Hot air drying characteristics of mango ginger: Prediction of drying kinetics by mathematical modeling and artificial neural network

Integrated drying model of lychee as a function of temperature and relative humidity

Drying is a universal method applied for food preservation. To date, several models have been developed to evaluate drying kinetics. In this study, lychee was dried employing a hot air dryer, and the drying kinetics was evaluated by comparing the Newtonian model, Henderson and Pabis model, Page model, and Logarithmic model. However, temperature and relative humidity, the key driving forces for drying kinetics, are not considered by these models. Thus, an integrated drying model, as a function of temperature and relative humidity, was developed to predict the hot air-drying kinetics and mass transfer phenomena of lychee followed by the calibration and validation of the model with independent experimental datasets. The model validation consisted of Nash- Sutcliffe model coefficient (E ), coefficient of determination ( R 2 ) and index of agreement ( d ) and all of them were found close to 1 indicating perfect model fit. Besides, the developed model was applied for process optimization and scenario analysis. The drying rate constant was found as a function of temperature and relative humidity that was high at high temperature and low relative humidity. Interestingly, temperature showed a higher effect on the drying rate constant compared to relative humidity. Overall, the present study will open a new window to developing further drying model of lychee to optimize quality its quality parameters.

Hot Air Impingement Drying Enhanced Drying Characteristics and Quality Attributes of Ophiopogonis Radix

The effects of drying temperature and air velocity on the drying characteristics, color, bioactive compounds, rehydration ratio, and microstructure of Ophiopogonis Radix during hot air impingement drying (HAID) were explored in the current study. The experimental results showed that the drying temperature and air velocity had a significant impact on the drying characteristics and quality attributes of dried products except for the rehydration ratio. The drying time decreased from 720 to 240 min with the increase of drying temperature from 50 to 70 °C. Increasing the air velocity from 6 to 12 m/s enhanced the drying process of Ophiopogonis Radix, while the extension of air velocity to 15 m/s lowered the drying rate. The samples that were dried at a lower drying temperature obtained lower color difference. Properly increasing the drying temperature or air velocity could increase the total polysaccharide and flavonoid contents of dried products. Additionally, a back-propagation neural network (BPNN) model was developed to predict the moisture ratio of Ophiopogonis Radix during the drying process. The optimal BPNN with 3-11-1 topology were obtained to predict the moisture ratio of Ophiopogonis Radix during HAID and performed with an acceptable performance.

Application of artificial neural network for the quality-based classification of spray-dried rhubarb juice powders

The aim of the study was to develop a neural model enabling classification of fruit spray dried powders, on the basis of graphic data acquired from a bitmap received in the process of spray drying. The neural model was developed with multi-layer perceptron topology. Input variables were expressed in 46 image descriptors based on RGB, YCbCr, HSV (B) and HSL models. Sensitivity analysis of input variables and principal component analysis determined the significance level of each attribute. The optimal model with the lowest error value root mean square, at the level of 0.04 contained 46 neurons in the input layer, 11 neurons in the hidden layer, 10 neurons in the output layer. The results allowed to show that dyeing force (color features) had influence on effective differentiation of the research material consisting of spray-dried powders of rhubarb juice with various dried juice content levels: 30, 40 and 50% as well as high ("H") and low ("L") level of saccharification a chosen carrier (potato maltodextrin).

Evaluation of processing mechanism in Astragali Radix by low-field nuclear magnetic resonance and magnetic resonance imaging

Astragali Radix (Huangqi) is an important herb medicine that is always processed into pieces for clinical use. Many operations need to be performed before use, among which drying of Astragali Radix (AR) pieces is a key step. Unfortunately, research on its drying mechanism is still limited. Low-field nuclear magnetic resonance (LF-NMR) and magnetic resonance imaging (MRI) techniques were applied to study the moisture state and distribution during drying. The content of bioactive components and texture changes were measured by HPLC and texture analyzer, respectively. The moisture content of the AR pieces decreased significantly during drying, and the time to reach the drying equilibrium were different at different temperatures. The time when at 70°C, 80°C, and 90°C reach complete drying are 180 min, 150 min and 120 min, respectively. 80°C was determined as the optimum drying temperature, and it was observed that the four flavonoids and astragaloside IV have some thermal stability in AR pieces. When dried at 80°C, although the total water content decreased, the free water content decreased from 99.38% to 15.49%, in contrast to the increase in bound water content from 0.62% to 84.51%. The texture parameters such as hardness changed to some extent, with the hardness rising most significantly from 686.23 g to 2656.67 g. Correlation analysis revealed some connection between moisture content and LF-NMR and texture analyzer parameters, but the springiness did not show a clear correlation with most parameters. This study shows that HPLC, LF-NMR, MRI, and texture analyzers provide a scientific basis for elucidating the drying principles of AR pieces. The method is useful and shows potential for extension and application; therefore, it can be easily extended to other natural herb medicines.

Hot-Air Drying Characteristics of Sea Cucumber (Apostichopus japonicus) and Its Rehydration Properties

Drying is one of the most common methods for processing and preserving sea cucumber. Based on the research of pretreatment and drying process, this paper develops a dried sea cucumber product that can be quickly rehydrated in only 8 hours. By setting the pretreatment at heated water temperature of 80°C and the drying temperature from 30°C to 60°C, the fitting model is found by comparing empirical formulas and artificial neural networks. The ANN-based model was demonstrated to fit the experimental data for the adequate drying of sea cucumber. Following the increased drying temperature, the drying time was decreased and the rehydration ratio was increased. The sensory evaluation and texture properties dried at 40°C and 50°C were much better than those dried at 30°C and 60°C. Microstructure of rehydrated dried sample showed that increasing the temperature leads to the increase of fiber pore space, and the rehydration rate increases. The results of drying time and the rehydration properties of sea cucumber showed that 50°C is the best drying condition for hot-air drying of sea cucumber. The developed rapid rehydration dried sea cucumber can effectively simplify the rehydration time and steps of the dried sea cucumber and improve the quality of the sea cucumber, and it is considered to be a good technology for drying the sea cucumber and making a fast rehydration dried sea cucumber that can be further used for value-added product.

The kinetics of thin-layer drying and modelling for mango slices and the influence of differing hot-air drying methods on quality

This study compared the thin-layer drying kinetics of hot-air methods, namely, convective oven drying (OVD), uncontrolled solar drying (UAD) and modified ventilation greenhouse solar drying (MVD). Additionally, the effects of these drying techniques on colour, rehydration characteristics and microstructure of Tommy Atkin mango slices were investigated. The experiments were conducted on mango slices of three different thicknesses: 3 mm, 6 mm and 9 mm. The drying curves generated from the experimental data revealed that the rate of drying increased with thickness and that a thickness of 3 mm is optimal. It was discovered that increased drying rates resulted in a decrease in the drying time. When 3 mm slices were dried using OVD and MVD, the duration of the drying process was reduced by 85% and 80%, respectively, in comparison to the samples dried under UAD conditions. Lemon juice pre-drying treatment had no significant (p < 0.05) effect on the drying rate or duration of the drying process. Non-linear regression analysis was used to optimise the drying coefficients by fitting the moisture ratio data to eleven suitable thin-layer models. The model parameters developed by Midilli et al. performed the best in terms of predicting the experimental moisture ratio (R² = 0.9810-0.9981, χ ² = 1.465 × 10^-6-3.081 × 10^-5 and RMSE = 0.0003-0.0004). Additionally, increasing the slice thickness to 6 mm and 9 mm prolonged the drying times, resulting in significant changes in sample quality, including the total colour (ΔE), rehydration and microstructure. In comparison to OVD- and MVD-dried samples, UAD-dried samples exhibited the greatest colour change and had the highest rehydration ratio values. Also, the surface of the UAD-dried samples developed a more porous structure with distinct cracks. Based on the results, MVD was determined to be a viable alternative method for drying 3 mm mango slices on a large scale.

Modelagem e propriedades termodinâmicas da secagem do epicarpo, mesocarpo e endocarpo do tucumã (Astrocaryum aculeatum)

Resumo O tucumã é um fruto amazônico, possui alto poder nutricional e é utilizado em diversos segmentos econômicos. Portanto, estudos para a redução de umidade devem ser realizados para o aumento do tempo de prateleira deste fruto. Este trabalho tem como objetivo realizar o estudo de secagem do epicarpo (casca), mesocarpo (polpa) e endocarpo (amêndoa) do tucumã nas temperaturas de 60 ºC, 70 ºC e 80 ºC. Foi observado que o aumento da temperatura reduziu o tempo de queda da razão de umidade do epicarpo, mesocarpo e endocarpo. Modelos matemáticos foram utilizados para estimar dados experimentais e foram calculadas propriedades termodinâmicas do processo. Baseado no maior R2 e o menor SE e DQM, o modelo Logístico apresentou melhor ajuste para cinética de secagem dentre os avaliados, estimando energia de ativação de 39,50 kJ mol-1, 46,62 kJ mol-1 e 17,76 kJ mol-1 para o epicarpo, mesocarpo e endocarpo, respectivamente. Os resultados das propriedades termodinâmicas mostraram que a entalpia (ΔH) caracteriza a secagem como um processo endotérmico. A entropia (ΔS) diminui com o aumento da temperatura. Os valores da energia de Gibbs são positivos, ou seja, o processo é não espontâneo e necessita de energia externa para difusividade da água no ar.

Comparative study of hot air and vacuum drying on the drying kinetics and physicochemical properties of chicory roots

The effect of the drying conditions on the retention quality for dried chicory roots (Cichorium intibyus L.) was investigated. Cubes of chicory roots were dried using hot air and vacuum dryers at 60 and 80 °C. Two different air velocities (0.2 and 0.7 m/s) were used in the hot air dryer, and two vacuum pressures (25 and 50 mmHg absolute) were set in the vacuum chamber. An exhaustive three dimensional mathematical model to describe mass transfer during drying of chicory roots of 1 cm of side was presented considering a polynomial functionality for the contraction kinetics. Experimental data obtained at laboratory scale were used to validate the proposed model showing good agreement between the experimental and estimated moisture profiles for both drying procedures. Moisture diffusivity was found to increase with the air drying temperature, velocity and vacuum pressure depending on the drying method. However, higher moisture diffusivity coefficients and lower activation energy values were obtained for the vacuum drying method. Samples dried using the vacuum drier at 60 °C and 25 mmHg presented better retention quality attributes, i.e., better rehydration, lower shrinkage and higher total phenolic content. The proposed mathematical model was able to satisfactorily predict the described behavior.

Recent developments of artificial intelligence in drying of fresh food: A review

Intellectualization is an important direction of drying development and artificial intelligence (AI) technologies have been widely used to solve problems of nonlinear function approximation, pattern detection, data interpretation, optimization, simulation, diagnosis, control, data sorting, clustering, and noise reduction in different food drying technologies due to the advantages of self-learning ability, adaptive ability, strong fault tolerance and high degree robustness to map the nonlinear structures of arbitrarily complex and dynamic phenomena. This article presents a comprehensive review on intelligent drying technologies and their applications. The paper starts with the introduction of basic theoretical knowledge of ANN, fuzzy logic and expert system. Then, we summarize the AI application of modeling, predicting, and optimization of heat and mass transfer, thermodynamic performance parameters, and quality indicators as well as physiochemical properties of dried products in artificial biomimetic technology (electronic nose, computer vision) and different conventional drying technologies. Furthermore, opportunities and limitations of AI technique in drying are also outlined to provide more ideas for researchers in this area.

Mathematical modelling of thin layer hot air drying of apricot with combined heat and power dryer

In this study thermal energy of an engine was used to dry apricot. For this purpose, experiments were conducted on thin layer drying apricot with combined heat and power dryer, in a laboratory dryer. The drying experiments were carried out for four levels of engine output power (25 %, 50 %, 75 % and full load), producing temperatures of 50, 60, 70, and 80 ° C in drying chamber respectively. The air velocity in drying chamber was about 0.5 ± 0.05 m/s. Different mathematical models were evaluated to predict the behavior of apricot drying in a combined heat and power dryer. Conventional statistical equations namely modeling efficiency (EF), Root mean square error (RMSE) and chi-square (χ2) were also used to determine the most suitable model. Assessments indicated that the Logarithmic model considering the values of EF = 0.998746, χ 2 = 0.000120 and RMSE = 0.004772, shows the best treatment of drying apricot with combined heat and power dryer among eleven models were used in this study. The average values of effective diffusivity ranged 1.6260 × 10(-9) to 4.3612 × 10(-9) m2/s for drying apricot at air temperatures between 50 and 80 °C and at the air flow rate of 0.5 ± 0.05 m/s; the values of Deff increased with the increase of drying temperature the effective diffusivities in the second falling rate period were about eight times greater than that in the first falling rate period.

Modelling solubility of phenolics of mango ginger extract in supercritical carbon dioxide using equation of state and empirical models

Solubility of phenolics of mango ginger extract in supercritical carbon dioxide was studied at 40-60 °C and 100-350 bar. Critical temperature, critical pressure and critical volume of caffeic acid, the principal component of the extract were calculated using group contribution methods and compared with the values obtained by CHEMDRAW®. Vapor pressure of caffeic acid was predicted by Reidel method. Solubility prediction in supercritical carbon dioxide was studied using two different equation of states (EOS) models and eight empirical models. Peng-Robinson EOS predicted the solubility very well with average deviation of 0.68 % from the experimental solubility. Empirical equations based on the simple error minimization using non-linear regression method which do not require complex physiochemical properties was also found suitable to predict the solubility at different extraction conditions. Jouyban et al. model showed very less deviation (2.25 %) for predicted solubility values from the experiment.

Hot air drying characteristics of mango ginger: Prediction of drying kinetics by mathematical modeling and artificial neural network

Mango ginger (Curcuma amada) was dried in a through-flow dryer system at different temperatures (40-70 °C) and air velocities (0.84 - 2.25 m/s) to determine the effect of drying on drying rate and effective diffusivity. As the temperature and air velocity increased, drying time significantly decreased. Among the ten different thin layer drying models considered to determine the kinetic drying parameters, semi empirical Midilli et al., model gave the best fit for all drying conditions. Effective moisture diffusivity varied from 3.7 × 10(-10) m(2)/s to 12.5 × 10(-10) m(2)/s over the temperature and air velocity range of study. Effective moisture diffusivity regressed well with Arrhenius model and activation energy of the model was found to be 32.6 kJ/mol. Artificial neural network modeling was also employed to predict the drying behaviour and found suitable to describe the drying kinetics with very high correlation coefficient of 0.998.