Effects of operating factors on osmotic dehydration of broccoli stalk slices

Figures & data

Table 1. Process variables and the levels for the osmotic dehydration of broccoli stalk slices

Coded (Xi)	Independent variables	Pattern and levels
		a	−	0	+	A
X1	Sucrose concentration (% w/v)	40	44.05	50	55.95	60
X2	Temperature (°C)	25	33.11	45	56.89	65
X3	Immersion time (hour)	1	1.81	3	4.19	5

Table 2. Experimental conditions with values of response variables

Run No.	Pattern	Independent variables			Independent variables
		Concentration	Temperature	Time	WL (%)	SG (%)
		(% w/v)	(°C)	(hour)
1	a00	40.00	45.00	3.00	40.76	4.89
2	−−−	44.05	33.11	1.81	36.97	4.55
3	−−+	44.05	33.11	4.19	39.57	6.43
4	−+−	44.05	56.89	1.81	42.14	8.99
5	−++	44.05	56.89	4.19	44.90	14.17
6	0a0	50.00	25.00	3.00	42.49	5.69
7	00a	50.00	45.00	1.00	40.30	3.68
8	0	50.00	45.00	3.00	57.22	5.96
9	0	50.00	45.00	3.00	56.24	5.93
10	0	50.00	45.00	3.00	57.27	6.34
11	0	50.00	45.00	3.00	57.30	5.93
12	0	50.00	45.00	3.00	57.37	6.12
13	0	50.00	45.00	3.00	56.73	5.82
14	00A	50.00	45.00	5.00	61.18	8.83
15	0A0	50.00	65.00	3.00	50.67	15.64
16	+−−	55.95	33.11	1.81	42.40	4.12
17	+−+	55.95	33.11	4.19	54.59	6.55
18	++−	55.95	56.89	1.81	46.6	7.27
19	+++	55.95	56.89	4.19	57.76	13.37
20	A00	60.00	45.00	3.00	65.02	4.94

Table 3. Regression coefficients and ANOVA of the second-order polynomial model for the overall effects of the three process variables (sucrose concentration (X₁), temperature (X₂), and immersion time (X₃)) on (A) water loss (WL) and (B) solute gain (SG)

(A) For WL (R^2 = 0.93, Adjusted R^2 = 0.87, Root Mean Square Error (RMSE) = 3.11) (df = Degree of Freedom)
Source	Sum of Square	df	Mean Square	F Ratio	Prob > F	Estimate
Intercept						57.15
Concentration	452.03	1	452.03	46.83	<.0001	9.68
Temperature	73.24	1	73.24	7.59	0.0203	3.89
Time	298.29	1	298.29	30.90	0.0002	7.86
Concentration*Temperature	1.22	1	1.22	0.13	0.7291	−1.11
Concentration*Time	40.46	1	40.46	4.19	0.0678	6.36
Temperature*Time	0.09	1	0.09	0.01	0.9231	−0.31
Concentration*Concentration	77.01	1	77.01	7.98	0.0180	−6.54
Temperature*Temperature	297.37	1	297.37	30.81	0.0002	−12.85
Time*Time	135.98	1	135.98	14.09	0.0038	−8.69
Model	1301.37	9	144.60	14.98	0.0001
Error	96.52	10
Corrected total	1397.89	19
Lack of fit	95.52	5	19.10	95.92	<.0001
Pure error	1.00	5	0.20
Total error	96.52	10
(B) For SG (R^2 = 0.98, Adjusted R^2 = 0.97, Root Mean Square Error (RMSE) = 0.59) (df = Degree of Freedom)
Intercept						5.99
Concentration	0.55	1	0.55	1.57	0.2388	−0.34
Temperature	110.71	1	110.71	314.75	<.0001	4.79
Time	43.06	1	43.06	122.43	<.0001	2.99
Concentration*Temperature	0.61	1	0.61	1.74	0.2171	−0.78
Concentration*Time	0.27	1	0.27	0.77	0.4014	0.52
Temperature*Time	6.07	1	6.07	17.26	0.002	2.46
Concentration*Concentration	0.67	1	0.67	1.90	0.1986	−0.61
Temperature*Temperature	47.62	1	47.62	135.39	<.0001	5.14
Time*Time	0.96	1	0.96	2.74	0.1288	0.73
Model	211.37	9	23.49	66.77	<.0001
Error	3.52	10	0.35
Corrected total	214.89	19
Lack of fit	3.35	5	0.67	19.43	0.0027
Pure error	0.17	5	0.03
Total error	3.52	10

Figure 1. 3D surface plot as a function of concentration and immersion time during osmotic dehydration of broccoli stalk slices on WL.

Figure 2. 3D surface plot as a function of temperature and concentration during osmotic dehydration of broccoli stalk slices on WL.

Figure 3. 3D surface plot as a function of immersion time and temperature during osmotic dehydration of broccoli stalk slices on SG.

Table 4. Summary of fit and ANOVA of the stepwise regression for water loss (WL) and solute gain (SG)

		WL					SG
Summary of fit	R^2	0.90					0.96
	Adj. R^2	0.86					0.96
	RMSE	3.26					0.63
Analysis of variance	Source	df	SS	MS	F Ratio	Prob > F	df	SS	MS	F Ratio	Prob > F
	Model	6	1259.60	209.93	19.73	<.0001	5	209.27	41.85	104.32	<.0001
	Error	13	138.29	10.64			14	5.62	0.40
	Corrected Total	19	1397.89				19	214.89
	Lack Of Fit	8	137.30	17.16	86.17	<.0001	3	1.72	0.57	1.62	0.2402
	Pure Error	5	1.00	0.20			11	3.89	0.35
	Total Error	13	138.29				14	5.62

Note: Adj. R² = Adjusted R², RMSE = Root Mean Square Error, df = degree of freedom, SS = Sum of Square, MS = Mean Square.

Figure 4. Predicted versus experimental values for WL.

Figure 5. Predicted versus experimental values for SG.