Comparative evaluation of greenhouse gas emissions and specific energy consumption of different drying techniques in pear slices

Kaveh, Mohammad; Çetin, Necati; Gilandeh, Yousef Abbaspour-; Sharifian, Faroogh; Szymanek, Mariusz

doi:10.1007/s00217-023-04346-2

Cited by 8 publications

(4 citation statements)

References 76 publications

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“…Seyfi et al 33 showed that increasing the thickness of Aloe vera gel increases GHG emission for different drying methods due to the increase in drying time. Taghinezhad et al 24 and Kaveh et al 81 also obtained similar results for drying carrots and pear in different thicknesses, respectively.…”

Section: Resultsmentioning

confidence: 54%

“…8 , 9 , and 10 , it can be seen that the more the microwave power increases in fixed thicknesses, the more GHG emission decreases. Motevali and Koloor 80 , Motevali et al 32 and Kaveh et al 81 stated that the process of reducing GHG emission with increasing microwave power was due to the reduction in energy consumption and drying time of the samples. While the increase in thickness in constant powers caused an increase in GHG emission.…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 54%

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

Self Cite

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“…The color difference of each sample is calculated based on the baseline measurement results. Referring to the method in [24] and making certain modifications, take a beaker, add 200 mL of deionized water, and then add about 3 g of O. raphanipes samples dried by HD, ID, and VD, respectively. Rehydrate at a constant temperature of 60 • C, and remove the samples at intervals of 20 min.…”

Section: Appearance Quality Evaluationmentioning

confidence: 99%

Effect of Different Drying Methods on the Quality of Oudemansiella raphanipes

Hou,

Zhang,

et al. 2024

Foods

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In this study, we used fresh Oudemansiella raphanipes as raw materials and pre-treated through hot air drying (HD), infrared radiation drying (ID), and vacuum freeze drying (VD) to investigate the effects of different drying methods on the rehydration rate, appearance quality, microstructure, and volatile flavor components of the dried products, as well as to determine the physicochemical properties and bioactivities of the polysaccharides in the dried O. raphanipes. The results showed that the VD O. raphanipes had the highest rehydration rate and the least shrinkage in appearance, and it better maintained the original color of the gills, but their aroma was not as strong as that of the HD samples. The scanning electron microscopy results indicate that VD maintains a good porous structure in the tissue, while HD and ID exhibit varying degrees of shrinkage and collapse. Seventy-five common volatile substances were detected in the three dried samples, mainly alkanes, alcohols, and esters. The polysaccharides (PS-H, PS-I, and PS-V) extracted from the dried samples of these three species of O. raphanipes had similar infrared spectral features, indicating that their structures are basically consistent. The highest yield was obtained for PS-V, and the polysaccharide content and glucuronic acid content of PS-I were higher than those of the remaining two polysaccharides. In addition, PS-V also showed better antioxidant activity and inhibitory activity against α-glucosidase as well as α-amylase. In conclusion, among the above three drying methods, the quality of O. raphanipes obtained by vacuum freeze drying is the best, and this experiment provides a theoretical basis for the selection of drying methods for O. raphanipes.

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“…2.5 | Specific energy consumption Specific energy consumption (SEC) during the drying of apricot samples using convective (E CV ), microwave (E MW ), and infrared (E IR ) methods were obtained from Equations ( 9)-( 11), respectively as following (Kaveh et al, 2023;Motevali et al, 2011):…”

Section: Effective Moisture Diffusivitymentioning

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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Comparative evaluation of greenhouse gas emissions and specific energy consumption of different drying techniques in pear slices

Cited by 8 publications

References 76 publications

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

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

Effect of Different Drying Methods on the Quality of Oudemansiella raphanipes

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

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