An ASABE Meeting Presentation Paper Number: 096498
Modelling and Optimization of Drying Variables in Desiccant Air Drying of Aloe vera (Aloe barbadensis Miller) Gel
Ramachandra C T Department of Agricultural Processing and Food Engineering College of Agricultural Engineering, P. B. No. 329, Raichur-584 102, India E-mail :
[email protected]
P Srinivasa Rao Agricultural and Food Engineering Department Indian Institute of Technology, Kharagpur-721 302, India
E-mail :
[email protected] Written for presentation at the 2009 ASABE Annual International Meeting Sponsored by ASABE Grand Sierra Resort and Casino Reno, Nevada June 21 – June 24, 2009
The authors are solely responsible for the content of this technical presentation. The technical presentation does not necessarily reflect the official position of the American Society of Agricultural and Biological Engineers (ASABE), and its printing and distribution does not constitute an endorsement of views which may be expressed. Technical presentations are not subject to the formal peer review process by ASABE editorial committees; therefore, they are not to be presented as refereed publications. Citation of this work should state that it is from an ASABE meeting paper. EXAMPLE: Author's Last Name, Initials. 2009. Title of Presentation. ASABE Paper No. 09----. St. Joseph, Mich.: ASABE. For information about securing permission to reprint or reproduce a technical presentation, please contact ASABE at
[email protected] or 269-429-0300 (2950 Niles Road, St. Joseph, MI 49085-9659 USA).
Abstract. Desiccant air drying characteristics of Aloe vera (Aloe barbadensis Miller) gel were investigated at temperature range of 40 to 70°C, relative humidity range of 15 to 30% and air velocity range of 0.5 to 2.0 m/s. The drying time varied from 330 to 900 minutes. The specific energy consumption during drying was found in the range of 201.60 to 415.17 MJ/kg. The diffusivity coefficient increased with the temperature from 4.93 to 16.38 x 10-10 m2/s. Aloin, a major anthroquinone responsible for therapeutic properties was quantified by High Performance Liquid Chromatography (HPLC) and found within acceptable limits for applications in foods. Response surface methodology (RSM) was applied to optimize the desiccant air drying process to produce best quality Aloe vera gel powder. The optimized process parameters were temperature 64°C, relative humidity 18% and air velocity 0.8 m/s. Three mathematical models namely, Newton, Page and Henderson-Pabis models were evaluated in the kinetics research. The fit quality of the proposed models was evaluated by using the linear regression coefficient (r2), sum square error (SSE), root mean square error (RMSE) and Chi-square statistic ( χ 2 ). The drying behaviour of Aloe vera gel was well predicted by Page model within the limits of the experiment. Keywords. Aloe vera; Desiccant air-drying; Aloin; Response surface methodology; HPLC.
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The authors are solely responsible for the content of this technical presentation. The technical presentation does not necessarily reflect the official position of the American Society of Agricultural and Biological Engineers (ASABE), and its printing and distribution does not constitute an endorsement of views which may be expressed. Technical presentations are not subject to the formal peer review process by ASABE editorial committees; therefore, they are not to be presented as refereed publications. Citation of this work should state that it is from an ASABE meeting paper. EXAMPLE: Author's Last Name, Initials. 2009. Title of Presentation. ASABE Paper No. 09----. St. Joseph, Mich.: ASABE. For information about securing permission to reprint or reproduce a technical presentation, please contact ASABE at
[email protected] or 269-429-0300 (2950 Niles Road, St. Joseph, MI 49085-9659 USA).
Introduction The genus Aloe belongs to the family Liliaceae, including the species Aloe barbadensis Miller, commercially known as "Aloe vera". The parenchyma cells of Aloe vera contain a transparent mucilaginous jelly, which is referred to as Aloe vera gel. It has thick, fleshy leaves covered with fleshy spikes and bears hanging, tubular, yellow flowers. The plant is xerophylous, being well adapted to dry land areas, and has tissues highly modified for water retention and storage (Denius and Homm, 1972). The potential use of Aloe vera products often involves some type of processing, e.g., heating, dehydration and grinding. Processing may cause irreversible modifications to the polysaccharides, affecting their original structure, which may promote important changes in the proposed physiological and pharmaceutical properties of these constituents. Processing of Aloe vera gel derived from the leaf pulp of the plant has become a big industry worldwide due to the application in the pharmaceutical, cosmetic and food industry. In the food industry Aloe vera has been utilized as a resource of functional food, especially for the preparation of health drinks and other beverages, including tea, which contain Aloe vera gel and have no laxative effects. Aloe vera gel is also used in other food products, for example, milk, ice cream confectionery and so on. Unfortunately, because of improper processing procedures, many of these so-called aloe products contain, very little or virtually no active ingredients, namely, mucopolysaccharides (Douglas and Reynolds, 1986). In view of the known wide spectrum of biological activities possessed by the leaves of the Aloe vera plant and its wide spread use, it has become imperative that the leaf be processed with the aim of retaining essential bioactive components. Aloe vera gel is commercialized as powdered concentrate. Traditionally, Aloe vera gel is used both, topically (treatment of wound, minor burns and skin irritations) and internally to treat constipation, cough, ulcers, diabetes, headache and immune system deficiencies. The powder is used in wide range of food products. It is presently used in health drinks, sport beverages, soft drinks, candies and chewing gum. It is even used to prepare hangover remedy. A few examples of product applications for food and beverage products are aloe soft drink (with electrolytes), diet drink with soluble fibre, hangover drink, healthy vegetable juice, tropical fruit juice with Aloe vera, yoghurt and yoghurt drinks, Aloe vera jelly desserts with chunks of aloe, instant Aloe vera tea granules, Aloe vera gums for sore or bleeding gums, Aloe vera candy, Aloe vera sorbet with citrus juice and Aloe vera fruit smoothies (http://www. aloecorp.org). The high temperature during drying of Aloe vera gel leads to irreversible modifications to the active substances, affecting their original structure, which may promote important changes in the food and pharmacological properties of Aloe vera gel. To retain higher degree of product freshness, biological activity, high level heat-stable components and sensory attributes the lowtemperature dehydration has very good potential compared to any other dehydration processes. Presently spray-drying and freeze-drying technologies are commonly followed in aloe industries, which are time consuming and are of high-end cost (He et al., 2002). To find an alternative
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technology to these, desiccant air drying system is thought of and is expected to retain the essential food and pharmacological properties of Aloe vera gel to the great extent. The kinetics of mass transfer (mainly water) during the drying process was dependent on the operational control of temperature, relative humidity and velocity of drying air, as well as product thickness, load density and shape. Good mathematical modelling of the drying process is an efficient tool for prevention of product deterioration, excessive energy consumption, equipment stress and increase in product yields. There are numerous empirical equations that simulate the drying processes which are useful in modelling its kinetics and can be used in the design of drying systems. Among these equations, Newton, Henderson-Pabis (Henderson and Pabis, 1962) and Page (Page, 1949) have appeared to be most applicable to the present study. All these models derive a direct relationship between average moisture content and drying time, and are strongly related to Fick’s second law of diffusional model (Akpinar, 2006). The aim of the present research was to study and model the kinetics of desiccant air drying characteristics of Aloe vera (Aloe barbadensis Miller) gel by using empirical equations, to evaluate the influence of process parameters on the specific energy consumption, aloin content, drying time, colour and to optimize the process parameters by response surface methodology (RSM).
Materials and methods Sample preparation The leaves of Aloe vera (Aloe Barbadensis Miller) were harvested from the farm of Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur, India. Leaves were selected for uniformity, according to harvest, colour, size and freshness. Hand filleting was done to separate fillet from the rind and the fillet was cut into flat slabs of 10 ± 1 mm in thickness. Moisture content was determined by AOAC methodology No. 934.06 (AOAC, 1990) by using a vacuum oven and an analytical balance.
Drying equipment The drying experiments were carried out at temperature range of 40 to 70°C, relative humidity range of 15 to 30% and air velocity range of 0.5 to 2.0 m/s. The dried samples were stored in sealed laminated aluminum foil. The desiccant air drying unit employed was designed and developed (Patel, 1995) at Agricultural and Food Engineering Department of Indian Institute of Technology, Kharagpur, India (Fig. 1).
Mathematical modelling of drying kinetics Fick’s second law was applied for the mathematical modelling of drying kinetics, which uses the relationship established on moisture ratio (MR) as a dependent variable described in Eq. (1) and which relates the gradient of sample moisture in real time with the initial moisture content and equilibrium moisture content (Akpinar et al., 2003; Babalis and Belessiotis, 2004; Simal et al., 2005). In the preset investigation, the integrated equation for long time periods and infinite slab
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geometry was used, representing the first term of the series (Eq. (2)) in Eq. (3) (Crank, 1975) with which the diffusion coefficient (Dwe) is obtained for experimental temperature.
MR =
X wt − X we X wo − X we
… (1)
MR =
⎡ − (2n + 1)2 Dweπ 2t ⎤ 1 exp ⎢ ⎥ ∑ 4 L2 π 2 n=0 (2n + 1)2 ⎦ ⎣
… (2)
MR =
⎡ − Dweπ 2t ⎤ exp ⎢ ⎥ 2 π2 ⎣ 4L ⎦
… (3)
8
∞
8
In addition, this process was modelled by the empirical equations proposed by Newton, Henderson-Pabis and Page commonly used in industrial design for dryers (Kiranoudis et al., 1992). These models are presented in the form of equations as follows: Newton: MR = exp(− k1t )
… (4)
Henderson-Pabis: MR = a. exp(− k 2 t )
… (5)
Page: MR = exp − k 3t n1
… (6)
(
)
Statistical analysis The statistical analysis was carried out using Systat 8.0 software, applying analysis of variance (ANOVA) to estimate average statistically significant differences for a confidence level of 95%. The fit quality of the proposed models on the experimental data were evaluated using linear regression coefficient (r2), sum square error (SSE) (Eq. (7)), root mean square error (RMSE) (Eq. (8)) and Chi-square ( χ 2 ) (Eq. (9)).
SSE =
1 N
N
∑ (MR
ei
i =1
⎡I RMSE = ⎢ ⎣N
∑ (MR i =1
∑ (MR i =1
2
N
N
χ2 =
− MRci )
ei
ci
… (7)
− MRei )
2
⎤ ⎥ ⎦
1/ 2
… (8)
− MRci )
N−z
2
… (9)
Quantification of Hydroxyanthracene derivatives in desiccant air dehydrated Aloe vera gel powder Hydroxyanthracene derivatives were analysed by High Performance Liquid Chromatography (HPLC). The analytical column used was C-18. Hydroxyanthracene derivatives were extracted with methanol. Samples were sonicated in an ultrasonic bath for 10 min, filtered through 0.2 µm membrane filters, and 20 µl of solution analyzed at a wavelength of 296 nm. The separation was carried out using acetonitrile (mobile phase A) and water/acetonitrile 90:10, v/v (mobile phase B). Trifluoroacetic acid (C2HF3O2) (0.1%v/v) was added to mobile phase B to minimise peak
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tailing. The detail of the gradient is as follows: Acquisition. Time 0 min = 5% A, 95% B (isocratic for 10 min); time 15 min = 15% A, 85% B (isocratic for 10 min); time 55 min = 22% A, 78% B; time 57 min = 5% A, 95% B. The flow rate was 1 ml/min (Bozi et al., 2007).
Results and discussion Drying process The variation of moisture content as a function of the time for the temperature range of 40 to 70°C, relative humidity range of 15 to 30% and air velocity range of 0.5 to 2.0 m/s is shown in figure 2. All the drying curves show a clear exponential tendency, and as expected, it is observed that the drying time decreased when the temperature increased until all the samples reached similar equilibrium moisture content. The time required to achieve a moisture content of lower than 5.0 g water/g (d.b.) at 40°C was 900 minutes which was approximately the triple the time for a temperature of 70°C (330 minutes). Input parameters and responses of desiccant air dehydrated Aloe vera gel powder during drying process is given in Table 1. Similar results were obtained by various researchers (Mwithiga and Olwal, 2004; Vega et al., 2007) working with kale, red bell pepper, various vegetables and kiwis, respectively.
Modelling of drying curves As shown in figure 2, the drying curves had an exponential form at all the experimental range and only demonstrated a falling rate period. Simal et al. (2000) drew the same conclusion working with Aloe vera cultivation in Spain. It was for this reason that we recommended the use of empirical exponential models proposed by Newton, Henderson-Pabis and Page model. The values of the model constants were shown in Table 2.
Statistical analysis of models Table 2 shows the results of statistical tests (r2, SSE, RMSE, P and χ 2 ) applied to the fits obtained with the proposed equations. These statistical tests evaluate the fit quality on the experimental data and have been used by the researchers in studies of food drying. According to the regression coefficient, Page model achieved the best fit though a good fit was also obtained by the Henderson-Pabis and Newton models (r2>0.96). When evaluating the fit quality with the other statistical tests on the calculated data of the proposed models, the Page model produced the lowest SSE
