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1

CONTENTS CHAPTER PAGE No.

1 INTRODUCTION 8 2 REVIEW OF LITERATURE 11

2.1 Nutritive value of apple and apple pomace 11 2.2 Basic concept of drying 11 2.3 Thin layer drying 16 2.4 Equilibrium moisture content 29 2.5 Factors affecting hot air drying 33

2.5.1 Blanching 33

2.5.2 Temperature, layer thickness, air velocity and relative humidity 34

2.5.3 Variety of agricultural product 37 2.6 Drying high moisture foods 37

2.7 Drying of apple, apple puree, apple pomace and other fruit pomaces 41

2.8 Utilization of apple processing wastes 45 2.8.1 Fuel 45 2.8.2 Food products 45 2.8.3 Pectin extraction 47 2.8.4 Cattle feed 48 2.8.5 Biotransformation 49 2.8.6 Source of fiber 51 2.8.7 Miscellaneous uses 52 3 MATERIALS AND METHODS 57

3.1 Materials and equipment 57 3.2 Preliminary experiments 57 3.3 Experimental design 58 3.4 Experimental setup 59 3.5 Measurement of variables 59

3.5.1 Air temperature and velocity 59 3.5.2 Moisture content 59 3.5.3 Equilibrium moisture content 60 3.5.4 Moisture ratio 61 3.6 Experimental procedures 62

3.6.1 Washing 62 3.6.2 Sample preparation 62 3.6.3 Blanching of apple 62 3.6.4 Crushing 62 3.6.5 Mechanical juice expression 62

3.6.5.1 Hydraulic press 62 3.6.5.2 Cylinder and piston assembly 63 3.6.5.3 Juice expression and apple pomace recovery 63 3.6.6 Drying on SATAKE dryer 63 3.6.7 Drying rate 64

3.6.7.1 Average drying rate 64 3.6.7.2 Overall drying rate 64 3.6.8 Grinding and milling of dried apple pomace 65 3.6.9 Colour 65

2

3.6.10 Sensory evaluation 65 3.7 Data analysis 65 4 RESULTS AND DISCUSSION 70

4.1 Drying behaviour 70 4.1.1 Moisture content 70 4.2 Drying rates 71

4.2.1 Average drying rate 71 4.2.2 Overall drying rate 72

4.2.2.1 Effect of moisture content on overall drying rate 72 4.3 Equilibrium moisture content 74 4.4 Moisture ratio 74 4.5 Validity of drying models 75 4.6 Error analysis for Pages model 88 4.7 Effect of drying on colour 88 4.8 Sensory evaluation 89

4.8.1 Colour 89 4.8.2 Odour 89 4.8.3 Texture 89 4.8.4 Overall acceptability 90 5 SUMMARY AND CONCLUSIONS 91 6 LITERATURE CITED 92

APPENDICES 112

3

LIST OF FIGURES FIG. No. TITLE PAGE 2.1 A Schematic description of transfers during drying process 56 3.1 Flow chart of the whole process involved during apple pomace drying 67 4.1 Variation in moisture content during drying at 50 C 77 4.2 Variation in moisture content during drying at 60 C 77 4.3 Variation in moisture content during drying at 70 C 78 4.4 Drying behavior of 2 mm thickness of apple pomace 78 4.5 Drying behavior of 4 mm thickness of apple pomace 79 4.6 Drying behavior of 6 mm thickness of apple pomace 79 4.7 Effect of temperature on average drying rate at 2 mm thickness 80 4.8 Effect of temperature on average drying rate at 4 mm thickness 80 4.9 Effect of temperature on average drying rate at 6 mm thickness 81 4.1 Effect of thickness on average drying rate at 50C 81 4.11 Effect of thickness on average drying rate at 60C 82 4.12 Effect of thickness on average drying rate at 70C 82 4.13 Effect of drying temperature on overall drying rate of apple pomace 83 4.14 Effect of velocity on average drying rate of apple pomace 83 4.15 Plot of data in terms of equation (3.6) for apple pomace at 2 mm thickness 84 4.16 Plot of data in terms of equation (3.6) for apple pomace at 4 mm thickness 84 4.17 Plot of data in terms of equation (3.6) for apple pomace at 6 mm thickness 85 4.18 Interrelation between moisture ratio and time at 50C 85 4.19 Interrelation between moisture ratio and time at 60C 86 4.2 Interrelation between moisture ratio and time at 70C 86

4.21 Effect of air temperature on drying constant k for average n for Pages model 87

LIST OF TABLES TABLE No. TITLE PAGE

2.1 Food value of apple 54 2.2 Composition of apple pomace 55 3.1 Experimental plan for apple pomace drying 69

LIST OF PLATES PLATE No. TITLE PAGE NO.

3.1 CARVER hydraulic press view 152 3.2 Apple pomace cake from CARVER press 152 3.3 SATAKE dryer view 153 3.4 Willey mill view 153 3.5 Pomace powder obtained at different drying conditions

LIST OF SYMBOLS AND ABBREVIATIONS A constant As absorbance

ANOVA analysis of variance AlCl3 aluminium chloride avg. average

4

a radius of sphere BIS Bureau of Indian Standards B0 constant B constant b average moisture content for a given absorption period, g/g, C constant

CO2 carbon dioxide c moisture gain due to initial hydration, g/g, d.b.

cm centimeter d.b. dry basis

dM/dt drying rate

average drying rate

div second partial differential DF degree of freedom D diffusion coefficient D1 equivalent diameter of solid etc. etcetera

et al. and others Eqs. equations

F.A.O Food and Agriculture Organization F F- value

Fig. figure g gram(s)

g/cm2 gram per square centimeter g/kg gram per kilogram grad gradient

h hour(s) I intensity of light transmitted by the sample, candela Io intensity of light transmitted by the blank, candela

i.e. that is K11, K22, K33 phenomenological coefficients K12, K13, K21, K23,K31,K32

coupling coefficients

K heating or cooling constant KMS potassium metabisulphite

k first drying rate constant Kg kilogram

kg/m2 kilogram per square meter L layer thickness l second drying rate constant

lbs pound Mn+1 moisture content at time tn+1 Mn moisture content at time tn Me equilibrium moisture content M average moisture content Mo initial moisture content Md dynamic equilibrium moisture content Mo moisture content of the sample in percent d.b.

M(r,0) boundary moisture at time t=0 M(r,t) boundary moisture at time t=t Mt moisture at time t MF final moisture content, % d.b.,

avidtdM

5

MR moisture ratio MS mean sum of square

MSE mean square of error M1 monolayer moisture content

effective moisture content at the bounding surface at time greater than zero, d.b.

mg milligram min minute(s) m/s meter per second m2/s meter square per second mm millimeter

n total readings NH4OH ammonium hydroxide

OD optical Density p probability

ppm parts per million

water activity

R2 coefficient of multiple determination SS sum of squares S surface area

SO2 sulphur dioxide s second T temperature Te external temperature To outside temperature t time t1 total time of drying V volume

Wn, Wn+i, Wn+2i sample weights taken at time interval at time interval i Wo weight of samples at zero time Ws initial solid weight of the sample We equilibrium weight of the sample Wt final weight Wi initial weight W watt

w.b. wet basis w/w weight per weight X1 Me/M1 X distance measured in the direction of diffusion yi observed value y predicted value % percent / per

C degree Celsius % T percent transmittance

second order differential

1 (pi/2)2 D

t

/S2, dimensionless

roots of the Bessel function of zero order

m

oPP

2

n

6

ACKNOWLEDGEMENT

. WHERE TIRELESS STRIVES, STRETCHES ITS ARMS TOWARDS PERFECTION.

-Rabindranath Tagore

Its the solemn benediction of God who has given the opportunity to prepare this manuscript and provided wonderful nature to live and work. All my sincere gratitude goes to Him for His Blessings.

It is rare to find people striving tirelessly towards perfection. Dr. D.K.Gupta, Professor, Department of Post Harvest Process and Food Engineering, G.B. Pant University of Agriculture & Technology, Pantnagar (U.S. Nagar), Uttarakhand, India, is one of such rare person, who not only strives himself but guides his students too towards perfection. His guidance, during course work and research, has been consistent and his continuous constructive criticism made me rectify my shortcomings and ultimately made this, an otherwise hard-to-do, task possible. I will be grateful to him forever, for the benefits of his guidance would be eternal.

I express my deepest sense of reverence and indebtedness to the esteemed members of my Advisory Committee, Dr B. K. Kumbhar, Professor and Head and Dr Anupama Singh, Senior Research Officer, Department of Post Harvest Process and Food Engineering, who helped me in all possible capacities and for their valuable suggestions and eternal encouragement at various stages of the investigation.

Sincere regards are due to Dean, College of Technology, Dean, College of Post Graduate Studies and Director, Experiment Station, G. B. Pant University of Agriculture and Technology, Pantnagar for providing necessary facilities to carry out the study.

It is often said good friends are rare to get. In this context I find myself very lucky to have friends like Shashwati, Surmila, Manju, Shweta, Vijaya, Pinki, Deepti, Bhawana and many others for helping me when I was in need .

Love, encouragement and enthusiasm are the fabric stories of intellectual foundation. This is the point which I have inculcated with the blessing of my parents. An enigma result, whenever I project my memory to consult my intellect upon some words to express my gratitude towards them and I successfully fail to transform their immaculate entities of deeds

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into mere frame code of language. I bow my mind, body and spirit to be shattered by the warmest, glorified, golden rays of my beloved parents.

A regard of love, affection of immeasurable inner rippling exclaims the entire viability of my brother Rupesh and Suhail, sister Noopur, and my loving husband Manu Srivastava and my in-laws without the support from them this work would not have been complete.

A special mention is required to thank my elder brother, Dr. Pankaj Srivastava who stood by me through all the thick and thin and was the source of constant encouragement and help throughout the progress of this work.

My abstruse regard goes to G.B. Pant University of Agriculture and Technology for the successful completion of the work. This list is obviously incomplete but allow me submit that the omissions are inadvertent and I once again record my deep felt gratitude to all those who cooperated with me in this endeavor.

Chandigarh (RACHANA SHALINI) Dated: September, 2010

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1. INTRODUCTION

Apple (Malus domestica Borkh.) is the most favoured fruit of millions of people and widely grown fruit in temperate regions of the globe (FAO, 1989; Kaushal and Joshi, 1995; Kaushal et al., 2002; Agrahari and Khurdiya, 2003).

The world production of apple at present is about 58 million tones from an area of about 5.26 million ha (FAO, 2004); seventy-one percent of the fruit is consumed as fresh apple while about 20% is processed into value added products of which 65% are processed into apple juice concentrate (AJC) and the balance quantity into other products which include packed natural RTS (Ready To Serve) apple juice, apple wine and cider, apple purees and jams, dried apple products etc. (Anonymous, 2004).

Presently, India is the ninth largest producer of apples in the world contributing about one-third of total apple production of the world with an annual production of 1.42 million tons from an area of 0.25million ha (Anonymous, 2004; FAO, 2004; GOI, 2004; Negi, 2004). It is the fourth major fruit crop of India (GOI, 2004).

The major apple growing states in India are Jammu and Kashmir, Himachal Pradesh and Uttaranchal (Sharma, 1994; Negi, 2004). It is a major horticultural produce and is the backbone of the rural economy of these states (Agrahari and Khurdiya, 2003). However, during the last 4-5 years, cultivation of apples has been extended to northeast Himalayan states also (Negi, 2004).

Uttaranchal, which was formed on 9 November, 2000, possesses a great potential for apple production (Negi, 2004). Its production is increasing tremendously every year. Most of the production of this fruit is used for table purposes but a portion is being processed into various products (Kaushal and Joshi, 1995). Apple is used for the production of single strength juice, apple juice concentrate, jam and fermented products like cider, wine and vermouth (Amerine et al., 1980; Downing, 1989; Joshi et al., 1991; Joshi, 1997; Kaushal et al., 2002). After processing into juice or juice concentrate, the left over material (byproduct) is pomace which is being thrown away causing environmental pollution (Joshi and Joshi, 1990; Kaushal and Joshi, 1995).

A conventional process removes 75 percent of fresh weight of apple as juice and 25 percent is the pomace (Sargent, 1984; Sargent et al., 1986; Wang and Thomas, 1989; Shah and Masoodi, 1994; Masoodi, 1998; Kaushal et al., 2002). In India, the states of

9

Himachal Pradesh and Jammu and Kashmir produce huge quantum of apple pomace which is not being utilized at present for human consumption but is dumped as such in the fields which creates pollution problems because of its uncontrolled fermentation and high C. O. D. i.e. Chemical Oxygen Demand of 250- 300 g/kg (Masoodi, 1998).

Being rich source of carbohydrate, pectin, crude fiber and minerals (Smock and Neubert, 1950), it is the good source of nutrition. Efforts in the past have been made to utilize this precious resource in one or the other form but the problem of apple pomace utilization still needs solution. Different microbial transformation of apple pomace have been proposed for obtaining valuable products like biogas (Lane, 1979), ethanol (Hang et al., 1982), butanol (Voget et al., 1985), citric acid (Hang and Woodams, 1986) and pectinases (Hours et al., 1988). Chemical analysis of apple pomace has revealed that it is not only a good source of total dietary fiber but contains a significant amount of soluble dietary fiber which comprises of pectin (Masoodi, 1998).

Efforts have been made in the past to extract pectin from apple pomace (Sharma et al., 1985) or to make citric acid (Hang and Walter, 1989; Sharma and Joshi, 2001; Kaushal et al., 2002). An innovative approach for the recovery of the ethanol and the production of animal feed concomitantly has also been advocated (Joshi and Sandhu, 1994, 1996; Kaushal et al., 2002). Efforts made to utilize it in the preparation of edible products like apple pomace jam and sauce have also been successful (Wang and Thomas, 1989; Kaushal and Joshi, 1995; Joshi et al., 1996). Pomace papad (fruit cloth), a form of high value low volume product have also been prepared from apple pomace (Kaushal et al., 2002; Thakur and Thakur, 2000). But the large quantity of apple pomace produced suggests that the preparation of single product would not be economically feasible and production of all possible products needs to be explored (Kaushal et al., 2002). Cookies were prepared and evaluated by incorporating different amounts (10-50%) of apple pomace powder in dough. Sensory evaluation of prepared cookies showed that 30% of apple pomace powder could be incorporated in preparation of cookies of good quality (Kaushal and Joshi, 1995).

Since, apple pomace is a part of the fruit; it has potential for conversion into edible products (Kaushal and Joshi, 1995). But, it being biodegradable in nature with high bio-chemical oxygen demand (BOD), disposal of apple pomace into the environment causes pollution, necessitating the efforts to find out the appropriate solution to this problem. The commercial utilization of pomace shall ultimately be determined by economics of products

10

and the cost of waste disposal coupled with pressure from environment protection agencies in implementing the laws (Kaushal et al., 2002).

Though traditionally utilized as cattle feed, only a fraction of apple pomace is used due to rapid spoilage of the wet pomace (Bates and Roberts, 2001). So apple pomace drying should be done to prevent the spoilage. Hence, the current problem was selected to conduct studies on drying characteristics of apple pomace so as to generate data for design of drying systems.

The specific objectives of the present study are

1. To study the drying behavior of wet apple pomace under thin layer drying conditions in relation to drying air temperature, air velocity and layer thickness.

2. To determine the equilibrium moisture content of apple pomace. 3. To test mathematical models for predicting wet apple pomace drying rates. 4. Sensory evaluation of the dried apple pomace powder at different conditions of

temperature and layer thickness. 5. To optimize the drying air temperature and layer thickness on the basis of sensory

evaluation.

11

2. REVIEW OF LITERATURE

This chapter deals with the review of literatures of apple pomace drying, its utilization and the explanation of the underlying theory and principles of drying of pomace and modelling of the drying process.

2.1 Nutritive value of Apple and apple pomace

Apple supplies good amount of calorie (calorific value-59) and is rich in minerals and vitamins. Of the total carbohydrate (13.4%) present in apple, about 80% constitute sugars. Fructose is the principal sugar (60%) followed by glucose (25%) and sucrose (15%). The food value of apple per hundred grams of edible portion is given in Table 2.1 (Anonymous, 2004).

Apple pomace is the main by-product of apple cider and juice processing industries and accounts for about 25% of the original fruit mass at 85% (w.b.) moisture content (Walter and Sherman, 1976). Apple pomace typically contains between 66.4% (w.b.) and 78.2% (w.b.) moisture and 9.5-22.0% carbohydrates (Smock and Neubert, 1950). Apple pomace contains 26.41% dry matter (DM), 3.95% proteins, 3.62% sugars, 6.81% cellulose, 0.38% ash, 0.42% acid and 8.7 mg calcium per 100 g of wet apple pomace; (Vasilev et al., 1976). The composition is given in Table 2.2. Fermentable sugars in apple pomace such as glucose, fructose and sucrose can be converted to ethanol using yeast (Hang et al., 1981; Miller et al., 1982; Hang, 1987). Ethanol is considered a possible alternative fuel source to supplement or totally replace petroleum (Coote, 1983).

2.2 Basic concept of drying

Drying refers to the removal of relatively small amount of moisture from solid material by evaporation (Chakraverty, 1995). Drying is a process of simultaneous heat and moisture transfer. Usually, air is used as an external drying medium for supplying the heat to evaporate the moisture from the solid surface. A number of biological materials, when dried as single particles under external conditions, exhibit a constant rate moisture loss during the initial drying period, followed by a falling rate period. Cereal grain kernels, however, do not display a constant rate drying period unless they are harvested at a very immature state or have had water condensed or rained on their surfaces. Because of the difference in the drying behaviour of individual kernels and a bed of kernels, drying process is divided into single-kernel drying, thin layer drying and deep bed drying. (Brooker et al., 1974).

12

Single-kernel refers to drying of single kernel exposed to drying air and thin layer drying refers to the grain drying process in which all grains are fully exposed to the drying air at constant air temperature, and humidity. Practically, up to 20 cm thickness of grain bed is taken as thin layer. In deep bed drying all the grains in the dryer are not fully exposed to the same conditions of drying air. The conditions of drying air at any point in the grain mass changes with time and at any times, it also changes with the depth of the grain bed. Over and above the rate of air flow per unit mass of grain is small compared to the thin layer drying of grain (Chakraverty, 1995).

Many theories have been proposed and mathematical models developed describing the drying behaviour of biological products, still the physics of drying is not fully known. A number of physical mechanisms have been proposed for describing migration of moisture in capillary porous products. A schematic description of transfers during drying process is given in Fig 2.1 (Mazumdar, 1987).

Newman (1931) described the basic equation of diffusion theory for porous media with the assumption that the resistance of flow of moisture was uniform throughout the material.

( )dM div D grad Mdt

= (2.1)

For constant diffusivity, Equation (2.1) reduces to:

2

dM D Mdt

= (2.2)

A number of analytical solutions of Equations 2.1 and 2.2 are available for average moisture content of various regularly shaped bodies under different initial and boundary conditions and can be used directly (Crank, 1975).

On the basis of equation 2.2, which was analogus to heat conduction in solids proposed by Fick in 1855, Crank, (1975) proposed the following relationship for describing the one dimensional moisture diffusion in solids:

2

2 dM MDdt X

=

(2.3)

13

This equation can be integrated to relate the time of drying with the initial and final moisture content under the assumptions that the initial moisture content is uniform throughout the thin layer of a solid and that the surface of the layer is in equilibrium with the surrounding atmosphere. This result in the following solution which is widely used for modelling the drying of biological materials (McCabe et al., 1985)

MR = (2.4)

Van Arsdel and Copley (1963) defined a generalized drying curve that includes (a) constant drying rate region and (b) falling rate region. All materials do not follow this pattern and in some cases, only the falling rate regions are observed. A substance undergoes a constant rate when a film of water is freely available at the drying surface and the process is similar to that of a pool of water evaporating into air. The rate, as such, is dependent upon the air temperature, air humidity and heat transfer coefficient for the water.

Luikov (1966) developed a complex mathematical model for describing the drying of capillary porous products based on the different physical flow mechanisms. The model equations were described as a system of partial differential equations with phenomenological and coupling coefficients. The proposed set of equations was:

PKTKMKt

M 213

212

211 ++=

(2.5)

PKTKMKt

T 223

222

221 ++=

(2.6)

PKTKMKt

P 233

232

231 ++=

(2.7)

The coupling results from the combined effects of moisture, temperature, total pressure gradient on the moisture, energy and total mass transfer. This system of equation has not yet been applied to grain due to unknown phenomenological transfer coefficients, however, simplification have been worked out to describe of hygroscopic biological solids.

Husain et al. (1972) investigated Luikovs equations (Eqs. 2.5 to 2.7) and found that the pressure terms could be neglected. They developed solution of following simplified set of equations:

+++=

...........

251

918

111 2592

pi

eeeMMMM

eo

e

14

TKMKt

M 212

211 +=

(2.8)

TKMKt

T 222

221 +=

(2.9)

The equation (2.8) and (2.9) describe the drying and thermal behaviour of cereal grains very well. They applied their solutions to a number of products and concluded that consideration of coupling effects was required in a very limited number of practical cases. A constant surface moisture content equal to equilibrium moisture content is commonly assumed in analysis of diffusion process (Brooker et al., 1974). Eqs. 2.8 and 2.9 were, therefore, further simplified by neglecting the coupling effects of heat and mass transfer. However, in practical analysis, the temperature gradients do not have to be considered and ultimate simplification of Luikov equation results in equation (2.10).

MKt

M 211 =

(2.10)

Since, it is generally agreed that moisture flow within a grain kernel takes place by diffusion; transfer of coefficient K11 is called D, diffusion coefficient. For a constant value of D, equation (2.10) can be written as;

+

=

x

Mx

c

x

MDt

M2

2

(2.11)

where, c is zero for planar symmetry, unity for a cylindrical body and two for a sphere.

A number of solutions to equation (2.11) for various solid shapes have been used as drying equations for grains with initial and boundary condition assumptions. The following initial and boundary conditions are usually assumed in solving equation.

etror MMandMM == ),()0,( 0 (2.12)

The analytical solutions of equation (2.11) for average moisture content of various regularly shaped bodies under different initial and boundary conditions are given in book of diffusion (Crank, 1957), such,

For an infinite plane:

15

+

+=

=4

)12(exp)12(

18 222

022

pipi

n

nMR

n

(2.13)

For sphere:

=

=9

exp16222

122

pipi

n

nMR

n

(2.14)

For an infinite cylinder:

=

=4

exp422

12

XMR nn n

(2.15)

(Perry, 1963). In the above equations, the average moisture content and the time are expressed as dimensionless quantities, MR and X, respectively:

eo

et

MMMM

MR

= (2.16)

)2/1()( DtVAX = (2.17)

For the plane, A/V= half thickness

For the sphere, A/V= (radius)/3

For the cylinder, A/V= (radius)/2

Brooker et al. (1974) described a number of physical mechanisms for transfer of moisture in capillary porous products. Due to complicated nature of drying phenomenon, success of developing the theoretically based drying equations has thus far been limited. Effect of initial moisture content, product temperature, and kernel volume/surface area and air humidity have not yet been fully incorporated in any of these drying equations.

16

2.3 Thin Layer Drying

Thin layer drying refers to the grain drying process in which all grains are fully exposed to the drying air at constant air temperature, and humidity. Practically, up to 20 cm thickness of grain bed is taken as thin layer (Chakraverty, 1995).

The diffusion models are often too complex in practical application as well as time consuming. The simplified rational equations do not describe the drying process of biological materials accurately, over the entire moisture content range encountered in the drying process. This is due to consideration of simplicity of the boundary conditions, the assumption of constant diffusivity and irregularities in shape and size of biological materials (Brooker et al., 1974; Pulavarti, 2004). To overcome the problems associated with the rational models, empirical thin layer drying equations have been an area of major interest in dryer designing and computer simulations.

A large number of empirical equations have been developed for most major grains. The equations describe the change in moisture content with time in thin layer drying of grain as a function of initial moisture content and the drying conditions. A well known empirical model analogous to Newtons law which refers to the heating or cooling of solids was developed by Lewis (1921) and later used by Huckill (1947). According to it, change in temperature between the body and the surrounding medium when temperature difference is small is mathematically expressed as:

)( eTTkdtdT

= (2.18)

The equation (2.18) on integration leads to;

kt

o

e eTTTT

=

(2.19)

By substituting moisture content, dry basis for the temperature in the above Equation

(2.19), following equation obtained kteMeMo

MeM

=

(2.20)

Equation (2.20) (Lewis, 1921; Brooker et al., 1974) often called exponential drying equation, assumes that the rate of moisture loss of grain kernel under constant drying

17

conditions is proportional to the difference between the kernel moisture and its equilibrium moisture content. The exponential model is one of the simplest models used in describing the thin layer drying of agricultural products.

A number of investigators, Henderson and Pabis (1961), Henderson (1974), White et al. (1981), Kulshreshtha (1983), Garg (1990), Kanawade (1990), Mishra (1991), Duggal (1984), Burande (1992), Pandey (2000), Faisal (2003) and Pulavarti (2004) have applied the above relationships (Eq.2.20) for describing thin layer behaviour of food grains as well as fruits and vegetables.

The experimental Equation (2.20) has been reported to be inadequate for describing thin layer behaviour by a number of investigators and several modifications have been proposed.

Page (1949) proposed following empirical model of drying of shelled corn:

nkteMR = (2.21)

Duggal (1984), Ajibola (1989), Garg (1990), Burande (1992), Basantpure (1997), Khiste (1997), Kar (1998), Pandey (2000), Pradeshi et al. (2001), Faisal (2003) and Pulavarti (2004) used above model for materials and found it to result in a better fit as compared to other models.

Becker (1959) proposed a specific model for a thin layer drying of wheat, of the form:

MR = 1 - 2/12/1

)()(2

piVDtS

(2.22)

Hustrulid and Flikke (1959) studied the theoretical drying curve for shelled corn and found that the diffusion equation for spheres describes very well the drying curve in the important early stages of drying.

ktn

ndo

de

nMMMMMR

2

122

16

=

=

=

pi (2.23)

Thompson (1967) and Thompson et al. (1968) proposed the following term of thin layer equation for corn drying in the temperature range of 140 to 300 0F.

18

t = A (ln MR) + B (ln MR)2 (2.24)

They found that A and B depend on temperature and

A=1.86178+0.00488 T (2.25)

B=427.3640 exp(-0.03301T) (2.26)

Van-Rest and Issac (1968) compared the prediction accuracy of Pages model Eq. (2.21) and the following Eq. (2.27). They found Eq. (2.27) to be most accurate in describing the thin layer data for corn.

MR = A + B ln(t) (2.27)

Log model (Eq.2.27) was shown to be most useful for empirical description of small grains like wheat and oat, over limited range of normal drying conditions and moisture ratio. Pages equation (2.21), however, has a wide application range over the Log model (Van-Rest and Issac , 1968).

Sabbah (1968) proposed the following empirical equation to thin layer drying data for corn in the temperature range of 36 to 70 0F:

MR= exp (-k t0.664) (2.28)

Where, K= exp (-XtY) and X and Y are the functions of temperature and relative humidity of drying air:

X= (6.0142 + 1.453x10-4(rh)2-T(3.353x10-4+3.0x10-8(rh)2)0.5 (2.29)

Y= 0.1245-2.197x10-3(rh)+ 2.3x10-5(rh)T-5.8x10-5T (2.30)

Nellist and OCallaghan (1971) proposed the following two term exponential equation for the thin layer drying of rice, soybean and corn.

e

tktk MBeAeM ++= 21 (2.31)

Overhults et al. (1973) found the following relationship for thin layer drying of soybean.

MR = exp (-Kt) N (2.32) Where, N = A+BT

19

Wang and Singh (1978) proposed the following empirical equation for thin layer drying of rough rice and parboiled rice.

MR= 1+ At+ Bt2 (2.33)

Agrawal and Singh (1984) studied thin layer drying of rice (paddy). The rice drying experiment were performed in triplicate of the following conditions : (1) constant relative humidity of 26% and at air temperature of 32, 35, 43, 47 and 51 oC and (2) constant air temperature of 51 oC and at an RH of 18.75, 26, 45, 65 and 85 percent. They conclude that the empirical equation proposed by Page (1949) for corn was found to the well described the all the thin layer data.

Narain and Bakker-Arkema (1986) summarized some of the empirical equations for drying grain as:

(1) 2/12/1

)()(21

piVDtSMR =

(Bakker, 1959) (2.34)

(2) t = A ln MR + B (ln MR)2 (Thompson et al., 1968) (2.35)

(3) )exp(0 KtBMR = (Singh et al., 1984) (2.36)

Ajibola (1989) studied the thin layer drying rates for melon seeds at different temperature (40-70 oC). A non linear least squares regression program was used to evaluate for three thin layer drying models (Exponential model, Pages model and Diffusion model) and concluded that the exponential model in which the drying constant is a function of temperature and relative humidity was found adequate for predicting thin-layer drying of melon seed.

Kulshreshtha et al. (1990) conducted studies on the drying of paddy in a 5-cm thin bed using heated air at eight drying temperatures from 30 65 oC and examined the performance of diffusion model and selected empirical models namely; exponential model, Pages model generalized exponential, Wang and Singh model and Thompsons quadratic model on the experimental data. They concluded that the theoretical diffusion model was not significantly different from empirical models of Page, Thompson, and the generalized form of the exponential model. Pages model performs the best in describing the thin layer paddy

20

drying data, even better than the more fundamental diffusion model on the basis of high coefficient of determination and low Standard error of estimate.

Mishra (1991) studied the drying behaviour of potato cubes and gave the following form of relationship for predicting drying rates:

)exp( CAtdt

dM+=

(2.37)

Equation (2.37) fits better with the experimental data for 40 80C temperature range and 100 180 m/min of air velocity range. Values of A ranged from -0.090 to -0.419 and C ranged from 4.466 to 6.123 for the specified range of temperature and air velocity.

The empirical models have the drawback of not giving any theoretical insight into the drying mechanism. These are preferred over rational models due to their simplicity of incorporation in the simulation and design.

Pathak et al. (1991) studied the thin layer drying rates of rape seed at eight levels of drying air temperature, four in the conventional drying temp range of 50-93 oC and four in the elevated temperature range of upto 200 oC. They developed a mathematical model of the form of Pages equation which predicted the moisture ratios well at the conventional as well as elevated drying temperatures.

Palipane and Driscoll (1994) studied the thin layer drying behaviour of in-shell nuts and kernels at the temperature range of 26-56 oC and 21-48 oC for testing validity of drying models. They found that two-term exponential model fitted the data best.

Jain and Singh (1997) studied the drying kinetics of green gram and conducted experiments on thin layer drying (thickness not specified) of greengram (Phaselous aureus Roxb.) at 40, 50, 60, 70 and 80 oC in a batch type cross flow dryer at an air flow rate of 3.45 m3/min. They concluded that the drying of green gram takes place in falling rate period and is governed by moisture diffusion. The total drying time for greengram decreases with increase in drying air temperature.

Joseph et al. (1997) studied the drying characteristics of large cardamom by using different dryers (Rotary dryer and natural convection dryer).The performance of these dryers was compared. It was found that it is possible to dry large cardamom from a moisture level of 80 % (wb) to 10% (wb),in natural convection dryer with a drying time of 24 hour to 28 our at

21

70 C. They also concluded that drying characteristics were independent of the dryer used and were controlled by the availability of moisture which in turn was dependent on the material characteristics.

Pezzutti and Crapiste (1997) studied drying characteristics of garlic including moisture sorption equilibrium, drying kinetics and pungency losses. Garlic slices were dried in a cabinet laboratory dryer at 45, 60 and 75 oC and three air velocities 2, 3 and 4 m/s and three air relative humilities 5, 30 and 50%. The final moisture content of the samples ranged from 6.3 to 17.1% on wet basis depending on drying conditions. Air drying experiments and a diffusive model taking into account the internal and external resistances to mass transfer were used to evaluate effective diffusivity and energy of activation for diffusion. It was concluded that the influence of temperature on adsorption was practically negligible and some hysteresis effect decreasing with temperature was observed. The effective diffusivity increased with temperature, ranging from 1.54 to 3.45 x 10-10 m2/s in the wet zone and from 0.34 to 0.58 x 10-10 m2/s in the dry zone. The effect of air velocity, air relative humidity and sample thickness on drying kinetics was also studied. Changes in garlic flavour or pungency during dehydration as a function of temperature, based on determination of pyruvic acid, was measured and modeled as a first order reaction.

Buser et al. (1999) determined the thin-layer drying characteristics of marigolds as a function of air temperature and airflow rate. A thin layer theoretical drying model was developed to describe the drying characteristics of marigolds. It was concluded that the optimum conditions for petal processing, in the temperature range of 55 to 70 oC and airflow range of 0.23 to 0.33 m3s-1m-2, were obtained at an air temperature of 70 oC and an airflow rate of 0.33 m3s-1m-2.

Sarsavadia et al. (1999) studied the thin-layer drying rates of brined onion slices at four levels of drying air temperature (50-80C), four levels of air flow velocity (0.25-1.00 m/s) and three levels of air relative humidity (10-20%). The experimental data obtained were fitted into a standard Arrhenius-type model and Power model using non-linear regression analysis. The Arrhenius-type model was found to be more suitable for predicting drying rate constants based on higher values of coefficient of determination and lower values of chi-square.

Temple and Boxtel (1999) studied the thin layer drying of black tea with tray load of 0.87 to 2.6 kg/m2. It was found that there was a very high rate of drying at the start of drying. A commonly used simple model, assuming that the resistance for water transport is all over

22

the surface of the particle, is represented by an equation analogous to Newton's law of cooling and also termed the Lewis equation (Jayas et al., 1991) satisfies:

( )eMMkdtdM

= (2.38) The

integrated form of this equation is termed the exponential drying model.

kt

e

ee

MMMM

=

0 (2.39)

They tested exponential model and derived a drying rate factor for the Lewis drying equation. The value for the rate factor was confirmed by independent experiment. The drying rate factor k for macerated tea particles was found to be dependent on air temperature as expected, and was observed to be strongly dependent on airflow velocity, in contrast to the results from other research. The reason why tea dries in a way different from most other agricultural produce may be because of the small particle size, the breakdown in cellular structure and the high concentration of soluble substances in the free cell sap. A single function describes the whole process of thin layer drying of tea from 70% m.c. w.b. down to 3%. The drying rate is directly proportional to the superficial airflow and the air, indicating that tea drying is not only diffusion limited but also a function of convection drying. The regression of the product of temperature and superficial velocity was of the following form:

k = (0.00028 x ( T- 45) u)- 0.00067 (2.40)

Hassan and Hobani (2000) studied thin layer drying rates for dates at different temperature (70, 80 and 90 oC) and evaluated for three drying models namely, Exponential, Pages and the diffusion model. They concluded that Pages model fitted the data best.

Freire et al. (2001) studied the thin layer (2-4 mm) drying kinetics of olive bagasse at high temperatures (125 to 250 0C). They experimentally analyzed the influence of product granularity, gas velocity and temperature on the drying kinetics of olive bagasse. The drying conditions, investigated in this study, included combustion products of air and propane with dry-bulb temperatures ranging from 125 to 250 0C, relative humidity lower than 1% and gas velocities ranging from 0.5 to 2.0 ms-1. The results of the drying kinetics experiments were fitted to a model based on Fick's law, which allowed for the determination of the effective moisture diffusivity as a function of temperature. Accurate agreement between experimental results and predicted curves was found for gas velocities above a critical velocity (the

23

minimum velocity above which the drying rate dependence on velocity becomes negligible) is quantified as 1.5 ms-1.

Tan et al. (2001) studied the thin layer drying characteristics of sweet potato chips and pressed grates. Drying conditions were: temperatures of 33, 51 and 70 oC, airflow rates of 0.084 and 0.145 m3/(s-m2) ; and absolute humidity of 1.003 X 10 -2 Kg H2O/kg dry air. The drying rates of pressed grates were higher than those of chips. The modified Pages equation describes thin layer drying of chips and pressed grates.

nkt)(eMR = (2.41)

The drying time required for chips to reach the moisture ratio of 0.5 varied between 2.4 and 6.1 times that of pressed grates. The chips and grates showed only falling rate periods; a constant rate period was not observed in any of the drying conditions. Drying air temperature and airflow rate had significant effects on the drying rate of chips and pressed grates. The drying rate and the K constant values for pressed grates were higher than those for chips at all drying conditions.

Dandamrongrak et al. (2002) examined the thin-layer drying behaviour of bananas in a heat pump dehumidifier dryer with two kilogram of sample per tray. Four pre-treatments (blanching, chilling, freezing and combined blanching and freezing) were applied to the bananas, which were dried at 50 oC with an air velocity of 3.1 ms_1 and with the relative humidity of the inlet air of 1035%. Three drying models, the simple model, the two-term exponential model and the Page model were examined. All models were evaluated using three statistical measures, correlation coefficient, root means square error, and mean absolute percent error. Moisture diffusivity was calculated based on the diffusion equation for an infinite cylindrical shape using the slope method. The rate of drying was higher for the pre-treatments involving freezing. The sample which was blanched only did not show any improvement in drying rate. In fact, a longer drying time resulted due to water absorption during blanching. There was no change in the rate for the chilled sample compared with the control. While all models closely fitted the drying data, the simple model showed greatest deviation from the experimental results. The two-term exponential model was found to be the best model for describing the drying curves of bananas. Constants were tabulated with different treatment. Moisture diffusivities of bananas were in the range 4.313.2 X 10_10 m2s-1.

24

Panchariya et al. (2002) the thin layer (layer thickness not specified) drying characteristics of tea using heated ambient air for the temperature range of 80120 oC and air flow velocity of 0.250.65 m/s. The drying data were then fitted to the different semi-theoretical models such as Lewis, Page, modified Page, two-term and Henderson and Pabis models. Based on the ratios of the difference between the initial and final moisture contents and the equilibrium moisture content, the Lewis model gave better predictions than other models, and satisfactorily described the thin-layer drying characteristics of black tea particles. The effective diffusivity varied from 1.14 X 10_11 to 2.98 X 10_11 m2/s over the temperature range. The temperature dependence of the diffusivity coefficient was described by the Arrhenius-type relationship. The activation energy for moisture diffusion was found to be 406.02 kJ/mol. The dependence of drying constant on temperature and air velocity was described by the Arrhenius type and Power-type relationships. The coefficients of determination were above 0.996 for both relationships. The Arrhenius-type model was used to predict the acceptable moisture ratios at the experimental drying conditions and to investigate the influence of drying variables on drying rate constant. The results illustrate that in spite of high initial moisture content, the drying of tea particles takes place only in the falling rate period.

Togrul and Pehlivan (2002) conducted thin layer (thickness not specified) solar drying of apricots. Drying curves obtained from the data were fitted to a number of mathematical models and the effects of drying air temperature, velocity and relative humidity on the model constants and coefficients were evaluated by the multiple regression and compared to previously given models. The logarithmic drying model was found to satisfactorily describe the solar drying curve of apricots with a correlation coefficient (r) of 0.994. The constants and coefficients of this model could be explained by the effect of drying air temperature, velocity and relative humidity with a correlation coefficient (r) of 1.000.

Akpinar et al. (2003 b) investigated the thin layer drying behaviour of red pepper slices in a convective dryer and performed mathematical modelling by using thin layer drying models in literature. Drying experiments were conducted at inlet temperatures of drying air of 55, 60 and 70 oC and at a drying air velocity of 1.5 m/s. Eleven different thin layer mathematical drying models were compared according to their coefficient of correlation to estimate drying curves which are:

25

1. Two term model

)exp()exp( 10 tkbtkaMR += (Henderson, 1974) (2.42) 2. Wang and Singh model

21 btatMR ++= (Wang and Singh, 1978) (2.43)

3. Modified pages model

( )[ ]nktMR = exp (White et al., 1978)(2.44)

4. Two -term Exponential model

)exp()1()exp( kataktaMR += (Sharaf-Elden et al., 1980)(2.45)

5. Verma et al.

)exp()1()exp( gtaktaMR += (Verma et al., 1985)(2.46)

6. Newtons model

)exp( ktMR = (Muzumdar, 1987)(2.47)

7. Henderson and Pabis

)exp( ktaMR = (Zhang and Litchfield, 1991)(2.48)

8. Pages model

)exp( nktMR = (Diamante and Munro, 1993)(2.49)

9. Modified Henderson and Pabis

)exp()exp()exp( htcgtbktaMR ++= (Karathanos, 1999)(2.50)

10. Logarithmic model

cktaMR += )exp( (Yagcioglu et al., 1999)(2.51)

11. Approximation of diffusion

)exp()1()exp( kbtaktaMR += (Yaldiz and Ertekin, 2001)(2.52)

The effects of drying air temperature on the model constants and coefficients were predicted by regression models. According to the results, an approximation of the diffusion model could satisfactorily describe the drying curve of red peppers with a correlation coefficient (r) of 0.9987. The constants and coefficients of this model could be explained by the effect of drying air temperature.

Cao et al., 2003 studied the thin layer drying characteristics of Maitake mushroom (Grifola frondosa) in a tray drier, having high initial moisture content of 98.28% w.b. They

26

analyzed using the modified plate drying model. This consists of three parameters; surface mass transfer coefficient H, dynamic equilibrium moisture content Md, and drying constant k.

)(1)()()( 1

1 11,,1,,1,

nnn

n

j jnjsjsnnnsnennns tktH

XXMMtkMMtHMM

+++

=

=

(2.53)

The model was simplified by presenting an approximate function expression for the plate drying model and can be used to simulate the drying process for Maitake mushroom. Samples were dried at various air temperatures (35, 40, 45, 50 and 55 oC) and relative humidities (30, 40, 50, 60 and 70%). Results indicated that the modified plate drying model, used to predict the moisture content and drying rate, fitted reasonably well with experimental results for the different drying conditions. Both the drying constant k and surface mass transfer coefficient H of the modified plate drying model were expressed as Arrhenius-type functions of temperature.

Doymaz and Pala (2003) studied the thin-layer drying characteristics of corn with single layer of uniform corn kernels having average radius 3.4 mm. The exponential equation and the Pages equation were used to determine the thin-layer drying characteristics. Both the equations fitted well to the experimental data. The Pages equation was found to better describe the thin-layer drying of corn than the single exponential equation. The effective diffusivity was determined to be 9.488 X 10_11 to 1.768 X 10_10 m2/s for the untreated corn and 1.424 X 10_10 to 2.716 X 10_10 m2/s for treated samples ( treated with alkali solution of four percent potassium carbonate containing two percent ethyl oleate) in the temperature range of 5575 oC. The activation energies for diffusion were calculated to be 29.56 kJ/kg mol (for untreated) and 30.56 kJ/kg mol (for treated).

Doymaz (2004 a) studied the drying kinetics of white mulberry. Six different thin layer mathematical drying models were compared according to their coefficient of determination to estimate drying curves. The effective moisture diffusivity values were estimated from Ficks diffusion model. These values were in the range 2.2316.909 X 10-10 m2/s. Comparing the r2, SSE and RMSE values of the six models, it was concluded that the logarithmic model represents drying characteristics better than the other equations.

Doymaz (2004 b) investigated the effects of air temperature, air-flow rate and sample thickness on drying kinetics of carrot cubes. Convective air drying characteristics of carrot cubes were evaluated in a cabinet dryer. Drying was carried out at 50, 60, 65, 70 oC and

27

drying data were analyzed to obtain diffusivity values from the period of falling drying rate. In the falling rate period, moisture transfer from carrot cubes was described by applying the Ficks diffusion model and effective moisture diffusion coefficients were calculated which were in the range of 0.776 x 109 and 9.335 x 109 m2/s. Effective diffusivity increased with increasing temperature. An Arrhenius relation with an activation energy value of 28.36 kJ/mol expressed effect of temperature on the diffusivity. Two mathematical models, namely, Pages model and Henderson and Pabis model, available in the literature were fitted to the experimental data. The Pages model is given better prediction than the Henderson and Pabis model and satisfactorily described drying characteristics of carrot cubes.

Erenturk et al. (2004) studied the thin-layer drying characteristics of rosehip, fruit of rose. The system was operated in an air temperature range of 5080 oC, air velocity range of 1.673.10ms_1 and air absolute humidity range of 0.0050.08 kg [vapor] kg_1 [dry air]. Six mathematical models available in the literature were fitted to the experimental data namely; Newtons model, Pages model, Henderson Pabis model, logarithmic model, two-term model and Wang and Singh model. By statistical comparison of the values for the six models, it was concluded that the logarithmic model represents drying characteristics better than the other equations.

Lahsasni et al. (2004 a) studied the thin layer solar drying of prickly pear cladode (Opuntia ficus indica ) at three drying air temperatures (50, 55 and 60 oC), and three drying air flow rates (100, 200 and 300 m3/h). They found that the drying air temperature was the main factor influencing the drying kinetics and that only the falling drying rate period exists the Pages model satisfactorily described the solar drying curves of cladode with an R2 of 0.9995 and constants k and are temperature dependent whose relationship was:

k = -0.2838 + 0.0103 T - 9.2326 x 10-5 T2 (2.54)

n = 13.3725-0.4483 T + 0.0041 T2 ...(2.55)

Lahsasni et al. (2004 b) studied the thin layer convective solar drying and mathematical modelling of prickly pear peel (Opuntia ficus indica) prickly pear peel is sufficiently dried in the ranges of 32 to 36 oC of ambient air temperature, 50 to 60 oC of drying air temperature, 23 to 34% of relative humidity, 0.0277 to 0.0833 m3/s of drying air flow rate and 200 to 950 W/m2 of daily solar radiation. The experimental drying curves show only a falling rate drying period. The main factor in controlling the drying rate was found to be the drying air temperature. The drying rate equation is determined empirically from the

28

characteristic drying curve. Also, the experimental drying curves obtained were fitted to a number of mathematical models namely, Newtons model, Pages model Modified Pages model, Modified Pages model, Henderson and Pabis model, Logarithmic model, Two term model, Two term exponential model, Wang and Singh model, Approximation of diffusion model, Modified Henderson and Pabis model and MidilliKucuk. The model given by Midilli and Kucuk (2003) was

bta += nkteMR

(2.56)

The MidilliKucuk drying model was found to satisfactorily describe the solar drying curves of prickly pear peel with a correlation coefficient (r) of 0.9998.

Togrul and Pehlivan (2004) conducted experiments to study the drying behaviour of fruits during open-air sun drying on apricots pre-sulphured with SO2 or NaHSO3, grapes, peaches, figs and plums, in the ranges of 2743 oC for ambient temperature and 0.722.93 MJ/m2h for solar radiation. The drying rate curves of these fruits contained no constant rate period, but showed a falling rate period. Twelve mathematical models were tested to fit the drying rates of the fruits namely; Newtons model , Pages model, Modified Pages model, Henderson Pabis model, logarithmic model, two-term model, Wang and Singh model, approximation of diffusion, Verma et al. model, Modified Henderson and Pabis model, two term exponential model and Modified Page Equation-II. They found that the approximation of the diffusion model for apricots (non-pre-treated or SO2-sulphured) and figs, the modified Henderson and Pabis model for apricot (NaHSO3-sulphured), grape and plum, and the model given by Verma et al. (1985) for peach were found to be the best fit for one layer open sun drying behaviour of the fruits.

Desmorieux and Decaen (2005) studied the convective drying of spirulina, which is microalgae with therapeutic and nutritional properties, in thin layer. Spirulina sorption isotherm was established at 25 and 40 oC. A simple Henderson model is proposed to represent the isotherm. The drying by convection was studied to investigate the influence of temperature and air velocity. Under conditions of temperature and air velocity of less than 40 oC and 2.5 m/s, the first drying phase appears on the curves. By normalizing, the use of the drying characteristic curve allows the regrouping of curves and the representation of thin layer spirulina convective drying by a polynomial function.

29

Mohapatra and Rao (2005) studied the thin layer drying characteristics of parboiled wheat for a temperature range of 4060 oC, using semi-theoretical and empirical models as reported by Henderson and Pabis model (Henderson and Pabis, 1961), Lewis model (Bruce, 1985), Two-term model (Henderson, 1974), Pages model (Page, 1949, cited by Bruce, 1985), Wang and Singh model (Wang and Singh, 1978), Thompsons model (Thompson et al., 1968).

The total drying occurred in falling rate period, signifying the influence of moisture diffusion during drying. The effective diffusivity varied from 1.218 X 10-10 to 2.861 X 10-10 m2/s over the experimental temperature range. Temperature dependence of the diffusivity was well documented by Arrhenius-type relationship. The activation energy of for moisture diffusion during drying was found to be 37.013 kJ/g mol K. The thin layer drying characteristics of parboiled wheat was well fitted with the Two-term model with the drying constants being in a linear relationship with drying temperatures.

2.4 Equilibrium Moisture Content

For solutions of most of the theoretical as well as empirical models, data on equilibrium moisture content is required. Me may be assumed, as was done by (Brooker et al., 1974) in the analysis of diffusion process or calculated by using techniques given by following authors: Ross and White (1972), Burande (1992), Khiste (1997), Basantpure (1997), Kar (1998), McMinn and Magee (1999), Pandey (2000), Faisal (2003) and Pulavarti (2004),

Udani et al. (1968) studied the rate of moisture adsorption by wheat flour at 40C and over the relative humidity range of 40 to 80 percent using the technique of Gur-Arieh and Nelson (1965). They found their data to obey the first order kinetics model of the form:

)exp(1k

t

WWWW

oe

o =

(2.57)

The rate constant k increased linearly with relative humidity of air and decreased with protein content of the flour but did not change with particle size.

Ross and White (1972) used the concept of dynamic EMC (Md) instead of equilibrium moisture content. (Me) was evaluated by least square regression of thin layer drying data. They found K and Md to be function of temperature and suggested the following form of

30

relationship for the temperature range of 40-70 0C and Relative humidity in the range of 6- 25%:

ln (K) = A B/T (2.58)

Md = A BT (2.59)

Iglesias et al. (1975 a) studied water sorption isotherms in sugarbeet root. They suggested that the multi-layer adsorption equation could be used to describe the sorption behaviour of a great variety of food materials. Halseys equation is :

=

ro XRT

a

PP

exp (2.60)

The equilibrium moisture content decreased linearly with increase in temperature.

Iglesias et al. (1975 b) also studied water vapour sorption by sugar beet root components. The predicted and experimental isotherms were in agreement at low relative humidities up to 25% probably because of phase transition of sucrose than at higher relative humidities. They also determined the water sorption isotherm of raw sugar beet root and its water insoluble components at various temperatures and attempted to describe the experimental equilibrium moisture content data using some existing theories on physical adsorption

Singh (1977) studied water vapor sorption kinetics by extruded Soya- products at six relative humidity levels of 50, 60, 70, 75, 80 and 100% and at three temperature levels of 30, 35 and 40 0C. In this study, he used whole and ground nutri-nugget, a product made from defatted extruded soy meal. He found that the following model described the data well.

kWkWt

WWo

o=

(2.61)

The rate constant and the equilibrium moisture content were independent of the initial moisture content, but the dependence of rate constant on temperature obeyed Arrhenius law. The rate constant was tabulated for different values of relative humidity at different experimental temperatures. Rate constant was, however, independent of temperature in the relative humidity range of 72.8 to 75.0 percent.

31

Bandyopadhyay and Roy (1978) concluded that the soaking of paddy, irrespective of variety, can be described by semi- empirical equation of the form

tKmmm mio'2 pi=

(2.62)

where,

( ) 1' DVS

mmK osm

=

(2.63)

Their equation predicted well the moisture gain by paddy as influenced by time and temperature of soaking. The data on large number of paddy varieties confirmed their hypothesis that soaking of paddy is influenced by two different mechanisms, one below and other above the gelatinization temperature of rice starch.

Chirife and Iglesias (1978) presented a review of literature on equations for fitting water sorption isotherms of various food products. Twenty- three equations, which have been reported in literature for correlating equilibrium moisture content in food materials were considered and analyzed for their origin, range of applicability (both to type of food and water activity) and uses. The equations are:

1. The B.E.T. equation 2. The B.E.T. modified equation 3. The Bradley equation 4. The Caurie equation 5. The Chen equation 6. The Chens modified equation 7. The Chen and Clayton 8. The Chung and Pfost equation 9. The Day and Nelson equation 10. The Hailwood and Horrobin equation 11. The Halsey equation 12. The Harkins-Jura equation 13. The Haynes equation 14. The Henderson equation 15. The Iglesias and Chirife equation

32

16. The Halseys modified equation 17. The Kuhn equation 18. The linear equation 19. The Mizrahi equation 20. The Oswin equation 21. The Smith equation 22. The Strohman and Yoerger equation 23. The Young and Nelson equation

Singh et al. (1981) studied the kinetics of water vapour sorption by wheat flour from saturated atmosphere and used the following equation for obtaining equilibrium moisture content.

Wn+1 = Wn.Z + We (Z-1) (2.64) Where, Z = exp (-0.25k) t = tn+1 tn

They used a method developed by Isaacs and Gaudy (1968) to calculate equilibrium moisture content Me as the K value was small in the experiment and so error in the determination of Z was large. For the present case Isaacs and Gaudy equation for parameter Me comes to (2.65) which leads to equation (2.66) and (2.67).

Dwivedi (1984) studied the kinetics of moisture absorption by pigeon pea and used the equation (2.68) for obtaining equilibrium moisture content. He also used a method developed by Isaacs and Gaudy (1968) to calculate equilibrium moisture content Me as the k value was small in the experiment and so error in the determination of Z was large. For the present case Isaacs and Gaudy equation for parameter Me comes to

ininn

ininne

WWWWWWW

++

++

+

=

2)(.

2

22

(2.65)

From We values, Me values were calculated using relation:

100)100( ' +=o

MWWM

o

ee

(2.66)

33

100)100(' += os

o

o MWWM (2.67)

Kanawade (1990) used the following equation for obtaining equilibrium moisture content.

Mn+1 = Mn.Z + Me (Z-1) (2.68)

Where,

Z = exp (kt)

t = tn+1 tn

Garg (1990) also used the equation (2.68) for obtaining equilibrium moisture content. He also concluded that the values of Me, computed from the drying data represent the limiting moisture content for the dynamic equilibrium, which is more logical in computation of moisture ratio of a drying process rather than the equilibrium moisture content obtained through the static method.

Burande (1992) and Khiste (1997) found EMC by trial error, which was tried at 1 or 2% lower than the final moisture content. They observed that 1% reduction in final moisture content gave information on equilibrium moisture content.

2.5 Factors Affecting Hot Air Drying

Thermal conductivity, specific heat and density depend on the chemical composition and physical structure of the material. Therefore, these properties vary from material to material. Moreover, they also change during drying process as the composition of solid matrix-change (Pulavarti, 2004). It is reported that the carrot pieces took five hours as against seven hours for Potato under same drying conditions and initial dimensions of the sample to reach final moisture content 0.06 (d.b.) (Van Arsdel and Copley, 1963).

2.5.1 Blanching

In case of agricultural products, the pretreatments are necessary before processing in order to retain colour, inactivate enzymes and/or enhance rate process. The ultimate aim of pretreatment is to improve quality of final product and reduce processing cost. The pretreatments differ from product to product. Blanching of fruits and vegetables is principally

34

followed to inactivate the enzymes responsible for enzymatic and oxidative browning. The common methods of blanching include hot water, steam and chemical blanching. The loss of nutrients takes place during blanching which depends on temperature and time of blanching. For high moisture products blanched material dries more rapidly than the unblanched one (Van Arsdel and Copley, 1963).

Jayaraman et al. (1982) studied the drying of diced ( in. cube) vegetables pieces. They were blanched in boiling water of 0.1% potassium metabisulphite for a period of 5 min for potato, carrot and yam. They dried in two stages, first high temperature for a short time of 160 to 180C for 8 min and subsequently dried to 5% moisture in a conventional dryer with 60 70C of hot air. They found the required drying time decreases to one third, bulk density also decreased to about half and there was also decrease in reconstitution time which was about less than half and rehydration ratio increases to about double than direct tray drying. These results are amenable for starch cell structure.

Katara and Nath (1985) worked on 0.75 cm, 1.25 cm and 1.75 cm cubes of three varieties of Kufri Badshah, Kufri Muthu and Kufri Jyoti. They reported blanching with 1% KMS and 2% brine solution improved colour, dehydration ratio and rehydration ratio compared to unblanched and only KMS blanched samples.

Jayaraman et al. (1990) developed dehydrated cauliflower by soaking the blanched pieces in solution of different concentrations of common salt and sucrose (cane sugar), alone and in combination and in a cabinet drier. They evaluated sensory, rehydration, storage, microbiological, histological and sorption characteristics. The optimum treatment found was soaking in 3% salt and 6% sucrose for 12-16 h at 4 oC; it markedly reduced shrinkage and improved rehydration without affecting palatability. It was necessary to boil the soak solution for 3 min and cool prior to soaking to reduce microbial contamination.

2.5.2 Temperature, layer thickness, air velocity and relative humidity

Chirife (1970) investigated the drying characteristics of freshly harvested tapioca root (Manihot utilissma Pohe) from posadas, Argentina in a laboratory through circulation dryer. Variables studied were bed depth (2-12 cm), air velocity (2,300-5,200 Kg / (hr) (sq.meter), and air temperature (55-100C). Static pressure drops of air passing through beds of dried and wet slices also were investigated. Straight lines are obtained plotting on semi logarithmic paper the non dimensional moisture content (w-we) / (wo-we) against time. He concludes that a diffusional mechanism is controlling the drying rate. Factors to be considered in the

35

design of a continuous through circulation dryer are bed depth, air velocity and air temperature.

Mazyak et al. (1973) established the effects of various technological parameters, for example, the shape and size of the material to be dried, velocity and temperature of heating medium, specific loading of the material, on fluidized bed drying behaviour of potatoes. The application of periodic aeration resulted in 1.5 times less energy consumption and 2.5 times less drying time.

Carpi et al. (1977) conducted experiments on dehydration of potato cubes (1.0 cm3) with an aspirated air flow. A centrifugal fan produced an air stream which hit the product vertically. Drying time was reduced compared with parallel or counter flow circulations. Residual moisture was 7.9% and degree of rehydration is 45.0.

Babenya et al. (1978) monitored the drying rate for the first falling period in a laboratory hot drier, equipped with a discharge unit for a weighing the product. The drying rate was studied as a function of drier potential, air speed, potato cube size and batch per m2, method for establishing the above variables was experimentally tested at 60-100C and speed of 0.5-3.5 m/s. Cubes were 5-10 mm in size and the batch was 10-16 kg/m2.

Syarief et al. (1984) determined the thin layer drying rates of sunflower seeds as a function of temperature, relative humidity, initial moisture content and air velocity. The experiments were conducted over a temperature range of 27 to 33 oC, initial moisture content of 21 and 26 percent, drying air velocities of 0.1, 0.3, 0.5, m3/s-m2 and 20 to 80% relative humidity. The drying air temperature had the greatest effect on drying rate. The experimental data were fitted to four drying models Log model, Pages equation, Thompson model and Glenns two lump kernel drying model. Out of which Pages equation with drying constant as a function of drying air temperature was found to be best to describe the data.

( )NtkMR = exp (2.69)

Where k = 5.16 X10-5 (T)1.8387

N = 1.009 0.0049 (T)

Chiang and Peterson (1985) studied thin layer air drying of French fried potatoes. During entire process of drying, the temperature had dominant effect at low relative humidity

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in comparison to high relative humidity while the air velocity had the least effect on drying rate.

Gowda et al. (1986) studied dehydration of onions in single stage drying at air temperature of 50, 60, 70 and 80C and loading rates of 5, 10, 20 and 30 kg/m2. Keeping the air flow rate at 55 m3/min/m2 of tray area and a constant slice thickness of 5 mm using a model dehydrator, they stated that the air temperature of 60C and loading rate of 30 kg/m2 gave satisfactory results with acceptable quality of end product.

Munde et al. (1988) developed a process for multistage dehydration of onion flakes. They dried 4 mm thick onion slices at 50, 60, 70, 80, 90 and 100C temperatures upto 30, 40, 50 and 60 per cent cutoff moisture levels and the remaining moisture was removed at the control temperature of 50C. On the basis of quality factors and production time, they recommended the two stage dehydration process and also stated that the four stage dehydration process saves 24% drying time at the cost of very marginal sacrifice in quality from the possible best two stage dehydration process.

Chand et al. (1990) dried chilli (Capsicum) fruits and studied the effect of drying temperature and storage on capsaicin content. They were dried at two air temperatures of 50 and 52.5 oC and at an air velocity of 1.5 m/s in a bed thickness of 10 and 15 cm. they found that the drying of chillies (capsicum) at 50 oC air temperature and 1.5 m/s air velocity was more desirable in a bed thickness of 10 cm. It required 15 hours to reduce the moisture level from 73.4 to 5.7-11.5% (d.b.) in the freshly harvested chillies. Hot air method of drying was found better than sun drying which took about 20-35 hours (3-5 days). Drying at more than 50 oC resulted in the loss of capsaicin (pigment found in chilli)

Mishra (1991) studied the drying behaviour of Potato cubes (cube size: 10mm x 10mm x 10mm) under single layer drying (1 cm) condition in relation to drying air temperatures (40, 50, 60, 70 and 80C) and air velocities (100, 120, 140, 160 and 180 m/min). On the basis of experimental results, he concluded that Potato cubes should be dried at air temperature of 70C or higher to obtain final moisture content below 12% (d.b.).

Kanawade and Narain (1993) studied the effect of pretreatment and drying temperature on quality of dehydrated peas. They observed that pricking and blanching before drying increases colour, texture, flavour and overall acceptability scores. But the increase in drying temperature from 60 90C significantly decreased colour score. The

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scores for texture, flavour and overall acceptability increased with increase in drying air temperature from 60 80C.

2.5.3 Variety of agricultural product

Singh and Kumar (1984) dehydrated 3/8 mm onion flakes of different varieties in a cross flow hot air dryer at 60C for 10 h and reported drying ratio, colour of the product, moisture content and rehydration characteristics. On the basis of above parameters, they found that Udaipur 101 and Udaipur 102 varieties of red and white onions respectively were most suitable for dehydration as compared to other varieties

Rajkumar and Sreenarayanan (2001) dehydrated white and red colour onion varieties in a cross flow drier at different temperatures viz., 50, 60 and 70C with different sulphitation levels as pre-treatments. They reported that drying at 50C and 0.4% sulphitation retained more ascorbic acid while, a temperature of 60C and 0.3% sulphitation showed less non-enzymatic browning and scored maximum points in organoleptic studies.

Davidson et al. (2004) studied the thin layer (thickness not specified) forced-air drying of ginseng roots. Based on analysis of drying rates, an exponential model was used to describe the relationship between moisture ratio and drying time. They concluded that exponential model parameters depended on root size and shape and were estimated from thin-layer drying experiments. Quality of roots dried by a three-stage process was not significantly different from roots dried at a constant air temperature of 38 oC in terms of internal colour and ginsenoside content. Drying times were in the range of 100150 h for the three-stage process compared to 180200 h at 38 oC.

2.6 Drying of High Moisture Foods

Most of the high moisture foods such as fruits and vegetables may undergo three phases of drying (a) constant rate (b) first falling rate (c) second falling rate (Sarvacos and Charm, 1962). Many researchers have observed that during the early phase of drying such as constant rate and first falling rate, the mechanisms of moisture transfer is mainly liquid diffusion. (Sarvacos and Charm, 1962; Van Arsdel and Copley, 1963; Chen and Johnson, 1969; Mishra, 1991; Pandey, 2000).

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During the constant rate period of drying the magnitude of the rate of drying is dependent upon the area exposed, difference in humidity between air stream and wet surface, the coefficient of mass transfer and the velocity of the drying air (Pulavarti, 2004).

The stage of critical moisture content occurs between the constant rate and falling rate periods. The critical moisture content is the minimum moisture content of the product that will sustain a rate of flow of free water to the surface of the product equal to the maximum rate of removal of water vapour from the product under the drying conditions (Pulavarti, 2004).

Husain et al. (1972) used mathematical modelling of desorption rates and heat transfer in food stuffs during dehydration studies and explained the mechanism of moisture movement. They observed falling rates with no constant rate period during the entire drying process. The Arrhenius equation for the moisture independent diffusivity was given as:

D(T) = 1.505 Exp. (-1562.25/Tabs) (2.70)

were significantly affected by the diffusion index and thermal diffusivity.

Jayaraman et al. (1982) studied the drying of diced vegetables pieces. The vegetable pieces were blanched in boiling water of 0.1% potassium metabisulphite for a period of 5 min for Potato, carrot and yam and was dried in two stages, first high temperature for a short time of 160 to 180C for 8 min and subsequently dried to 5% moisture in a conventional dryer with 6070C of hot air. They found that the required drying time decreases to about one third, considerable decrease in bulk density (i.e. about half ), decrease reconstitution time about less than half and rehydration ratio increases about double due to high temperature drying than direct tray drying.

Shrivastava and Nath (1985) studied the drying of fresh and brined cauliflower. They blanched the cauliflower cloves snow-ball for 6 min. in boiling water, 5% NaCl and 0.05% citric acid, 0.25% EDTA and 1% citric acid, or 0.75% sodium metabisulphite and 0.25% sodium sulphite (sulphite mixture). They observed that dried product obtained from raw cauliflower was unacceptable due to dark colour and poor rehydration ratio. Blanching alone in water did not improve colour of dried product. However they observed that use of 0.75% sodium metabisulphite and 0.25% sodium sulphite mixture reduces the enzymatic browning to 0.3372 O.D.(Optical Density).

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Diamante and Munro (1991) studied the effect of air dry bulb temperature, (50 70C), air relative humidity (10 15%), air velocity (0.5 3.0 m/sec) and slice thickness (3 9 mm) on the thin layer air drying of sweet Potato slices was investigated. They found drying rate curve consisted of two falling rate periods and contained no constant rate period. It was found that the modified Page equation best describe the thin layer air drying of sweet Potato slices down to moisture content of 10% dry basis.

Hawaldar et al. (1991) studied the drying characteristics of tomatoes under various operating conditions. Experiments were conducted at different temperature (40, 50, 60, 70 and 80C) and air flow velocities (0.4, 0.7, 1.0, 1.4 and 1.5 m/s) to determine the drying characteristics of tomatoes. Diffusion model was used to study the drying of sliced tomato specimens. Shrinkage was observed and this effect was taken into account in the basic diffusion model through the use of power law expression that related apparent shrinkage to moisture content. Analysis of experimental data yielded correlations between the effective diffusivity and both temperature and air velocity.

Mantri and Agrawal (1991) studied on the dehydration of ginger. They used multistage dehydration process they reported ginger dehydrated at 85C upto a moisture content of 50% (w.b.) during the first stage and then dried upto 12% moisture content (w.b.) by 65C temperature of air. This process reduces the drying time and improving or at least maintaining the quality of ginger.

Wang and Brennan (1991) studied the drying rate of Potato slices in hot air dryer at 40, 50, 60 and 70C and air flow rate 4.0 m/s. Drying behaviour of potato depends on temperature and shrinkage and should be taken into account in predictive models.

Nougeria and Park (1992) conducted the drying experiments of banana fruits to obtain banana passa (dried banana). The drying process was accomplished using three temperatures (50, 60 and 70C) and three air velocities (0.5, 1.0 and 1.5 m/sec). The parameters that produced the highest effective diffusivity were 60 m2/s at temperature of 70C and air velocity of 1.5 m/sec.

Ronald et al. (1992) investigated the effects of air velocity, slice thickness and pretreatment with sodium chloride solution and surface active agents( influences capillary flow in liquid transfer in a porous solid and can be used in food drying to identify the controlling mechanism) on drying of potato slices. They found that the drying occurred entirely in the falling rate period. Diffusion coefficients increased with the addition of sodium

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chloride and surface active agents. Diffusion coefficients were also influenced by air velocity and slice thickness.

Madamba et al. (1996) studied the thin layer (2-4 mm thick) drying characteristics of garlic slices (2-4 mm) for a temperature range of 50-90C, a relative humidity range of 8-24% and an airflow range of 0.5 1.0 m/s. They found that the temperature and slice thickness significantly affect the drying rate while relative humidity and air flow rate were insignificant during drying.

Karathanos and Belessiotis (1997) conducted dying experiments for various products, such as sultana grapes, currants, figs, plums and apricots. It was observed that most perishable fruits and vegetables were dried in the falling rate period.

Khiste (1997) studied the dehydration characteristics of vegetable kofta at temperatures in range of 60 to 90 0C and air velocities of 60, 90 and 120 m/min. He concluded that kofta should not be dried at air temperatures of 90C or higher. Medium drying conditions resulted in best quality of the product. As far as possible, lower temperature at 60C with medium velocity 90 m/min or medium temperatures (70 and 80C) with lower velocity of 60 m/min should be used.

Baig and Chakraverty (2002) studied the effects of pre treatments of carrot slices on drying characteristics at air temperatures of 40, 50 and 600C at an air velocity of 0.75 m/s using hot water blanching at 80 oC for 3 min by treatment with 2% NaCl solution for 30 min. They found that the air temperature of 500C appears to be suitable for dehydration of carrot slices.

Pandey (2000) studied the dehydration characteristics of cauliflower using blanched samples in 0.10-0.30% of KMS concentration and then soaked for 15-75 min range of dipping time in starch solution of 1.5-3.5% concentration and then dried at 40-80C temperature of air. He found that the browning was affected by increase in temperature. The experimental data was tried on exponential model, generalized exponential model, Pages model, logarithmic model and power law model. Pages model was found best fit in predicting the moisture ratio at different conditions with coefficient of determination values varying in the range of 0.9992 to 0.9510.the average value of n was 1.032. The optimum values of dehydration and rehydration were 14.75 and 10.64 respectively.

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Singh et al. (2002) studied the dehydration kinetics of onion slices using a tray drier at 55- 60oC. The kinetics revealed that the dehydration of onion occurred in the falling rate period. The sorption isotherms and rehydration studies were also conducted on the dried product. The overall acceptability was found to be very good for dehydrated onion slices.

Arora et al (2003) studied the drying kinetics of Agaricus biporus and Pleurotus florida Mushrooms in a tray drier at 45, 50,55,60,65 oC. They found that the drying took place in the falling rate period and the drying kinetics was adequately described by Pages model.

Faisal (2003) studied the optimization of hot air drying of cauliflower in the temperature range of 43-76 oC and air velocity in the range of 0.47 to 6.52 m/s for tray load of 0.5-2.5 kg/m2. He found browning was affected by increase in temperature. Pages model was shown satisfactory results for drying data on the basis of high coefficient of determination and low standard error of estimate. The compromise optimum levels of the three variables for blanched samples was found to be 57.45 oC, 5.21 m/s and 2.36 kg air temperature, air velocity and sample size respectively. The compromise optimum levels of the three variables for blanched and soaked samples were found to be 55.88oC of air temperature, 5.13 m/s of air velocity and 2.35 kg of sample size.

Mittal et al. (2003) studied the dehydration characteristics of plum (Prunus domestica L.) pulp. The extracted pulp was dehydrated in a cabinet drier at temperatures of 50, 60 and 70 oC and tray load of 0.2, 0.4, 0.6, 0.8, 1.0 and 1.2 g/cm2. The regression analysis was performed to develop mathematical model using dehydration time, dehydration temperature and tray load as independent variables and moisture content as dependent variable. The R2 value (0.94) obtained for the model indicates that it can be used to predict plum pulp moisture content under various dehydration conditions.

2.7 Drying of Apple, Apple Puree, Apple Pomace and other Fruit Pomaces

Pruthi and Agarwal (1971) studied the effect of different treatments and sun-drying of Mandarin orange pomace and peel on the recovery and quality of pectin extracted there from. They reported that Mandarin orange pomace took 4 days to sun-dry while Mandarin peel took 3 days to dry (when spread at the tray load of 4 and 5 lb per tray (1.81 and 2.27 kg per tray ) of 16 inches X 32 inches to a moisture content of about 4.4-6.8 percent without significantly affecting the recovery and quality of pectin. The rate of drying was faster in peel than in pomace. Mixing of peel with pomace accelerated the rate of drying of pomace, which

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otherwise retards the rate of heat transfer. Combined drying of peel and pomace has been suggested. Blanching of the material somewhat improved the rate of drying.

Agarwal and Pruthi (1972) studied the effect of different methods of dehydration of Mandarin orange waste (peel and pomace) on the quality and recovery of pectin. Of the two methods of dehydration employed, viz., through-draught and cross draught, the former was better for efficient and quicker dehydration of mandarin waste at air temperature 60 oC. The optimum tray load was found out to be 40-50 lbs per tray (18.18-22.73 kg per tray) of 2.5 inches X 2.5 inches X 0.5 inches. Depending upon the initial moisture content of the material, the drying times for peel, pomace and mixture of peel and pomace (2:1) were 3, 5.45 and 3.16 hours respectively . The mean dehydration ratios for peel, pomace and peel-pomace mixture were 6.0, 11.7 and 10.0 and the optimum rehydration ratios were 6.0, 9.0 and 9.6 respectively.

Krailo (1984) has done work on the modernization of apple pomace drying unit for ultimate production of a dried milled product for confectionary use (particle size 0.1 mm) or animal feedstuff (particle size greater than 0.1 mm). The unit had a drying capacity of 350 kg/h and energy consumption of 135 kW/t pomace.

Monroy et al. (1986) conducted studies on the drying of olive and grape pomace constituents on a fluidized bed system based on their aerodynamic behaviour. Separation kinetics were explained through an empirical modification of the Leva Equation. They proposed that during the first stage of operation particle entrainment is controlled by the drying rate and during the second stage by mechanical effects. Laboratory experiments were conducted to obtain data on equilibrium moisture content. Thin layer drying equations were developed for defatted olive flesh and grape seed.

Peraza et al. (1986) conducted studies on the dehydration and separation of grape pomace in a fluidized bed system. The grape pomace components (seed and skin) were separated in a fluidized bed system based on their aerodynamic behaviour. Seed dehydration was characterized by a first period of constant drying rate and a second period in which drying rate decreased following Ficks liquid molecular diffusion model.

Bains et al. (1989) studied the drying behaviour of apple puree in a forced-air circulation cabinet drier with a cross-flow tray arrangement at air temperatures of 70 and 94C, air flow rate of 2.0 and 4.1 m/s and relative humidity of 5 and 15 %. Drying curves were used to study the influence of temperature, air flow rate and relative humidity on the

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rate of moisture removal and to predict the time required to dry the product to a final moisture content of 20% d.b. They concluded that all the three factors influenced the rate of drying with the higher temperature, higher air velocity and lower relative humidity condition yielding the fastest drying rate, but it also adversely affected the quality of the product. So, a two- stage drying operation which involved a high temperature, low humidity and high flow rate in the first stage followed by a lower temperature at the finishing stage of the experiment was found to give a better quality product on the basis of colour and flavour of the dried apple puree.

Karathanos et al. (1995) reported that the effective diffusivity varied from 4 to 21 X 10-10 m2/s for the apple in nature samples.

Uretir (1995) conducted an experimental drying of apple samples with 0.6-1.8 mm layer thickness in 1.7-3.0 m/s at 78-94C by using a computer controlled- tunnel- type dryer. She mod