Optimization of drying process for Rosa pimpinellifolia L. fruit (black rose hips) based on bioactive compounds and modeling of drying process

Figures & data

Table 1. Actual and coded values of variables

Coded values	Actual values
	A	B
− 1.41	55.86	.54
−1.00	60	1
.00	70	1.25
1.00	80	1.75
1.41	84.14	1.96

**A = Temperature (°C); B = Air velocity (m.s⁻¹)

Table 2. Coded central composite design with the responses

	Coded		Actual		Responses
Run	A	B	Temperature	Velocity	TPC	TACN	DPPH	FRAP
1	1.41	.00	84.14	1.25	1590.29	241.73	134.42	795.09
2	.00	1.41	70.00	1.96	1294.86	540.00	108.86	764.03
3	.00	.00	70.00	1.25	1496.00	460.00	109.33	647.57
4	.00	−1.41	70.00	.54	1428.43	347.10	111.23	587.39
5	.00	.00	70.00	1.25	1477.14	466.56	112.65	730.00
6	.00	.00	70.00	1.25	1489.70	405.95	109.81	686.39
7	−1.00	1.00	60.00	1.75	1312.14	443.79	119.27	585.45
8	.00	.00	70.00	1.25	1437.43	396.76	111.64	663.09
9	−1.41	.00	55.86	1.25	1555.71	292.40	128.74	523.33
10	1.00	1.00	80.00	1.75	1401.71	321.00	115.49	780.10
11	1.00	−1.00	80.00	.75	1461.43	291.33	120.22	630.20
12	−1.00	−1.00	60.00	.75	1637.43	368.11	108.86	600.00
13	.00	.00	70.00	1.25	1458.29	420.00	100.00	617.45

** Temperature (°C); Velocity (m.s⁻¹); TPC = Total phenolic content (mg/100 g); TACN = Total anthocyanins (mg c3g E/100 g); DPPH = DPPH radical scavenging (mmol TE/g); FRAP = Ferric Reducing Antioxidant Power (mmol ISE/g).

Table 3. Models used to describe the drying curves of R. pimpinellifolia.

Models	Equations	References
Newton	MR = exp (-kt)	^[Citation10]
Page	MR = exp (-kt^n)	^[Citation7]
Henderson and Pabis	MR = α exp (-kt^n)	^[Citation7]
Logarithmic	MR = α exp (-kt^n) + c	^[Citation11]
Two-term	MR = α exp (-kt) + b exp (-gt)	^[Citation11]
Two-term exponential	MR = α exp (-kt)+(1 + α) exp (-kαt)	^[Citation12]
Wang and Singh	MR = 1 + at + bt^2	^[Citation11]
Approximation of Diffusion	MR = α exp (-k t) + (1 – α) exp (-kbt)	^[Citation13]
Verma	MR = α exp (-kt) + (1-α) exp (-gt)	^[Citation11]
Modifiye Henderson and Pabis	MR = α exp (-kt) + b exp (-gt) + c exp (-ht)	^[Citation13]
Logistic	MR = α/(1 + b exp (kt))	^[Citation14]
Midilli	MR = α exp (-kt^n) + bt	^[Citation7]
Aghabashlo model	MR = exp [-kt/(1 + gt)]	^[Citation15]

**MR is the moisture ratio, t is the time and α, b, c, g, h and k are the constants of models.

Table 4. Models used to describe the rehydration kinetics of R. pimpinellifolia.

Models	Equations	References
Peleg model	$R_r=\frac{1}{X_e-X_0}\times \frac{t}{\left(a+bt\right)}$	^[Citation21]
First order kinetic	$R_r=1-\mathrm{e}\mathrm{x}\mathrm{p}\left(-at\right)$	^[Citation19,Citation21]
Exponential related equation	$R_r=1-exp\left(-at\right)$	^[Citation19,Citation22]
Exponential model	$R_r=1-\mathrm{e}\mathrm{x}\mathrm{p}\left(a{t}^k\right)$	^[Citation21]
Weibull model	$R_r=1-\mathrm{e}\mathrm{x}\mathrm{p}(-(\frac{t}{b}){)}^a$	^[Citation20,Citation21]
Vega-gálves model	$R_r=\alpha \mathrm{e}\mathrm{x}\mathrm{p}\left[\frac{-b}{{\left(1+t\right)}^k}\right]$	^[Citation19,Citation23]

** Rr is the rehydration ratio, t is the time and α, b and k are the constants of models.

Table 5. Physical and chemical characteristics of fresh R. pimpinellifolia fruits

Characteristics	Amounts
Flesh/seed ratio	3.09 ± .42
Dry matter, %	39 ± 2
TACN, mg/100 g	675 ± 19
TPC, mg/100 g	1331 ± 48
DPPH radical scavenging, mmol TE/g	170.9 ± .7
FRAP, mmol ISE/g	897 ± 50
Color
L*	30.35 ± .75
a*	2.53 ± .56
b*	2.20 ± .20

Figure 1. Effect temperature and air velocity on drying time

Figure 2. Effect temperature and air velocity on total color change

Figure 3. 3D graphics showing the effect of independent variables on the responses

Table 6. Results of statistical analysis of the thirteen selected drying models applied to experimental drying data of R. pimpinellifolia fruits

Models	R^2	RMSE	X^2	Constants
Newton	.98	.04	.00	k = .10
Page	.99	.01	.00	k = .05; n = 1.29
Henderson ve Pabis	.99	.03	.00	α = 1.08;k = .11
Logarithmic	.99	.01	.00	α = 1.16; b = −.11;k = .09
Two-term	.99	.01	.00	α = 4.27; b = −3.26;g = .21; k = .17
Two-term exponential	.99	.01	.00	α = 1.85; k = .15
Wang ve Singh	.99	.02	.00	α = −.08; b = .00
Approximation of Diffusion	.99	.02	.00	α = 1.16; b = −20.51; k = .09; t = −.22
Verma	.99	.01	.00	α = 12.65; g = .19; k = .1834
Modifiye Henderson and Pabis	.99	.01	.00	α = .11; b = 3.54; c = 2.6386; g = .17; h = .21; k = .17
Logistic	.99	.01	.00	α = .70; b = 1.73; k = .17
Midilli	.99	.01	.00	α = 1.00 b = −.00; k = .05; n = 1.27
Aghabashlo model	.99	.02	.00	g = −.02; k = .08

Figure 4. Perturbation plot showing the effects of variables on total anthocyanin

Figure 5. Moisture content versus time for drying curve of R. pimpinellifolia fruits at 67. 21°C; 1.75 m.s⁻¹

Table 7. Textural and shrinkage characteristics

Textural characteristics
Distance (10%)	Hardness (N)	Springiness	Cohesiveness	Gumminess	Chewiness	Resilience
Fresh fruit	7.63 ± 1.73	.91 ± .03	.30 ± .04	6.40 ± .04	5.81 ± 1.56	.43 ± .06
Dried fruit	47.85 ± 5.71	.78 ± .04	.65 ± .02	31.04 ± 3.80	24.31 ± 2.89	.26 ±.01
Shrinkage
Plant material			Projected Area (cm^2)		Shrinkage (%)
Fresh fruit			.02 ± .00		-
Dried fruit			.01 ± .00		56.32 ± 2.92

Figure 6. Microstructures of fresh (a) and dried (b) dried R. pimpinellifolia.

Figure 7. Rehydration behavior of dried R. pimpinellifolia fruits at 20, 40 and 60°C

Table 8. Results of statistical analysis of the modeling of rehydration data

Temperature (°C)	Models	R^2	RMSE	X^2	Model constants
20	Peleg model	.99	.03	.00	α =3.84; b = .66
	First order kinetic	.98	.04	.00	α = .25
	Exponential related equation	.98	.04	.00	α = .25
	Exponential model	.99	.03	.00	α = .21; k = 1.11
	Weibull	.99	.04	.00	α = 1.11; b = 4.01
	Vegá-galves model	.99	.03	.03	α = 1.49; b = 3.57; k = .87
40
	Peleg model	.99	.03	.0012	α = 3.15; b = .79
	First order kinetic	.97	.05	.00	α = .25
	Exponential related equation	.97	.05	.00	α = .25
	Exponential model	.98	.04	.00	α = .31; k = .86
	Weibull model	.98	.04	.00	α = .86; b = 3.90
	Vegá-galves model	.98	.04	.00	α =1.31; b = 3.01; k = .88
60
	Peleg model	.99	.03	.00	α = 7.13; b = .36
	Firstorderkinetic	.95	.08	.00	α = .19
	Exponential related equation	.95	.08	.01	α = .19
	Exponential model	.99	.03	.00	α = .08; k = 1.51
	Weibull	.99	.03	.00	α =1.51; b =5.47
	Vegá-galves model	.99	.02	.00	α =1.84; b =5.68; k =.88

Table 9. LC/MS/MS quantification of phenolic compounds identified in fresh and dried fruit (ppm)

No:	Phenolic compounds	RT, min	Quantity, ppm
			Fresh material	Dried material
1	Fumaric acid	3.17 ± .03	1.70 ± .03	16.71 ± .27
2	Gallic acid	4.20 ± .02	9.23 ± .42	15.06 ± .14
3	Protocatechuic acid	7.42 ± .03	3.84 ± .08	34.37 ± .10
4	Catechin	11.48 ± .03	609.35 ± 10.18	1581.58 ± 1.40
5	Tannic acid	11.64 ± .04	5.26 ± .11	6.38 ± .03
6	Chlorogenic acid	14.80 ± .03	11.51 ± .12	31.62 ± .12
7	Epicatechin	18.16 ± .03	42.38 ± 1.06	169.99 ± 2.42
8	p-coumaric acid	24.30 ± .02	.93 ± .01	6.86 ± .08
9	Quercetin-3-glucoside	39.41 ± .03	31.89 ± 2.53	70.93 ± 1.01
10	Rutin	39.57 ± .05	25.18 ± .10	24.08 ± .07
11	Naringenin	60.36 ± .04	.68 ± .01	.82 ± .01
12	Quercetin	63.02 ± .03	10.56 ± .10	28.70 ± .60
Total			752.51	1987.10

Tunde-Akintunde, T. Y. Mathematical Modeling of Sun and Solar Drying of Chilli Pepper. Renew. Energy. 2011, 36(8), 2139–2145. DOI: https://doi.org/10.1016/j.renene.2011.01.017.

 Web of Science ®Google Scholar

Doymaz, I. Convective Drying Kinetics of Strawberry. Chem. Eng. Process. Process Intensif. 2008, 47(5), 914–919. DOI: https://doi.org/10.1016/j.cep.2007.02.003.

 Web of Science ®Google Scholar

Nurafifah, F.; Chuah, A. L.; Wahida, M. A. P. F. Drying of Plectranthus Amboinicus (Lour) Spreng Leaves by Using Oven Dryer. Eng. Agric. Environ. Food. 2018, 11(4), 239–244. DOI: https://doi.org/10.1016/j.eaef.2018.08.002.

 Google Scholar

Chielle, D. P.; Bertuol, D. A.; Meili, L.; Tanabe, E. H.; Dotto, G. L. Convective Drying of Papaya Seeds (Carica Papaya L.) And Optimization of Oil Extraction. Ind. Crops Prod. 2016, 85, 221–228. DOI: https://doi.org/10.1016/j.indcrop.2016.03.010.

 Web of Science ®Google Scholar

do Nascimento Silveira Dorneles, L.; Luís, A.; Goneli, D.; Andrea, C.; Cardoso, L.; Bezerra, C.; Rosemari, M.; Cardoso, G.; Schoeninger, V. Effect of Air Temperature and Velocity on Drying Kinetics and Essential Oil Composition of Piper Umbellatum L. Leaves. Ind. Crop. Prod. 2019, 142, 111846. DOI: https://doi.org/10.1016/j.indcrop.2019.111846.

 Web of Science ®Google Scholar

Hosseinzadeh Samani, B.; Gudarzi, H.; Rostami, S.; Lorigooini, Z.; Esmaeili, Z.; Jamshidi-kia, F. Development and Optimization of the New Ultrasonic-infrared-vacuum Dryer in Drying Kelussia Odoratissima and Its Comparison with Conventional Methods. Ind. Crops Prod. 2018, 123, 46–54. DOI: https://doi.org/10.1016/j.indcrop.2018.06.053.

 Web of Science ®Google Scholar

Kumar, S.; Mahanand, S. S.; Kumar, P. Mathematical Modelling and Experimental Analysis of Broccoli (Brassica Oleracea I.) In Tray Dryer. J. Pharmacog. Phytochem. 2017, 6(6), 1049–1052.

 Google Scholar

Benseddik, A.; Azzi, A.; Zidoune, M. N.; Khanniche, R.; Besombes, C. Empirical and Diffusion Models of Rehydration Process of Differently Dried Pumpkin Slices. J. Saudi Soc. Agric. Sci. 2019, 18, 401–410. DOI: https://doi.org/10.1016/j.jssas.2018.01.003.

 Google Scholar

Ghellam, M.; Koca, I. Modelling of Rehydration Kinetics of Desert Truffles (Terfezia Spp.) Dried by Microwave Oven. Turk. J. Agric. Food Sci. Technol. 2020, 8(2), 407–415. DOI:https://doi.org/10.24925/turjaf.v8i2.407-415.3083.

 Google Scholar

Noshad, M.; Mohebbi, M.; Shahidi, F.; Mortazavi, S. A. L. I. Kinetic Modeling of Rehydration in Air-dried Quinces Pretreated Wıth Osmotic Dehydration and Ultrasonic. J. Food Process. Preserv. 2012, 36(5), 383–392. DOI: https://doi.org/10.1111/j.1745-4549.2011.00593.x.

 Web of Science ®Google Scholar

Pramiu, P. V.; Rizzi, R. L.; Do Prado, N. V.; Coelho, S. R. M.; Bassinello, P. Z. Numerical Modeling of Chickpea (Cicer Arietinum) Hydration: The Effects of Temperature and Low Pressure. J. Food Eng. 2015, 165, 112–123. DOI: https://doi.org/10.1016/j.jfoodeng.2015.05.020.

 Web of Science ®Google Scholar

Vega-Gálvez, A.; Notte-Cuello, E.; Lemus-Mondaca, R.; Zura, L.; Miranda, M. Mathematical Modelling of Mass Transfer during Rehydration Process of Aloe Vera (Aloe Barbadensis Miller). Food Bioprod. Process. 2009, 87(4), 254–260. DOI: https://doi.org/10.1016/j.fbp.2008.10.004.

 Web of Science ®Google Scholar