Shrinkage, porosity and density behaviour during convective drying of bio-porous material

Liu, Guohong; Chen, Junruo; Liu, Meihong; Wan, Xinxin

doi:10.1016/j.proeng.2012.01.1078

Cited by 25 publications

(8 citation statements)

References 10 publications

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“…The largest reductions were observed for samples dried by the KMFD (46%) and KMFD-VD (53%) processes as shown in Figure 3(b). These reductions in bulk density are explained by both water loss and the creation and enlargement of porous space during drying [32] . Using a longer vacuum step in the KMFD-VD process tends to further reduce the value of this property because the pressure difference associated with the vacuum promotes material expansion [33] .…”

Section: Properties Of Dehydrated Bananamentioning

confidence: 99%

Processes for controlling the structure and texture of dehydrated banana

2015

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This study is about how to control the microstructure of dehydrated banana during drying by a multi-flash drying process. The influence of different drying methods and process variables on the formation of fruit microstructure and texture were evaluated. These processes were: a conductive multi-flash drying process, conductive multi-flash drying combined with classical vacuum drying, convective drying in an oven, and vacuum drying. Density, porosity, and shrinkage of dehydrated fruit were strongly influenced by the drying process being applied. Results showed that it is possible to control the porous structure of dehydrated banana by choosing the correct dehydration process.

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Section: Properties Of Dehydrated Bananamentioning

confidence: 99%

Processes for controlling the structure and texture of dehydrated banana

2015

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“…Moreover to that, the diffusion coefficient is highly influenced by shrinkage and, ignoring this factor affects the accuracy and credibility of simulation results. Liu, Chen, Liu, and Wan (2012) reported that the drying rates were underestimated and diffusivities overestimated when drying data are not corrected for shrinkage. However, despite the importance of shrinkage on the drying process, limited models were developed in the literature.…”

Section: Introductionmentioning

confidence: 99%

A comparative study on the efficiency of two modeling approaches for predicting moisture content of apple slice during drying

Dalvi‐Isfahan

2020

J Food Process Engineering

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The efficiency of two different modeling approaches for predicting moisture content of apple slices during drying were evaluated and compared. The experiments were performed at four air‐drying temperatures and at three levels of air velocity in the convective hot air dryer. Moisture content of apple during drying was predicted using theoretical model and Artificial Neural Networks (ANNs) models. The theoretical model was developed by solving heat and mass transfer equations simultaneously using numerical technique. A multilayer perceptron (MLP) neural network and radial basis function (RBF) network were used to develop neural network modeling. The agreement between the experimental results and the theoretical model predictions was quite good by considering the shrinkage dependent effective diffusivity in the model. Among different ANN structures, the best results were obtained for the MLP network with two hidden layers based on the statistical criteria (the coefficient of determination [R2] and mean squared error [MSE]). Although both two approaches could reasonably forecast the moisture content of apple slice during drying, the ANN model demonstrated better goodness of fit than theoretical model. Practical application Moisture content and water activity are important parameters in production of dried fruit, which have traditionally been measured by destructive and time‐consuming methods. Hence, quick tools are required to evaluate the moisture concentration and monitor changes during drying. Modeling can be considered as a helpful tool for prediction of moisture content as a function of time and space. The results of this study show that the two types of models can successfully predict changes in moisture content during drying. Applying these models at the commercial level can reduce variability in product quality and improve repeatability of the drying process.

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“…T m = Lama waktu masak T 1 = Waktu awal perebusan T 2 = Waktu akhir perebusan Pengukuran Densitas (Liu et al, 2012dan Madiouli et al, 2012 Densitas atau massa jenis adalah suatu besaran kerapatan massa benda yang dinyatakan dalam berat benda per satuan volume benda dalam satuan kg/m 3 atau g/cm 3 . Pengukuran densitas selain berdasarkan formula dari Liu et al (2012), dan Madiouli et al (2012), juga menggunakan formula dari Zogzas et al (1994), Krokida et al (1997Krokida et al ( dan 2000, Koc et al (2008), Martynenko (2008), Khalloufi et al (2009), dan Mayor et al (2011) yaitu perbandingan massa benda (m) dengan volume (cm 3 ). Adapun untuk menghitung densitas diseuaikan dengan bendanya yang dapat dilakukan menghitung dengan menggunakan rumus:…”

Section: Metode Analisisunclassified

Karakteristik Lama Masak Dan Warna Pempek Instan Dengan Metode Freeze Drying

Alhanannasir

Rejo

Saputra

et al. 2018

J-AGT

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Pempek is a typical food of Palembang, South Sumatra, Indonesia, mostly processed in the form of wet with a moisture content of 50-60%, so that it only lasts for 3-4 days. In order for pempek to long last, it was processed into instant pempek using the freeze drying method. Pempek had tube form with a size of 3 cm in high and 4 cm in diameter. This study was aimed to see the freeze drying pressure and time to length of cooking, density, and lightness of instant pempek. Stored pempek at temperature of -50° C was carried out freeze drying with a pressure of 0.002 bar, 004 bar, 0.006 bar, and 0.008 bar for 38 hours, 40 hours, 42 hours, and 44 hours with a methodology of Split the Duncan Real Distance Difference Test Plot Design. The result showed that the pressure and time had very significant effect on the length of cooking, density and lightness of pempek. P3 (0.006 bar) pressure treatment affected shorter cooking time of pempek about 10.83 minutes, L2 treatment has 14.17 minutes for cooking times and the P3L2 treatment combination has low density of 0.71 g / cm3 and color brightness (lightness) about 85.38, almost resemble with white color. Making instant pempek with freeze drying method gave better results in ways of shorter and faster cooking time than instant pempek by other drying methods. Keywords: cooking length, freeze drying, instant pempek

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Shrinkage, porosity and density behaviour during convective drying of bio-porous material

Cited by 25 publications

References 10 publications

Processes for controlling the structure and texture of dehydrated banana

Processes for controlling the structure and texture of dehydrated banana

A comparative study on the efficiency of two modeling approaches for predicting moisture content of apple slice during drying

Karakteristik Lama Masak Dan Warna Pempek Instan Dengan Metode Freeze Drying

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