Evaluation of the effect of high-power ultrasound waves on conventional air drying of cumin seeds

Namjoo, Moslem; Moradi, Mehdi; Niakousari, Mehrdad

doi:10.1016/j.seta.2022.102262

Cited by 10 publications

(8 citation statements)

References 40 publications

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“…Three important steps in the process optimization with RSM include a statistical design of experiments, a prediction of coefficients in a mathematical model, and an estimation of response parameters and a study of model accuracy in the experimental designs [18]. Optimum operating conditions were achieved using optimization with RSM.…”

Section: Independent Variables Coded Variablesmentioning

confidence: 99%

“…In the RSM, the relationship between the response and independent variables can be determined through mathematical modeling [17]. Numerous studies have optimized and modeled the impact of the drying variables (such as air speed and temperature, microwave power (MP), and slice thickness (ST)) on the response parameters including mechanical and thermal qualities as well as the quality of the agricultural products such as cumin seeds [18], sweet potato slices [8], and Cornus mas [10]. In all the mentioned studies, the researchers have reported the best drying variables and pretreatments to achieve the best parameters; however, no similar study was found for carrot slices.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying of the Best Model for Prediction of Greenhouse Gas Emission, Quality, and Thermal Property Values during Drying Using RSM (Case Study: Carrot)

Taghinezhad

Kaveh²,

Szumny

et al. 2023

Applied Sciences

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The aim of this study is to use the response surface methodology (RSM) to mathematically model the response parameters and emission of greenhouse gases (GHG) and optimize the drying variables for a carrot dried with the microwave method using various pretreatments. To this end, the influence of the drying parameters (independent), such as microwave power and slice thickness dried by two pretreatments of ultrasonication at 30 °C for 10 min and blanching at 70 ℃ for 2 min, was explored on the dependent (response) parameters including the thermal properties (drying time, effective moisture diffusion coefficient (Deff), specific energy consumption, energy efficiency, quality features (color changes and shrinkage), and GHG emission (including CO2 and NOx). It should be mentioned that the emission of GHG was determined based on the energy consumption of various types of power plants such as the gas turbine steam power turbine, and combined cycle turbines using various fuels such as natural gas, heavy oil, and gas oil. The results indicated that the ultrasonication and blanching pretreatments can decrement the drying time (linearly), energy consumption (linearly or quadratically), shrinkage(quadratically), and color changes(quadratically) and enhance the Deff (linearly) and energy efficiency (linearly or quadratically) in all samples with R2 > 0.86. Moreover, the shortest drying time (42 min), lowest SEC (9.51 MJ/kg), and GHG emission ((4279.74 g CO2 in the combined cycle turbines plant, and 18.16 g NOX in the gas turbine plant) with natural gas for both plants) were recorded for the samples pretreated with blanching while the lowest color changes (13.69) and shrinkage (21.29) were observed in the ultrasonicated samples. Based on the optimization results, a microwave power of 300 W and steam power turbine of 2 mm were the best variables with a desirability of about 80% which resulted in the highest-quality products at the lowest GHG emission.

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Section: Independent Variables Coded Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantifying of the Best Model for Prediction of Greenhouse Gas Emission, Quality, and Thermal Property Values during Drying Using RSM (Case Study: Carrot)

Taghinezhad

Kaveh²,

Szumny

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Namjoo et al 76 studied convective drying with ultrasonication pretreatment of cumin seeds and found that the EF varied from 0.689 and 1.542% for 30 ºC without pretreatment and 40 ºC with us pretreatment, respectively. Abbaspour-Gilandeh et al 75 showed that the maximum EF for infrared and convective drying of terebinth was obtained using ultrasonication pretreatments and blanching.…”

Section: Resultsmentioning

confidence: 99%

Qualitative, energy and environmental aspects of microwave drying of pre-treated apple slices

Taghinezhad,

Kaveh,

Szumny

et al. 2023

Sci Rep

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In the present research, response parameters such as specific energy consumption (SEC), thermal efficiency (TE), energy efficiency (EF), drying time (DT), greenhouse gas (GHG) emission (such as CO2 and NOx), and quality features (color variation and shrinkage) were modeled by response surface methodology (RSM) for apple slices dried in a microwave dryer under ultrasonication (30 ℃—10 min) and blanching (80 °C—2 min) pretreatments. Also, RSM was applied to optimize two independent parameters including microwave power and sample thickness in the levels 100, 200, and 300 W and 2, 4, and 6 mm, respectively. The results indicated the significant influence (P < 0.01) of the independent parameters on the response parameters. The vales of SEC, DT, GHG emission, shrinkage, and color difference were linearly decreased with the declining sample thickness and increasing microwave power, while the energy and thermal efficiencies were increased by a quadratic equation. The use of ultrasonication and blanching pretreatments decreased the SEC, GHG emissions, and DT; while improving the quality of the samples as compared to the non-treated slices. The optimization results showed the optimal drying times (31.55, 82.19, and 50.55 min), SEC (3.42, 10.07, and 4.37 MJ/kg), CO2 with natural gas (1539.75, 1518.75, and 4585 g), CO2 with gas oil (3662.53, 2099.25, 2721.25 g), NOx with natural gas (10.094, 9.956, and 12.906 g), and NOx with gas oil (12.934, 12.758, and 16.538 g) at a microwave power of 300 W and sample thickness of 2 mm with desirability of 0.921, 0.935, and 0.916 for control samples, ultrasonicated, and blanched, respectively.

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“…Now, the D eff can be obtained from the slope method. Accordingly, the logarithm value of the moisture ratio obtained of the dried samples against time gives a linear equation of K slope (Namjoo et al, 2022).

\ln (MR) = \ln \frac{8}{π^{2}} - \frac{- D_{eff} π^{2}}{4 L^{2}} t

K = (\frac{D_{eff} π^{2}}{4 L^{2}})

…”

Section: Methodsmentioning

confidence: 99%

Machine learning approaches for estimating apricot drying characteristics in various advanced and conventional dryers

Kaveh,

Çetin,

Khalife

et al. 2023

J Food Process Engineering

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Drying plays a crucial role in preserving the quality of agricultural products. Nevertheless, suboptimal conditions in drying systems have an adverse effect on drying characteristics and energy efficiency. Machine learning approaches are innovative and reliable that have been successfully used to solve such challenges and achieve optimization in drying processes. In this study, five machine learning approaches (multilayer perceptron [MLP], gaussian processes [GP], support vector regression [SVR], k‐nearest neighbors [kN], and random forest [RF]) were used to estimate moisture content and moisture ratio of apricot in five various dryers (convective [CV], microwave [MW], infrared [IR], microwave‐convective [MW‐CV], and infrared‐convective [IR‐CV]). Also, the values of specific energy consumption (SEC) and effective moisture diffusivity (Deff) were calculated in these dryers. Accordingly, the best result of the Deff (3.14 × 10−10 m2/s) and the minimum value of the drying time (130 min) and SEC (18.67 MJ/kg) were obtained using MW‐CV hybrid dryer. While the lowest values of Deff (2.09 × 10−11 m2/s) and highest drying time (18.5 h) and SEC (209.34 MJ/kg) were detected in CV dryer at 50°C. The best correlation coefficients (R) for the estimation of moisture content were gained using RF technique for k‐fold cross validation and train‐test split with the values of 0.9908 and 0.9912, respectively. Moreover, moisture ratio results showed that the MLP achieved the highest R value over 0.9985 for both validation methodologies. In the discrimination of the drying methods, the MLP had the greatest accuracy as 82.00% and 86.00% for k‐fold cross validation and train‐test split, respectively. The results showed that the RF and ML models could potentially be used for estimation and discrimination for drying applications.Practical ApplicationsRecently, there has been an increased interest in healthy food choices such as foodstuffs, snacks, and dried products. This trend has captured the attention of both dietitians and conscious consumers. Apricots are a prime example of a valuable dried product that can be dry in various conditions. Machine learning techniques can be used for rapid and non‐destructive determination of drying characteristics and such techniques yield objective and accurate results. Present findings revealed that texture machine learning models could be used as an effective and reliable discrimination tool for dried products.

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Evaluation of the effect of high-power ultrasound waves on conventional air drying of cumin seeds

Cited by 10 publications

References 40 publications

Quantifying of the Best Model for Prediction of Greenhouse Gas Emission, Quality, and Thermal Property Values during Drying Using RSM (Case Study: Carrot)

Quantifying of the Best Model for Prediction of Greenhouse Gas Emission, Quality, and Thermal Property Values during Drying Using RSM (Case Study: Carrot)

Qualitative, energy and environmental aspects of microwave drying of pre-treated apple slices

Machine learning approaches for estimating apricot drying characteristics in various advanced and conventional dryers

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