Mathematical and artificial neural network modeling for vacuum drying kinetics of Moringa olifera leaves followed by determination of energy consumption and mass transfer parameters

Tarafdar, Ayon; Jothi, Nandhini; Kaur, Barjinder Pal

doi:10.1016/j.jarmap.2021.100306

Cited by 30 publications

(45 citation statements)

References 35 publications

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“…ANN provided a relatively reasonable fit at all drying conditions, which was in the range of the maximum R 2 and minimum MSE values of the semiempirical model. is illustrates that ANN is more capable of correlating all external and internal variables and is more likely to provide accurate predictions at other unseen processing conditions [22,23].…”

Section: Comparative Assessment Of Semiempirical and Artificialmentioning

confidence: 98%

“…Similar trends were observed in cocoyam dried in a convective dryer [52], in apples (cv. Golden Delicious) dried in a vacuum drying with the temperature escalated from 50 °C to 70 °C [53], and moringa leaves vacuum-dried in the range of 40 °C to 60 °C [22].…”

Section: Moisture Diffusivity and Activation Energymentioning

confidence: 99%

“…For that purpose, a neural network approach was also attempted for this study considering the novel nature of the drying process. Artificial neural networks (ANN) are a machine learning method applied to predict intricate correlations among input and output conditions of drying and nonlinear problems [22]. Many researchers used ANN to explain moisture content, rate of drying, moisture ratio, effective diffusivity, and specific energy consumption for different agriculture products [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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Vibro-Fluidized Bed Drying of Pumpkin Seeds: Assessment of Mathematical and Artificial Neural Network Models for Drying Kinetics

Dhurve

Tarafdar

Arora

2021

Journal of Food Quality

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Pumpkin seeds were dried in a vibro-fluidized bed dryer (VFBD) at different temperatures at optimized vibration intensity of 4.26 and 4 m/s air velocity. The drying characteristics were mapped employing semiempirical models and Artificial Neural Network (ANN). Prediction of drying behavior of pumpkin seeds was done using semiempirical models, of which, one was preferred as it indicated the best statistical indicators. Two-term model showed the best fit of data with R2 − 0.999, and lowest χ2 − 1.03 × 10−4 and MSE 7.55 × 10−5. A feedforward backpropagation ANN model was trained by the Levenberg–Marquardt training algorithm using a TANSIGMOID activation function with 2-10-2 topology. Performance assessment of ANN showed better prediction of drying behavior with R2 = 0.9967 and MSE = 5.21 × 10−5 for moisture content, and R2 = 0.9963 and MSE = 2.42 × 10−5 for moisture ratio than mathematical models. In general, the prediction of drying kinetics and other drying parameters was more precise in the ANN technique as compared to semiempirical models. The diffusion coefficient, Biot number, and hm increased from 1.12 × 10−9 ± 3.62 × 10−10 to 1.98 × 10−9 ± 4.61 × 10−10 m2/s, 0.51 ± 0.01 to 0.60 ± 0.01, and 1.49 × 10−7 ± 4.89 × 10−8 to 3.10 × 10−7 ± 7.13 × 10−8 m/s, respectively, as temperature elevated from 40 to 60°C. Arrhenius’s equation was used to the obtain the activation energy of 32.71 ± 1.05 kJ/mol.

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Section: Comparative Assessment Of Semiempirical and Artificialmentioning

confidence: 98%

Section: Moisture Diffusivity and Activation Energymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vibro-Fluidized Bed Drying of Pumpkin Seeds: Assessment of Mathematical and Artificial Neural Network Models for Drying Kinetics

Dhurve

Tarafdar

Arora

2021

Journal of Food Quality

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“…Nevertheless, there is a rising demand for superior quality of dehydrated vegetables and leaves globally, which stimulated the need to explore for other potential drying technologies (Tarafdar, Jothi, & Kaur, 2021). That is why microwave drying has been applied as a popular alternative for drying in the food industry.…”

Section: Introductionmentioning

confidence: 99%

Convective and microwave drying of coriander leaves: Kinetics characteristics and modeling, phenolic contents, antioxidant activity, and principal component analysis

Mouhoubi

Boulekbache‐Makhlouf

Mehaba

et al. 2021

J Food Process Engineering

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In this study, coriander leaves were subjected to two different drying techniques: convective (40, 80, and 120 C) and microwave (100, 500, and 1,000 W). The effects of both processes on the kinetic parameters variation, modeling, and phytochemical quality were studied. Kinetic parameters variation showed that time reduction (2.53 min), high drying rate (3.08 kg water/kg dm min), and high diffusivity (1.21 Â 10 À10 m 2 /s) were obtained during microwave drying. For energy consumption, an opposite trend was observed between the variation in the specific electrical energy consumption (SEC e ), which is significant during convective drying (208.33 Â 10 +07 MJ/kg H 2 O), and the energy efficiency (EE), which is obtained during microwave one (12.16 Â 10 À03 %). The kinetic data modeling done on 23 empirical models allowed us to choose the Sledz model as the best describing the drying kinetics of coriander since it presented better predictive performances with .9947 < adjusted R 2 < .9995, 0.0001 < χ 2 < 0.0005, and 0.0070 < RMSE<0.0228 values. In relation to the bioactive compounds, the highest values of the total phenolic content (TPC) and antioxidant activities (ABTs and DPPH) were observed during microwave drying with 0.37 mg GAE/mL, 83.33, and 71.94%, respectively. Correlations between kinetic parameters, energy consumption as well as phytochemical quality were established using principal component analysis (PCA). As a result, the innovative microwave process proved to be the best method for drying coriander leaves. Practical applications:The evolution of technological conservation processes has led to the development of a wide range of modern drying processes. The success of these processes can be explained by the many advantages attributed to them: shorter drying times, high drying rate, high energy efficiency, and product quality. The most difficult is the appropriate choice of a drying method with highly energy efficient and consumes less energy while maintaining the best quality. Fresh coriander leaves are widely used to flavor foods. However, they are highly perishable and require

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“…Inlet air temperature can be actively controlled and each particle is in direct contact with hot air which results in uniform and efficient drying at reduced process times (Sivakumar et al, 2016). As per a study, drying temperature between 50 and 60 ℃ results in higher drying efficiency, lower drying time, and energy consumption and therefore, suggested for drying of Moringa leaves (Tarafdar et al, 2021). Prakash et al (2004), studied the drying characteristics of carrots by comparing solar cabinet drying, FBD, and microwave oven drying.…”

mentioning

confidence: 99%

Effect of fluidized‐bed and freeze‐drying techniques on physicochemical, nutritional, thermal, and structural properties of Moringa oleifera flowers, leaves, and seeds

Manju

Rekha

Priyanka

et al. 2021

J. Food Process. Preserv.

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Moringa oleifera Lam. (Moringaceae) is called a "Miracle Tree" with multiple medicinal and industrial applications. This tree has originated in India and is cultivated in tropical and sub-tropical countries. Almost all the parts of this plant are valuable and edible as they high nutritional and medicinal values (Arora et al., 2013). It is the most abundant source of minerals, vitamins, amino acids, and phytochemicals such as phenols, ascorbic acid, and flavonoids that are important for its high antioxidant activity (Coz-Bolaños et al., 2018). Nutritional composition, phytochemical profile, and antioxidant activity differ significantly with crop variety, drying conditions, and pre-treatments given before use (Attkan et al., 2014;Cho et al., 2013;Singh et al., 2020). In the current scenario, drying is a common practice to obtain superior quality of dehydrated fruits, vegetables, and leaves. Convectional drying may lead to undesirable effects on the physiochemical, organoleptical, and functional properties of food. Lyophilization or freeze-drying (FD) is a novel technique which is carried out by reducing and maintaining the effective product temperature below 0˚C under reduced pressure (usually around 600-700 Pa) simultaneously so that all the moisture of the product is sublimated from solid state to directly into the vapor state. FD is comparitively better than the other drying methods in retention of active components of produce due to drying at sub-zero temperatures which causes no thermal degradation and resist function of degradative enzymes by reduction of moisture

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Mathematical and artificial neural network modeling for vacuum drying kinetics of Moringa olifera leaves followed by determination of energy consumption and mass transfer parameters

Cited by 30 publications

References 35 publications

Vibro-Fluidized Bed Drying of Pumpkin Seeds: Assessment of Mathematical and Artificial Neural Network Models for Drying Kinetics

Vibro-Fluidized Bed Drying of Pumpkin Seeds: Assessment of Mathematical and Artificial Neural Network Models for Drying Kinetics

Convective and microwave drying of coriander leaves: Kinetics characteristics and modeling, phenolic contents, antioxidant activity, and principal component analysis

Effect of fluidized‐bed and freeze‐drying techniques on physicochemical, nutritional, thermal, and structural properties of Moringa oleifera flowers, leaves, and seeds

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