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. 2022 Sep 16;87(10):4394–4415. doi: 10.1111/1750-3841.16322

Drying date plum (Diospyros lotus L.) fruit: Assessing rehydration properties, antioxidant activity, and phenolic compounds

Awadalgeed M A Hassan 1, Oscar Zannou 1,✉, Hojjat Pashazadeh 1, Ali Ali Redha 2,3,✉, Ilkay Koca 1
PMCID: PMC9826110  PMID: 36112569

Abstract

Date plum (Diospyrus lotus L.) is an edible fruit from the Ebenaceae family, rich in nutrients, and having tremendous medicinal properties. This paper attempted to show the influence of different parameters of convective drying such as temperature (50, 60, 70, and 80°C) and air velocity (0.5, 1.0, and 1.5 m/s) on the shrinkage and microstructure, rehydration properties, antioxidant activity, and phenolic compounds of date plum. The drying caused significant changes in the color, actual size, and distribution of the fruit cells of date plum. The total phenolic content (TPC), total flavonoid content (TFC), ferric reducing antioxidant power (FRAP), and 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH) of fresh date plum were 0.81 ± 0.00 mg GAE/g, 0.23 ± 0.10 mg ECE/g, 7.15 ± 1.09 mmol ISE/g, and 14.92 ± 0.88 mmol/TE, respectively. The drying at 70°C had the highest values of TPC, TFC, gallic acid, chlorogenic and syringic acids, catechin, quercetin‐3‐glucoside, resveratrol, and DPPH. The drying air velocities showed no significant effects on the antioxidant contents and the antioxidant activity. Of the models applied to the drying kinetics, the Midilli model was found as the best model to describe the drying kinetics of date plum. In addition, the Weibull model was found as the most successful among the models applied to the rehydration kinetics of date plum. According to the achieved findings, the convective drying temperature of 70°C is the optimum temperature to produce the dehydrated date plum.

Practical Application

This work has revealed the drying conditions responsible for preserving the phenolic compounds, total flavonoid content, and antioxidant features of D. lotus L. The study found the optimum drying conditions, and Midilli and Weibull models were the most fitted models to describe the drying and rehydration behaviors of D. lotus L. fruits, respectively. The drying provides a reasonable value of the possibility of continuous consumption of the fruits dried afforded on off‐seasons. The dried fruits are widely used for multipurpose and have been extensively used in food industries due to their rich nutraceutical and antioxidant compounds.

Keywords: antioxidant activity, date plum, drying, phenolic compounds, rehydration

1. INTRODUCTION

Date plum or Caucasian persimmon (D. lotus L.) is from the Ebenaceae family and is native to China and Asia. Date plum is cultivated in several countries for its edible fruits, owing higher nutritive and medicinal properties. The consumption of fruits is recommended for their beneficial effects on human health including sedative, antiseptic, antidiabetic, antitumor, laxative, antidiarrhea, dry cough reliever, and tension regulator (Rashed et al., 2012; Uddin et al., 2011). Studies have reported the effectiveness of date plum of having antinociceptive (Uddin et al., 2014), muscle relaxative (Rauf et al., 2015), anti‐inflammatory (Uddin et al., 2014), antioxidant, antiproliferative (Loizzo et al., 2009; Nabavi et al., 2009), skin lesion recoverer (Azadbakht et al., 2011; Cho et al., 2017), sedative (Rauf et al., 2015; Uddin et al., 2014), and evidence anti‐HIV (Rashed et al., 2012) activities. The phytochemical studies of date plum revealed that it is a rich source of gallic acid, vanillic acid, caffeic acid, ferulic acid, salicylic acid, protocatechuic acid, myricetin, 3,4‐dihydroxybenzoic acid, quercetin, 4‐hydroxybenzoic acid, and ρ‐coumaric acid (Ayaz et al., 1997; H. Gao et al., 2014). Similarly, Rashed et al. (2012) have reported ellagic acid, methyl gallate, gallic acid, myricetin‐3‐O‐β‐glucuronide, myricetin‐3‐O‐α‐rhamnoside, myricetin, and quercetin as major in Egyptian date plum. These bioactive compounds have got potential application in the food, cosmetic, and pharmaceutical industries. Therefore, applying preservation techniques to have long‐term storage date plum with high functional properties is essential.

Although the date plum contains many essential bioactive compounds and has displayed many biological activities, it is a seasonal fruit with higher water content and is subjected naturally and rapidly to biochemical alterations. Thus, drying date plums seems opportune and timely to decrease the water content, limit microbiological and biological alterations, and preserve the nutritional and bioactive compounds as well as biological activities (A. Ahmad et al., 2012; Pashazadeh et al., 2020a, 2020b). Many drying techniques have been developed to preserve food quality and reduce environmental issues and energy consumption (Doymaz, 2008; Erbay & Icier, 2009; Figiel, 2010; Giri & Prasad, 2007). Convective drying is a hot air reported to be adequate for drying fruits and vegetables, providing faster, hygienic, and safe dried products (Pashazadeh et al., 2020a, 2020b; Zhu et al., 2020). The convective drying has many advantages that justify its wide use in the food industry. It corresponds to the basic treatment mode of drying food products for their conservation and gain benefits to prolonging the shelf life of the bioproducts such as fruits and vegetables (Kouhila et al., 2020). According to Almeida et al. (2016), the convective drying, compared to other types of drying has the advantages to be the low price of facilities, easy, and cheap process control as well as flexibility. It is a comparably economical drying technique with a well‐known theoretical framework (Kouhila et al., 2020). By a selection of a convenient convective mode of drying, the appropriate physical parameters such as temperature, humidity, and airflow rate, with a reasonable heat dosage are very essential for a better performance of drying (Almeida et al., 2016; Kouhila et al., 2020). Moreover, investigating the optimum drying conditions and kinetics became essential to evaluate the drying impacts on preserving bioactive compounds, nutritional quality, and antioxidant activity of the dried fruits (Pashazadeh et al., 2020a, 2020b).

The degree of rehydration is one of the most important factors determining the quality of the dried products. Usually, most dried agricultural products are rehydrated for their uses. The rehydration comprises three simultaneous processes: the imbibition of water into dried material, the swelling, and the leaching of solubles (Krokida & Marinos‐Kouris, 2003; Pashazadeh et al., 2020a). However, to date, no study has investigated the drying and rehydration conditions and kinetics of date plum. Thus, the main aim was to determine the drying conditions to have maximum total phenolic content (TPC), total flavonoid content (TFC), and the antioxidant activity of date plum. Furthermore, the kinetics and modelling of drying and rehydration as well as the microstructural changes occurring during the drying process were evaluated.

2. MATERIALS AND METHODS

2.1. Plant material

Date plum (D. lotus L.) fruits were collected from Samsun (Turkey) in 2019 during the Winter harvest season. Fruits were sorted, filled in PTE bags (ca. 300 g), and kept in the refrigerator.

2.2. Chemical and reagents

2,2‐Diphenyl‐1‐picrylhydrazyl (DPPH), 2,4,6‐tris(2‐pyridyl)−1,3,5‐triazine (TPTZ), acetone, Trolox, sodium nitrite, hydrochloric acid, methanol, sodium hydroxide, Folin‐Ciocalteau reagent, (−)‐ epicatechin, hesperidin, catechin, fumaric acid, and quercetin were purchased from Sigma‐Aldrich. Gallic acid (GA) and sodium carbonate were purchased from Riedel‐de Haen. Sodium acetate and glacial acetic acid were bought from Carlo Erba. Aluminum (III) chloride and iron (III) chloride were acquired from Merck.

2.3. Physicochemical characteristics

The moisture content was measured in an oven at 70°C for 24 h according to the preliminary tests. The color was determined using a colorimeter (DP‐400, Minolta, Japan) and the CIE L*a*b* scale was measured. The total soluble solids were evaluated at 25°C using an Abbe refractometer (Atago, Japan), and pH was measured with a pH meter.

2.4. Drying system and experiments

The drying system was a convective regime cabinet dryer (EKSIS, Turkey) described in our previous study (Pashazadeh et al., 2021, 2020a). The drying process was carried out at four temperatures (50, 60, 70, and 80°C) and three air velocities (0.5, 1.0, and 1.5 m/s). After the drying process, the samples were taken out to room temperature and sealed into polyethylene LDPE bags.

2.5. Modeling of drying curves

The moisture ratio (MR) was determined using Equation (3):

MR=MtM0 (1)

where M 0 and Mt are initial moisture and at time t, respectively.

The drying data was applied to six models (Table 1), and the coefficient of determination (R 2), chi‐square (χ2) and root mean square error (RMSE) were generated by MATLAB software (R2016d) to validate the fitness of the models (Doymaz, 2008; Zannou et al., 2021). The R 2, χ2, and RMSE were expressed as follows:

R2=1−∑i=1N(MRp,i−MRe,i)2∑I˙=1N(MRe,i−MR¯e,i)2 (2)
χ2=∑i=1NMRe,i−MRp,i2N−n (3)
RMSE=1N∑i=1NMRe,i−MRp,i21/2 (4)

where MRe ,i and MRp ,i are the ith experimental and predicted moisture rates, respectively. MR¯e,i is the mean of the experimental moisture ratio. N and n are the number of treatments and the number of constants in the models, respectively.

TABLE 1.

Empirical models applied to drying kinetics of date plum.

No: Models Equations References
1 Newton MR = exp (−kt) Bengtston et al. (1998) et al. (1998); Tunde‐Akintunde (2011)
2 Page MR = exp (−ktn ) Keneni et al. (2019)
3 Henderson and Pabis MR = α exp (−ktn ) Özdemir & Devres, 1999; Yaldız et al. (2001)
4 Two‐term MR = α exp (−kt) + b exp (−gt) Nurafifah et al. (2018); Özdemir and Devres (1999)
5 Two‐term exponential MR = α exp (−kt)+(1+α) exp (−kαt) Chielle et al. (2016); Padoin et al. (2016)
6 Wang and Singh MR = 1 + at + bt 2 Özdemir and Devres (1999); Nurafifah et al. (2018)
7 Approximation of diffusion MR = α exp (−kt) + (1−α) exp (−kbt) Ertekin and Yaldız (2004)
Logistic MR = α/(1 + b exp (kt)) Yaldız et al., 2001; Yaldız and Ertekin (2001); Samani et al. (2018)
8 Midilli MR = α exp (−ktn ) + bt Nurafifah et al. (2018)
9 Aghabashlo model MR = exp [−kt/(1+gt)] Kumar et al. (2017)
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Note: MR is the moisture ratio; t is the time; and α, b, c, g, k, and n are the constants of models; exp is the exponential function.

2.6. Sample extraction

A portion of 1 g of the crushed samples was mixed with 20 ml of 80% methanol and left to macerate for 12 h at 25°C. The mixtures were filtered and properly diluted for the analyses.

2.7. Total phenolic content

The TPC was determined by the Folin‐Ciocalteu method adopted from Zannou and Koca (2020). Briefly, 150 µl of the diluted sample was mixed 750 µl of 10% Folin‐Ciocalteu reagent (stood for 5 min) and 600 µl of 7.5% (w/v) Na2CO3. The mixture was placed in the dark for 2 h. The absorbance was read at 760 nm using a UV‐spectrophotometer (Thermo Spectronic) and expressed as mg gallic acid equivalent per g (mg GAE/g).

2.8. Total flavonoid content

The TFC was determined using the protocol of Zannou and Koca (2020). One milliliter of the diluted solution was combined with 0.3 ml of 5% NaNO2 and left to stand for 5 min, followed by the addition of 0.5 ml of 5% AlCl3. The mixture was kept for 6 min before adding 0.5 ml of 1 M NaOH. After 10 min, the absorbance was read at 510 nm. The TFC was estimated based on a calibration curve using epicatechin as standard. The results were given as mg epicatechin equivalents (ECE)/g dw.

2.9. DPPH radical scavenging activity

The DPPH radical scavenging was determined using the method described in Zannou and Koca (2020). Briefly, an aliquot of 50 µl sample was added with 1 ml DPPH solution (0.06 mM in 80% methanol). The mixture was shaken and left to stand in dark for 1 h until the reaction completed. Thereafter, the absorbance at 517 nm was recorded. The DPPH solution was used as control. The reduction ratio of DPPH was determined with the following equation:

Reduction%=Ac−AsAc×100 (5)

where Ac is the absorbance of the control and As is the absorbance of extract.

2.10. Ferric reducing antioxidant power

Ferric reducing antioxidant power (FRAP) assay was performed according to the procedure of Zannou and Koca (2020). Briefly, a portion if 50 ml volume of sample was mixed with 950 mM of FRAP solution constituted of 100 mM acetate buffer:10 mM FeCl3:10 mM TPTZ (2,4,6‐tripyridyl‐s‐triazine). The assembly was shaken for about 5 min, and the absorbance was read at 593 nm against a blank. The FRAP values of the extracts were calculated from the calibration curve using FeSO4 as a standard. The results were given as mmol FeSO4 equivalents (mmol ISE/g dw).

2.11. Rehydration curves and modeling

The rehydration was conducted at room temperature (25°C), and the rehydration features such as moisture content (Mc ) and rehydration ratio (Rr ) were calculated according to Pashazadeh, Zannou, Koca (2020). Peleg, Weibull, and Vegas‐Gálves models (Tables 2). were applied to the rehydration data, and the statistically significant results were shown based on R 2, χ2, and RMSE as expressed in Equations (2)–(4).

TABLE 2.

Models applied to the rehydration kinetics of date plum.

Models Equations References
Peleg
M(t)=M0+t(α+bt)
 Vega‐Gálvez et al. (2009)
First‐order kinetic M(t) = Meq + (M 0−Meq) exp (−α t) Benseddik et al. (2019), Ghellam and Koca (2020), and Krokida and Marinos‐Kouris (2003)
Exponential‐related equation M(t) = Meq (1−exp (−α t)) Ghellam and Koca (2020) and Noshad et al. (2012)
Exponential model M(t) = Meq + (M 0−Meq) exp (−α tk) Benseddik et al. (2019) and Saguy et al. (2005)
Weibull M(t) = Meq+(M 0−Meq) exp (−(t/b) α) Benseddik et al. (2019), Pramiu et al. (2015), and Vega‐Gálvez et al. (2009)
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Note: Mt , M 0, and Me are the water contents at time t, before rehydration, and end of rehydration, respectively, and α, b, and k are the constants of models.

2.12. LC‐MS/MS analysis

The phenolic compounds of fresh and dried samples were determined using liquid chromatography coupled to a mass spectrometer detector (LC‐MS/MS, Shimadzu LC‐MS 8040) as described in Zannou et al. (2021). The liquid chromatography coupled to a mass spectrometer detector (LC‐MS/MS, Shimadzu LC‐MS 8040) via electrospray ionization (ESI) and two pumps (LC‐30 AD), a column oven (CTO‐10AS VP), an autosampler (SIL‐30AC), and a degassing unit (DGU‐20A 3R). The MS/MS system functioned at 300°C capillary temperature, 350°C vaporizer temperature, 30 arb sheath gas pressure, 13 Arb Aux gas pressure, 4000 V spray voltage (positive polarity), 2500 V spray voltage (negative polarity), and 4 µA discharge current. A 0.45‐µm nylon filter was used to filter the samples and standards before injecting 20 µl into a C18 reversed‐phase column (ODS hypersil 5 µm, 4.6 × 250 mm). The column temperature was set at 30°C, and analysis was performed for 34 min. The mobile phase was constituted of water: formic acid in 99.9:0.1 v:v (mobile phase A) and HPLC grade methanol (mobile phase B). The flow rate of the solvents was 0.7 ml/min, and the following gradient solution was used: 0 min, 100% A; 1 min, 100% A; 22 min, 5% A and 95% B; 25 min, 5% A and 95% B; 30 min, and 0% A and 100% B. The phenolic compounds were identified based on their elution time and quantified from their peak area. The phenolic compounds were identified based on their elution time and quantified from their peak area. The identified compounds were quantified using a mixture of external standards (gallic acid, catechin, fumaric acid, and quercetin) prepared by dissolving standards in methanol at concentrations of 0, 50, 75, 100, 150, and 200 ppm.

2.13. Scanning electron microscope

The fresh and dried samples’ microstructures were obtained using scanning electron microscope (SEM) (JEOL JSM‐7001F) (Zannou et al., 2021).

2.14. Data analysis

The MATLAB software (R2016d) was used for the modelling and the Design‐Expert software 9.0 (Trial version, Stat‐Ease Inc., Minneapolis, USA) to generate the optimization models and one‐factor graphics. The statistical significance of independent variables and the correlation between them was evaluated using ANOVA. The adequacy of the optimization model was determined based on the coefficient of determination (R2), adjusted coefficient of determination (adj. R2), coefficient of variation (CV), and Fisher's test value (F‐value). The regression coefficients were considered significant at p < 0.05. The optimum parameters were estimated considering the desirability function. The analyses were carried out in triplicate, and the one‐way ANOVA with post hoc Duncan's test was used (SPSS, version 21). The significance of the results was given at p≤ 0.05.

3. RESULTS AND DISCUSSION

3.1. Characteristics of raw material

The date plum studied in the present work is almost spherical (19.90 mm in width and 18.06 mm in length) with a flesh/seed ratio of 2.81 and the number of grains is about 220 for each kilogram. As shown in Table 3, the dry matter, soluble solids, and pH were 44.60%, 13.66%, and 6.82, respectively. It is encouraging to compare soluble solids and dry matter results with that found by Ayoub et al. (2020), who found that total soluble solids and moisture of date plum pulps are 13% and 70.5%, respectively. The color of the date plum was determined as L*, a*, and b* values were 37.20, 6.40, and 14.48, respectively.

TABLE 3.

Physicochemical characteristics of fresh date plum.

Fresh fruit characteristics Values a
Number of grains per kg 220 ± 10
Width, mm 19.90 ± 5.03
Length, mm 18.06 ± 2.75
Flesh/seed ratio 2.81 ± 0.95
Dry matter, % 44.60 ± 1.99
Soluble solids, % 13.66 ± 1.96
pH 6.82 ± 0.17
TPC, mg GAE/g 0.81 ± 0.01
TFC, mg ECE/g 0.23 ± 0.10
FRAP, mmol ISE/g 7.15 ± 1.09
DPPH, mmol TE/g 14.92 ± 0.88
Color features
L* 37.20 ± 3.86
a* 6.40 ± 2.49
b* 14.48 ± 3.90
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a

Means values of three replicates and corresponding standard deviations.

In this study, the TPC, TFC, and antioxidant activities (FRAP and DPPH) were determined (Table 1). Evidence suggested that the potent medicinal properties agents of date plum could be correlated with various bioactive compounds (Uddin et al., 2011). The present results showed that the FRAP and DPPH values of date plum extracts were 7.15 ± 1.09 mmol ISE/g and 14.92 ± 0.88 mmol TE/g, respectively. The TPC and TFC were determined as 0.81 ± 0.01 mg GAE/g and 0.23 ± 0.10 mg ECE/g, respectively. There are similarities between our findings in this study and those described by Murathan (2020), who found that TPC was 1.3 mg/g and TFC was 0.12 mg/g. It has previously been observed that the flavonoid content of date plum ranged from 30.52% to 34.42%, and the phenol content ranged from 16.05% to 17.40% (M. Ahmad et al., 2014).

3.2. Modeling of drying kinetics

The results of modeling applied to the drying kinetics of date plum are shown in Table 2. As it can be observed, an increase in temperature resulted in an important reduction in drying time (Figure 1). The fruit dried at 50°C showed the longest drying time, and the drying time decreased as the temperature increased. These results were in agreement with those obtained by Koca et al. (2009) who reported that the drying time decreased as the airflow rate and temperature increased. They also found the drying time increased as the temperature decreased. The drying curves were applied to Newton, Page, Henderson and Pabis’, Two‐term, Two‐term exponential, Logistic, Midilli, and Aghabashlo models to find out the well‐fitted models describing the dehydration behavior. This approach used in this investigation is similar to that used by (Pashazadeh, Zannou, & Koca, 2020; Zannou et al., 2021). Furthermore, as shown in Figure 1, the results of (MR, t) obtained for the different temperatures indicated that the Midilli model was the well fitted with the experimental data giving the highest coefficient of determination (R 2), the lowest RMSE and lowest chi‐square (χ2) (Table 4). These results were found similar to those of the drying kinetics of R. pimpinellifolia (Pashazadeh et al., 2020a). In the Midilli model at 50, 60, 70, and 80°C at air velocity 0.5, 1.0, and 1.5 m/s, the results of R 2 varying from 0.9968251 to 0.9995381, RMSE from 0.0165964 to 0.0074116, and χ2 from 0.0002754 to 0.000054. Midilli model was the best fitted at the drying conditions of 70°C and 1.5 m/s, giving R 2 of 0.9995381, RMSE of 0.0074116, and χ2 of 0.000054.

FIGURE 1.

FIGURE 1

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Drying curves of Diospyros lotus L fruits.

TABLE 4.

Mathematical models applied to experimental drying kinetics of date plum.

Model Drying temperature (°C) Air velocity, m/s R 2 RMSE χ2 Constants
Newton 50 0.5 0.981368 0.0396265 0.0015702 k = 0.0017103
1.0 0.989209 0.0281931 0.0007948 k = 0.0015122
1.5 0.896501 0.1036942 0.0107525 k = 0.0022623
60 0.5 0.977901 0.0437773 0.0019164 k = 0.0032194
1.0 0.983023 0.0377495 0.0014250 k = 0.0030109
1.5 0.980623 0.0412749 0.0017036 k = 0.0030194
70 0.5 0.953837 0.0684128 0.0046803 k = 0.0046568
1.0 0.958493 0.0658683 0.0043386 k = 0.0061028
1.5 0.955910 0.0679314 0.0046146 k = 0.0059595
80 0.5 0.947262 0.0722411 0.0052187 k = 0.0059928
1.0 0.963218 0.0626485 0.0039248 k = 0.0086279
1.5 0.960261 0.0655185 0.0042926 k = 0.0094659
Page 50 0.5 0.996181 0.0180266 0.0003249 k = 0.0003604
n = 1.2393
1.0 0.993688 0.0216710 0.0004696 k = 0.000711
n = 1.1146
1.5 0.9847422 0.0401946 0.0016156 k = 1.5625
n = 1.8188
60 0.5 0.9944488 0.0221686 0.0004914 k = 0.0007454
n = 1.2506
1.0 0.9946105 0.0214773 0.0004612 k = 0.000924
n = 1.2001
1.5 0.9975685 0.0147533 0.0002176 k = 0.0006554
n = 1.2579
70 0.5 0.9964337 0.0193294 0.0003736 k = 0.0003624
n = 1.4701
1.0 0.9970025 0.0180513 0.0003258 k = 0.000578
n = 1.453
1.5 0.9971543 0.0176139 0.0003102 k = 0.0005283
n = 1.4656
80 0.5 0.9971949 0.0170936 0.0002921 k = 0.0004347
n = 1.5158
1.0 0.9966150 0.0195563 0.0003824 k = 0.0011696
n = 1.4128
1.5 0.9987755 0.0119047 0.0001417 k = 0.0011264
n = 1.4514
Hendeon vs. Pabis 50 0.5 0.9873884 0.0327583 0.0010731 a = 1.0733
k = 0.0018356
1.0 0.9909311 0.0259757 0.0006747 a = 1.0345
k = 0.0015688
1.5 0.9247374 0.0892714 0.0079693 a = 1.1484
k = 0.0026284
60 0.5 0.9849675 0.0364804 0.0013308 a = 1.0746
k = 0.003468
1.0 0.9894251 0.0300846 0.0009050 a = 1.0702
k = 0.0032332
1.5 0.9884351 0.0321757 0.0010352 a = 1.0815
k = 0.0032685
70 0.5 0.9727583 0.0534230 0.0028540 a = 1.1244
k = 0.0052522
1.0 0.9737137 0.0534564 0.0028575 a = 1.1145
k = 0.0067923
g = 0.012407
k = 0.011737
a = 1.1196
1.5 0.9732444 0.0540097 0.0029170 k = 0.0066786
80 0.5 0.9695994 0.0562732 0.0031666 a = 1.122
k = 0.00681
1.0 0.9768348 0.0511597 0.0026173 a = 1.1046
k = 0.0095281
1.5 0.9753737 0.0533877 0.0028502 a = 1.1055
k = 0.010492
Two term 50 0.5 0.9962413 0.0179598 0.0003225 a = −23.472
b = 24.4828
g = 0.0028147
k = 0.0028864
1.0 0.9939897 0.0211464 0.0004471 a = 1.6022
k = 0.0019137
k = 0.0011247
1.5 0.9704335 0.0570610 0.0032559 a = −25.5853
b = 26.606
g = 0.0049774
k = 0.0051809
60 0.5 0.9865846 0.0352037 0.0012393 a = 1.0922
b = −0.092202
g = 2.0202
k = 0.0035271
1.0 0.9945081 0.0221184 0.0004892 a = 10.1482
b = −9.1271
g = 0.0050411
k = 0.0047642
1.5 0.9960761 0.0190924 0.0003645 a = 18.0476
b = −17.0042
g = 0.0050348
k = 0.0048794
70 0.5 0.9795309 0.0479338 0.0022976 a = 1.1716
b = −0.17158
g = 2.1855
k = 0.0054782
1.0 0.9952665 0.0236498 0.0005593 a = 16.2909
b = −15.2832
g = 0.012407
k = 0.011737
1.5 0.9814357 0.0469891 0.0022079 a = 1.1799
b = −0.17993
g = 1.4036
k = 0.0070421
80 0.5 0.9956269 0.0225634 0.0005091 a = −10.8136
b = 11.8299
g = 0.012334
k = 0.013409
1.0 0.9939033 0.0279404 0.0007806 a = −85.5128
b = 86.5554
g = 0.015664
k = 0.015795
1.5 0.9885335 0.0393488 0.0015483 a = 1.2045
b = −0.20446
g = 1.3864
k = 0.011452
Two‐term exponential 50 0.5 0.9813502 0.0398358 0.0015868 a = 0.0001079
k = 15.833
1.0 0.9939897 0.0211464 0.0004471 a = 1.6021
k = 0.0019135
1.5 0.9678549 0.0583418 0.0034037 a = 2.0847
k = 0.0038087
60 0.5 0.9938541 0.0233257 0.0005440 a = 1.7883
k = 0.0045222
1.0 0.9941247 0.0224244 0.0005028 a = 1.7307
k = 0.0040986
1.5 0.9970141 0.0163491 0.0002672 a = 1.8005
k = 0.0042533
70 0.5 0.9538163 0.0695593 0.0048385 a = 6.7219
k = 69.2663
1.0 0.9942688 0.0249606 0.0006230 a = 1.9669
k = 0.0094004
1.5 0.9941453 0.0252648 0.0006383 a = 1.9768
k = 0.0092801
80 0.5 0.9472488 0.0741271 0.0054948 a = 3.561
k = 168.2749
1.0 0.9632011 0.0644804 0.0041577 a = 6.7217
k = 128.3327
1.5 0.9602085 0.0678637 0.0046054 a = 0.0001838
k = 51.4765
Wang vs. Singh 50 0.5 0.9962787 0.0177943 0.0003166 a = −0.0012899
b = 4.35827
1.0 0.9930802 0.0226902 0.0005148 a = −0.0011687
b = 3.66697
1.5 0.9827875 0.0426917 0.0018225 a = −0.0013805
b = 7.72998
60 0.5 0.9965325 0.0175207 0.0003069 a = −0.0024063
b = 1.48496
1.0 0.9955790 0.0194520 0.0003783 a = −0.0022756
b = 1.35046
1.5 0.9985556 0.0113708 0.0001292 a = −0.002271
b = 1.34136
70 0.5 0.9938465 0.0253905 0.0006446 a = −0.0033461
b = 2.57616
1.0 0.9948559 0.0236475 0.0005592 a = −0.0044437
b = 4.8176
1.5 0.9964650 0.0196316 0.0003854 a = −0.0042538
b = 4.17596
80 0.5 0.9921204 0.0286491 0.0008207 a = −0.0041916
b = 3.24356
1.0 0.9931008 0.0279195 0.0007795 a = −0.0063349
b = 9.92096
1.5 0.9945827 0.0250398 0.0006269 a = −0.0068896
b = 1.1219
Approximation of diffusion 50 0.5 0.9939385 0.0229319 0.0005258 a = 1.1481
b = 0.28388
k = 0.0012933
t = 0.68994
1.0 0.9976263 0.0134242 0.0001802 a = 1.1481
b = 0.079938
k = 0.0011625
t = 0.75166
1.5 0.9843555 0.0415069 0.0017228 a = 5.5398
b = 4.8603
k = 0.0002715
t = 4.8873
60 0.5 0.9968036 0.0171835 0.0002952 a = 1.2557
b = 0.32915
k = 0.0021166
t = 0.46508
1.0 0.9973144 0.0154672 0.0002392 a = 1.2129
b = 0.16192
k = 0.0020983
t = −0.26934
1.5 0.9967076 0.0174886 0.0003058 a = 1.1982
b = 0.51316
k = 0.0021289
t = 0.51046
70 0.5 0.9922183 0.0295549 0.0008734 a = 1.5962
b = 1.07
k = 0.002183
t = 1.3554
1.0 0.9913359 0.0319964 0.0010237 a = 1.41
b = −0.017547
k = 0.0033407
t = 0.54081
1.5 0.9949093 0.0246062 0.0006054 a = 1.5651
b = 0.98357
k = 0.002848
t = 1.2507
80 0.5 0.9953948 0.0231544 0.0005361 a = 1.7546
b = 5.0548
k = 0.0028761
t = 5.0522
1.0 0.9907122 0.0344861 0.0011892 a = 1.374
b = 0.47945
k = 0.0049329
t = 0.91542
1.5 0.9929367 0.0308830 0.0009537 a = 1.5158
b = 1.1072
k = 0.004788
t = 1.0918
Logistic 50 0.5 0.9972175 0.0154614 0.0002390 a = 0.64501
b = 1.6216
k = 0.0027349
1.0 0.9945248 0.0202848 0.0004114 a = 1.3609
b = 2.325
k = 0.0020193
1.5 0.9913300 0.0305947 0.0009360 a = 0.074276
b = 1.0255
k = 0.0071504
60 0.5 0.9950770 0.0210975 0.0004451 a = 0.67543
b = 1.6663
k = 0.0051582
1.0 0.9947958 0.0213149 0.0004543 a = 1.0758
b = 2.0978
k = 0.0043452
1.5 0.9980795 0.0132325 0.0001751 a = 0.68312
b = 1.6822
k = 0.0048235
70 0.5 0.9967460 0.0187791 0.0003526 a = 0.32046
b = 1.3326
k = 0.009528
1.0 0.9975146 0.0167762 0.0002814 a = 0.31711
b = 1.318
k = 0.012278
1.5 0.9973940 0.0172184 0.0002964 a = 0.32245
b = 1.332
k = 0.012071
80 0.5 0.9971574 0.0176788 0.0003125 a = 0.29696
b = 1.3206
k = 0.013054
1.0 0.9961558 0.0214821 0.0004614 a = 0.41778
b = 1.4401
k = 0.01624
1.5 0.9984013 0.0141159 0.0001992 a = 0.36085
b = 1.3815
k = 0.018802
Midilli 50 0.5 0.9968251 0.0165964 0.0002754 a = 0.97471
b = −7.6114e‐06
k = 0.0002867
n = 1.2668
1.0 0.9985253 0.0105810 0.0001119 a = 1.0265
b = 0.0001078
k = 0.0029099
n = 0.86537
1.5 0.9907348 0.0319422 0.0010203 a = 0.95364
b = −9.4683
k = 8.94356
n = 1.8744
60 0.5 0.9973938 0.0155162 0.0002407 a = 1.0094
b = −0.000142
k = 0.001765
n = 1.075
1.0 0.9980940 0.0130302 0.0001697 a = 1.0332
b = −0.0001585
k = 0.0030256
n = 0.97142
1.5 0.9986268 0.0112942 0.0001275 a = 1.0022
b = −5.81835
k = 0.0010282
n = 1.1682
70 0.5 0.9978387 0.0155755 0.0002425 a = 1.009
b = −0.000154
k = 0.0007054
n = 1.3265
1.0 0.9980341 0.0152410 0.0002322 a = 0.99694
b = −0.00011378
k = 0.000805
n = 1.3733
1.5 0.9995381 0.0074116 0.000054 a = 1.0121
b = −0.0002408
k = 0.0011829
n = 1.2831
80 0.5 0.9981349 0.0147351 0.0002171 a = 1.0149
b = −0.0002550
k = 0.0008396
n = 1.3629
1.0 0.9971854 0.0189842 0.0003604 a = 1.0103
b −0.00014253
k = 0.0017498
n = 1.3178
1.5 0.9994114 0.0089146 0.000079 a = 1.0108
b = −0.0002027
k = 0.0017355
n = 1.345
Aghabashlo model 50 0.5 0.9965496 0.0171344 0.0002935 g = 0.00029098
k = 0.0013299
1.0 0.9960815 0.0170746 0.0002915 g = 0.00018775
k = 0.00128
1.5 0.9907674 0.0312668 0.0009776 g = −0.0010345
k = 0.0011353
60 0.5 0.9966765 0.0171530 0.0002942 g 0.00063324
k = 0.002422
1.0 0.9964123 0.0175232 0.0003070 g = 0.0005108
k = 0.0023698
1.5 0.9985907 0.0112320 0.0001261 g = −0.000572
k = 0.0022829
70 0.5 0.9961928 0.0199715 0.0003988 g = −0.0013991
k = 0.0029749
1.0 0.9973775 0.0168843 0.0002850 g = –0.001741
k = 0.0039225
1.5 0.9982474 0.0138230 0.0001910 g = 0.0017752
k = 0.0037793
80 0.5 0.9949600 0.0229125 0.0005249 g = −0.0020746
k = 0.0037337
1.0 0.9942199 0.0255549 0.0006530 g = −0.0022188
k = 0.0058633
1.5 0.9966071 0.0198163 0.0003926 g = −0.0027227
k = 0.006204
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Abbreviations: R 2, coefficient of determination; χ2, Chi square; RMSE, root mean square error.

3.3. Effects of drying on rehydration kinetics

The rehydration ratio is one of the quality parameters of the dried products (Sharma et al., 2005). Experimental rehydration was performed on the dried date plum to evaluate the moisture uptake. The findings obtained after the rehydration different temperatures indicated that the drying temperature affected the behavior of the dried fruit. Similar to our findings, Chenlo et al. (2018) who reported that the rehydration capacity and water uptake permeability are associated with the increase in drying temperature. This phenomenon could be attributed to some substances being dragged by water flow during drying. Table 4 shows the rehydration parameters of dried fruit. The empirical models such as Peleg, first‐order kinetic, Exponential‐related equation, exponential, and Weibull were considered in this work. The dried samples showed fast rehydration in the first hours (5 h), followed by slower water absorption which achieved equilibrium after 6 h. As Figure 2, the rehydration is faster at 70°C, followed by 60, 80, and 50°C, respectively. Also, the Peleg, Vega‐Gálvez, and Weibull models depicted better rehydration behavior. These outcomes corroborate with various studies which mentioned that these models were adequate to the rehydration kinetics of various fruits (Benseddik et al., 2019; Pramiu et al., 2015; Vega‐Gálvez et al., 2009). Weibull model fit the experimental data well with R 2 higher than 0.99, lower χ2 (0.0005), and lower RMSE (0.0243) (Table 5).

FIGURE 2.

FIGURE 2

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Rehydration curves changes in moisture content of dried samples for different temperatures of 50°C (a), 70°C (b), 80°C (c), and 60°C (d).

TABLE 5.

Results of the mathematical models for rehydration kinetics.

Model Drying temperature (°C) Air velocity, m/s R 2 RMSE χ2 Constants
Peleg 50 0.5 0.9686929 0.0667288 0.0044527 a = 17.3486
b = −0.65285
1.0 0.9728760 0.0616083 0.0037955 a = 7.2852
b = 0.31459
1.5 0.9732346 0.0542582 0.0029439 a = 6.1211
b = 0.43799
60 0.5 0.9913442 0.0331405 0.0010982 a = 2.6859
b = 0.67551
1.0 0.9613429 0.0752573 0.0056636 a = 5.3121
b = 0.44827
1.5 0.9284872 0.0845211 0.0071438 a = 4.6841
b = 0.58621
70 0.5 0.9657222 0.0645664 0.0041688 a = 4.1571
b = 0.5218
1.0 0.9445493 0.1038668 0.0107883 a = 4.9321
b = 0.28925
1.5 0.9323755 0.0892904 0.0079727 a = 5.6554
b = 0.34262
80 0.5 0.9692896 0.0601125 0.0036135 a = 6.8437
b = 0.29861
1.0 0.9518885 0.0681313 0.0046418 a = 1.7346
b = 0.85804
1.5 0.9386015 0.0853826 0.0072902 a = 7.1537
b = 0.13063
First‐order kinetic 50 0.5 0.7929317 0.1636260 0.0267734 a = 0.13068
1.0 0.9126146 0.105874 0.011209 a = 0.19639
1.5 0.9402696 0.0772823 0.0059725 a = 0.20391
60 0.5 0.9865899 0.0388906 0.0015124 a = 0.35809
1.0 0.9264738 0.0989598 0.0097930 a = 0.23947
1.5 0.9058394 0.0924723 0.0085511 a = 0.22575
70 0.5 0.9410786 0.0798105 0.0063697 a = 0.27817
1.0 0.8824753 0.1414464 0.0200071 a = 0.29788
1.5 0.9008602 0.1011302 0.0102273 a = 0.23437
80 0.5 0.9196772 0.0922280 0.0085060 a = 0.20637
1.0 0.9218901 0.0823563 0.0067825 a = 0.37678
1.5 0.8834898 0.1100213 0.0121046 a = 0.21805
Exponential‐related equation 50 0.5 0.7929317 0.1636260 0.0267734 a = 0.13068
1.0 0.9126146 0.105874 0.011209 a = 0.19639
1.5 0.9402696 0.0772823 0.0059725 a = 0.20392
60 0.5 0.9865899 0.0388906 0.0015124 a = 0.35809
1.0 0.9264738 0.0989598 0.0097930 a = 0.23947
1.5 0.9058394 0.0924723 0.0085511 a = 0.22575
70 0.5 0.9410786 0.0798105 0.0063697 a = 0.27818
1.0 0.8824753 0.1414464 0.0200071 a = 0.29788
1.5 0.9008602 0.1011302 0.0102273 a = 0.23437
80 0.5 0.9196772 0.0922280 0.0085060 a = 0.20636
1.0 0.9218901 0.0823563 0.0067825 a = 0.37677
1.5 0.8834898 0.1100213 0.0121046 a = 0.21805
Exponential model 0.5 0.9630622 0.0724815 0.0052535 a = 0.0040572
k = 2.7812
1.0 0.974159 0.060134 0.003616 a = 0.052923
k = 1.7389
1.5 0.9462393 0.0768973 0.0059131 a = 0.15479
k = 1.1588
60 0.5 0.9909628 0.0338627 0.0011466 a = 0.29713
k = 1.1489
1.0 0.9829703 0.0499501 0.0024950 a = 0.075472
k = 1.7362
1.5 0.9069525 0.0964108 0.0092950 a = 0.25092
k = 0.93778
70 0.5 0.9457065 0.0812594 0.0066031 a = 0.2224
k = 1.151
1.0 0.9969621 0.0243113 0.0005910 a = 0.050986
k = 2.3637
1.5 0.9046163 0.1060449 0.0112455 a = 0.19404
k = 1.1253
80 0.5 0.9354478 0.0871520 0.0075954 a = 0.12642
k = 1.2893
1.0 0.9539740 0.0666383 0.0044406 a = 0.55556
k = 0.70384
1.5 0.8957626 0.1112506 0.0123767 a = 0.14683
k = 1.2549
Weibull 50 0.5 0.9630622 0.0724815 0.0052535 a = 2.7802
b = 7.2437
1.0 0.9741590 0.060134 0.003616 a = 1.7386
b = 5.4198
1.5 0.9462393 0.0768973 0.0059131 a = 1.1587
b = 5.0025
60 0.5 0.9909628 0.0338627 0.0011466 a = 1.149
b = 2.8755
1.0 0.9829703 0.0499501 0.0024950 a = 1.7361
b = 4.4295
1.5 0.9069525 0.0964108 0.0092950 a = 0.93764
b = 4.3674
70 0.5 0.9457064 0.0812595 0.0066031 a = 1.1507
b = 3.6909
1.0 0.9969621 0.0243113 0.0005910 a = 2.3638
b = 3.5223
1.5 0.9046163 0.1060449 0.0112455 a = 1.1253
b = 4.2936
80 0.5 0.9354478 0.0871520 0.0075954 a = 1.2891
b = 4.9733
1.0 0.9539740 0.0666383 0.0044406 a = 0.70375
b = 2.3051
1.5 0.8957626 0.1112506 0.0123767 a = 1.2545
b = 4.6127
Proposed model 50 0.5 0.9676514 0.0714987 0.0051120 a = 30,355,130.031
b = 21.0753
k = 0.081862
1.0 0.9760471 0.060721 0.003687 a = 2.4604
b = 5.42
k = 0.731
1.5 0.9771205 0.0528786 0.002796 a = 19.2216
b = 5.4711
k = 0.25114
60 0.5 0.9908628 0.0364004 0.0013249 a = 1.2596
b = 3.6482
k = 1.2225
1.0 0.9751682 0.0635794 0.0040423 a = 1.3496
b = 7.4978
k = 1.3517
1.5 0.9473324 0.0764582 0.0058458 a = 51.3675
b = 5.9818
k = 0.17081
70 0.5 0.9666538 0.0680799 0.0046348 a = 3.3499
b = 3.747
k = 0.50151
1.0 0.9939106 0.0371776 0.0013821 a = 1.0957
b = 43.4438
k = 2.9432
1.5 0.9359955 0.0938278 0.0088036 a = 303.3513
b = 8.1708
k = 0.16057
80 0.5 0.9738120 0.0588775 0.0034665 a = 316.7003
b = 8.3097
k = 0.15532
1.0 0.9409324 0.0800706 0.0064113 a = 1.2269
b = 2.3532
k = 0.98937
1.5 0.9458233 0.0866304 0.0075048 a = 12,722.084
b = 12.0156
k = 0.10785
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Abbreviation: RMSE, root mean square error.

3.4. Effects of drying on microstructure

The effects of drying on microstructure and distribution of cells in fresh and dried apples are presented in Figure 3. The SEM micrographs on the surfaces of raw and dried date plum fruits showed a distinct difference in the microstructure of fruits (Figure 3). Accordingly, Witrowa‐Rajchert and Rząca (2009) determined that the drying causes many changes in the structure and properties of plant material. The fresh date plum fruit tissue showed a well‐organized structure consisting of small and clear spherical to oval cells and intercellular spaces. The fruit dried at 50°C has large undistinguished cells spaces with the fruit dried at 60°C and 70°C showed a decreased intercell contact and collapse of cell structure up to the breakdown of cell walls. The SEM micrographs of fruit dried at 80°C have organized large cells, intercell contact, and well structure (Figure 3). Seemingly, Witrowa‐Rajchert and Rząca (2009) reported the drying of apples causes significant changes in the size of the cells and their distribution. In a previous study, the tissue of the fresh autumn olives showed large spherical and organized cell walls, while dried convective berries cells became very large, deformed, irregular, and wrinkled (Zannou et al., 2021). The results of the microstructure showed significant differences between fresh and dried date plum structural characteristics as well as significant changes in the cell size and distribution (Figure 3) Table 5.

FIGURE 3.

FIGURE 3

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Microstructures (scanning electron microscopy) of the fresh sample (a) and dried Diospyros lotus L. fruits, (b) Fruits dehydrated at 50°C, (c) sample dehydrated at 60°C, (d) fruit dehydrated at 70°C, (e) fruits dehydrated at 80°C; all micrographs were taken on the surface of the sample.

3.5. Effects of drying on antioxidant properties

The values of TPC, TFC, FRAP, and DPPH of dried date plum are shown in Table 6. As can be seen, the TPC, TFC, and DPPH of dried fruit increased with increasing the temperature from 50°C to 70°C, while the FRAP did not vary irrespectively of the increase of temperature. The lower TPC, TFC, and antioxidant activity obtained at 50°C is due to the longer exposure of fruit which occurs in the degradation of the compounds with antioxidant activity (Koca et al., 2009; Pashazadeh et al., 2020a). The drying at 70°C gave the highest TPC, TFC, and DPPH of 4.44 mg GAE/g, 1.66 mg ECE/g, and 54.03 mmol TE/g, respectively. The drying at 80°C led to a decrease in TPC (from 4.44 mg GAE/g to 3.96 mg GAE/g), TFC (from 1.66 mg ECE/g to 0.79 mg ECE/g), and DPPH (from 54.03 mmol TE/g to 32.54 mmol TE/g). The increase of the drying temperature above 80°C induced the destruction of antioxidant substances in date plum. Similarly, it has been reported in previous studies that the drying agricultural material at higher temperatures (above 70°C) is harmful to antioxidants compounds, leading to the destruction of the structure of some phenolic compounds (Erbay & Icier, 2009; Nicoli et al., 1999; Pashazadeh et al., 2020a; Zannou et al., 2021). Nonetheless, no statistical difference had been detected in the anthocyanin content of sweet cherries dried at 60, 70, and 80°C (Ouaabou et al., 2020). These findings suggested that not only the drying conditions can affect the antioxidant properties of the agricultural products but also the type and intrinsic characteristics of the raw material are determinants.

TABLE 6.

Antioxidant properties of date plum dried in different drying conditions.

Temperature (°C) Air velocity, m/s Total phenolic, mg/g Total flavonoid, mg/g DPPH, mmol/g FRAP, mmol/g
50 0.5 3.93 ± 0.43c 1.11 ± 0.15d 43.72 ± 2.46a 41.14 ± 1.68b
1.0 2.49 ± 0.32fe 0.85 ± 0.11gf 29.37 ± 8.55a 41.86 ± 3.69 g
1.5 3.50 ± 0.03d 1.27 ± 0.11c 37.01 ± 1.38a 43.66 ± 1.35fedc
60 0.5 1.47 ± 0.06 g 1.00 ± 0.11fed 31.76 ± 3.37a 39.81 ± 0.40gf
1.0 2.24 ± 0.18f 0.99 ± 0.09fed 44.21 ± 3.41a 42.86 ± 0.80b
1.5 2.66 ± 0.12e 1.47 ± 0.08b 39.45 ± 2.85a 43.52 ± 1.93dcb
70 0.5 4.57 ± 0.04a 1.37 ± 0.03cb 35.92 ± 2.50a 42.4 ± 1.62fed
1.0 4.38 ± 0.18ba 1.66 ± 0.07a 54.03 ± 2.95a 42.99 ± 1.39a
1.5 4.36 ± 0.39cba 1.03 ± 0.02ed 42.38 ± 0.43a 41.00 ± 1.24cb
80 0.5 3.43 ± 0.17d 0.68 ± 0.04h 42.97 ± 2.49a 42.73 ± 2.19cb
1.0 4.12 ± 0.23cb 0.79 ± 0.07hg 38.61 ± 1.27a 44.85 ± 1.13edcb
1.5 4.35 ± 0.22cba 0.89 ± 0.07gfe 32.54 ± 2.17a 42.8 ± 2.49gfe
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Note: Data with the same superscript letters in a column are not significantly different (p > 0.05).

Abbreviations: DPPH, 2,2‐diphenyl‐1‐picrylhydrazyl; FRAP, ferric reducing antioxidant power.

3.6. Effects of drying on phenolic compounds

Thirteen phenolic compounds include gallic acid, catechin, chlorogenic, caffeic acid, vanillic acid, epicatechin, syringic, p‐coumaric acid, ferulic acid, sinapic acid, quercetin‐3‐glucoside, salicylic acid, and resveratrol were identified from date plum samples (Table 7). Previous studies have reported that date plum is a rich source of phenolic compounds such as gallic acid, vanillic acid, caffeic acid, ferulic acid, salicylic acid, quercetin, and ρ‐coumaric acid (Ayaz et al., 1997; H. Gao et al., 2014). In the present study, the phenolic compounds showed significant differences among drying conditions (p < 0.05). As can be seen in Table 7, the increase in drying temperature induced the increase of phenolic acid content, except for caffeic acid and ferulic acid. The values of gallic acid, catechin, chlorogenic acid, caffeic acid, vanillic acid, syringic acid, and p‐coumaric acid were lower in fresh than in dried fruit. On the other hand, the values of epicatechin, ferulic acid, sinapic acid, quercetin‐3‐glucoside, salicylic acid, and resveratrol were higher in fresh than in dried fruit (Table 7). The samples dried at 70°C had the highest values of gallic acid (341.79 mg/kg), catechin (65.80 mg/kg), chlorogenic acid (4.49 mg/kg), syringic acid (5.65 mg/kg), quercetin‐3‐glucoside (14.28 mg/kg), and resveratrol (2.26 mg/kg). Moreover, the highest values of vanillic acid (73.16 mg/kg), ferulic acid (8.66 mg/kg), and salicylic acid (13.75 mg/kg) were found at 60°C. In the same agreement with our findings, several studies have reported that the drying at the temperatures ranging from 60°C to 70°C preserve the most phenolic compounds (X. Gao et al., 1996; Erbay & Icier, 2009; Nicol et al., 1999; Ouaabou et al., 2020; Pashazadeh et al., 2020a; Zannou et al., 2021).

TABLE 7.

Effect of drying conditions on the phenolic compounds date plum (mg/kg).

Temperature (°C) Air velocity, m/s Gallic acid Catechin Chlorogenic acid Caffeic acid Vanillic acid Epicatechin Syringic acid p ‐Coumaric acid Ferulic acid Sinapic acid Quercetin‐3‐glucoside Salicylic acid Resveratrol
Fresh Sample 169.18 ± 0.05 28 ± 0.23 0.00 ± 0.00 2.23 ± 0.01 52.68 ± 0.13 20.3 ± 0.34 2.65 ± 0.02 8.45 ± 0.21 8.2 ± 0.10 134.53 ± 0.34 21.25 ± 0.48 20.03 ± 0.25 3.125 ± 0.01
50 0.5 162.31 ± 8.50f 4.70 ± 0.51f 2.11 ± 0.02cb 7.61 ± 0.01a 53.93 ± 2.40dc 8.08 ± 0.35dcb 2.25 ± 0.10g 5.87 ± 0.74g 7.58 ± 0.33ba 13.80 ± 0.24a 3.96 ± 0.05g 8.55 ± 0.98dc 2.14 ± 0.01edc
1.0 288.84 ± 8.69c 49.68 ± 6.67cb 2.13 ± 0.10cb 5.12 ± 0.05c 51.61 ± 0.04dc 7.56 ± 0.11dcb 3.83 ± 0.26ed 10.43 ± 0.22fed 7.67 ± 1.77ba 5.59 ± 1.22dc 6.54 ± 1.12fe 13.40 ± 1.78b 2.15 ± 0.02c
1.5 259.97 ± 18.32d 47.97 ± 2.05c 2.30 ± 0.60cb 3.07 ± 0.21f 45.62 ± 8.79ed 7.38 ± 0.30dcb 3.48 ± 0.25e 9.14 ± 1.10f 6.39 ± 0.07cb 6.85 ± 0.87c 5.66 ± 0.03f 10.06 ± 0.53c 2.12 ± 0.00ed
60 0.5 292.11 ± 5.78c 50.25 ± 3.13cb 2.68 ± 0.26b 6.75 ± 0.30b 49.17 ± 0.06dc 10.52 ± 0.09a 4.16 ± 0.56dc 10.72 ± 1.40edc 7.64 ± 1.87ba 14.48 ± 0.13a 8.29 ± 0.15d 13.15 ± 0.33b 2.15 ± 0.0c
1.0 276.68 ± 16.03dc 29.58 ± 4.20d 2.63 ± 0.18b 6.81 ± 0.22b 73.16 ± 0.83a 10.38 ± 0.29a 4.40 ± 0.22c 9.64 ± 0.12ed 8.66 ± 0.42a 14.70 ± 0.44a 7.86 ± 0.45ed 13.75 ± 1.17b 2.14 ± 0.01edc
1.5 184.29 ± 10.47f 21.12 ± 0.57e 1.76 ± 0.01c 4.49 ± 0.19d 54.40 ± 0.87dc 8.70 ± 0.38b 5.65 ± 0.04a 5.30 ± 0.62g 6.45 ± 0.87cb 10.97 ± 2.31b 3.91 ± 0.28g 7.26 ± 1.42ed 2.12 ± 0.02e
70 0.5 341.79 ± 11.45a 60.54 ± 0.23ba 4.49 ± 0.01a 4.04 ± 0.13e 63.33 ± 2.47b 7.36 ± 0.05dcb 5.46 ± 0.11ba 12.14 ± 0.03c 6.73 ± 0.29cba 9.17 ± 0.93b 14.28 ± 0.43a 5.91 ± 0.65fe 2.26 ± 0.00a
1.0 277.14 ± 15.05dc 65.80 ± 1.18a 2.35 ± 0.33b 4.64 ± 0.36d 48.85 ± 1.79dc 6.90 ± 0.75dc 4.24 ± 0.12dc 11.55 ± 0.74dc 6.49 ± 0.07cb 13.19 ± 0.29a 10.97 ± 0.35c 4.99 ± 0.20gf 2.26 ± 0.00b
1.5 258.85 ± 1.57d 49.68 ± 6.29cb 2.54 ± 0.31b 3.09 ± 0.07f 44.58 ± 3.85ed 7.17 ± 0.69dc 4.00 ± 0.09dc 10.29 ± 0.54fed 5.07 ± 0.46c 4.35 ± 1.73d 8.94 ± 0.14d 3.32 ± 0.41g 2.19 ± 0.00b
80 0.5 314.75 ± 2.44b 48.60 ± 0.73cb 2.43 ± 0.09b 2.07 ± 0.01 g 50.57 ± 2.47dc 8.18 ± 0.15cb 2.92 ± 0.15f 19.75 ± 0.23a 6.41 ± 0.18cb 5.27 ± 0.20dc 11.28 ± 0.71c 10.42 ± 0.06c 2.12 ± 0.00ed
1.0 342.23 ± 1.22a 56.47 ± 0.40cb 2.48 ± 0.02b 2.23 ± 0.05 g 41.15 ± 1.35e 5.81 ± 0.18e 4.99 ± 0.10b 14.73 ± 0.35b 6.35 ± 0.11cb 13.97 ± 0.89a 10.69 ± 0.09c 17.41 ± 1.01a 2.12 ± 0.00ed
1.5 234.79 ± 3.74e 46.65 ± 4.83c 2.68 ± 0.04b 1.65 ± 0.02h 57.94 ± 1.98cb 6.65 ± 0.51ed 5.35 ± 0.03ba 13.96 ± 0.21b 7.36 ± 0.45ba 9.98 ± 0.06b 12.77 ± 1.58b 14.19 ± 0.85b 2.14 ± 0.01dc
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Note: Different lowercase letters in the same column mean statistical difference at p≤ 0.05.

4. CONCLUSION

The present study attempted to examine the characterization of dried date plum and the influence of the drying process on its total phenolic compound, rehydration, microstructure, antioxidant activity, and phenolic compounds. The results showed that the convective drying of the date plum increased the total phenolic content, flavonoid content, antioxidant activity, and individual phenolic compounds when increasing the temperature up to 70°C. Additionally, the drying process showed significant effects on microstructure, drying, and rehydration kinetics. The best mathematical models of drying and rehydration kinetics were found to be Midilli and Weibull models, respectively. These findings have significant implications for understanding how to assess the drying parameters of rehydration, antioxidant activity, and phenolic compounds of date plum. This study revealed that the date plum is rich in bioactive ingredients, and the dried product can be used for multipurpose in food, cosmetic, and drug industries. Further research should focus on other drying methods to produce interesting findings on the best drying conditions for date plums.

AUTHOR CONTRIBUTIONS

Awadalgeed M. A. Hassan: conceptualization; data curation; methodology; visualization; writing – original draft. Oscar Zannou: conceptualization; data curation; methodology; visualization; writing – original draft; writing – review & editing. Hojjat Pashazadeh: Conceptualization; Data curation; Methodology; Visualization; Writing – original draft; Writing – review & editing. Ali Ali Redha: data curation; funding acquisition; validation; visualization; writing – original draft; writing – review & editing. Ilkay Koca: conceptualization; data curation; funding acquisition; methodology; project administration; supervision; validation; visualization; writing – original draft; writing – review & editing.

CONFLICT OF INTEREST

None to declare.

Hassan, A. M. A. , Zannou, O. , Pashazadeh, H. , Ali Redha, A. , & Koca, I. (2022). Drying date plum (Diospyros lotus L.) fruit: Assessing rehydration properties, antioxidant activity, and phenolic compounds. Journal of Food Science, 87, 4394–4415. 10.1111/1750-3841.16322

Contributor Information

Oscar Zannou, Email: zannouoscar@gmail.com.

Ali Ali Redha, Email: aa1249@exeter.ac.uk.

REFERENCES

  1. Ahmad, A. , Ali, M. , Barat, G. , Mohammad Hadi, K. , & Saeidi, M. (2012). Effect of air velocity and temperature on energy and effective moisture diffusivity for Russian olive (Elaeagnusan gastifolial L.) in thin‐layer drying. Iranian Journal of Chemistry and Chemical Engineering, 31(1), 75–79. [Google Scholar]
  2. Ahmad, M. , Gul, H. , Ahmad, M. , Sarwar, N. , Abbasi, S. , & Gulfraz, M. (2014). Phytochemical analysis and biological activities of Diospyros lotus L. fruit extracts. International Journal of Pharmaceutical Chemistry, 4(4), 155–159. [Google Scholar]
  3. Almeida, G. , Lancha, J. P. , Pierre, F. , Casalinho, J. , & Perré, P. (2016). Physical behavior of highly deformable products during convective drying assessed by a new experimental device. Drying Technology, 35(8), 906–917. 10.1080/07373937.2016.1233883 [DOI] [Google Scholar]
  4. Ayaz, F. A. , Kadıoǧlu, A. , & Reunanen, M. (1997). Changes in phenolic acid contents of Diospyros lotus L. during fruit development. Journal of Agricultural and Food Chemistry, 45(7), 2539–2541. [Google Scholar]
  5. Ayoub, A. , Singh, J. , Hameed, F. , & Mushtaq, M. (2020). Evaluation of secondary metabolites (antibacterial and antioxidant activity) of Amlok (Diospyros lotus L) fruit extracts of Jammu region. Journal of Pharmaceutical Research International, 32, 8–19. [Google Scholar]
  6. Azadbakht, M. , Hosseinimehr, S. J. , Shokrzadeh, M. , Habibi, E. , & Ahmadi, A. (2011). Diospyros lotus L. fruit extract protects G6PD‐deficient erythrocytes from hemolytic injury in vitro and in vivo: prevention of favism disorder. European Review for Medical and Pharmacological Sciences, 15, 1270–1281. [DOI] [PubMed] [Google Scholar]
  7. Bengtston, G. , Rahman, M. S. , Stanley, R. , & Perera, C. O . (1998). Effect Specific Pretreatment on the Drying Behaviour of Apple Rings. New Zealand Inst. of Food Science and Technology and The Nutrition Society of New Zealand Conference.
  8. Benseddik, A. , Azzi, A. , Zidoune, M. N. , Khanniche, R. , & Besombes, C. (2019). Empirical and diffusion models of rehydration process of differently dried pumpkin slices. Journal of the Saudi Society of Agricultural Sciences, 18(4), 401–410. 10.1016/j.jssas.2018.01.003 [DOI] [Google Scholar]
  9. Chenlo, F. , Arufe, S. , Díaz, D. , Torres, M. D. , Sineiro, J. , & Moreira, R. (2018). Air‐drying and rehydration characteristics of the brown seaweeds, Ascophylum nodosum and Undaria pinnatifida . Journal of Applied Phycology, 30(2), 1259–1270. [Google Scholar]
  10. Chielle, D. P. , Bertuol, D. A. , Meili, L. , Tanabe, E. H. , & Dotto, G. L. (2016). Convective drying of papaya seeds (Carica papaya L.) and optimization of oil extraction. Industrial Crops and Products, 85, 221–228. 10.1016/j.indcrop.2016.03.010 [DOI] [Google Scholar]
  11. Cho, B. O. , Che, D. N. , Yin, H. H. , Shin, J. Y. , & Jang, S. I. (2017). Diospyros lotus leaf and grapefruit stem extract synergistically ameliorate atopic dermatitis‐like skin lesion in mice by suppressing infiltration of mast cells in skin lesions. Biomedicine & Pharmacotherapy, 89, 819–826. 10.1016/j.biopha.2017.01.145 [DOI] [PubMed] [Google Scholar]
  12. Doymaz, I. (2008). Convective drying kinetics of strawberry. Chemical Engineering and Processing: Process Intensification, 47(5), 914–919. 10.1016/j.cep.2007.02.003 [DOI] [Google Scholar]
  13. Erbay, Z. , & Icier, F. (2009). Optimization of hot air drying of olive leaves using response surface methodology. Journal of Food Engineering, 91, 533–541. 10.1016/j.jfoodeng.2008.10.004 [DOI] [Google Scholar]
  14. Ertekin, C. , & Yaldiz, O. S. M. A. N. (2004). Drying of eggplant and selection of a suitable thin layer drying model. Journal of food engineering, 63(3), 349–359. [Google Scholar]
  15. Figiel, A. (2010). Drying kinetics and quality of beetroots dehydrated by combination of convective and vacuum‐microwave methods. Journal of Food Engineering, 98, 461–470. 10.1016/j.jfoodeng.2010.01.029 [DOI] [Google Scholar]
  16. Gao, H. , Cheng, N. , Zhou, J. , Wang, B. , Deng, J. , & Cao, W. (2014). Antioxidant activities and phenolic compounds of date plum persimmon (Diospyros lotus L.) fruits. Journal of Food Science and Technology, 51(5), 950–956. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Gao, X. , Uggla, M. , & Rumpunen, K. (1996). Antioxidant activity of dried and boiled rose hips. Acta Horticulturae, 690, 239–244. [Google Scholar]
  18. Ghellam, M. , & Koca, I. (2020). Modelling of rehydration kinetics of desert truffles (Terfezia spp.) dried by microwave oven. Turkish Journal of Agriculture – Food Science and Technology, 8, 407–415. 10.24925/turjaf.v8i2.407-415.3083 [DOI] [Google Scholar]
  19. Giri, S. K. , & Prasad, S. (2007). Drying kinetics and rehydration characteristics of microwave‐vacuum and convective hot‐air dried mushrooms. Journal of Food Engineering, 78, 512–521. 10.1016/j.jfoodeng.2005.10.021 [DOI] [Google Scholar]
  20. Keneni, Y. G. , Hvoslef‐eide, A. K. T. , & Marchetti, J. M. (2019). Mathematical modelling of the drying kinetics of Jatropha curcas L. seeds. Industrial Crops and Products, 132, 12–20. 10.1016/j.indcrop.2019.02.012 [DOI] [Google Scholar]
  21. Koca, I. , Ustun, N. S. , & Koyuncu, T. (2009). Effect of drying conditions on antioxidant properties of rosehip fruits (Rosa canina sp.). Asian Journal of Chemistry, 21(2), 1061–1068. [Google Scholar]
  22. Kouhila, M. , Bahammou, Y. , Lamsyehe, H. , Moussaoui, H. , Tagnamas, Z. , Idlimam, A. , Lamharrar, A. , & Mouhanni, H. (2020). Cyclical variation of drying air temperature on Mytilus galloprovincialis convective drying. Solar Energy, 211, 1070–1083. 10.1016/j.solener.2020.10.020 [DOI] [Google Scholar]
  23. Krokida, M. , & Marinos‐Kouris, D. (2003). Rehydration kinetics of dehydrated products. Journal of Food Engineering, 57(1), 1–7. 10.1016/s0260-8774(02)00214-5 [DOI] [Google Scholar]
  24. Kumar, S. , Mahanand, S. S. , & Kumar, P. (2017). Mathematical modelling and experimental analysis of broccoli (Brassica oleracea I.) In tray dryer. Journal of Pharmacognosy and Phytochemistry, 6(6), 1049–1052. [Google Scholar]
  25. Loizzo, M. R. , Said, A. , Tundis, R. , Hawas, U. W. , Rashed, K. , Menichini, F. , Frega, N. G. , & Menichini, F. (2009). Antioxidant and antiproliferative activity of Diospyros lotus L. extract and isolated compounds. Plant Foods for Human Nutrition, 64(4), 264. [DOI] [PubMed] [Google Scholar]
  26. Murathan, Z. T. (2020). Phytochemical screening and antioxidant activity of Diospyros lotus L. fruits grown in Turkey. Acta Scientiarum Polonorum Hortorum Cultus, 19(2), 49–55. [Google Scholar]
  27. Nabavia, S. M. , Ebrahimzadeha, M. A. , Nabavia, S. F. , Fazeliana, M. , & Eslami, B. (2009). In vitro antioxidant and free radical scavenging activity of Diospyros lotus and Pyrus boissieriana growing in Iran. Pharmacognosy Magazine, 4(18), 122–126. [Google Scholar]
  28. Nicoli, M. C. , Anese, M. , & Parpinel, M. (1999). Influence of processing on the antioxidant properties of fruit and vegetables. Trends in Food Science and Technology, 10, 94–100. 10.1016/S0924-2244(99)00023-0 [DOI] [Google Scholar]
  29. Noshad, M. , Mohebbi, M. , Shahidi, F. , & Mortazavi, S. A. (2012). Kinetic modeling of rehydration in air‐dried quinces pretreated with osmotic dehydration and ultrasonic. Journal of Food Processing and Preservation, 36(5), 383–392. [Google Scholar]
  30. Nurafifah, F. , Chuah, A. L. , & Wahida, M. A. P. F. (2018). Drying of Plectranthus amboinicus (lour) spreng leaves by using oven dryer. Engineering in Agriculture, Environment and Food, 11, 239–244. 10.1016/j.eaef.2018.08.002 [DOI] [Google Scholar]
  31. Ouaabou, R. , Nabil, B. , Ouhammou, M. , Idlimam, A. , Lamharrar, A. , Ennahli, S. , Hanineb, H. , & Mahrouz, M. (2020). Impact of solar drying process on drying kinetics, and on bioactive profile of Moroccan sweet cherry. Renewable Energy, 151, 908–918. 10.1016/j.renene.2019.11.078 [DOI] [Google Scholar]
  32. Özdemir, M. , & Devres, Y. O. (1999). The thin layer drying characteristics of hazelnuts during roasting. Journal of Food Engineering, 42(4), 225–233. [Google Scholar]
  33. Padoin, D. , Assumpc, D. , Meili, L. , Tanabe, E. H. , & Luiz, G. (2016). Convective drying of papaya seeds (Carica papaya L.) and optimization of oil extraction. Industrial Crops and Products, 85, 221–228. 10.1016/j.indcrop.2016.03.010 [DOI] [Google Scholar]
  34. Pashazadeh, H. , Zannou, O. , Galanakis, C. M. , Aldawoud, T. M. S. , Ibrahim, S. A. , & Koca, K. (2021). Optimization of drying process for Rosa pimpinellifolia L. fruit (black rose hips) based on bioactive compounds and modeling of drying process. Journal Journal of Food Properties, 24(1), 1367–1386. [Google Scholar]
  35. Pashazadeh, H. , Zannou, O. , & Koca, I. (2020a). Modeling of drying and rehydration kinetics of Rosa pimpinellifolia fruits: Toward formulation and optimization of a new tea with high antioxidant properties. Journal of Food Process Engineering, 43(10), e13486. [Google Scholar]
  36. Pashazadeh, H. , Zannou, O. , & Koca, I. (2020b). Modeling and optimization of drying conditions of dog rose for preparation of a functional tea. Journal of Food Process Engineering, e13632.1–14. 10.1111/jfpe.13632 [DOI] [Google Scholar]
  37. Pramiu, P. V. , Rizzi, R. L. , do Prado, N. V. , Coelho, S. R. M. , & Bassinello, P. Z. (2015). Numerical modeling of chickpea (Cicer arietinum) hydration: The effects of temperature and low pressure. Journal of Food Engineering, 165, 112–123. 10.1016/j.jfoodeng.2015.05.020 [DOI] [Google Scholar]
  38. Rashed, K. , Zhang, X. ‐J. , Luo, M.‐T. , & Zheng, Y. ‐T. (2012). Anti‐HIV‐1 activity of phenolic compounds isolated from Diospyros lotus fruits. Phytopharmacology, 3(2), 199–207. [Google Scholar]
  39. Rauf, A. , Uddin, G. , Siddiqui, B. S. , & Khan, H. (2015). In vivo sedative and muscle relaxants activity of Diospyros lotus L. Asian Pacific Journal of Tropical Biomedicine, 5(4), 277–280. 10.1016/s2221-1691(15)30345-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Saguy, I. S. , Marabi, A. , & Wallach, R. (2005). Liquid imbibition during rehydration of dry porous foods. Innovative Food Science and Emerging Technologies, 6, 37–43. [Google Scholar]
  41. Samani, B. H. , Gudarzi, H. , Rostami, S. , Lorigooini, Z. , Esmaeili, Z. , & Jamshidi‐Kia, F. (2018). Development and optimization of the new ultrasonic‐infrared‐vacuum dryer in drying Kelussia odoratissima and its comparison with conventional methods. Industrial Crops and Products, 123, 46–54. [Google Scholar]
  42. Sharma, G. , Verma, R. , & Pathare, P. (2005). Thin‐layer infrared radiation drying of onion slices. Journal of Food Engineering, 67(3), 361–366. [Google Scholar]
  43. Tunde‐Akintunde, T. Y. (2011). Mathematical modeling of sun and solar drying of chilli pepper. Renewable Energy, 36(8), 2139–2145. 10.1016/j.renene.2011.01.017 [DOI] [Google Scholar]
  44. Uddin, G. , Rauf, A. , Siddiqui, B. S. , Muhammad, N. , Khan, A. , & Shah, S. U. A. (2014). Anti‐nociceptive, anti‐inflammatory and sedative activities of the extracts and chemical constituents of Diospyros lotus L. Phytomedicine, 21(7), 954–959. [DOI] [PubMed] [Google Scholar]
  45. Uddin, G. , Rauf, A. , Siddiqui, B. S. , & Shah, S. Q. (2011). Preliminary comparative phytochemical screening of Diospyros lotus Stewart. Middle‐East Journal of Scientific Research, 10(1), 78–81. [Google Scholar]
  46. Vega‐Gálvez, A. , Notte‐cuello, E. , Lemus‐mondaca, R. , Zura, L. , & Miranda, M. (2009). Mathematical modelling of mass transfer during rehydration process of aloe vera (Aloe barbadensis Miller). Food and Bioproducts Processing, 87(4), 254–260. 10.1016/j.fbp.2008.10.004 [DOI] [Google Scholar]
  47. Witrowa‐Rajchert, D. , & Rząca, M. (2009). Effect of drying method on the microstructure and physical properties of dried apples. Drying Technology, 27(7–8), 903–909. [Google Scholar]
  48. Yaldız, O. , & Ertekin, C. (2001). Thin layer solar drying of some vegetables. Drying Technology, 19, 583–597. [Google Scholar]
  49. Yaldız, O. , Ertekin, C. , & Uzun, H. I. (2001). Mathematical modeling of thin layer solar drying of sultana grapes. Energy, 26, 457–465. [Google Scholar]
  50. Zannou, O. , Pashazadeh, H. , Ghellam, M. , Hassan, A. M. , & Koca, I. (2021). Optimization of drying temperature for the assessment of functional and physical characteristics of autumn olive berries. Journal of Food Processing and Preservation, 45(9), e15658. [Google Scholar]
  51. Zannou, O. , & Koca, I. (2020). Optimization and stabilization of the antioxidant properties from Alkanet (Alkanna tinctoria) with natural deep eutectic solvents. Arabian Journal of Chemistry, 13, 6437–6450. 10.1016/j.arabjc.2020.06.002 [DOI] [Google Scholar]
  52. Zhu, A. , Zhao, J. , & Wu, Y. (2020). Modeling and mass transfer performance of Dioscorea alata L. slices drying in convection air dryer. Journal of Food Process Engineering, 43(7), e13427. 10.1111/jfpe.13427 [DOI] [Google Scholar]

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