Optimization of drying parameters and color changes of pretreated ...

Optimization of drying parameters and color changes of pretreated organic apple slices STELA JOKIĆ*, JASMINA LUKINAC, DARKO VELIĆ, MATE BILIĆ, DAMIR MAGDIĆ, MIRELA PLANINIĆ Department of Process Engineering, Faculty of Food Technology, University J.J. Strossmayer of Osijek, F. Kuhaca 18, P.O. Box 709, 31000 Osijek, Croatia

*

Corresponding author. Tel.: +385 98 1666629, fax: +385 31 207 115, E-mail address: [email protected]

ABSTRACT The aim in this research was to determine the optimal drying parameters for the production of dried apple slices of good texture, good rehydration ability and suitable color. Organic apple samples variety “Florina” were pretreated and dried in laboratory tray drier at different temperatures. Different chemical pretreatments were applied on samples (dipping in 0.5% ascorbic acid solution; 0.3% L–cysteine solution; 0.1% 4–hexyl resorcinol solution and 1% sodium metabisulfite solution). Drying temperatures for nontreated samples were 50, 60, and 70 °C at airflow velocity of 1.5 ms-1. Color changes were measured by chromameter and digital image analysis. The Page’s mathematical model was used to calculate the drying kinetic parameters. The obtained results showed a good agreement with experimental data. According to drying time, rehydration and color characteristics the optimal drying temperature was found to be 60 °C. The best results were achieved when samples were pretreated with 4–hexyl resorcinol. Keywords: Drying kinetics; Color; Rehydration; Pretreatment; Organic apple; Image analysis

ABBREVIATIONS a

red-green

AK

ascorbic acid solution

b

yellow-blue

B

blue

BL

blanching in hot water

G

green

k, n

parameters in model (6)

L

lightness-darkness

LC

L–cysteine solution

m

weight (g)

NaB

sodium metabisulfite solution

NT

non treated samples

R

red

RR

rehydration ratio

t

drying time (min)

T

temperature (C)

W

weight (g)

X

moisture (kgwkgdb-1)

X´

dimensionless moisture

dX´/dt drying rate (min-1) 4H

4–hexyl resorcinol solution

∆ELab

color changes in CIE Lab color model

∆ERGB color changes in RGB color model

Subscripts d

dried sample

db

dry basis

e

equilibrium

r

rehydrated sample

w

water

0

initial

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INTRODUCTION During the last decade there is a strong tendency towards organic apple cultivation. Defining the optimal preservation and storage conditions for fresh apples is beneficial since unsuitable preservation and storage methods cause losses of fruits and vegetables that range from 10% to 30% (Yaldiz and Ertekin 2001). The development of new, high quality and consumer attractive dried fruit products is necessary to widen product availability and diversify markets, particularly since fresh fruit consumption is generally below the levels recommended in normal diet (Contreras et al. 2008). Color is an important fruit quality attribute of fruit which occurs in the interaction among light, observed object and observer (Yam and Papadakis 2004). Color changes are mostly related to browning reactions that take place during drying of fruits and vegetables. There are many studies about pretreatments of fruit in order to minimize adverse changes occurring during drying and rehydration (Son et al. 2001; GuerreroBeltran et al. 2005; Doymaz, 2006 and 2007). The browning of fruits and vegetables during drying appears due to both enzymatic and non-enzymatic reactions (Vadivambal and Jayas 2007). To define and display color it is necessary to select a color space which is a mathematical representation of a set of colors. The three most common color spaces are: RGB (used for television, computer screens, scanners and digital cameras), CMYK (used by the printing industry) and the CIE Lab space (used in laboratory colorimeters). Colorimeters measure color parameters on small rounded area and give nonobjective results on colored samples with different color area. Most objective color assessment can be obtained using image analysis of all visible surface of analyzed sample. These techniques can be applied on both sides of apple, reddish and greenish to ensure more objective results because almost 100% of apple surface is captured in an image. Color changes measured in RGB color model can be separated in color channels with intensity values for red, green and blue color from 0 to 255 (Magdić and Dobričević 2007). For the food technologist properties such as color, shape (shrinkage) and rehydration capacity are determinant for the quality of the dried product (Fernandez et al. 2005). Drying is probably one of the oldest methods of food preservation and also well researched scientific area (Lewicki and Jakubczyk 2004; Velić et al. 2004; Lewicki 2006; Sacilik and Elicin 2006; Kaya et al. 2007; Margaris and Ghiaus 2007; Doymaz 2008; Lertworasirikul 2008). Thousands of years of experience and trial-error methods as well as research done during the last hundred years resulted in development of a variety of drying methods and drying equipment. The aim of this research was to investigate the impact of drying kinetics and different chemical pretreatments on color changes of organic apple slices during the drying process. The effect of temperatures and

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pretreatments on the quality of dried apple samples was determined on the basis of color and rehydration characteristics. For this purpose color changes were measured by two different methods and results were compared.

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MATERIAL AND METHODS Material Organically grown apples (var. Florina) were obtained from the local small family farm and stored at +4 °C. After stabilization at the ambient temperature, apples were hand peeled and cut into tube-shaped samples, 20 mm diameter and 5 mm height. Drying method Drying was performed in a pilot plant tray dryer (UOP 8 Tray Dryer, Armfield, UK). The dryer operates on the thermogravimetric principle. The dryer (Fig. 1) is equipped with controllers for controlling the temperature and airflow velocity. Air was drawn into the duct through a diffuser by a motor driven axial flow fan impeller. In the tunnel of the dryer there were carriers for trays with samples, which were connected to a balance. The balance was placed outside the dryer and continuously determined and displayed the sample weight. FIG. 1. The drying temperatures for nontreated apple samples had varied from 50 °C, 60 °C and 70 °C. The dryer was operated at air velocity of 1.5 ms-1. Prior to drying at temperature of 60 °C, apple samples were treated for four groups of analysis as follows: dipping in 0.5% ascorbic acid solution; dipping in 0.3% L–cysteine solution; dipping in 0.1% 4–hexyl resorcinol solution and dipping in 1 % sodium metabisulfite solution. The apple samples on trays were placed into the tunnel of the dryer and the measurement started from this point. “Testo 350” probes, placed into the drying chamber, were used to measure the drying air temperature. Sample weight loss and airflow velocity were recorded in five minutes interval during the drying process using a digital balance (Ohaus, Explorer, USA) and digital anemometer (Armfield, UK). Dehydration lasted until a moisture content of about 12% (wet base) was achieved. Dried samples were kept in airtight glass jars until the beginning of rehydration experiments. Determination of the total solid/moisture content The moisture content of the dried samples was determined using a standard laboratory method. Small quantities of each sample were dried in a vacuum oven (24 hours at 105 °C). Time dependent moisture content of the samples was calculated from the sample weight and dry basis weight. Weight loss data allowed the moisture content to be calculated such as follows:

X(t)=

mw m db

(1)

4

Color measurements The color characteristics were used as quality parameter of the dried apple samples. Color measurement was done using Minolta CR-400 Chromameter and image analysis system. Data were stored in CIE Lab and RGB color models and color changes during this period were evaluated. The total color difference in CIE Lab color model was calculated as follows: 2 2 2 ∆E Lab = ( ∆L ) + ( ∆a ) + ( ∆b )   

(2)

Parameter L refers to the lightness of the samples, and ranges from black = 0 to white = 100. A negative value of parameter a indicates green, while a positive one indicates red–purple color. Positive value of parameter b indicates yellow while negative value indicates blue color. Samples were placed on the measure head of CR-400 and measurements of color were performed for all prepared samples. A standard white color was used for calibration. Color changes in RGB color model were followed by image analysis. Basic elements of image analysis system shown in Fig 2. were lightening chamber with low voltage halogen lamps with reflector (provided illumination of sample area of 760±5 Lux), digital camera (Panasonic Lumix DMC-FZ30) and software for image preprocessing and analysis (IrfanView, Adobe Photoshop, Global Lab Image/2). Samples for imaging were placed at 60 cm from camera. FIG 2. Images were stored in bitmap (BMP) graphic format with 8-bit pallet (28 = 256 colors) and after that were processed and analyzed. This graphic format stores information about colors in RGB-triplets for every pixel on the image where red (R), green (G) and blue (B) are intensities of mentioned colors in range from 0 to 255. Program calculated average percentage of red (R), green (G) and blue (B) color on a sample area. An average share of each color on sample surface was presented as the final result. Color changes in RGB color model were defined as: 2 2 2 ∆E RGB = ( ∆R ) + ( ∆G ) + ( ∆B )   

(3)

where ∆R, ∆G and ∆B were differences between color values of fresh apple samples and color values of dried samples. Average values of color and color changes of apples were calculated for both color models.

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Rehydration Rehydration characteristics of the dried products were used as a quality index and they indicated the physical and chemical changes that occurred during the drying and were influenced by processing conditions, sample compositions, sample preparation and extent of structural and chemical disruptions induced by drying (Krokida and Maroulis 2001). Approximately 3 g (±0.01 g) of dried samples were placed in a 250 ml laboratory glass (two measurements for each sample), 150 ml distilled water was added, the glass was covered and heated for 3 min up to the boiling point. The content of the laboratory glass was then cooked for 10 min by mild boiling and cooled. Cooled content was filtered for 5 min under vacuum and weighted. The rehydration ratio (RR) was used to express ability of the dried material to absorb water. It was determined by the following equation:

RR=

Wr Wd

(4)

Drying rate curve determination Page's exponential model successfully describes the drying kinetics of food materials (Velić et al. 2004; Simal et al. 2005; Bozkir 2006; Wang et al. 2007; Singh et al. 2008). The authors also used this model to describe the changes of moisture content and drying rates. To avoid some ambiguity in results due to differences in initial sample moisture, the sample moisture was expressed as dimensionless moisture ratio ( X'=X(t)/X 0 ) . The drying curve for each experiment was obtained by plotting the dimensionless moisture of the sample vs. the drying time. For approximation of the experimental data and calculating drying curves (Eq. 5) and drying rate curves (Eq. 6), the simplified model was used, as follows:

X' (t ) = e (-kt

−

n

)

dX' = k ⋅ n ⋅ t (n −1) ⋅ X' (t ) dt

(5) (6)

The parameters k and n were calculated by non-linear regression method (Quasi-Newton) using Statistica 6.0 computer program. The correlation coefficient (r2) was used as a measure of model adequacy.

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RESULTS AND DISCUSSION Fig. 3 shows experimental data of moisture contents at 50 °C, 60 °C and 70 °C and the fitting to Page's model (mod_50, mod_60 and mod_70) versus drying time at different temperatures. It can be seen that a good agreement exists between the experimental data and the chosen mathematical model (Page's model), which is confirmed by high values of correlation coefficient in all runs (R2 = 0.99981 - 0.99989). Results show that the temperatures had a significant effect on the drying rates of apple. The variation of moisture content and drying time was obtained at each drying temperature. Drying of apple samples at higher temperature resulted in shorter drying time, as it was expected. FIG. 3. Fig. 4 shows typical drying curves versus drying time for different drying temperatures. Apples did not exhibit a constant rate period of drying. The entire drying took place in the falling rate period. FIG. 4. Fig. 5 shows experimental moisture content versus drying time for different pretreatments at air velocity of 1.5 m s-1 and drying temperature at 60 °C. FIG. 5. Fig. 6 shows drying rate vs. drying time for different pretreatments at air velocity of 1.5 m s-1 and drying temperature at 60 °C. It can be seen that different pretreatments decrease the drying time compared to nontreated apple samples. FIG. 6. Fig. 7 shows total color difference in both color models CIE Lab and RGB versus different drying temperatures for nontreated apple samples. ∆ELab values varied from 9.4 to 11.42, whereas ∆ERGB values varied from 12.39 to 21.74. The smaller color changes were observed where apple samples were dried at 60 °C. FIG. 7. Fig. 8 shows total color changes in both color models CIE Lab and RGB of pretreated apple samples versus different pretreatments at air velocity of 1.5 m s-1 and drying temperature at 60 °C. ∆ELab values for pretreated apple samples varied from 5.72 to 9.38, whereas ∆ERGB values varied from 2.01 to 39.22. Chemical pretreatment with 0.1% 4–hexyl resorcinol give the smaller change of color in both color models. It can be seen the big difference in color changes between two chosen color models in the case of pretreatments with ascorbic acid. It depends on chosen area of analyzed apple sample surface. Lab values differs because of small diameter

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of measuring head of instrument Meanwhile, using image analysis in RGB color model all area of analyzed apple sample is included. FIG. 8. Fig. 9 shows RGB color space in pixels vs. luminosity of dried apples for different pretreatments and drying temperature at 60 °C. The biggest luminosity changes were observed on samples pretreated with ascorbic acid. Color content is expressed in nuances from 0 to 255. Samples closer to nuance 255 were lighter and the samples closer to 0 showed darker color. FIG. 9. Fig. 10 shows rehydration ratio versus different drying temperatures for drying of nontreated apple samples. The rehydration ratio was affected significantly by the drying temperatures. Rehydration ratio for nontreated apple samples decreased as the drying temperature increased. The results show that the longer the exposure time of apple samples to certain temperature is the higher are the irreversible degradation changes. FIG. 10. Fig. 11 shows rehydration ratio versus different pretreatments of apple samples. It can be seen that dipping in 0.1 % 4–hexyl resorcinol solution resulted in the highest rehydration, compared to other pretreatments. FIG. 11.

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CONCLUSIONS Air drying of apple samples could be modeled using Page’s equation. The results of the estimation exhibited correspondence to the experimental results. Increase in the drying air temperature caused a decrease in the drying time and an increase in the drying rate. Rehydration rates and color characteristic of apple samples were found to be dependent on drying conditions. The rehydration ratio decreased as the drying temperature increased. Also, rehydration ratio of all treated samples was higher as compared to nontreated samples. In view of the color measurements for nontreated apple samples calculated correlation among used color models was found to be 0.97, and for pretreated apple samples correlation was equal to 0.37. According to drying time, rehydration and color characteristics the optimal drying temperature was found to be 60 °C. The best results (reduced drying time, high rehydration ratio and minimum color change) were achieved when samples were pretreated with 4–hexyl resorcinol. Today’s consumer expectation for better food quality drives research and improvement of drying technologies. The observed differences in the drying kinetics and pretreatments should be considered when selecting the best drying condition in order to improve the final product quality. The investigation of economic parameters of different drying pretreatments should be considered as well.

ACKNOWLEDGMENTS This work was financially supported by Ministry of Science, Education and Sports of the Republic of Croatia, projects 113-0000000-3497 and 113-1130471-0592.

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REFERENCES BOZKIR, O. 2006. Thin-layer drying and mathematical modeling for washed dry apricots. J. Food Eng. 77, 146-151. CONTRERAS, C., MARTIN-ESPARZA, M.E., CHIRALT, A. and MARTINEZ-NAVARRETE, N. 2008. Influence of microwave application on convective drying. Effects on drying kinetics, and optical and mechanical properties of apple and strawberry. J. Food Eng. 88, 55–64. DOYMAZ, I. 2006. Drying kinetics of black grapes treated with different solutions. J. Food Eng. 76, 212-217. DOYMAZ, I. 2007. Influence of pretreatment solution on the drying of sour cherry. J. Food Eng. 78, 591-596. DOYMAZ, I. 2008. Convective drying kinetics of strawberry. Chem. Eng. Process. 47, 914-919. FERNANDEZ, L., CASTILLERO, C. and AGUILERA, J.M. 2005. An application of image analysis to dehydration of apple discs. J. Food Eng. 67, 185-193. GUERRERO-BELTRAN, J.A., SWANSON, B.G. and BARBOSA-CANOVAS, G.V. 2005. Inhibition of polyphenoloxidase in mango puree with 4-hexylresorcinol, cysteine and ascorbic acid. LWT. 38, 625630. KAYA, A., AYDIN, O. and DEMIRTAS, C. 2007. Drying Kinetics of Red Delicious Apple. Biosystems Eng. 96 (4), 517-524. KROKIDA, M.K. and MAROULIS, Z.B. 2001. Quality changes during drying of food materials. In Drying Technology in Agriculture and Food Sciences (Mujumdar A S, ed). Oxford IBH, Delhi, India. LERTWORASIRIKUL, S. 2008. Drying kinetics of semi-finished cassava crackers. A comparative study. LWT, 41, 1360–1371. LEWICKI, P.P. 2006. Design of hot air drying for better foods. Trends Food Sci. Technol. 17, 153-163. LEWICKI, P.P. and JAKUBCZYK, E. 2004. Effect of hot air temperature on mechanical properties of dried apples. J. Food Eng. 64, 307-314. MAGDIĆ, D. and DOBRIČEVIĆ, N. 2007. Statistical Evaluation of Dynamic Changes of ‘Idared’ Apples Color During Storage. Agriculturae Conspectus Scientificus, 72 (4), 311-316.

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MARGARIS, D.P. and GHIAUS, A.G. 2007. Experimental study of hot air dehydration of Sultana grapes. J. Food Eng. 79, 1115-1121. SACILIK, K. and ELICIN, A.K. 2006. The thin layer drying characteristics of organic apple slices. J. Food Eng. 73, 281-289. SINGH, G.D., SHARMA, R., BAWA, A.S. and SAXENA, D.C. 2008. Drying and rehydration characteristics of water chestnut (Trapa natans) as a function of drying air temperature. J. Food Eng. 87, 213-221. SIMAL, S., FEMENIA, A., GARAU, M.C. and ROSSELLO, C. 2005. Use of exponential, Page’s and diffusional models to simulate the drying kinetics of kiwi fruit. J. Food Eng. 66, 323-328. SON, S. M., MOON, K.D. and LEE, C.Y. 2001. Inhibitory effects of various antibrowning agents on apple slices. Food Chem. 73, 23-30. VADIVAMBAL, R. and JAYAS, D.S. 2007. Changes in quality of microwave-treated agricultural products-a review. Biosystems Eng. 98, 1-16. VELIĆ, D., PLANINIĆ, M., TOMAS, S. and BILIĆ, M. 2004. Influence of airflow velocity on kinetics of convection apple drying. J. Food Eng. 64(1), 97-102. WANG, Z., SUN, J., CHEN, F., LIAO, X. and HU, X. 2007. Mathematical modeling on thin layer microwave drying of apple pomace with and without hot air pre-drying. J. Food Eng. 80, 536-544. YALDIZ, O. and ERTEKIN, C. 2001. Thin layer solar drying of some vegetables. Drying Technol 19, 583-596. YAM, K.L. and PAPADAKIS, S.E. 2004. A simple digital imaging method for measuring and analyzing color of food surfaces. J. Food Eng. 61(1), 137-142.

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Figures

Thermocouples to «Testo 350»

Heat power control

Digital balance

Air inlet

Thermocouples to PC Air outlet

Heaters

Door

Trays

Fan speed control

Digital anemometer

Relative humidity couples to «Testo 350»

Fig. 1. Schematic diagram of the convection drying equipment

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1. Lightning chamber 2. Light source 3. Digital camera 4. Background for sample 5. Sample for analysis 6. Computer

Fig. 2. Image analysis system

13

1.2

X'

50 °C 1.0

60 °C

0.8

70 °C mod_50 °C

0.6

mod_60 °C 0.4

mod_70 °C

0.2

0.0 0

50

100

150

200

250

300

350

400

t [min] Fig. 3. Experimental and approximated moisture content of nontreated dried apple samples

14

0.015

50 °C

-1

-dX'/dt [min ]

60 °C 0.010

70 °C

0.005

0.000 0

100

200

300

400

t [min] Fig. 4. Drying rate dynamics of nontreated apple samples at different temperatures

15

X'

1.2

NT

1.0

AK

0.8

LC 4H

0.6

NaB 0.4 0.2 0.0 0

50

100

150

200

250

300

t [min] Fig. 5. Changes of apple moisture content at 60 °C drying temperature after different pretreatments

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-dX'/dt [min -1]

0.012

NT AK

0.009

LC 0.006

4H NaB

0.003

0.000 0

40

80

120

160

200

240

1 280

t [min]

Fig. 6. Drying rate dynamics of pretreated apple samples at 60 °C drying temperature

17

25

Lab RGB

20

∆E

15

10

5

0 50 ºC

60 ºC

70 ºC

Fig.7. Total color changes (∆ELab and ∆ERGB) of nontreated apple samples at different drying temperatures

18

45

Lab

40

RGB

35

∆E

30 25 20 15 10 5 0 NT

AK

LC

4H

NaB

Fig. 8. Total color changes (∆ELab and ∆ERGB) of pretreated apple samples at 60 °C drying temperature

19

4500

1_NT

5

4000 2_4H 3500 3_AK pixels

3000 2500 2000

1

4_LC 5_NaB 4

1500 1000 500 0 100

2

3 120

140

160

180 luminosity

200

220

240

Fig. 9. Dried apples luminosity after different pretreatments at 60 °C drying temperature

20

6.80 6.60 6.40

RR

6.20 6.00 5.80 5.60 5.40 5.20

50 °C

60 °C

70 °C

Fig. 10. Rehydration ratio (RR) of nontreated apple samples at different drying temperatures

21

10 9 8 7

RR

6 5 4 3 2 1 0

NT

AK

LC

4H

NaB

Fig. 11. Rehydration ratio (RR) of pretreated apple samples at 60 °C drying temperature

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