Drying of Cataloglu Apricots: The Effect of Sodium Metabisulfite Solution on Drying Kinetics, Diffusion Coefficient, and Color Parameters

Figures & data

Figure 1. Experimental setup for the drying experimental studies

Table 1. Thin-layer drying models used for apricot drying

Model name	Model^1)	Reference
Lewis	$MR=aexp(-kt)$	Roberts et al. (2008)
Henderson & Pabis	$MR=aexp(-kt)+c$	Henderson and Pabis (1961)
Logarithmic	$MR=aexp(-kt)+(1-a)exp(-gt)$	Wang et al. (2017)
Verma et al.	$MR=a+bt+c{t}^2$	Ihns et al. (2011)
Parabolic	$MR=1+at+b{t}^2$	Sharma and Prasad (2004)
Wang & Singh	$MR=aexp(-k{t}^n)+bt$	Izli et al. (2014)
Page	$MR=exp\left(-{\left(\frac{t}{b}\right)}^a\right)$	Tiwari et al. (2018)
Midilli & Kucuk	$MR=exp\left(-\left(\frac{at}{1+bt}\right)\right)$	Midilli and Kucuk (2003)
Weibull	$MR=exp(-k{t}^n)$	Corzo et al. (2008)
Aghbashlo et al.	$MR=exp\left(-\left(\frac{at}{1+bt}\right)\right)$	Aghbashlo et al. (2009)

a, b, c, g, k, k₀, k₁, n: empirical constants and coefficients in drying models

Figure 2. Drying curves of apricot at different temperature

Figure 3. Drying rate vs. time for apricots pretreated and without treatment

Table 2. Statistical results obtained from the selected drying models for SMB samples

T (^oC)	Model	R^2	P	x^2	RMSE
50	Lewis	0.9911	9.7146	0.000635	0.195522
	Henderson & Pabis	0.9943	7.6206	0.000406	0.151029
	Logarithmic	0.9998	1.0468	0.000013	0.024500
	Verma et al.	0.9997	0.9735	0.000016	0.022580
	Page	0.9984	3.5909	0.000110	0.078563
	Midilli & Kucuk	0.9998	1.1056	0.000014	0.026446
	Parabolic	0.9994	1.4132	0.000038	0.041488
	Wang & Singh	0.9993	1.8464	0.000047	0.049881
	Weibull	0.9984	3.5909	0.000110	0.078563
	Aghbashlo et al.	0.9997	0.8757	0.000017	0.027636
60	Lewis	0.9983	2.1044	0.000116	0.046760
	Henderson & Pabis	0.9990	2.4235	0.000069	0.044024
	Logarithmic	0.9992	1.8663	0.000055	0.032928
	Verma et al.	0.9992	2.5240	0.000055	0.039739
	Page	0.9985	2.6615	0.000104	0.050437
	Midilli & Kucuk	0.9994	2.2038	0.000048	0.033490
	Parabolic	0.9948	6.7371	0.000386	0.101760
	Wang & Singh	0.9944	7.3435	0.000408	0.104586
	Weibull	0.9985	2.6615	0.000104	0.050437
	Aghbashlo et al.	0.9983	2.2697	0.000118	0.048553
70	Lewis	0.9924	6.7761	0.000610	0.117345
	Henderson & Pabis	0.9973	3.1783	0.000220	0.061133
	Logarithmic	0.9980	3.6340	0.000172	0.055551
	Verma et al.	0.9995	1.8534	0.000035	0.027628
	Page	0.9988	3.2888	0.000098	0.040818
	Midilli & Kucuk	0.9997	0.6863	0.000029	0.019731
	Parabolic	0.9986	3.7145	0.000119	0.051358
	Wang & Singh	0.9982	3.1944	0.000148	0.048517
	Weibull	0.9988	3.2888	0.000098	0.040818
	Aghbashlo et al.	0.9968	4.8268	0.000265	0.069919
80	Lewis	0.9951	4.1504	0.000398	0.071818
	Henderson & Pabis	0.9981	2.3315	0.000157	0.040566
	Logarithmic	0.9982	2.7305	0.000157	0.041799
	Verma et al.	0.9997	1.6680	0.000018	0.013852
	Page	0.9984	3.5104	0.000131	0.041846
	Midilli & Kucuk	0.9996	1.1535	0.000032	0.020224
	Parabolic	0.9973	5.2638	0.000234	0.064594
	Wang & Singh	0.9973	5.0165	0.000228	0.062792
	Weibull	0.9984	3.5105	0.000132	0.041846
	Aghbashlo et al.	0.9969	4.1221	0.000261	0.057026

Table 3. Statistical results obtained from the selected drying models for control samples

T (^oC)	Model	R^2	P	x^2	RMSE
50	Lewis	0.9901	10.2404	0.000708	0.227239
	Henderson & Pabis	0.9935	7.9953	0.000472	0.183742
	Logarithmic	0.9993	2.5550	0.000046	0.054788
	Verma et al.	0.9994	2.4176	0.000040	0.048702
	Page	0.9988	2.7478	0.000087	0.074530
	Midilli & Kucuk	0.9997	1.5905	0.000023	0.038139
	Parabolic	0.9999	0.3214	0.000003	0.012011
	Wang & Singh	0.9998	0.5889	0.000009	0.021323
	Weibull	0.9988	2.7478	0.000087	0.074530
	Aghbashlo et al.	0.9998	1.2837	0.000011	0.026567
60	Lewis	0.9992	1.8648	0.000054	0.036195
	Henderson & Pabis	0.9996	1.6514	0.000027	0.030034
	Logarithmic	0.9996	1.6520	0.000028	0.030030
	Verma et al.	0.9993	1.5856	0.000048	0.036272
	Page	0.9994	1.6715	0.000039	0.034986
	Midilli & Kucuk	0.9996	1.6001	0.000028	0.029947
	Parabolic	0.9955	5.9364	0.000321	0.105574
	Wang & Singh	0.9944	7.0136	0.000380	0.115797
	Weibull	0.9994	1.6715	0.000039	0.034986
	Aghbashlo et al.	0.9993	1.6245	0.000047	0.036499
70	Lewis	0.9931	6.7151	0.000535	0.127040
	Henderson & Pabis	0.9975	3.3907	0.000199	0.068221
	Logarithmic	0.9984	2.9757	0.000127	0.054959
	Verma et al.	0.9997	1.1920	0.000024	0.023788
	Page	0.9993	2.4109	0.000050	0.034661
	Midilli & Kucuk	0.9998	0.9005	0.000015	0.019622
	Parabolic	0.9990	3.2506	0.000078	0.045779
	Wang & Singh	0.9988	2.9412	0.000095	0.047325
	Weibull	0.9993	2.4109	0.000050	0.034661
	Aghbashlo et al.	0.9977	3.9403	0.000178	0.068381
80	Lewis	0.9915	6.5631	0.000695	0.111643
	Henderson & Pabis	0.9971	2.8777	0.000246	0.055814
	Logarithmic	0.9976	3.2086	0.000208	0.05427
	Verma et al.	0.9998	1.1434	0.000100	0.011923
	Page	0.9984	3.7515	0.000130	0.049703
	Midilli & Kucuk	0.9997	1.2204	0.000025	0.020604
	Parabolic	0.9980	4.3337	0.000173	0.060231
	Wang & Singh	0.9974	3.7000	0.000220	0.061349
	Weibull	0.9984	3.7515	0.000130	0.049703
	Aghbashlo et al.	0.9960	4.9583	0.000342	0.077758

Figure 4. Experimental and predicted moisture ratio from the Midilli & Kucuk model distributions of apricots at different temperature

Figure 5. Effective moisture diffusivity versus temperature

Figure 6. Arrhenius-type relationship between effective moisture diffusivity and temperature

Table 4. The color values of the fresh and dried samples at different temperatures

Code	T (°C)	L	a	b	ΔE	C
Fresh	-	61.75±3.01	9.08±0.424	34.47±1.68	-	35.65±1.67
Control	50	36.31±1.65	8.13±0.39	1.67±0.07	41.52±1.95	8.30±0.38
	60	37.03±1.69	9.23±0.44	3.14±0.11	39.91±1.89	9.75±0.44
	70	38.03±1.77	9.56±0.46	3.36±0.14	39.12±1.85	10.13±0.47
	80	37.29±1.71	9.27±0.45	3.19±0.12	39.71±1.81	9.80±0.45
SMB	50	41.29±1.99	11.59±0.54	9.39±0.43	32.46±1.47	14.92±0.70
	60	45.23±2.11	11.78±0.56	15.88±1.02	25.02±1.19	19.77±0.94
	70	46.65±2.22	11.91±0.58	16.40±1.13	23.72±1.17	20.27±0.97
	80	43.66±2.05	11.87±0.57	14.38±0.95	27.18±1.32	18.65±0.88

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