of the effective drying time, the moisture content of continuous drying at 35, 40, and 45 ºC were 9.38 (±0.00), 8.69 (±0.17), and 7.70 % (±0.48), respectively; while the moisture content of intermittent drying at 35, 40, and 45 ºC were 8.28 (±0.21), 7.31 (±0.41), and 6.97 % (±0.07), respectively. The image analysis method for detection of the crack in soybean demonstrated that at the target moisture content (7.7 %), cracked grain ratios with intermittent drying at 35, 40, and 45 ºC were reduced by 52.08, 27.59, and 18.24 %, respectively. With the effective drying time, the activation energy for intermittent drying (9.33 kJ/mol) was significantly lower than that value for continuous drying (21.23 kJ/mol).

scholarly journals Drying Kinetics of Sliced Pineapples in a Solar Conduction Dryer

2017 ◽  
Vol 7 (2) ◽  
pp. 14 ◽  
Author(s):  
Luqman Ebow Ibn Daud ◽  
Isaac Nyambe Simate
Keyword(s):  
Thin Layer ◽  
Average Moisture Content ◽  
Drying Time ◽  
Sun Drying ◽  
Thin Layer Drying ◽  
Drying Models ◽  
Drying Model ◽  
Thin Layer Drying Models ◽  
Drying Curves ◽  
Kinetics Of

As a means of adding value to pineapple production and minimising post-harvest losses, sliced pineapples were dried using a Solar Conduction Dryer (SCD) and appropriate thin layer drying models to predict drying were developed whilst the performance of the SCD was also investigated. For the period of the experiment, ambient temperature and temperature in the dryer ranged from 24 to 37 °C and 25 to 46 ℃ respectively. The performance of the dryer was compared to open sun drying using pineapple slices of 3-5 mm in thickness where the slices were reduced from an average moisture content of 85.42 % (w.b.) to 12.23 % (w.b.) by the SCD and to 51.51 % (w.b.) by the open sun drying in 8 hours effective drying time. Pineapple slices of thicknesses 3 mm, 5 mm, 7 mm and 10 mm were simultaneously dried in the four drying chambers of the SCD and their drying curves simulated with twelve thin layer drying models. The Middilli model was found as the best fitted thin layer drying model for sliced pineapples. The optimum fraction of drying tray area that should be loaded with pineapples was also investigated by simultaneously loading 7 mm slices of pineapples at 50, 75, and 100 percent of drying tray area. Loading the slices at 50, 75 and 100 percent of drying tray area gave overall thermal efficiencies of 23, 32 and 44 percent, respectively, hence loading pineapple slices at 100 percent drying tray area was recommended as the best.

scholarly journals Dehydration Kinetics of Cassava, Yam and Potato Slices Using a Refractance WindowTM Dryer

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Akinjide A Akinola ◽  
Stanley N. Ezeorah
Keyword(s):  
Moisture Content ◽  
Thin Layer ◽  
Coefficient Of Determination ◽  
Plastic Film ◽  
Thin Layer Drying ◽  
Drying Models ◽  
Thin Layer Drying Models ◽  
Drying Curves ◽  
Kinetics Of ◽  
Potato Slices

 This study aims to investigate the drying characteristics of cassava, yam, and potato slices using a laboratory scale batch Refractance Window™ (RW) dryer. The experimental dryer was constructed by modifying a laboratory water bath. The bath was covered with a transparent Polyethylene terephthalate (PET) plastic film held in-place with angled edges. The cassava, yam, and potato slices were dried on the Refractance WindowTM dryer, and the variation of the moisture content of the slices during the drying process was measured. The water temperature beneath the plastic film was maintained at 60oC. The dehydration data were fitted to thin-layer drying models. Regression analysis suggested that the Haghi and Ghanadzadeh model best describes the dehydration behaviour for the 3 mm thick slices for the cassava, yam, and potato tubers. The coefficient of determination (R2) values of 0.999, 0.998, and 0.998 for the cassava, yam, and potato slices respectively were reported in all the models studied. The drying curves, the drying rate curves, and the Krischer curves, from the experimental drying data, was plotted. Observations indicate that the cassava, yams, and potatoes slices dried to below 0.11 g water/g-solid moisture content in about 150 min. This study was performed to facilitate the understanding of the design, modelling, and operations of a continuously operating RW dryer. Keywords: Refractance Window Drying, Thin Layer Drying Models, Yams, Cassava, Potatoes.

scholarly journals Evaluation and Selection of Thin-layer Drying Models for Paddy Dried in Static Flat-bed Batch Dryer

2020 ◽  
pp. 55-65
Author(s):  
Lubna Sadaf Anchal ◽  
Abhinav Dubey ◽  
Prassana Kumar
Keyword(s):  
Moisture Content ◽  
Thin Layer ◽  
Statistical Parameters ◽  
Heating Chamber ◽  
Thin Layer Drying ◽  
Drying Models ◽  
Thin Layer Drying Models ◽  
Drying Conditions ◽  
Kinetics Of ◽  
Selection Of

A Static flat-bed batch dryer was developed for drying paddy from harvesting moisture content (20 – 22%) to 12% for safe storage. The dryer mainly consisted of Blower, Heating chamber, Plenum chamber and drying chamber. Twenty kg paddy was dried in the developed dryer at two different inlet air flow rate (1 m3/min. and 1.26 m3/min). The machine has a capacity of 20 kg and temperature of drying air was 60 and 55°C respectively. The moisture content was recorded at every 15 minutes interval and moisture ratio plots were generated. The experimental data were fit in 8 different thin-layer drying models and statistical parameters along with the model constants were obtained. It was found that the Wang and Singh model with the highest values for R2 and the least values of RMSE in selected drying conditions has the best fit. Henderson & Pabis and Newton models were also found suitable for describing the drying kinetics of paddy in the developed dryer. 

scholarly journals CURVAS DE SECAGEM E AVALIAÇÃO DA ATIVIDADE DE ÁGUA DA BANANA PASSA

2004 ◽  
Vol 22 (1) ◽  
Author(s):  
MILTON CANO-CHAUCA ◽  
AFONSO M. RAMOS ◽  
PAULO C. STRINGHETA ◽  
JOSÉ ANTONIO MARQUES ◽  
POLLYANNA IBRAHIM SILVA
Keyword(s):  
Experimental Data ◽  
Linear Regression ◽  
Moisture Content ◽  
Water Activity ◽  
Linear Regression Analysis ◽  
Sorption Isotherms ◽  
Exponential Model ◽  
Drying Time ◽  
Non Linear ◽  
Drying Curves

Curvas de secagem de banana passa foram determinadas, utilizando-se três temperaturas do ar de secagem. Os resultados indicaram que para reduzir o teor de umidade do produto até 23,5% foram necessários tempos de secagem de 51, 36 e 30 horas paras as temperaturas de 50, 60 e 70ºC, respectivamente. O modelo exponencial U/Uo = exp(-kt) foi ajustado para os dados experimentais mediante análise de regressão não-linear, encontrandose alto coeficiente de regressão linear. Determinou-se a atividade de água do produto ao longo do processo de secagem para as três temperaturas testadas. Estudou-se a correlação entre a atividade de água e o teor de umidade do produto, determinando-se as isotermas de dessorção da banana passa a 25ºC. Observou-se que a atividade de água diminuiu em função do tempo de secagem e do teor de umidade para as três temperaturas de secagem. Os dados experimentais foram ajustados mediante regressão não-linear ao modelo polinomial e a seguinte equação foi obtida: U = -1844,93 + 7293,53Aa – 9515,52Aa2 + 4157,196Aa3. O ajuste mostrou-se satisfatório (R2 > 0,90). DRYING CURVES AND WATER ACTIVITY EVALUATION OF THE BANANA-PASSES Abstract Banana drying curves were determined by utilizing three drying air temperatures. The results indicated that to reduce the moisture content of the product until 23.5% it were necessary drying times of 51, 36 and 30 hours for temperatures of 50, 60 and 70ºC, respectively. The exponential model U/Uo = exp(-kt) was adjusted for the experimental data by means of non linear regression analysis, and a high coefficient of linear regression was found. The water activity of the product was determined throughout the drying process for the three tested temperatures. The correlation between the water activity and moisture content of the product was studied, and the sorption isotherms were determined at 25º C. It was observed that the water activity decreased in function to the drying time and moisture content for the three drying temperatures. The experimental data were adjusted by means of non linear regression to the polynomial model and the following equation was obtained: U = - 1844.93 + 7293.53A a – 9515.52 Aa 2 + 4157.196A a 3. The final adjust was satisfactory (R2 > 0.90).

scholarly journals Thin layer drying of zucchini in solar dryer located in Osmaniye region

2018 ◽  
Vol 12 (2) ◽  
pp. 79-85 ◽  
Author(s):  
Kamil Neyfel Çerçi ◽  
Özge Sufer
Keyword(s):  
Thin Layer ◽  
Linear Regression Analysis ◽  
Chi Square ◽  
Mathematical Expressions ◽  
Thin Layer Drying ◽  
Drying Models ◽  
Drying System ◽  
Thin Layer Drying Models ◽  
Dehydration Behavior ◽  
And Diffusion

In this study, the dehydration behavior of zucchini using solar assisted drying system was examined according to 22 thin layer drying models available in literature. The correlation coefficient (R2), chi-square (χ2) and root mean square error (RMSE) values were calculated to check the suitability of models by non-linear regression analysis. It was found that Cubic and Modified Midilli-1 models were the most suitable equations and their R2 values were calculated as 0.99963. χ2 and RMSE values of related mathematical expressions were 1.89343×10‒5, 1.91692×10‒5 and 0.01685×10‒3, 0.01721×10‒3 respectively. In addition, heat transfer, mass transfer and diffusion coefficients, which were important parameters in design of drying systems were also determined as 5.18124 W/m2°C, 1.57129×10‒7 m/s and 2.335718×10‒9 m2/s respectively.

A Mathematical Model for Thin-Layer Drying of Litchi Pulp

2012 ◽  
Vol 192 ◽  
pp. 51-56
Author(s):  
Zhi Qiang Guan ◽  
Xiu Zhi Wang ◽  
Min Li ◽  
Xiao Qiang Jiang
Keyword(s):  
Experimental Data ◽  
Thin Layer ◽  
Heat Pump ◽  
Moisture Ratio ◽  
Drying Time ◽  
Page Model ◽  
Thin Layer Drying ◽  
Drying Models ◽  
Heat Pump Drying ◽  
Predicted Values

A drying experiment of litchi pulp was done with a self-built heat-pump drying system. A few commonly-used thin-layer drying models for foods were linearized and fitted with the drying experimental data to select a relatively optimal model of depicting the relationship between moisture ratio and drying time of the heat-pump drying of litchi pulp. It was found that the Page model is relatively optimal. The multivariate linear regression approach was employed to solve for the parameters of the Page model based on experimental data and an experimental verification was conducted; the verification results show that the predicted values of the Page model have a good fitness with the measured values and thus the Page model can predict more accurately the moisture ratio and drying rate of litchi pulp in a heat-pump drying process.

scholarly journals The Experimental Research of Drying Kinetics of Nagpur Orange Fruit (Citrus Sinesis- L) by Hot Air Electrical Dryer

2019 ◽  
Vol 8 (6S4) ◽  
pp. 1131-1134
Keyword(s):  
Thin Layer ◽  
Drying Kinetics ◽  
Drying Time ◽  
Drying Characteristics ◽  
Drying Temperature ◽  
Hot Air ◽  
Thin Layer Drying ◽  
Drying Behavior ◽  
Orange Fruit ◽  
Kinetics Of

The study is aimed experimentally and compared with the theoretical results of drying kinetics of Nagpur orange fruit dried in a hot air electrical dryer. Orange fruit is highly perishable and needs to be consumed or processed immediately after harvest. Drying or dehydration is one of the most practical methods of preserving food products. Therefore, thin layer drying characteristics of falling rate of Nagpur orange are determined experimentally under different conditions of drying air temperatures, relative humidity and air velocities for different moisture contents. Thin layer models like Wang and Singh, Page and Henderson have been compared with Experimental results. The knowledge of drying kinetics helps for identification of exact drying time and air flow velocity for different moisture content. Here drying operation is carried out at a velocity of 1m/sec and 1.25 m/sec for different temperature of 55°C, 65°C and 75°C. This analysis reveals that drying temperature has a more significant effect on moisture removal while velocity has the least effect. Drying rate is found to increase with the increase in drying temperature and reduce with drying time. Experimental data is statistically correlated by plotting the drying characteristics curve. The analysis reveals that Wang and Singh's model is a better model to explain the drying behavior of Nagpur Orange fruit (R2=0.9888).

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