Abstract-- The effect of different infrared (IR) powers on drying of orange slices was investigated in infrared dryer. The orange slices dried at 62, 74 and 88 W infrared powers and constant slice thickness of 6 mm. Results showed that drying, colour and rehydration characteristics of orange slices were greatly influenced by infrared power. The drying data were fitted with five mathematical models available in the literature. Based on the statistical tests applied to make an assessment, the model of Midilli and Kucuk was found to satisfactorily explain drying kinetics of orange slices for all drying conditions. The Fick’s diffusion model was used to calculate the effective moisture diffusivity (Deff) of orange slices. The values of Deff varied from 1.59×10-10 to 2.49×10-10 m2/s. It was found that the Deff increased with increasing IR power. Activation energy was estimated by a modified Arrhenius type equation as 2.11 kW/kg. As the infrared power increased, the rehydration ratio was found to be reduced. Furthermore, with increase of infrared power, the values of a and DE increased, whereas the values of L, b and C decreased.
Keywords-- Activation energy; effective moisture diffusivity; infrared drying; modelling; orange slices.
I. INTRODUCTION
Orange (Citrus sinensin L.), one of the most important fruit among citrus fruits, is consumed mostly as fresh or juice because of the its high level of vitamin C content and polyphenol contents like the other citrus fruits (Bozkir, 2020; Acoglu and Yolci Omeroglu, 2021). The worldwide production of oranges in 2019 was approximately 78699604 tons. The leading countries in orange production in the world are Brazil, China, India, United States of America, Mexico, Spain and Egypt, respectively. Orange production in Turkey was about 1700000 tons in 2019 (FAO, 2020).
High moisture content of orange leads to biochemical and microbial spoilage. Therefore, it is processed into frozen, dried, canned, jam and fruit juice forms in order to increase its shelf life (Ozkan-Karabacak et al., 2020). Drying, which is one of the oldest method for preserving food, can be applied under optimum conditions to extend the shelf life of the citrus by decreasing the moisture content to a low level to inactivate physiological, enzymatic and microbial degradation, as well lower the weight and transportation cost of products (Deng et al., 2020). Drying of orange is a common way to produce healthy snack foods, additives and confectionery and bakery products, powdered forms for beverages and fortification materials as a valuable bioactive source (Acoglu and Yolci Omeroglu, 2021).
There are many methods in the literature for drying food products. One of them is infrared drying method. Infrared drying has many more advantages over traditional drying methods, such as high energy efficiency and short drying times. This advantages are is due to quick absorption of energy by water molecules, which causes rapid evaporation of water and results in high drying rates of food and, thus, leads to a decrease of drying time and, therefore, to lower energy consumption and better quality of the dried food (Doymaz, 2015).
Several researches have studied the drying of orange slices, peels and skins such as convective (Garau et al., 2006; Rafiee et al., 2010; Deng et al., 2020), microwave + hot air drying (Diaz et al., 2003; de Pilli et al., 2008; Acoglu and Yolci Omeroglu, 2021), and vacuum drying (Ozkan-Karabacak et al., 2020). However, no study on infrared drying of orange has been reported in literature. The purpose of this research is to investigate the effect of infrared power on drying, rehydration and colour characteristics, and compute effective moisture diffusivity and activation energy of orange slices.
II. MATERIAL AND METHODS
A. Material
The orange fruits (Citrus sinensin var. Washington) were purchased from a local market in Istanbul and immediately brought to the laboratory where the experiments would take place. Then, the oranges were washed thoroughly to remove dust and other foreign materials. Prior to the drying experiment, the orange fruits were cut into 6±0.3 mm thick slices with peel using a sharp knife. The initial moisture content of oranges was determined by using an oven at 105°C for 24 h. Triplicate samples were used for the determination of moisture content and the average values were reported as 85.21%, w.b. (5.43 kg water/kg dry matter, d.b.).
B. Experimental procedure
Drying experiments were carried out in a moisture analyzer with one 250 W halogen lamp (Snijders Moisture Balance, Snijders b.v., Tilburg, Holland). Before infrared drying process, orange slices (approximately 45±0.5 g) were separated evenly and homogeneously over the pan. The drying experiments were performed at infrared power level varying from 62 to 88 W, determined with a digital energy meter (PeakTech 9035, Germany). The infrared power was set in control unit of equipment. Weight loss of samples was recorded at regular intervals of 15 min during drying using a digital balance (model BB3000, Mettler-Toledo AG, Grefensee, Switzerland), which has 0-3000 g measurement range with reading accuracy of 0.1 g. Drying was stopped when the moisture content of samples were approximately 14.9%, w.b. (0.175 kg water /kg dry matter, d.b.). The dried product was cooled and packaged in low-density polyethylene bags then heat-sealed. All experiments were carried out in triplicate. Drying data were analysed using two-way analysis of variance at p<0.05.
C. Moisture content
The moisture content of orange slices was calculated by the following equation:
, (1)
where 
is the moisture content (kg water/kg dry matter), 
is the weight
of sample (kg), and 
is the dry
matter content of sample (kg).
D. Drying rate
The drying rate of orange slices at a particular time period was calculated as follows:
, (2)
where 
is drying rate [kg water/(kg dry matter´min)], 
and 
are drying
times (min), and 
and 
are the moisture
contents (d.b.) at times and ,
respectively.
E. Modelling
The moisture ratio (
) of orange
slices was calculated from Eq. (3).
, (3)
where 
, 
and 
are the moisture content at initial, equilibrium and at any time ‘
’ (min) during
drying process, respectively, expressed as kg water/kg dry matter). However, the equilibrium moisture content () is
relatively small compared with and , especially
for infrared drying. So, Eq. (1) 
can be
simplified to 
(Izli et al., 2021).
F. Data analysis
The data from
drying of orange slices was fitted with five thin-layer
drying models (Table 1) typically used for the modeling of drying curves.
The data of were analyzed
using Statistica 8.0.550 (StatSoft Inc., Tulsa, OK, USA) software packa-ge. The
parameters of models were estimated using a non-linear regression procedure
based on the Levenberg-Marquardt algorithm. The fitting quality of the
experimental data to all models was evaluated using the
coefficient of determination (R2), reduced chi-square (c2) and root mean square error (RMSE).
Higher values of R2 and lower c2 and RMSE indicate better
goodness of fit (Salehi and Satorabi, 2021). These
parameters were calculated from the following formulas:
, (4)
, (5)
. (6)
Table 1. Mathematical models applied to the drying curves.
|
Model name |
Model 1) |
|
Lewis |
|
|
Henderson and Pabis |
|
|
Logarithmic |
|
|
Page |
|
|
Midilli and Kucuk |
|
1) a, b, c, k and n: constants and coefficients in drying models
where 
and 
are
experimental and predicted dimensionless moisture ratios, respectively, 
is number of
observations, and 
is number of constants.
G. Calculation of effective moisture diffusivity
Fick’s second law of diffusion equation, symbolized as a mass-diffusion equation for drying of agricultural products drying in a falling rate period, is shown in the following equation:
(7)
Fick’s second law of unsteady state diffusion given in Eq. (7) can be used to determine the moisture ratio in Eq. (8). The solution of diffusion equation for infinite slab given by Crank (1975), and supposed uniform initial moisture distribution, negligible external resistance, constant diffusivity and negligible shrinkage, is:
(8)
where 
is the
effective moisture diffusivity (m2/s), is the time (s), 
is the
half-thickness of samples (m) and 
is a positive
integer. Eq. (8) can be simplified to the following for long drying times.
. (9)
The Eq. (9) can be
linearized by applying natural logarithmic at both sides as shown in Eq. (10),
where the slope of the graph (
versus time)
was used to determine the 
, (10)
. (11)
H. Estimation of activation energy
Activation energy (
) represents
the minimum energy required for water molecules to migrate within the food
during drying (Martynenko and Kudra, 2016). For the calculation of activation
energy, modified form of Arrhenius equation shows the relationship between the
effective moisture diffusivity and the infrared power to sample weight (Kayran and
Doymaz, 2019).
, (12)
where 
is the
pre-exponential factor of Arrhenius equation (m2/s), is the
activation energy (W/kg), 
is the
infrared power level (W), and 
is the sample
weight (kg).
I. Colour
The colour parameters of fresh and dried orange slices were determined using a Konica Minolta CR-400 (Tokyo, Japan) chroma meter equipped with a D65 illuminant and operating with CIE Lab [L (whiteness/darkness), a (redness/greenness), and b (yellowness/blueness)] colour spaces. Calibration was performed with the colour calibration tile before the colour measurements. Four separate measurements were randomly taken from the surface of the orange slices, and the results were given as the average of four separate measurements. The total colour differences (DE) and Chroma (C) calculated using Eqs. (13) and (14), respectively (Isik et al., 2019; Kayran and Doymaz, 2019):
, (13)
, (14)
where
, 
and 
are the colour values of fresh samples before drying.
J. Rehydration
Two grams of dried
orange slices were soaked in 300 mL of distilled water at 20°C. After 6 h, the
samples were removed from water, wiped with tissue paper, then drained and
weighed. The rehydration was expressed by moisture content of the orange slices
during rehydration process. Rehydration ratio (
) of the
orange slices was calculated as follow (Kutlu, 2021):
(15)
where 
is weight of
rehydrated samples (g) and 
is weight
dried samples (g). The experiments were replicated three times and the average
values were calculated.
III. RESULTS AND DISCUSSION
A. Drying curves
The effect of infrared power on drying curves of the orange slices during drying is shown in Fig. 1. The drying curves are typical to ones for similar fruits and vegetables. The moisture content decreased exponentially with elapsed duration of drying and decreased faster at higher infrared powers in all cases, as expected. As can be seen, an increase in the infrared power decreased the moisture content due to increasing temperature. The drying time required reaching the final moisture content of samples were 315, 240 and 210 min at the infrared power levels of 62, 74 and 88 W, respectively. The average drying rate of samples increased 1.5 times as infrared power increased from 62 W to 88 W. As expected at higher infrared power level the higher heat absorption resulted in higher product temperature, higher mass transfer driving force, faster drying rate and consequently shorter drying time. Similar studies have been previously reported by some researchers (Corrêa et al., 2012; Darvishi et al., 2014; Kayran and Doymaz, 2019).
B. Drying rate
The drying rate curves of orange slices
are shown in Fig. 2. Figure 2 reveals that, in general, two distinct periods
are identifiable, namely warming-up and falling-rate periods. The initial short
warming-up stage corresponds to sample heating, and consequently to
non-isothermal drying conditions this followed by a falling-rate period. A
constant-rate period could not be detected in drying rate curves in Fig. 2.
Similar results have been observed in the drying of lemon slices (Darvishi et
al., 2014) and orange peels (Deng et al., 2020). As shown in Fig. 2,
the higher
Fig. 1. Variations of moisture content with drying time of orange slices at different infrared powers.
Fig. 2. Variations of drying rate as a function of drying time for different infrared powers.
the infrared power, the greater the increase in drying rates. The drying rates were higher in the beginning of the process, and thereafter decreased with decrease of moisture content in the samples during drying process. The results were consistent with observations made by different authors on drying various agricultural products (Hafezi et al., 2015; Doymaz, 2015).
C. Evaluation of the models
The moisture content data obtained in the 6 mm thick orange slices were converted into the moisture ratio (MR) and fitted to the five thin-layer drying models listed in the Table 1. The model having the highest R2 and the lowest c2 and RMSE values was determined as the best. The results obtained by statistical calculation are given in Table 2.
Among the different models examined,
Midilli and Kucuk model could be considered as the most suitable drying model
with the highest R2 and lowest c2 and RMSE
Table 2. Thin-layer drying model constants and statistical parameters of orange slices
Fig. 3. Comparison of experimental moisture ratio with predicted moisture ratio from the Midilli and Kucuk model for orange slices.
values for all drying experiments (Table 2). The R2, c2, and RMSE values of this model varied within the ranges of 0.9993-0.9995, 0.000047-0.000083, and 0.019952-0.024625, respectively. To validate the selected model, plots of experimental MR and predicted MR by Midilli and Kucuk model are shown in Fig. 3.
A good agreement was observed between experimental and predicted MR values. That is, the data points generally banded around a 45° straight line on the plots. This trend provides extra evidence for the suitability of the model to forecast the drying characteristics of orange slices. Similar results were reported by Darvishi et al. (2014) and Maftoonazad et al. (2020).
D. Effective moisture diffusivity
The effective moisture diffusivity (Deff) values for different infrared power levels, calculated from Eq. (11), are given in Fig. 4 and ranged from 1.59×10-10 to 2.49×10-10 m2/s and were within the range of moisture diffusion values of food ingredients (10-12 to 10-8 m2/s) (Zogzas et al., 1996).
The Deff values increased with increase in infrared power due to high mass
transfer at high temperatures. Drying at 88 W had the highest value of
effective mois-
Fig. 4. Variation of effective moisture diffusivity with infrared powers.
Fig. 5. Arrhenius-type relationship between effective moisture diffusivity and infrared power.
ture diffusivity and the lowest value was obtained for 62 W. The Deff values of orange slices was reported as 6.27´10-10 to 3.5´10-10 m2/s in temperature range of 40 to 80°C by Rafiee et al. (2010). Garau et al. (2006) determined that the Deff values changed between 0.81´10-10 to 5.11´10-10 m2/s in temperature range of 30 to 90°C as a result of drying the orange skins. The Deff values obtained from the studies mentioned above seem to agree with the values obtained in this study. The relationship between effective moisture diffusivity and infrared power can be represented as:
(16)
E. Activation energy
The activation energy
can be determined from the slope of Arrhenius plot, ln(Deff) versus 
(Eq. 12). The ln (Deff) as a
function of the sample weight/infrared power level was plotted in Fig. 5. The
slope of the line is (-Ea) and the intercept equals to ln (D0).
The results show a linear relationship due to Arrhenius type dependence. Equation (17) shows the effect of sample weight/power level on Deff of samples with the following coefficients:
Table 3. Results of colour values for dried orange slices.
|
IR power (W) |
L |
a |
b |
C |
DE |
|
62 |
48.37±1.29 |
6.48±0.08 |
19.21±0.62 |
20.27±0.56 |
38.24±0.47 |
|
74 |
46.91±1.08 |
7.23±0.06 |
17.88±0.55 |
19.28±0.48 |
39.56±0.52 |
|
88 |
59.85±1.35 |
7.81±0.09 |
8.59+0.19 |
11.60±0.22 |
49.80±0.60 |
Fig. 6. Values of rehydration ratio of the orange slices dried at different infrared powers.
(17)
The calculated values of D0 and Ea from modified Arrhenius type exponential Eq. (17) were 8.14×10-10 m2/s and 2.11 kW/kg, respectively.
F. Colour
Colour is very important characteristic which influences the consumer acceptability. The colour values of fresh orange slices were measured as L, a, and b equal to 49.39, 3.28, and 36.46, respectively. Table 3 represents L, a, b, ΔE, and C values of dried orange slices.
As shown in Table 3, the results obtained from colour measurements of orange slices dried under various conditions showed that the infrared radiation power has a significant effect on the colour of the orange slices. After all drying processes, the dried samples had lower L and b values and higher a values. If the colour values of the fresh and dried samples are compared, a decrease in the L and b values of the dried samples, whereas an increase in the a values was observed. According to the results presented in Table 3, L values varied between 20.27 and 11.60 depending on the infrared power level. It has been stated that the variation in whiteness of dried samples can be taken as a measurement of browning. Under the same conditions, the b values fell from 19.21 to 8.59. In contrast, a values increased from 6.48 to 7.81. Infrared drying produced an increase in a value compared to fresh orange, probably due to isomerization or the increase in the concentration of some types of red carotenoids as lycopene (which has an intensely red tonality) because of heat irradiated and reduction of moisture content. Similar opinion has been expressed by Vega-Galvez et al. (2021) before.
The values of DE, which expresses the total colour change of orange slices after drying process. With increasing in infrared power from 62 to 88 W, ΔE was increased from 38.24 to 49.80. This is attributed to the process of Maillard browning reaction that might have occurred drying process. Alaei and Chayjan (2015) reported that the increase in the DE values of dried nectarine due to the increasing temperature of the vacuum-infrared dryer from 50 to 70°C. The chroma values were decreased 20.27 to 11.60 when infrared power increased from 62 to 88 W, and showed a decrease during drying process. The obtained values for chroma show the saturation degree of colour and are corresponding to the colour strength (Aidani et al., 2017).
G. Rehydration ratio
Rehydration can be defined as a measurement of food material damages during drying and pre-treatments. The amount and rate of water absorption is important points that affect the sensorial properties and preparation time during reconstitution of dried products (Mahtoonazad et al., 2020). To investigate the effect of drying conditions on final product quality, the values rehydration ratio of dried orange slices were calculated by using Eq. (15) and shown in Fig. 6.
As the infrared power increased, the rehydration ratio was found to be reduced. Thus in order to produce dried products with desirable rehydration ratio, very high infrared power during drying process should be avoided. Similar results were also presented in apple slices (Taiwo et al., 2002), carrot slices (Doymaz, 2015) and pear (Antal et al., 2017).
IV. CONCLUSIONS
Drying characteristics of orange slices were investigated in an infrared dryer at different infrared powers. The drying, colour and rehydration were significantly influenced by infrared power. The drying time decreased with the increase in infrared power. The entire drying process occurred in the falling-rate period and constant-rate period was not observed. The Midilli and Kucuk model described the best fit of the experimental data with higher R2 value and lowest c2 and RMSE values. The values of effective moisture diffusivity were varied between 1.59×10-10 and 2.49×10-10 m2/s for the range of infrared powers studies, between 62 W and 88 W. With the increase of power level, the effective moisture diffusivity increased. Activation energy was estimated by a modified Arrhenius type equation as 2.11 kW/kg. The values of colour parameters, a and DE increased, and L, b and C decreased with increasing infrared power during drying process.
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Received: July 9 2021
Sent to Subject Editor: August 2, 2021
Accepted: September 23, 2021
Recommended by Subject Editor Jose Luis Diaz de Tuesta