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09603085/03/$23.50+0.00# Institution of Chemical Engineers

www.ingentaselect.com=titles=09603085.htm Trans IChemE, Vol 81, Part C, June 2003

THE USE OF EXPERIMENTAL FACTORIAL DESIGN FORANALYSING THE EFFECT OF SPRAY DRYER OPERATING

VARIABLES ON THE PRODUCTION OF TOMATO POWDER

S. AL-ASHEH, R. JUMAH, F. BANAT and S. HAMMAD

Department of Chemical Engineering, Jordan University of Science and Technology, Irb id, Jordan

Atwo-level factorial experimental design technique was used to investigate the inuenceof the operating parameters on the production of tomato powder from tomato pasteduring the spray drying operation. This techniquewas applied to quantify the inuence

of feed total solids, feed ow rate, inlet air temperature and air ow rate on the processvariables, namely, product total solids, particle size, bulk density and solubility. A factorialmodel was constructed and used to study all interactions among the considered parameters. Theresults showed that, at a 95% condence interval, the effect of air ow rate was relativelyinsignicant, while the effects of feed total solids, feed ow rate and inlet air temperature wereat the same signicance level. Most interactions between the studied parameters wereinsignicant.

Keywords: spray drying; tomato powder; factorial design.

INTRODUCTION

The world annual production of tomatoes is about 100.7million metric ton and is considered one of the importantfood products. Jordan produces annually about 0.3 millionmetric ton (FAO, 2001), in four different areas and seasons.The southern part of the Jordan valley produces tomatoes inJanuary and February, the middle part of the Jordan valley inMay and June, the northern part of the Jordan valley in Juneand July, and the hilly areas in July and November. It isestimated that about 37% of Jordans tomatoes are producedas triple-concentrated tomato paste (Amitom, 2001).

Preservation of tomatoes is of commercial importance.It isused as a component in various vegetable and spicy dishes

and, in many countries, canned tomato is one of the maincanned vegetable products. It is characterized by its taste,color and avor, and providesseveral vitamins, e.g. vitaminC,carotenes and other valuable nutrients (Balochet al., 1997).

Powdered tomato has many advantages, including ease ofpacking, transportation and mixing, and no drum-clingingloss (Masters, 1985). In addition, tomato powder is much indemand by dehydrated soup manufacturers. It is nowproduced in many countries where tomatoes are an indige-nous outdoor crop (Greensmith, 1998).

Tomatoes can be sun-dried, dehydrated or spray-dried. Theoldest technology is sun drying, where ripened tomatoes arerst washed, halved and then usually kept in a water bathcontaining sulfur dioxide. They are then transferred intodrying trays which are exposed to the sun for 710 days.Thereafter, they are cut and packaged. The result is aproduct with typically 1224% moisture, robust in taste,which darkens after an expiry time of 912 months.

Convective dehydration is another technique for tomatodrying. Tomatoes are washed, cut and then typically

passed through long tunnels where they are dehydrated bywarm air. This process is quite capital-intensive but theprocess is easier to control. Dehydrated tomatoes producedby this method have less than 7% moisture, a less sharptaste, and lighten slightly in color after 1215 monthsstorage.

Among the industrial dryer types available, there are afew that accept pumpable food material suspensions at thedryer inlet and discharge a dry particulate at the outlet.Spray drying is a unique technique which is able to producepowder of specic particle size and moisture content irre-spective of dryer capacity and product heat sensitivity. In

many cases, spray drying is the only rational choice to dryuid feedstocks (Masters, 1985).

Spray drying refers to the removal of moisture from aslurry by breaking it into small droplets in the presence ofhot air to obtain a solid, dry powder. In the spray-dryingprocess, the liquid feed is pumped into the drying chamberthrough an atomizing system. Inside the drying chamber, astream of heated gas traps the droplets and carries themfrom the drying chamber to the product recovery system.Evaporation takes place in a few seconds as the relativelycool droplets come in contact with the hot gas.

Tomatoes have very low solid content, less than 6%. Spraydrying must be preceded by evaporating the pulped tomatoto produce a paste containing 30% solids (Greensmith,1998). The physical form of tomatoes, as a powder, providesa stable, natural, easily dosable ingredient which may beused to impart color and taste for food products (Bhandariet al., 1993).

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The objective of this work is to study the effect ofoperational variables such as feed concentration, feed owrate, air ow rate and inlet air temperature on producttomato powder properties including total solids, particlesize, bulk density and solubility. The study was performedusing the two-level factorial experimental design. Theinteraction among the variables is also considered.

MATERIALS AND METHODS

Preparation of Tomato Paste Feed

Tomato paste purchased from the market and consistingof 25% solids was used as a feed to the spray dryer. It wasdiluted to the required feed concentration by addition ofdistilled water.

Spray Drying Process

The tomato paste was spray-dried using a pilot spray

dryer (Mobile Minor, Niro Atomizer, Seborg, Denmark)with a vaned centrifugal atomizer driven by an air turbine.The drying chamber is 800 mm in diameter, 600 mmcylindrical height and conical based. The cone angle is60. All internal surfaces that contact the product arestainless steel AISI 316. The nominal atomizer wheelsupplied with the unit rotates within the range 25,00035,000rpm.

The feed, at specic concentration, was introduced andmetered into the dryer by means of a peristaltic pump(Watson Marlow 503 S) in the range 816 ml min1 andatomized into ne droplets using the wheel atomizer. The

drying air was electrically heated and controlled at thespecied temperature. It entered the drying chamber througha ceiling disperser designed to create a swirling airowdirectly around the vaned atomizer wheel. A schematicdiagram of the experimental set-up is shown in Figure 1.

Product Analysis

Product total solids

A sample of tomato powder (about 12.0 g) was weighedand placed in an oven at 105C for 24 h. The total solidpercentage (weight basis) was calculated as:

Total solids (%) weight of dried sampleweight of sample

100 (%)

Particle size

Average particle diameter of the product was measuredusing a particle size analyser (Fritsch analyser).

Bulk density

The bulk density of the product was determined bypouring about 5 g of the powder into a 10 ml graduatedcylinder. The volume occupied by the sample was recorded

and bulk density was calculated (Wade and Waller, 1994).

Solubility

The solubility of the product was measured using themethod adopted by Wade and Waller (1994). Saturatedsolutions were prepared by adding 7, 8, and 9 g of tomatopowder into 100 ml distilled water. These solutions were leftfor 48 h to ensure equilibrium. Then they were ltered, andtheir absorbance was read at 328 nm wavelength. A calibra-tion curve was prepared from which the solubility of thesamples was calculated.

TWO-LEVEL FACTORIAL EXPERIMENTAL DESIGN

The response variables in this study are the product totalsolids (y1), particle size (y2), bulk density (y3), and solu-bility (y4). In order to determine the effect of the operating

Figure 1. Schematic diagram of the spray drying process.

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variables on the response variables, a set of designedexperiments was performed. Two feed total solids (x1),two feed ow rates (x2), two inlet air temperatures (x3),and two air ow rates (x4) were selected to perform a two-level factorial design for the present study. Each of theseoperating variables was selected at lower and upper levelsand expressed in the following dimensionless form:

xi

{value of operating variable i}(1=2){its upper limit its lower limit}

(1=2){its upper limit its lower limit}

Therefore, each variable is ranked as 1 and 1 at lowerand upper levels, respectively. For ease of notation, theeffects were designated as in Table 1, which shows thevalues of the operating variables selected in this study.

RESULTS AND DISCUSSION

A 24complete factorial design can be performed with thevalues of the operating variables shown in Table 1. Thisresults in 16 tests with all possible combinations ofx1,x2,x3and x4. The response variables, y1, y2, y3 and y4 were

measured for each of these tests as shown in Table 2.Replicates for some runs for the purpose of statisticalanalyses are shown in Table 3.

The complete factorial model that can be used to t thedata in Table 2 is:

E(Yi) b0 b1x1 b2x2 b3x3 b4x4 b12x1x2

b13x1x3 b14x1x4 b23x2x3 b24x2x4 b34x3x4 b123x1x2x3 b124x1x2x4

b234x2x3x4 b134x1x3x4 b1234x1x2x3x4 (1)

where the parameterbi is responsible for the inuence of theoperating variables xion the response yi, while bij, bijkandbijkl are responsible for the possible interactions, amongoperating variables, on the response. Values of these 16parameters can be obtained by least square estimates.

Using the matrix X of the operating variables at differentruns and Yas the matrix of the response variable for thisprocess, the least square tted model, according to equation

(1), provides:

y1 98:1325 0:3712x1 0:6325x2 0:3038x3

0:0112x4 0:1288x1x2 0:0275x1x3

0:005x1x4 0:0438x2x3 0:0188x2x4 0:01x3x4 0:0325x1x2x3 0:025x1x2x4

0:0062x1x3x4 0:015x2x3x4

0:0012x1x2x3x4 (2)

y2 4:9681 0:5494x1 2:2881x2 0:7119x3

0:0131x4 0:0531x1x2 0:0656x1x3

0:0419x1x4 0:2794x2x3 0:0156x2x4

0:0106x3x4 0:0106x1x2x3 0:0419x1x2x4

0:0206x1x3x4 0:0044x2x3x4 0:0169x1x2x3x4 (3)

y3 0:5962 0:0575x1 0:05x2 0:0238x3

0:0012x4 0:0038x1x2 0:0025x1x3

0:0075x1x4 0:005x2x3 0:0012x3x4 0:0038x1x2x3 0:0038x1x2x4 0:0025x2x3x4

0:0038x1x2x3x4 (4)

y4 0:03968 0:00166x1 0:00176x2 0:00291x3 0:00021x4 0:00022x1x2 0:00025x1x3

0:00018x2x3 0:00002x2x4 0:00006x1x2x3

0:00009x1x2x4 0:00004x1x3x4

0:00006x2x3x4 0:00005x1x2x3x4 (5)

Estimation of Interaction Effects

The interaction effect is calculated as the averageresponse difference between one half of the factorial runs

Table 1.Values of operating variables in the d esigned setof experiments.

Operating variable 1 1

x1 (feed total solids), % 4.66 9.11x2 (feed ow rate), ml min

1 8 16x3 (inlet air temperature),

C 130 160x4 (air ow rate), cm

3s1 630 800

Table 2. Experimental results of 24designs for the response variables.

Test no. x1 x2 x3 x4 y1 (%) y2 (mm) y3(gcm3) y4(gml

1)

1 1 1 1 1 97.57 7.74 0.55 0.04252 1 1 1 1 97.52 7.61 0.57 0.04303 1 1 1 1 97.02 5.89 0.60 0.03604 1 1 1 1 96.92 5.80 0.62 0.03595 1 1 1 1 98.5 2.51 0.46 0.04606 1 1 1 1 98.56 2.40 0.47 0.04657 1 1 1 1 97.93 1.65 0.52 0.04008 1 1 1 1 98.07 1.75 0.52 0.04089 1 1 1 1 98.13 8.71 0.70 0.039

10 1 1 1 1 98.17 8.93 0.69 0.0395

11 1 1 1 1 97.31 6.57 0.74 0.033412 1 1 1 1 97.36 6.80 0.70 0.034013 1 1 1 1 99.54 3.75 0.58 0.042014 1 1 1 1 99.5 3.79 0.56 0.042215 1 1 1 1 98.97 2.82 0.63 0.036816 1 1 1 1 99.05 2.77 0.63 0.0372


EXPERIMENTAL FACTORIAL DESIGN 83

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and the other half. The design table must be augmented toinclude columns for interactions. An interaction column isformed from the column of the two factors, three factors

or four factors that comprise the interaction, by multi-plying the entries in the factor column. This is shown inTables 47.

It is seen that all three-variable and four-variable inter-actions are insignicant. Only the interaction betweenx1and

x2has an effect on y1, and the interaction between x2and x3has an effect on y2. Other two-variable interactions areinsignicant. Evidence of large negative (x1x2in y1) andpositive (x2x3 in y2) interaction is strong and thereforecannot be neglected from the model.

Estimation of Main Effects

From a statistical point of view, the main effect can beestimated from the difference between the average high-andlow-factor-level responses,

main effect of xi

P{response at high xi}P

{response at low x i}

(1=2){the number of factorial runs}

(6)

Thus, all experimental data (Tables 2 and 3) are used toestimate each main effect which is estimated independentlyof the other main effects. This feature of 2n design is

known as a hidden replication, giving maximum infor-mation per experimental run (Murphy, 1976). A summaryof the calculation procedure is shown in Tables 47 forthe response variables y1, y2, y3 and y4, respectively,where the average response has been taken for the repli-cate runs.

Therefore, the effect of increasing the feed total solidsfrom 4.66 to 9.11%, averaged over all levels of feed owrate, inlet air temperature and air ow rate is to increaseproduct total solids by 0.7425%, increase particle size by1.099mm, increase bulk density by 0.115 g cm

3, and to

decrease solubility by 0.00332 g ml1. Also, the effect ofincreasing the feed ow rate from 8 to 16 ml min1 is todecrease product total solids by 1.265%, increase particlesize by 4.576mm, increase bulk density by 0.1 g cm3, anddecrease solubility by 0.00352 g ml1. The effect of increas-ing inlet air temperature from 130 to 160C is to increaseproduct total solids by 0.6075%, increase particle size by

1.424mm, decrease bulk density by 0.0475g cm3, andincrease solubility by 0.00582 g ml

1. Finally, increasingair ow rate from 630 to 800 cm3s

1 has only a slight

effect on the response variables: product total solidsdecreases by 0.0225%, particle size decreases by0.0262mm, bulk density increases by 0.0025g cm

3, andsolubility decreases by 0.00042g ml1.

In another study (Banatet al., 2002) these inuences havebeen investigated individually rather than using two levelfactorial design. It was found that the increase in the feedtotal solids increased tomato powder total solids, particlesize and bulk density, while the increase in the feed ow ratedecreased tomato powder total solids and solubility, andincreased particle size and bulk density. These conclusionsare consistent with the results of factorial design obtained inthis work.

Increasing the feed total solids, i.e. decreasing the moist-ure content, decreases the drying loads which ease theproduction of dried product. The increase in particle sizewith the increase in the feed total solids is not entirely due tothe larger volume occupied by the higher solids concentra-tions. The actual particle size is apparently inuenced by thephysical properties of the feed, mainly viscosity (Wallmanand Blyth, 1951). The increase in the feed concentrationcauses an increase in viscosity. The particles formed areheavy-walled spheroids and become solid spheres, having arelatively high bulk density (Chuet al., 1951). Furthermore,larger particles have a larger diffusion boundary layer,

retarding transport of dissolved material from the particlesurface (Brittian, 1995).

The increase in feed ow rate results in a decrease inproduct total solids and solubility, and an increase in particlesize and bulk density. Increasing feed ow rate increases theamount of water introduced to the dryer, which conse-quently increases the water content of the product andthus decreases product total solids. The increase in particlesize does not necessarily indicate that larger droplets wereformed as they left the atomizer wheel. This increase inparticle size could be due to the greater probability ofcollision and subsequent coalescence of the droplets(Wallman and Blyth, 1951). Powder bulk density increaseswith feed ow rate as a result of higher water content of theproduct particles because water has a higher densitycompared to the dry solid (Jumahet al., 2000). The decreasein solubility is due to the increase in particle size asexplained earlier.

Table 3. Experimental results of replicates.

Degrees ofStandard deviation

Repeated test y1 (%) y2 (mm) y3(gml1) y4 (gml

1) freedom (mi1) y1 y2 y3 y4

2 97.52 7.61 0.57 0.043097.41 7.45 0.57 0.0422 2 0.11 0.16 0 0.000897.63 7.77 0.57 0.0438

10 98.17 8.93 0.69 0.0395

98.26 8.75 0.67 0.0410 2 0.09 0.18 0.02 0.001598.08 9.11 0.71 0.038012 97.36 6.80 0.75 0.0340

97.3 6.71 0.72 0.0332 2 0.06 0.09 0.03 0.000897.42 6.89 0.78 0.0348

14 99.5 3.79 0.56 0.042299.47 3.90 0.58 0.0420 2 0.03 0.11 0.02 0.000299.53 3.68 0.54 0.0424


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Table4.

Calculationsandresults

formaineffectsfortheresponsevariable

y1.

Operatingfactors

Dummyfactors

y1(%)

x1

x2

x3

x4

x12

x13

x14

x23

x24

x34

x123

x124

x234

x134

x1234

97.5

7

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

97.5

2

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

97.0

2

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

96.9

2

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

98.5

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

98.5

6

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

97.9

3

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

98.0

7

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

98.1

3

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

98.1

7

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

97.3

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

97.3

6

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

99.5

4

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

99.5

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

98.9

7

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

99.0

5

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Sa

788.0

3

780

787.4

9

784.9

7

784.0

3

785.2

8

785.0

2

785.4

1

785.2

1

785.1

4

785.3

2

784.8

6

784.9

4

785.1

1

785.0

7

Sa

782.0

9

790.1

2

782.6

3

785.1

5

786.0

9

784.8

4

785.1

784.7

1

784.9

1

784.9

8

784.8

785.2

6

786.1

8

785.0

1

785.0

5

Differenceeffect

0.7

425

1.2

65

0.6

075

0.0

225

0.2

575

0.0

55

0.0

1

0.0

875

0.0

375

0.0

2

0.0

65

0.0

5

0.0

3

0.0

125

0.0

025

aS

correspondstothesum

ofresponsesathighxi,while

S

correspondstothesum

ofresponses

atlow

xi.

Table5.



y2.

Operatingfactors

Dumm

yfactors

y2(mm)

x1

x2

x3

x4

x12

x13

x14

x23

x24

x34

x123

x124

x23

4

x134

x1234

7.7

4

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

7.6

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

5.8

9

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

5.8

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

2.5

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

2.4

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1.6

5

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1.7

5

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

8.7

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

8.9

3

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

6.5

7

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

6.8

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

3.7

5

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

3.7

9

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

2.8

2

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

2.7

7

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Sa

44.1

4

58.0

5

45.4

4

39.6

4

39.3

2

40.2

7

39.4

1

41.9

8

39.6

2

39.8

3

39.8

3

39.4

1

39.7

1

39.5

8

39.8

8

Sa

35.3

5

21.4

4

34.0

5

39.8

5

40.1

7

39.2

2

40.0

8

37.5

1

39.8

7

39.6

6

39.6

6

40.0

8

39.7

8

39.9

1

39.6

1

Differenceeffect

1.0

99

4.5

76

1.4

24

0.0

26

0.1

06

0.1

31

0.0

84

0.5

59

0.0

31

0.0

21

0.0

21

0.0

84

0.0

09

0.0

41

0.0

34

aS

correspondstothesum


S

correspondstothesum

ofresponses

atlow

xi.



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Table6.



y3.

Operatingfactors

Dumm

yfactors

y3(gcm3)

x1

x2

x3

x4

x12

x13

x14

x23

x24

x3

4

x123

x124

x234

x134

x1234

0.5

5

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.5

7

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.6

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.6

2

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.4

6

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.4

7

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.5

2

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.5

2

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.7

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.6

9

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.7

4

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.7

0

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.5

8

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.5

6

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.6

3

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.6

3

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Sa

5.2

3

5.1

7

4.5

8

4.7

8

4.8

0

4.7

9

4.8

3

4.8

1

4.7

7

4.7

6

4.8

4.8

4.7

5

4.7

7

4.7

4

Sa

4.3

1

4.3

7

4.9

6

4.7

6

4.7

4

4.7

5

4.7

1

4.7

3

4.7

7

4.7

8

4.7

4

4.7

4

4.7

9

4.7

7

4.8

0

Differenceeffect

0.1

15

0.1

0.0

475

0.0

025

0.0

075

0.0

05

0.0

15

0.0

1

0

0.0

025

0.0

075

0.0

075

0.0

05

0

0.0

075

aS

correspondstothesum


S

correspondstothesum

ofresponses

atlow

xi.

Table7.



y4.

Operatingfactors

Dum

myfactors

y4(gml1)

x1

x2

x3

x4

x12

x13

x14

x23

x24

x34

x123

x124

x23

4

x134

x1234

0.0

425

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.0

430

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.0

360

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.0

359

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.0

460

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.0

465

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.0

40

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.0

408

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.0

39

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.0

395

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.0

334

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.0

34

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.0

420

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.0

422

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.0

368

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

0.0

375

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Sa

0.3

041

0.3

033

0.34

07

0.3

157

0.3

192

0.3

154

0.3

174

0.3

188

0.3

176

0.3

174

0.3

169

0.3

167

0.3169

0.3

177

0.3

178

Sa

0.3

307

0.3

315

0.29

41

0.3

191

0.3

156

0.3

194

0.3

174

0.3

16

0.3

172

0.3

174

0.3

179

0.3

181

0.3179

0.3

171

0.3

17

Differenceeffect

102

0.3

32

0.3

52

0.58

2

0.0

425

0.0

45

0.0

5

0

0.0

35

0.0

05

0

0.0

125

0.0

175

0.0125

0.0

075

0.0

1

aS

correspondstothesum


S

correspondstothesum

ofresponses

atlow

xi.


86 AL-ASHEH et al.

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Increasing inlet air temperature from 130 to 160C results inan increase in product total solids, particle size and solubi-lity, and a decrease in bulk density. It is obvious that at lowertemperatures the rate of heat transfer decreases and thusdrying becomes less complete than at higher temperatures(Chu et al., 1951). The decrease in bulk density withincreasing air inlet temperature, although partly due todecreased moisture, is due to case hardening of the droplet

at higher temperatures followed by expansion of theentrapped vapor (Wallman and Blyth, 1951). Case harden-ing would explain the increase in particle size with inlet airtemperature. Also, coarse particles t together more loosely,while small particle pack together with fewer voids. Looserpacking means lower bulk density (Deis, 1997). Case hard-ening is a phenomenon associated with many fruit andvegetables powders. Tomato paste is a lm forming materialwhere drying rate can drop to zero; when evaporation ratefrom the surface of the particle is higher than the rate ofmoisture supply from the interior to the surface of the dryingmaterial, the surface layer becomes substantially dried. This

causes a shell volume at the surface to form an impermeablecrust that encapsulates the moisture in the interior (Karatasand Esin, 1994). The increase in solubility with inlet airtemperature was not completely understood. This requiresfurther investigation.

As for air ow rate, it can be concluded that it has nosignicant effect on any of the response variables. Evapora-tion from the surface is enhanced by improved convectivemass transfer rates as a result of increased air velocity. Thus,increasing air velocity shortens the constant drying rateperiod, which is typically quite short in spray drying opera-tions, lasting perhaps for a few seconds. Consequently, mostof the drying occurs in the falling rate period. Here, the rate

of moisture transport from within the droplet surface limitsthe amount of moisture lost to the drying air and drying isnot limited by external conditions. Thus, increasing airowrate has little effect on nal product total solids. The rate ofairow controls to a certain extent the residence time of theproducts in the drying chamber. The increase in the resi-dence time leads to a greater degree of moisture removalwhile reducing air velocity assists product recovery from thedrying chamber (Jumah et al., 2000).

It can also be observed that the inuence of feed ow rateon product total solids is greater than that of feed total solidsfollowed by inlet air temperature. As for product particle

size, feed ow rate has the greatest effect followed by inletair temperature followed by feed total solids. Feed totalsolids has the greatest effect on bulk density followed byfeed ow rate followed by inlet air temperature. Finally, itcan be seen that the inuence of inlet air temperature onsolubility is greater than that of feed ow rate followed byfeed total solids.

The effects of dummy factors can be similarly calculated.These factors represent a measure of any interactions in thesystem. It is seen that some of these interaction factors aresignicant compared to the effects of the main operatingvariables, therefore their inuence on responses cannot beneglected.

Assessment of Signi cance of Main Effect

To explore the importance of each term in equation (3), acondence interval can be calculated for each parameter

associated with that term. If the condence interval for agiven parameter contains the point zero, it means that theterm associated with such parameter is not important andcan be excluded from the model [equation (1)]. The con-dence interval for the least square parameter estimates isgiven by (Montgomery and Runger, 1994):

bbi tn,a[V(bbi)]1=2 (7)

where tn,a is the Students statistic, n is the degrees offreedom associated with the pure error variance, s2, and ais the probability limit. The pooled variance,s2p, can be usedas an estimate of the pure error variance and is given by:

s2p

Pli1(mi 1)s

2i

Pli1(mi 1)

(8)

wherel is the total number of replicates available in the dataset (i.e. l 4), mi, is the number of data points in theith setof replicates ands2i is the estimate of the pure error variancein the ith set of replicates. The term

Pi 1

l(mi 1)

represents the degree of freedom associated with s2p. Thesample variance [

Pi1n (yiy)

2=(n 1)] can be used as anestimate of the variances2i . Therefore, the resulting degreesof freedom is n 8 and an estimate of pure error variancefory1iss

2p 0:006175, fory2it iss

2p 0:01955, fory3it is

s2p 0:000425, and for y4 it is s2p 8:925 10

7. SinceVbb of each parameter estimate in equation (3) is s2=24,then the 95% condence interval for each parameter in eachresponse variable, according to equation (7) is: for y1b 0.0453; for y2b 0.0806; for y3b 0.0119; for

y4b0.00054.Applying these values of condence interval, equations

(2)(5) reduce to:y1 98:1325 0:3712x1 0:6325x2 0:3038x3

0:1288x1x2 (9)

y2 4:9681 0:54938x1 2:2881x2 0:7119x3

0:2794x2x3 (10)

y3 0:5962 0:0575x1 0:05x2 0:0238x3 (11)

y4 0:03968 0:00166x1 0:00176x2

0:00291x3 (12)

It can be concluded that for all four variables neither the

effect of x4 (air ow rate) nor its interactions with othervariables are signicant at 95% condence interval. It is alsoseen that equations (11) and (12), which represent the ttedmodel for responses variablesy3andy4, respectively, do notcontain any interaction terms. The interaction effect isobvious in y1 (product total solids) and y2 (particle size);

y1 is affected by the interaction between x1and x2, whiley2is affected by the interaction betweenx2 and x3.

CONCLUSIONS

Experiments on the production of tomato powder fromtomato paste were conducted using a spray dryer. The maineffects and interactions among the studied parameters suchas feed total solids, feed ow rate, inlet air temperature, andair ow rate on tomato powder properties, including totalsolids, particle size, bulk density and solubility, wereanalysed using a two-level factorial design model. It was



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found that increasing feed total solids increased tomatopowder total solids, particle size and bulk density anddecreased its solubility. Increasing feed ow rate decreasedtomato powder total solids and solubility and increased theaverage particle size and bulk density. Feed temperatureincreased particle size and decreased bulk density. Inlet airtemperature had an effect of increasing product total solids,particle size and solubility, and decreasing bulk density. The

effect of the parameters was assessed at 95% condenceinterval. It was found that air ow rate had no signicanteffect, at 95% condence interval, on response variables atthe range of airow rate used. It was also found that feedow rate had the greatest effect on product total solids andparticle size. Feed total solids had the greatest effect on bulkdensity and inlet air temperature had the most pronouncedeffect on powder solubility. The interaction between feedtotal solids and feed ow rate had a slight effect on producttotal solids, while only the interaction between feed ow rateand inlet temperature had a slight effect on product particlesize. No other interactions among the parameters were

observed in the other response variables.

REFERENCES

Amitom, 2001, www.tomate.org=Amitom.htm, Jordan.Baloch, W.A., Khan, S. and Baloch, K., 1997, Inuence of chemical

additives on stability of dried tomato powder, Int J Food Sci Technol,32: 117120.

Banat, F., Jumah, R., Al-Asheh, S. and Hammad, S., 2002, Effect ofoperating parameters on the spray drying of tomato pase, Eng Life Sci,2: 15.

Bhandari, B., Senoussi, A., Dumoulin,E. and Lebert, A., 1993,Spraydryingof concentrated fruit juices, Drying Technol, 11(5): 10811092.

Brittian, H.G., 1995, Physical Characterization of Pharmaceutical Solids(Marcel Dekker, New York), pp 179180.

Box, G.E.P. and Hunter, J.S., 1961, The 2ki fractional factorial designs,part I, Technometrics, 3: 311351.

Chu, J.C., Stout, L.E. and Bushe, R.M., 1951, Spray drying of santomerse,Chem Eng Prog, 47(1): 2938.

Deis, R., 1997, www.foodproductdesign.comFAO, 2001, www.apps.fao.org=default.htm, Italy.Greensmith, M., 1998, Practical Dehydration, 2nd edition (CRC Press,

London), pp 192196.

Jumah, R., Tashtoush, B., Shaker, R. and Zraiy, A., 2000, Manufacturing,parameters and quality characteristics of spray dried jameed, DryingTechnol, 18(45): 967984.

Karatas, S. and Esin, A., 1994, Determination of moisture diffusivitybehavior of tomato concentrate droplets during drying in air, DryingTechnol, 12(4): 799822.

Masters, K., 1985, Spray Drying Handbook, 4th edition (John Wiley andSons, New York), p 5 and 618.

Montgomery, D.C. and Runger, G.C., 1994, Applied Statistics andProbability for Engineers(John Wiley and Sons, New York).

Murphy Jr., T.D., 1976, Design and analysis of industrial experiments,ChemEng, 6: 168183.

Wade, A. and Waller, P.J., 1994, Handbook of Pharmaceutical Exipient, 2ndedition (American Pharmaceutical Association, Washington, DC)

Wallman, H. and Blyth, H.A., 1951, Product control in Bowen-type spray

dryer, Ind Eng Chem, 43(6): 14801486.

ADDRESS

Correspondence concerning this paper should be addressed toDr S. Al-Asheh, Department of Chemical Engineering, Jordan Universityof Science and Technology, P.O. Box 3030, Irbid 22110, Jordan.E-mail: [email protected]

The manuscript was communicated via our Regional Editor ProfessorJ. A. Howell. It was received 16 May 2002 and accepted for publication

after revision 3 April 2003.


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