alexa Thin Layer Drying Kinetics of Solar-Dried Amaranthus hybridus and Xanthosoma sagittifolium Leaves | OMICS International
ISSN: 2157-7110
Journal of Food Processing & Technology
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Thin Layer Drying Kinetics of Solar-Dried Amaranthus hybridus and Xanthosoma sagittifolium Leaves

P.T. Akonor1* and E.A. Amankwah2

1P.T. Akonor, Food Processing and Engineering Division, CSIR – Food Research Institute, P.O. Box M30, Accra, Ghana

2Department of Biochemistry and Biotechnology, Kwame Nkrumah University of Science and Technology, Kumasi

*Corresponding Author:
P.T. Akonor
Food Processing and Engineering Division
CSIR – Food Research Institute
P. O. Box M30, Accra, Ghana
E-mail: [email protected]

Received date:June 11, 2012; Accepted date: July 23, 2012; Published date: July 27, 2012

Citation: Akonor PT, Amankwah EA (2012) Thin Layer Drying Kinetics of Solar- Dried Amaranthus hybridus and Xanthosoma sagittifolium Leaves. J Food Process Technol 3:174. doi:10.4172/2157-7110.1000174

Copyright: © 2012 Akonor PT, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

The aim of the study was to model the solar drying characteristics of the leaves of Amaranthus hybridus and
Xanthosoma sagittifolium dried in thin layers. Fresh leaves were obtained from Centre for Biodiversity Utilisation and Development (CBUD) farms, trimmed into strips of 0.3 cm x 3 cm and loaded into cabinet solar dryers up to a 5 mm layer. Drying was monitored and moisture loss was determined by loss in weight of samples at hourly interval. Drying data were fitted to five thin layer models, namely; Newton’s, Page’s, Modified Page, Handerson and Pabis and Logarithmic models by Non-linear Regression Analysis, the effective diffusivity was also determined for the two leafy vegetables. All five models showed a good fit between observed and predicted values, with Page’s model resulting in the highest r2 and lowest RMSE and X2 and hence the best model to describe the solar-drying characteristics of the two vegetables.

Keywords

Drying curves; Effective Moisture Diffusivity; Leafy vegetables; Mathematical modelling; Solar drying

Nomenclature

a, b, c, k, y Constants in models  
Deff Effective diffusivity m2/s
L Half-thickness of slab m
MR Moisture ratio  
M Moisture content  
Mo Initial moisture content  
N Number of observation  
r2 Coefficient of determination  
t Drying time min
z Number of constants  
exp Experimental  
pre Predicted  

Introduction

Leafy vegetables have gained commercial importance and form an essential part of the Ghanaian diet, providing vitamins and micronutrients. As a result of their high moisture and short shelf life, there is the need to process them into stabilized forms with controlled water activity [1] that can store for longer periods [2] so that they will be available all year round [3]. Drying presents one of the most effective methods of food preservation. The process broadly describes the thermal removal of moisture from a product to yield a solid product [4]. It is a dual process of heat transfer to the product from the heating source and the mass transfer of moisture from the interior of the product to its surface and from the surface to the surrounding environment [5] and therefore the rate at which drying proceeds depends primarily on the rate at which these two processes proceed [4].

Drying method and the physical and biochemical changes that occur during drying seem to affect the quality properties of the dehydrated product. The proper handling of these reactions ensures that the dried product has a high nutritional value as well as a significantly extended shelf life. It is, therefore, essential to model and study the drying characteristics of food products in order to predict the suitable drying conditions as part of process control [6] and in the design and manufacture of dryers.

The study aimed at observing the drying characteristics of solardried Amaranthus spp and Xanthosoma spp leaves in thin layers using five analytical models in order to ascertain the model which best describes these drying characteristics.

Materials and Methods

Drying experiments

Fresh fully mature and edible Amaranthus spp (months) and Xanthosoma spp leaves were obtained from the Centre for Biodiversity Utilisation and Development (CBUD) farms at the Amanfrom Prison Camp in Kumasi. The leaves were detached from their stalks and inedible parts removed. The leaves were washed and trimmed into thin strips of dimension (0.3×3 cm) spread evenly on drying trays and loaded into a solar cabinet dryer (made of wood with glass windows, schematic in Figure 1 at a density of 1.5 kg/m2, in a single layer of 5 mm. Moisture loss during drying was determined by measuring the loss in weight of samples at hourly interval and at the beginning and end of drying and the representative samples are then taken for moisture content determination [7]. The leaves, with initial moisture (wb) content 85.8% and 82.7%, were dried to a final moisture content of 8.5% and 8.9% for Amaranthus spp and Xanthosoma spp leaves respectively. Average drying temperatures over the period of drying in the dryers were 49.8°C (RH 31.3%) and 48.7°C (RH 32.8) (Thermo hygrometer, Hanna HI 91610) for Amaranthus spp and Xanthosoma spp respectively. In order to reduce the incidence of moisture reabsorption, drying was discontinued at sundown (30.1°C, RH 47.5% and 30.8°C RH 46.3% for Amaranthus spp and Xanthosoma spp respectively) and the products were sealed air-tight in polypropylene bags overnight and drying continued the next morning (ambient 24.9°C, RH 69.8%).

food-processing-technology-Schematics-Solar

Figure 1: Schematics of Solar Cabinet Dryer.

Model fitting of drying data

Drying data was fitted to the Newton’s, Page’s, Modified Page, Handerson and Pabis and Logarithmic models by Non-linear Regression Analysis (STATGRAPHICS Centurion, 15.1) and coefficient of correlation and goodness of fit of predicted to experimental data determined. The moisture ratio was simplified as M/Mo because the relative humidity of the inlet air could not be controlled and Me is very small as compared to Mo. (Table 1)

The reduced chi square was calculated as:

image

Effective moisture diffusivity

During drying, a general diffusion transport mechanism in which the rate of moisture movement is described by an effective diffusivity value, Deff is often assumed, regardless of which mechanism is really involved in moisture movement. In this approach, Fick’s diffusion equation is used to explain the effective diffusivity. Parameters required in this approach are only sample dimensions and the effective diffusion coefficient. This method is very practical and convenient in describing moisture content change during processing. In using Fick’s equation, the leaves used were assumed as slabs and all assumptions for slab-shaped objects were proposed by [8] observed. The effective diffusivity was calculated by a linearised version [9-11].

image

Where, Deff is the effective diffusivity (m2/s); L is half the thickness of the slab (m).

Results and Discussion

Drying curves

The drying curves for the two leafy vegetables show a sample heat-up period where there is little or no drying. This may probably have been due to low temperatures at the beginning of the drying process, and not actually a sample heat-up period that characterises most drying processes.

The drying curves (Figure 2) show an “elbowing” along the curve, which indicates the period between the discontinuation and resumption of drying. The curves show a very short constant rate period as is observed in the drying of most food products [12] and a longer falling rate period, a phenomenon characteristic of food products with water activity less than 1 [13]. This occurrence demonstrates that diffusion is the dominant physical mechanism governing the removal of moisture from the samples. Similar observations were made by [14] for green beans, [15] for red chilli and [16] for okra.

food-processing-technology-Drying-curves

Figure 2: Drying curves for Amaranthus spp and Xanthosoma spp.

Mathematical modelling

The r2, RMSE and reduced chi square values for Amaranthus and Xanthosoma leaves for the chosen models are shown (Table 1). The appropriateness of a model for describing the drying characteristics of samples was based on its r2, RMSE and reduced chi-square. The higher the r2 values and lower the reduced X2 and RMSE values, the better the goodness of fit [17-21] (Table 2).

Model Name Reference
MR = exp (-kt) Newton Liu and Bakker-Arkema (1997)
MR = exp (-kty) Page’s Doymaz (2005)
MR = exp (-kt)y Modified Page’s Waewsak et al. (2006)
MR = a exp (-kt) Henderson and Pabis Waewsak et al. (2006)
MR = a exp (-kt) + c Logarithmic Yaldiz et al. (2001)

Table 1: Mathematical models as given by various authors.

Model Leafy vegetable
Amaranthus spp Xanthosoma spp.
r2 RMSE X2 r2 RMSE X2
MR = exp (-kt) 0.9236 0.1017 0.0100 0.9343 0.0914 0.0084
MR = exp (-kty) 0.9917 0.0349 0.0012 0.9905 0.0363 0.0013
MR = exp (-kt)y 0.9236 0.1058 0.0100 0.9343 0.0951 0.0091
MR = a exp (-kt) 0.9483 0.0870 0.0070 0.9573 0.0767 0.0058
MR = a exp (-kt) + c 0.9699 0.0693 0.0046 0.9791 0.0561 0.0033

Table 2: Models and their respective r2, RMSE and X2 for Amaranthus spp. and Xanthosoma spp. leaves.

All five models showed very good fit with r2 greater than 0.9 (Table 2). For the two samples studied, the Page model resulted in the highest r2 values and corresponding least values for RMSE and reduced chi-square whilst the Newton and Modified Page’s models gave the lowest r2 and highest RMSE and reduced chi-square. The high r2 for the Page model is an indication that it best describes the thin layer solar drying characteristics of the two leafy vegetables and fitted curve for the model is shown in Figure 3. Similar findings were detailed by [22] for [23], [24] for thin layer drying of diced cassava roots, and [25] for garlic slices.

food-processing-technology-Fitted-drying-curve

Figure 3: Fitted drying curve for leafy vegetables using Page’s model.

Effective moisture diffusivity calculation

A plot of ln (MR) against time t, gives a line with slope:

image

The slope, effective diffusivity values (Deff), the corresponding values of coefficients of determination r2 and the reduced chi square (X2) for the two indigenous leafy vegetables are presented (Table 3) (Figures 4 and 5).

food-processing-technology-Linear-relationship

Figure 4: Linear relationship between ln MR and drying time for Amaranthus spp.

food-processing-technology-drying-time-Xanthosoma

Figure 5: Linear relationship between ln MR and drying time for Xanthosoma spp.

Leafy Vegetable Slope Deff x10-9 m2s-1 X2 r2
Amaranthus spp 0.1766 1.95 0.0200 0.9700
Xanthosoma spp 0.1682 2.09 0.0086 0.9859

Table 3: Effective diffusivity, slope, reduced X2 and co-efficient of determination for the leafy vegetables.

The effective diffusivity, Deff , for the two leafy vegetables are comparable to 6.4 x10-9m2s-1 as obtained by [26] for drying carrots at 64°C and 1.2 x10-9m2s-1 – 5.9 x10-9m2s-1, by [27] for carrots dried between 30 and 70°C. Again it was within the range of 3.8 x10-10m2s-1-1.2 x10-9m2s-1 for drying green pepper at 60-80°C [28] and 6.03x10-9m2s-1–3.15 x10-8m2s-1 for vegetable wastes in a temperature range of 50–150°C [27]. At higher drying temperatures, the effective diffusivity is expected to be higher, especially with an increase in the drying air velocity [28,21,29].

Conclusion

The results show that the change in moisture ratio over time for solar drying the two leafy vegetables can be best described by the Page’s model. Under similar drying conditions, the model is appropriate for simulating the outcome of drying these vegetables during process control. Effective Moisture Diffusivity ranged between 1.9x10-9m2s-1 and 2.1 x 10-9m2s-1 for the two leafy vegetables.

References

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