thin layer drying of sliced mango

Central Luzon State UniversityINSTITUTE OF GRADUATE STUDIESScience City of Muñoz, Nueva Ecija 3119

------------------------------------

THIN LAYER DRYING MODELLING OF SLICED MANGO

CPE 726Advanced Process Design and Optimization

2nd Semester, SY 2009-2010.

Submitted to:

RUEL G. PENEYRAProfessor

Submitted by:

MARIBEL B. PENEYRAMS Agricultural Engineering Student

Table of Contents

1 INTRODUCTION................................................................................................................3

2 MATERIALS AND METHODS.......................................................................................4

2.1 Sample Preparation 4

2.2 Drying Experiments 4

2.3 Operation Principle 5

3 RESULTS AND DISCUSSION.........................................................................................7

3.1 Diffusion Coefficient k 7

3.2 Moisture Content Reduction 11

3.3 Analysis of Drying Air Temperature & Air Velocity ………………………… 16on Moisture Content Reduction

3.3 Analysis of Moisture Content Reduction on linear trends 18

4 CONCLUSIONS...............................................................................................................24

5 REFERENCES...................................................................................................................25

Thin Layer Drying Modelling for Sliced MangoSubmitted by: Maribel B. Peneyra, MSAE Student 2

I. INTRODUCTION

Mango (Mangifera indica L.) is one of the tropical and subtropical fruit of great

importance for both economical and nutritional points of view. It is considered to be a good

source of carbohydrates, vitamin C and very rich source of pro-vitamin A. In spite of its

excellence, the perishable nature of this fruit and its short harvest season severely limit

utilization. Drying may be an interesting method in order to prevent fresh fruit deterioration.

Drying is one of the most widely used primary methods of food preservation. The

objective drying is the removal of water to the level at which microbial spoilage and

deterioration reactions are greatly minimized (Akpinar and Bicer, 2004). It also provides

longer shelf-life, smaller space for storage and lighter weight for transportation (Ertekin and

Yaldiz, 2004). Sun drying is the most common method used to preserve agricultural products

in tropical and subtropical countries. However, being unprotected from rain, wind-borne dirt

and dust, infestation by insects, rodents and other animal, products may be seriously degraded

to the extent that sometimes become inedible and the resulted loss of food quality in the dried

products may have adverse economic effects on domestics and international markets.

Therefore, the drying process of agricultural products should be undertaken in closed

equipment (solar or industrial dryer) to improve the quality of the final product.

Objectives of this study were:

1. To investigate the thin layer drying kinetics of sliced mango.

2. To evaluate a suitable drying model for describing the drying process of mango fruit.

3. To investigate the combined effect of drying temperature and drying air velocity on

Moisture Content Reduction and Diffusion Coefficient “k”.


II. MATERIALS AND METHODS

Sample Preparation

Ripe mangoes were bought at the retail market in Munoz. First, fresh fruits were

washed and manually peeled using stainless steel knife and sliced at 3 - 5 mm thickness.

Sliced mango were mixed with white sugar in a ratio of 1.5 kg of mango to ¼ kg of white

sugar and left for 4 hours until the mixture produces syrup. Then, samples were drained and

the syrup was boiled. Mango samples were put back into the syrup for 3 minutes and then this

mixture was left for 8 hrs. Finally, samples were washed with warm water and drained before

placing into drying chamber.

Drying Experiments

Drying experiments were performed in a laboratory cross flow dryer, fabricated by the

researchers. This is consisted of heating unit, temperature control unit, drying chamber and 3

small axial fans. The average initial moisture content of the mango fruit was 62.5 % ( w.b.),

as determined by convective air drying oven for 8 hrs.

Before the start of experiments, the dryer was preheated without sample for 15

minutes to reach thermal stabilization. Then the samples were uniformly spread in the tray in

single layer. Samples were weighed in an interval of 1 minute in the first 20minutes of the

drying process. Readings of temperature in the data logger were also taken. For the

succeeding time, samples were weighed every 5 minutes until the drying process was

finished. For measuring the mass of sample at any time during experimentation, a digital

balance was placed under the drying chamber. The drying process was stopped when the

moisture content decreases to about 14 - 15 % (w.b). All the experiments were replicated


three times at each combination of drying air temperature and velocity, and the average

values were used for calculation.

In this study, the variables were investigated namely:

a) Independent variables: Drying air Temperature (at two levels: 50 oC and 60 oC) and

Velocity of drying air flow (at two levels: 0.1056 m/s and 0.2533 m/s).

b) Dependent variables: Moisture Content Reduction and Diffusion Coefficient “k”.

A Randomized Complete Block Design was chosen to conduct these experiments.

Operation Principle

This laboratory dryer has one tray in the drying chamber that can hold 150 grams

sliced mango. Heat is provided from the top by a heater, which is controlled by a thermostat.

Ambient air was heated by the heater located at the top. Airflow can be adjusted by

controlling the power supply for the fans. Moisture emitted from mango samples are

exhausted through outlet.


Figure 1. Schematic of sliced mango thin layer laboratory dryer

1- Housing; 2- Heater; 3- Insulator; 4- Fans;

5- Tray; 6- Thermostat; 7- Electronic Balance


III. RESULTS AND DISCUSSION

Diffusion Coefficient k

Values of diffusion coefficient “k” obtained from drying equation:

(1)

Where:

MR: Moisture Ratio

Mt

: Moisture Content at time t (d.b.), decimal

Mi

: Initial moisture Content (d.b.), decimal

Me

: Equilibrium Moisture Content (d.b.), decimal

t: time, minute (from beginning)

k: diffusion Coefficient, 1/minute

Table 1. Table of “k” values:

Factor B SUM BLOCK

A b1 b2 MEAN

a1 0.0263 0.0320 Block 1

0.0207 0.0273 Block 2

0.0164 0.0234 Block 3


SUM 0.0634 0.0827 0.1461

MEAN 0.02113 0.02757 0.02435

a2 0.0171 0.0313 Block 1

0.0234 0.0342 Block 2

0.0168 0.0243 Block 3

SUM 0.0573 0.0898 0.1471

MEAN 0.01910 0.02993 0.02452

GRAND SUM 0.1207 0.1725 0.2932

MEAN 0.02012 0.02875 0.02443

Where:

Factor A: Drying air Velocity, m/s

- level a1: a1 = 0.1056 m/s

- level a2: a2 = 0.2533 m/s

Factor B: Drying air Temperature, oC

- level b1: b1 = 50 oC

- level b2: b2 = 60 oC

ANOVA Table (RCBD) on “k”:

Source of Variation df Sum of Square, SS MS Fc Ftab.

Blocks 2 0.00011 ( = 0.05) ( = 0.01)

Treatments 0.00024

A 1 0.000000 0.00000008 0.008 5.987 13.745

B 1 0.000224 0.00022360 22.530** 5.987 13.745

A*B 1 0.000015 0.00001452 1.463 5.987 13.745

Expt'l Error 6 0.00006 0.00000992

Total 11 0.00040

** highly significant


Since 22.530 > 13.745, that means that impact of factor B on diffusion coefficient k is highly

significant.

Since 1.463 < 5.987, there is no interaction between factor A and factor B. Therefore, we can

compare means among levels of each factor.

Computation of the means between two levels of factor B:

Using Scheffe’s Test:

Compute Scheffe’s Value:

Where: a = 2; b = 2; n = 3; choose = 0.01; values of dfB, dfE, MSE from ANOVA Table.

Therefore,

Comparision between b1 and b2:

Since

Thus, is significantly different from at = 0.01


Figure 2. Graph of Diffusion Coefficient “k1” at T1 = 50 oC and V1 = 0.1056 m/s



Moisture Content Reduction

Table 2. Table of “MC Reduction” values:

Order Independent Variables MC Reduction, %/min MEAN, (Y)

X1 = Temp. X2 = Air Velocity R1 R2 R3 %/min

1 50.0 0.1056 0.3887 0.4437 0.4107 0.4144

2 50.0 0.2533 0.3834 0.5410 0.4198 0.4481

3 60.0 0.1056 0.5634 0.6493 0.5845 0.5991

4 60.0 0.2533 0.6231 0.6848 0.6471 0.6517

Let Y = Moisture Content Reduction

X1 = drying temperature, oC.

X2 = Air Velocity, m/s.

Thus, the multiple linear regression equation will be:

Results of regression Analysis:

Regression Statistics

Multiple R 0.998873101

R Square 0.997747472

Adjusted R Square 0.993242415

Standard Error 0.00945

Observations 4

ANOVA Table


df SS MS F Significance F

Regression 2 0.039556145 0.019778073 221.4727751 0.04746081

Residual 1 8.93025E-05 8.93025E-05

Total 3 0.039645448

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%

Intercept -0.591958977 0.053437335 -11.07762912 0.057313609 -1.270944693 0.08702674

X Variable 1 0.019415 0.000945 20.54497354 0.030962208 0.007407637 0.031422363

X Variable 2 0.292146242 0.063981043 4.566137566 0.13725496 -0.520809985 1.105102469

RESIDUAL OUTPUT

Observation Predicted Y Residuals Standard Residuals

1 0.409641667 0.004725 0.866025404

2 0.452791667 -0.004725 -0.866025404

3 0.603791667 -0.004725 -0.866025404

4 0.646941667 0.004725 0.866025404

Therefore, there is only Variable X1 (Drying Temperature) that related to Y because its

confident interval does not include value of “0”.

Thus, the linear regression equation obtain will be:


Figure 6. Drying Curve of sliced mango at T1 = 50 oC and V1 = 0.1056 m/s

Figure 7. Drying Curve of sliced mango at T1 = 50 oC and V2 = 0.2533 m/s


Figure 8. Drying Curve of sliced mango at T2 = 60oC and V1 = 0.1056 m/s



Figure 10. Drying Curve of sliced mango at different temperatures and air velocities

Analysis of Drying Air Temperature and Air Velocity on Moisture Content Reduction (MCR)

Table 3. Table of “Moisture Content Reduction (% / min)”:

Factor B SUM BLOCKA b1 b2 MEAN a1 0.3887 0.5634 Block 1

0.4437 0.6493 Block 2 0.4107 0.5845 Block 3SUM 1.2431 1.7972 3.0403 MEAN 0.41437 0.59907 0.50672

a2 0.3834 0.6231 Block 1 0.5410 0.6848 Block 2 0.4198 0.6471 Block 3SUM 1.3442 1.9550 3.2992 MEAN 0.44807 0.65167 0.54987 GRAND SUM 2.5873 3.7522 6.3395 MEAN 0.43122 0.62537 0.52829

ANOVA Table


Source of Variation df

Sum of Square,

SS MS Fc Ftab.

Blocks 2 0.01720 = 0.05 = 0.01

Treatments 0.11894

A 1 0.005586 0.005586 8.601* 5.987 13.745

B 1 0.113083 0.113083 174.128** 5.987 13.745

A*B 1 0.000268 0.000268 0.413 5.987 13.745

Expt'l Error 6 0.00390 0.000649

Total 11 0.14003

* significant


Since 174.128 > 13.745, that means that impact of factor B on “MCR” is highly significant.

Since 8.601 > 5.987, that means that impact of factor A on “MCR” coefficient is significant.

Since 0.413 < 5.987, there is no interaction between factor A and factor B on “MCR”.

Therefore, we can compare means among levels of each factor.

Comparision of the means between two levels of factor A:



Where: a = 2; b = 2; n = 3; choose = 0.05; values of dfA, dfE, MSE from ANOVA Table

above.

Therefore,


Comparision between levels a1 and a2:

Since

Thus, is different from at = 0.05




Where: a = 2; b = 2; n = 3; choose = 0.01; values of dfB, dfE, MSE from ANOVA Table

above.

Therefore,


Since



Analysis of Moisture Content Reduction on linear trends




Table 3. Table of “m” values:

Factor B SUM BLOCKA b1 b2 MEAN a1 -0.0090 -0.0120 Block 1

-0.0080 -0.0130 Block 2 -0.0090 -0.0140 Block 3SUM -0.0260 -0.0390 -0.0650 MEAN -0.00867 -0.01300 -0.01083

a2 -0.0080 -0.0120 Block 1 -0.0080 -0.0110 Block 2 -0.0090 -0.0120 Block 3SUM -0.0250 -0.0350 -0.0600 MEAN -0.00833 -0.01167 -0.01000 GRAND SUM -0.0510 -0.0740 -0.1250 MEAN -0.00850 -0.01233 -0.01042

ANOVA Table (RCBD) on “m” coefficient

Source of Variation df Sum of Square, SS MS Fc Ftab.


Blocks 2 0.0000022 ( = 0.05) ( = 0.01)

Treatments 0.0000469

A 1 0.0000021 0.00000208 6.818* 5.987 13.745

B 1 0.0000441 0.00004408 144.273** 5.987 13.745

A*B 1 0.0000007 0.00000075 2.455 5.987 13.745

Experimental Error 6 0.0000018 0.00000031

Total 11 0.0000509


* significant

Since 144.273 > 13.745, that means that impact of factor B on “m” coefficient is highly

significant.

Since 6.818 > 5.987, that means that impact of factor A on “m” coefficient is significant.

Since 2.45 < 5.987, there is no interaction between factor A and factor B on “m”. Therefore,

we can compare means among levels of each factor.

Comparision of the means between two levels of factor A:



Where: a = 2; b = 2; n = 3; choose = 0.05; values of dfA, dfE, MSE from ANOVA Table

above.

Therefore,

Comparision between levels a1 and a2:


Since





Where: a = 2; b = 2; n = 3; choose = 0.01; values of dfB, dfE, MSE from ANOVA Table

above.

Therefore,


Since



Figure 15. Sliced mango after drying


IV. CONCLUSIONS

Base on the equation (1), diffusion coefficient ‘k’ was determined at various drying

temperatures and air velocities. The result showed that the ‘k’ value increased with increase

in drying temperature.

Both drying air temperature and air velocity influenced the Moisture Content Reduction.

At level of 60oC and 0.1056 m/s, Moisture Content Reduction is fast, and drying time is

shortest. With this combination of factors, this is the best combination for sliced mango

drying.

In general, the color of dried product is bright yellow and the taste is delicious.


REFERENCES

Rajkumar, P. and R. Kailappan. 2006. Optimizing the process parameters for Foam Mat Drying of Totapuri Mango Pulp. An article.

Rajkumar, P. and R. Kailappan, R. Viswanathan, K. Parvathi, G. S. V. Raghavan and V. Orsat. 2007. Thin Layer Drying Study on Foamed Mango Pulp. the CIGR Ejournal Manuscript. FP 06 024. Vol. IX. March, 2007.

Ruiz Celmaa, A. and F. López-Rodríguezb, F. Cuadros Blázquezc. 2008. Experimental modelling of infrared drying of industrial grape by-products. An article.

El-Amin Omda Mohamed Akoy, Dieter Von Hörsten, Wolfgang Luecke. 2008. Drying Kinetics and Colour Change of Mango Slices as Affected by Drying Temperature and Time. Written for presentation at the 2008 Tropentag International Conference on “Competition for Resources in a Changing World: New Drive for Rural Development”, Hohenheim, Germany.

Kwanchai A. Gomez and Arturo A. Gomez. 1984. Statistical Procedures for Agricultural Research, 2nd Edition.

Ronald E. Walpole. 1982. Introduction to Satistics, 3rd Edition.


thin layer drying of sliced mango

Documents