Infrared Drying of Apricot Pomace

Kayran, Seda; Doymaz, İ̇brahim

doi:10.52292/j.laar.2019.199

Cited by 4 publications

(5 citation statements)

References 30 publications

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“…In this study, the D eff values of GP purée are within the general ranges mentioned in the literature (10 -12 to 10 -8 m 2 /s) for biological materials ( Zogzas et al, 1996 ). Similar results were obtained for carrot pomace dried through CD ( Kumar et al, 2012 ) and for apricot pomace dried with IR radiation ( Kayran & Doymaz, 2019 ).…”

Section: Resultssupporting

confidence: 80%

Dried grape pomace with lactic acid bacteria as a potential source for probiotic and antidiabetic value-added powders

et al. 2023

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Section: Resultssupporting

confidence: 80%

Dried grape pomace with lactic acid bacteria as a potential source for probiotic and antidiabetic value-added powders

et al. 2023

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“…Similarly, an increase in drying temperature increased the drying rate due to the increased water vapor pressure which enforced more pore opening in samples thus increased drying rate was observed. A similar observation was reported for IR drying of whole longan (Nuthong et al, 2011), kiwifruit (M. C. Zeng et al, 2014), bean seeds (Doymaz, 2014a), sour cherry (Chayjan, Kaveh, & Khayati, 2014), terebinth fruit (Kaveh & Chayjan, 2014), potato (Ruhanian & Movagharnejad, 2016), shredded carrot (Aktas et al, 2017), corn (Nejadi & Nikbakht, 2017), pork slice (Ling, Teng, Lin, & Wen, 2017), sweet potato (Onwude et al, 2019), and apricot pomace (Kayran & Doymaz, 2019).…”

Section: Drying Kinetics Of Food Materials In Ir and Ir‐assisted Dryingmentioning

confidence: 99%

“…Higher intensity might have increased the molecular vibration, rotation and electronic states of atom which leads to increased temperature and vapor pressure of samples thus increased the diffusion of moisture toward sample surface. Similar results reported in IR drying of whole longan (Nuthong et al, 2011), sour cherry (Chayjan, Kaveh, & Khayati, 2014), kiwifruit (M. C. Zeng et al, 2014), terebinth fruit (Kaveh & Chayjan, 2014), potato (Ruhanian & Movagharnejad, 2016), melon slice (Aktas et al, 2016), pumpkin slice (Ghaboos et al, 2016), peppermint leaves (Ashtiani et al, 2017), apricot (Kayran & Doymaz, 2017), corn (Rahmanian‐Koushkaki et al, 2017), button mushroom slices (Salehi et al, 2017), kiwifruit (Aidani et al, 2017), hazelnut kernel (Ghavidelan & Chayjan, 2017), sweet potato slice (Onwude et al, 2018a, 2018b), lemon slice (Salehi & Kashaninejad, 2018a), grape fruit slice (Salehi & Kashaninejad, 2018b), terebinth fruit (Kaveh et al, 2018), apricot pomace (Kayran & Doymaz, 2019), and dill leaves (Tezcan et al (2021). Doymaz and Kipcak (2019) described the effect of IR power ( p ) on D eff values of black mulberry by the following equation:

D_{eff} = 5 \times 10^{- 10} p - 6 \times 10^{- 10}, R^{2} = 0.9864 .

…”

Section: Drying Kinetics Of Food Materials In Ir and Ir‐assisted Dryingmentioning

confidence: 99%

Drying kinetics of food materials in infrared radiation drying: A review

Delfiya

Prashob

Murali

et al. 2021

J Food Process Engineering

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In recent years, many infrared (IR) and IR-assisted drying methods such as IR-hot air, IRvacuum, IR-microwave, and vibration aided IR-dryer have been reported for drying of food materials due to its rapid drying behavior and less processing time due to high drying rate, consumes less energy, offers better control over temperature, superior quality products, less ecological footprint and also easily it can be combined with other modes of heating technologies. The combination of IR radiation with other drying methods is very promising as it not only improves the drying rate but also preserve the food quality.IR and IR-assisted drying have been proved as a potential drying method for many food products. Drying kinetics of food materials during IR drying is very critical for the design of IR dryer, selection of appropriate drying parameters, product quality maintenance, and control and optimization of the drying process. Therefore, this paper aims to report the optimum process parameters for IR and IR-assisted drying of food materials and its drying kinetics. Similarly, critically reviewed the effect of various factors on the IR drying process such as drying rate, specific energy consumption, effective moisture diffusivity, and activation energy during drying of various food materials.

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“…The amount of M e compared to the values M 0 and M t are very small if the drying time is prolonged (Polat & Izli, 2022). Therefore, the equation of MR during drying can be written as Equation (3), and there is no need to measure the equilibrium moisture to calculate the MR (Kayran & Doymaz, 2019).

MR = \frac{M_{t}}{M_{O}}

…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, the equation of MR during drying can be written as Equation (3), and there is no need to measure the equilibrium moisture to calculate the MR (Kayran & Doymaz, 2019).…”

Section: Moisture Ratiomentioning

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.

show abstract

Infrared Drying of Apricot Pomace

Cited by 4 publications

References 30 publications

Dried grape pomace with lactic acid bacteria as a potential source for probiotic and antidiabetic value-added powders

Dried grape pomace with lactic acid bacteria as a potential source for probiotic and antidiabetic value-added powders

Drying kinetics of food materials in infrared radiation drying: A review

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

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