Mathematical modelling and characterization of drying of pre-treated sweet corn (Zea mays L.) kernels

Abstract

The pre-treated sweet corn samples were dried at temperatures of 55, 60, 65 and 70 °C and thin layer drying characteristics of sweet corn were assessed. Mathematical models were fitted and evaluated using R², χ², RMSE values. The effective diffusivities for the drying process were 4.32 × 10^–10 to 1.08 × 10^–9 m²/s and activation energies were 34.51 to 38.99 kJ/mol. Total sugar and ascorbic acid of dehydrated sweet corn kernels varied from 5.50 to 13.00 g/100 g and 3.30 to 10.50 mg/100 g respectively. The sample pre-treated with microwave blanching and dried at 70 °C obtained higher sensory score after rehydration, indicating suitability of the dehydrated sweet corn.

Introduction

Sweet corn is processed in hot water or steam before consumption. It is highly perishable seasonal crop and it needs to be processed to retain eating quality and increase storage stability for longer duration. Drying of sweet corn remains an important method for extending the shelf life along with other accepted preservation techniques like canning and freezing. Dried products have longer shelf life due to low water activity, which prevents growth of microorganisms, enzymatic and other deteriorative reactions (Jin et al. 2014) and remains economical in comparison to frozen and canned products due to storage of lesser amount of product and storage at normal temperatures.

Fresh sweet corn should be consumed within two weeks even after keeping at 4 °C temperature due to short shelf life. The enzymes are generally responsible for faster change in nutritional and sensory characteristics of sweet corn kernels. The blanching is used as one of the most extensively pre-treatments for enzymes inactivation that adversely affect product quality too (Sanjuan et al. 2001). The blanching also offers additional advantages like surface cleaning, retention of colour and vitamins (Xiao et al. 2017). The hot water, steam and microwave blanching are used to inactivate the indicator enzyme by reducing the enzymatic activity to the extent of 90% in sweet corn (Kachhadiya et al. 2018). The microwave blanching as an alternative to hot water / steam blanching is getting momentum due to lower energy use, lower shrinkage, and an overall enhancement in sensory attributes (James et al. 2006). The blanching also provides good nutritional quality dried product by reducing the drying time and increase in drying rate (Kingsly et al. 2007; Gupta et al. 2014).

Thin layer drying is an effective method owing to rapid drying rates and slow nutrients loss. It considers drying of product in single layer of sample for describing the process. Drying phenomenon can be described by development of models. Empirical drying models are being preferred due to providing accurate and consistent prediction of drying behaviour. A direct relation of moisture content and drying time is formulated in empirical models with consideration of drying fundamentals. The developed models can be used confidently within the studied range of temperature and moisture content. These models also have capabilities for the use in automation for economic operations and faster calculations (Ertekin and Firat 2017).

The drying of pre-treated green peas in thin layer and mathematical modelling was also reported by Doymaz and Kocayigit (2011). However, thin layer drying of blanched sweet corn and mathematical modelling is not reported so far. Therefore, the study was conducted to perform mathematical modelling of drying of blanched sweet corn kernels and evaluate the effect of drying on quality characteristics of sweet corn kernels.

Materials and methods

Experimental design

The completely randomized design (CRD) was used to formulate the experiments and analyse the samples. The samples were blanched followed by drying in tray dryer to reduce the moisture content for safe storage of sweet corn. The independent factors were blanching methods (Hot water blanching, steam blanching, microwave blanching and control) and drying temperatures (55, 60, 65, 70 °C). The samples were analysed at 5% level of significance. The regression analysis was carried out using OriginPro 9.2 software (M/s Origin Lab, Massachusetts, USA). Levenberg–Marquardt (L-M) algorithm was used for nonlinear fitting of models. The adjusted R² value, chi-square (χ²) and root mean square error were calculated for mathematical models evaluation.

Blanching

Fresh sweet corn (Variety: Madhuri) was procured from local market, Godhra, Gujarat, India. Before drying process, kernels were detached from the cob manually. Sweet corn kernels of 100 g samples were soaked in boiling water with sample to water ratio of 1:10 w/w in hot water blanching for 120 s. Steam blanching was performed by keeping 100 g sweet corn kernels in perforated bowl, which was placed in water containing vessel for steam generation on heating for 90 s. Microwave blanching was performed in microwave (M/S LG MC2841SPS, India) at 900 W for 60 s. A transparent glass beaker with diameter of 100 mm and capacity of 1000 mL was used to keep 100 g of sweet corn kernels, which was coved with a glass plate during blanching. The time for blanching was selected based on preliminary study on appropriateness of blanching reported by Kachhadiya et al. (2018). The samples were kept on blotting paper after blanching to remove excess moisture for 15 min and allowed the samples to cool to the room temperature. The fresh samples were kept as control for the drying study.

Drying

Drying of samples was carried out at 55, 60, 65 and 70 °C in a tray dryer (M/s Nova Instruments Pvt. Ltd., India) with a thickness of 10 mm. The tray dryer had a precision of ± 1 °C and could be used to maintain a maximum temperature of 100 °C. The direction of air flow was parallel to the surface of the kernels and circulation air flow rate was 0.03 m³/min. The sample mass was kept 100 ± 1 g per trial. The samples were taken out at every 10 min from the dryer for weighing and put back. The measurement of mass lasted in 10 ± 0.25 s only. The drying process continued until attaining constant moisture constant at that temperature. The moisture content (g water/ g dry matter) was determined using the following formula:

$$Moisturecontent=\frac{Massoftotalsample-Massofdrymatter}{Massofdrymatter}$$ 1

Mathematical modelling

Modelling is being extensively used for analysis of drying of various agricultural products. The process is considered as isothermal with diffusion as main mass transfer mechanism, and shrinkages and deformations of sweet corn kernels throughout drying process were considered as insignificant for establishing equations (Erbay and Icier 2010). The convection drying of various agricultural produce occur in falling rate drying period. Therefore, already established models can be fitted to the moisture ratio (MR) of the products with respect to time. Eight most commonly applied models used for drying process are listed in Table 1. Though, moisture ratio was simplified from (M–Me)/(Mo–Me) to M/Mo (Yaldiz et al. 2001); where M = the moisture contents at time t; Mo = initial moisture contents; and Me = equilibrium moisture contents in decimal dry basis. The values of Me are relatively small compared with M or Mo for long drying time and the relative humidity of ambient air can not be regulated for EMC determination (Doymaz 2005).

Table 1.

List of models and description

S. No	Model name	Model
1	Lewis	MR = exp (− kt)
2	Page	MR = exp (− ktn)
3	Henderson & Pabis	MR = a exp(− kt)
4	Wang & Singh	MR = 1 + at + bt2
5	Approximation of diffusion	MR = a exp(− kt) + (1 − a) exp ( − kat)
6	Modified page -II	MR = exp ( − c (t/L2)n)
7	Hii et al	MR = a exp (− ktn) + c exp(− gtn)
8	Verma	MR = a exp (− kt) + (1 − a) exp (− g t)

Effective diffusivity

The diffusivity was estimated using Fick’s second law of diffusion by plotting logarithmic of moisture ratio (MR) with time (t) using the following equation (Kumar et al. 2012a):

$$lnMR=ln\frac{8}{{\pi }^2}-\frac{{\pi }^2{D}_{\mathit{eff}}t}{4L_o^2}$$ 2

where D_eff represents effective diffusivity (m²/s) and L_o is half of drying thickness (m).

Activation energy

Activation energy was obtained by plotting natural logarithm of effective diffusivity (D_eff) against reciprocal of absolute temperature (1/T) in Arrhenius type log liner relationship. Arrhenius type relationship was used to describe effect of temperature on effective diffusivity (Doymaz and Kocayigit 2011):

$$D_{\mathit{eff}}=D_{o}exp\left(-\frac{E_a}{\mathit{RT}}\right)$$ 3

where E_a, D_o, R and T represents activation energy (kJ/mol), Arrhenius equation factor (m²/s), gas constant (kJ/mol K), temperature (K) respectively.

Determination of quality characteristics

The total sugar was determined by titration method proposed by Lane and Eynon (1934) with methylene blue as an internal indicator. The ascorbic acid of dehydrated sweet corn was estimated using 2,6- dichlorophenol-indophenol method (Ranganna 2017). Sensory characteristics were also evaluated after cooking of dried sample (10 g) held for 20 min in 1000 ml of boiling water (Eshtiaghi et al. 1994). Sensory evaluation was conducted by a panel of 15 semi trained panellists of faculty members and post graduate students. They were asked to assess the rehydrated samples on a hedonic rating test (1 = dislike extremely, 5 = neither like nor dislike, and 9 = like extremely) in accordance with their opinion for colour, taste and overall acceptability (Ranganna 2017).

Results and discussion

Drying characteristics

Pre-treated sweet corn kernels were dried at 55, 60, 65, 70 °C in thin layers initial thickness of about 10 mm in hot air dryer. The initial moisture contents of sweet corn kernels were 3.17, 3.81, 3.67 and 3.05 g of water /g of dry matter in control, hot water, steam and microwave blanched samples, which was reduced to the minimum consistent final moisture content.

The variation of moisture content for sweet corn kernels using various pre-treatments along with control at selected temperature is presented in Fig. 1. The duration required for drying process of sweet corn kernels were 16.4 to 22.8 h, 14.5 to 20.2 h, 13.7 to 19.3 h and 12 to 17.17 h with control, hot water blanched, steam blanched and microwave blanched respectively for selected temperature range (55–70 °C). Shorter drying period for samples blanched with microwave were observed. The samples pre-treated with microwave took about 22–25% lower time than control samples. The shorter drying period is due to volumetric heating by direct and internal absorption of microwave energy and difference in dimensions of samples with other blanching methods. This generates heat with in the material and resulting in rapid drying rates in comparison with conventional conduction heating. Similar approach for various agricultural products was also discussed by Vadivambal and Jayas (2007). It can also be noted from Fig. 1 that moisture content decrease was almost similar in the starting at selected temperatures, whereas moisture content reduced faster at higher temperature thereafter.

Fig. 1.

Thin layer drying curve of sweet corn kernels with pre-treatment at different treatment A Control B Hot-water C Steam D Microwave

The moisture ratio of pre-treated and control samples also decreased with the progress of drying at all the temperatures. It took 281 to 536, 256 to 395, 263 to 463 and 251 to 383 min to take away the initial half moisture (moisture ratio, MR = 0.5) for control, hot water blanched, steam blanched and microwave blanched respectively for selected temperature range (55–70 °C), which is about 1/3 of total drying time. However, it took about 2/3 of total drying time to remove the remaining half moisture from all the samples. Requirement of more drying time is evident due to decrease in molecular diffusion caused by reduction in moisture content gradient between the samples and the drying air in later stage of drying. Similar result during thin layer drying was also reported by Kumar et al. (2012b).

The maximum drying rates were 0.0091, 0.0115, 0.0083 and 0.0105 g water/g dry matter per min for control, hot water blanched, steam blanched and microwave blanched sweet corn samples respectively. However, the average rates for drying were 0.00275, 0.00368, 0.00385 and 0.00415 g water/g dry matter per min for control, hot water blanched, steam blanched samples and for microwave blanched samples respectively. The drying rate decreased with increase in drying time (data not shown) indicating that the drying took place in falling rate period similar to the decrease in water absorption rate reported in literature (Vishwakarma et al. 2013). It can be noted form Fig. 2 that rates of drying were faster at elevated drying temperatures, which is evident due to providing more energy to the moisture available in sweet corn kernels. Similar increase in drying rates with increase in temperature was also reported by Kumar et al. (2012b). Higher drying rates were also supported by Sigge et al. (1998) to maintain the product quality and reduction in power requirements. It can also be noted that final moisture content was lower at higher temperatures and drying rates are affected by the moisture content of the samples, which may be attributed to case hardening and glass transition.

Fig. 2.

Drying rate versus drying time of sweet corn kernels at selected temperature with pre-treatment A control samples B hot water blanched C steam blanched D microwave blanched

The minimum time of 12 h was observed for drying of sweet corn at 70 °C temperature for the samples blanched with microwave at 900 W for 60 due to maximum drying rate of 0.0105 g water/g dry matter per min.

Mathematical modelling

The values of coefficient of determination (R²), reduced chi-square (χ²) and root mean square error (RMSE) for the models (Table 1) varied from 0.95 to 0.99, 4.0 x 10⁻⁵ to 0.035 and 0.107 to 0.006 with an average of 0.98, 0.0237 and 0.040 respectively (Table 2). The fitting of drying data remained successful due to higher R² and lower χ² and RMSE in all the cases. The Hii et al. (2009) model fitted better in comparison to other models with higher R² and lower χ² and RMSE at all temperatures and treatments.

Table 2.

Statistical results obtained from different thin layer drying models

Model	Pre-treatment	Drying air temperature												Residual plot type
		55 °C			60 °C			65 °C			70 °C
		R2	χ2	RMSE	R2	χ2	RMSE	R2	χ2	RMSE	R2	χ2	RMSE
Lewis	C	0.96	0.0040	0.063	0.95	0.0053	0.07	0.96	0.0037	0.060	0.96	0.0037	0.061	Random
	HW	0.98	0.0015	0.040	0.98	0.0019	0.04	0.99	0.0010	0.030	0.98	0.0019	0.043
	SM	0.96	0.0040	0.063	0.98	0.0019	0.04	0.97	0.0025	0.048	0.98	0.0020	0.042
	MW	0.96	0.0032	0.058	0.96	0.0036	0.06	0.97	0.0031	0.055	0.96	0.0040	0.063
Page	C	0.99	0.0006	0.025	0.99	0.0006	0.02	1.00	0.0003	0.018	1.00	0.0002	0.013	Random
	HW	1.00	0.0354	0.090	1.00	0.0181	0.10	1.00	0.0072	0.104	1.00	0.0200	0.107
	SM	1.00	0.0003	0.018	1.00	0.0003	0.02	1.00	0.0001	0.008	1.00	0.0001	0.009
	MW	0.99	0.0005	0.024	1.00	0.0005	0.02	1.00	0.0003	0.016	0.99	0.0005	0.023
Henderson & Pabis	C	0.97	0.0028	0.052	0.96	0.0037	0.06	0.98	0.0024	0.048	0.98	0.0023	0.048	Random
	HW	0.99	0.0008	0.030	0.99	0.0012	0.03	0.99	0.0006	0.024	0.99	0.0012	0.034
	SM	0.97	0.0025	0.050	0.98	0.0013	0.04	0.98	0.0015	0.037	0.99	0.0012	0.032
	MW	0.97	0.0023	0.050	0.97	0.0026	0.05	0.98	0.0021	0.045	0.97	0.0029	0.053
Wang & Singh	C	1.00	0.0046	0.085	1.00	0.0541	0.09	1.00	0.0153	0.095	1.00	0.0262	0.101	Random
	HW	1.00	0.0003	0.018	1.00	0.0000	0.01	1.00	0.0000	0.003	1.00	0.0002	0.012
	SM	1.00	0.0004	0.020	1.00	0.0000	0.01	1.00	0.0002	0.014	1.00	0.0002	0.013
	MW	1.00	0.0002	0.013	1.00	0.0001	0.01	1.00	0.0002	0.012	1.00	0.0003	0.017
Approx diffusion	C	0.96	0.0040	0.063	0.95	0.0054	0.07	0.99	0.0007	0.025	1.00	0.0005	0.021	Random
	HW	0.98	0.0015	0.040	0.98	0.0019	0.04	1.00	0.0001	0.008	1.00	0.0003	0.016
	SM	0.96	0.0040	0.063	0.98	0.0019	0.04	1.00	0.0002	0.012	1.00	0.0001	0.010
	MW	0.96	0.0032	0.058	0.96	0.0037	0.06	1.00	0.0005	0.021	0.99	0.0009	0.029
Modified page	C	0.96	0.0041	0.063	0.95	0.0054	0.07	0.96	0.0037	0.060	0.96	0.0038	0.061	Random
	HW	0.98	0.0015	0.040	0.98	0.0019	0.04	0.99	0.0010	0.030	0.98	0.0019	0.043
	SM	0.96	0.0040	0.063	0.98	0.0020	0.04	0.97	0.0026	0.048	0.98	0.0020	0.042
	MW	0.96	0.0032	0.058	0.96	0.0037	0.06	0.97	0.0031	0.055	0.96	0.0041	0.063
Hii et al	C	1.00	0.0004	0.020	1.00	0.0003	0.02	1.00	0.0002	0.014	1.00	0.0001	0.009	Random
	HW	1.00	0.0001	0.011	1.00	0.0001	0.01	1.00	0.0001	0.007	1.00	0.0001	0.011
	SM	1.00	0.0001	0.012	1.00	0.0002	0.01	1.00	0.0000	0.006	1.00	0.0000	0.006
	MW	1.00	0.0001	0.012	1.00	0.0002	0.02	1.00	0.0001	0.011	1.00	0.0002	0.012
Verma	C	0.97	0.0027	0.052	0.96	0.0036	0.06	0.98	0.0023	0.047	0.98	0.0021	0.046	Random
	HW	0.99	0.0008	0.029	0.99	0.0012	0.03	0.99	0.0006	0.023	0.99	0.0011	0.032
	SM	0.97	0.0024	0.049	0.98	0.0012	0.03	0.98	0.0013	0.035	0.99	0.0011	0.031
	MW	0.98	0.0023	0.049	0.97	0.0025	0.05	0.98	0.0020	0.043	0.97	0.0027	0.052

C control samples, HW hot water blanched, SM steam blanched, MW microwave Blanched

The coefficients of the Hii et al. (2009) model for hot water blanched, steam blanched, microwave blanched and control at 55, 60, 65 and 70 °C were also estimated (Table 3). Polynomial equations of third order were also regressed for all the coefficients for microwave blanched samples at drying temperatures (T,°C).

$$\text{a}=0.1573{\text{T}}^{{}^2}-19.75\text{T}+619.5,{\text{R}}^2=0.99$$ 4

$$\text{k}=0.000001{\text{T}}^2-0.0002\text{T}+0.0057,{\text{R}}^2=0.90$$ 5

$$\text{n}=-0.0034{\text{T}}^2+0.4231\text{T}-11.772,{\text{R}}^2=0.93$$ 6

$$\text{c}=-0.1571{\text{T}}^2+19.729\text{T}-617.89,{\text{R}}^2=0.99$$ 7

$$\text{g}=0.000001{\text{T}}^2-0.0002\text{T}+0.0049,{\text{R}}^2=0.88$$ 8

Table 3.

Coefficient value of Hill et al. model at various temperature and pre-treatment

Temp. (°C)	Coefficient	C	HW	SM	MW
55	a	0.48	0.95	9.10	9.10
	k	0.00	0.00	0.00	0.00
	n	1.52	1.42	1.39	1.30
	c	0.48	0.02	− 8.13	− 8.13
	g	0.00	0.00	0.00	0.00
60	a	0.47	0.51	0.51	0.50
	k	0.00	0.00	0.00	0.00
	n	1.64	1.37	1.36	1.52
	c	0.47	0.45	0.46	0.45
	g	0.00	0.00	0.00	0.00
65	a	0.48	0.48	0.48	0.47
	k	0.00	0.00	0.00	0.00
	n	1.49	1.25	1.40	1.47
	c	0.48	0.50	0.50	0.49
	g	0.00	0.00	0.00	0.00
70	a	0.48	0.96	0.97	7.60
	k	0.00	0.00	0.00	0.00
	n	1.50	1.39	1.38	1.36
	c	0.48	0.00	0.00	−  6.64
	g	0.00	0.00	0.00	0.00

C control samples, HW hot water blanched, SM steam blanched, MW microwave Blanched

Effective diffusivity

The effective diffusivities of sweet corn kernel varied between 4.32 × 10⁻¹⁰ and 1.08 × 10⁻⁹ m²/s. The effective diffusivities of control, hot water, steam and microwave blanched samples varied from 4.33 × 10⁻¹⁰ to 7.94 × 10⁻¹⁰ m²/s, 4.86 × 10⁻¹⁰ to 8.95 × 10⁻¹⁰ m²/s, 5.11 × 10⁻¹⁰ to 9.49 × 10⁻¹⁰ m²/s and 6.42 × 10⁻¹⁰ to 1.08 × 10⁻⁹ m²/s respectively at drying temperature ranges from 55 to 70°C. It can also be observed effective diffusivity was higher for microwave pre-treated samples, which is evident due to volumetric heating during microwave blanching. The difference is diffusivities of all blanched samples is also due to the difference in equilibrium moisture of samples at different temperature and blanching method. The volumetric heating reduces the migration of water soluble constituents to the surface and may increase the internal porosity (Regier 2017), which might have resulted in increased effective diffusivity. The internal mass transfer resistance governed the drying process. The values obtained for effective diffusivities remained with in the range reported ranging from 10⁻¹¹ to 10⁻⁰⁹ for food materials (Wang et al. 2007). The effect of pre-treatments and temperature on effective diffusivity was also significant (P<0.01). More thermal energy in material dried at higher temperatures due to increase in the activity of water molecule is evident and might have led to higher effective diffusivity. The effective diffusivities increased with temperature of drying air in control and all pre-treated samples due to more diffusion of moisture at higher temperature by increasing the activity of water molecules. Chen et al. (2009) and Kumar et al. (2012b) also reported similar dependence of effective diffusivities on temperature.

Activation energy

The activation energy values were 38.99, 36.89, 37.61 and 34.51 kJ/mol for control, hot water blanched, steam blanched and microwave blanched, respectively, indicating less amount of heat is required for removal of water in microwave treated samples. The activation energy of the samples was between 34.51 and 38.99 kJ/mol with an average value of 37.00 kJ/mol, which remained inline with the reported range for food materials by Zogzas et al. (1996).

Quality of dehydrated sweet corn

Total sugar was 35 g/ 100 g on dry matter basis in fresh sweet corn samples, which reduced during blanching and dehydration. Total sugar after dehydration of sweet corn kernel of control, hot water blanched, steam blanched and microwave blanched samples were 5.67 to 6.35 g/100 g, 6.89 to 8.93 g/100 g, 8.94 to 10.55 g/100 g and 10.34 to 13.37 g/100 g on dry matter basis at drying temperature varied between 55 and 70 °C significantly (P < 0.05), indicating retention of higher sugar in microwave blanched samples followed by drying at 70 °C (Table 4). Samples treated with microwave blanching retained more sugar as compared to other blanching methods (Kachhadiya et al. 2018). The ascorbic acid in fresh sample was 29.29 mg/ 100 g on dry matter basis, which reduced during blanching and dehydration. The ascorbic acid after dehydration of sweet corn kernel of control, hot water blanched, steam blanched & microwave blanched samples were 3.40 to 4.47 mg/100 g, 3.39 to 4.98 mg/100 g, 5.45 to 6.62 mg/100 g and 9.11 to 10.80 mg/100 g on dry matter basis respectively (P < 0.05), indicating higher retention of ascorbic acid in microwave blanched samples too followed by drying at 70 °C. The higher nutritional value of microwaves pre-treated samples is obvious as a result of short blanching duration and restriction of leaching losses in microwave blanching. It was also observed that microwave blanched and dehydrated sweet corn at 70 °C obtained higher sensory score after rehydration, indicating the suitability of the use of dehydrated sweet corn. Higher sensory score and nutritional values may be due to shorter drying time of microwave blanching samples (Fig. 1). Shorter drying time in microwave blanching is evident due to the reduction in moisture during microwave blanching rather than absorbing moisture as in hot water and steam blanching, which might have worked as preliminary drying. The drying rate should preferably be as high as possible as longer drying times lead to final products of poor quality due to caramelization, Maillard reactions, enzymatic reactions, pigment degradation and ascorbic acid oxidation as well as higher energy requirements for the dehydration process (Sigge et al. 1998).

Table 4.

Variation of ascorbic acid, total sugar of dehydrated sweet corn and sensory score of rehydrated sweet corn

Temperature, °C	Samples
	Control	Hot water blanched	Steam blanched	Microwave blanched
Total Sugar, g/100 g
55	5.50a	6.70a	8.70a	10.10a
60	5.50a	7.00b	9.60b	11.10b
65	5.70a	7.60c	10.10c	11.70c
70	6.30b	8.60d	10.20c	13.00d
Ascorbic acid, mg/100 g
55	3.30a	3.30a	5.30a	8.90a
60	3.50a	3.80b	5.40a	9.70b
65	3.60a	4.60c	6.30b	10.00b
70	4.30b	4.80c	6.40b	10.50c
Sensory score
55	4.6a	7.4b	6.2a	7.6b
60	6.1b	7.1a	6.9b	7.2a
65	6.2b	7.7c	7.8d	7.9c
70	6.1b	7.2a	7.5c	8.6d

^a–dMeans within the same column with different letters are significantly different at P < 0.05

Conclusion

Minimum time in drying was occurred for sweet corn kernels treated with microwave. Effective diffusivities were 4.32 × 10^–10 to 1.08 × 10^–9 m²/s. Activation energy values were 34.51 to 38.99 kJ/mol. The effective diffusivity was more and activation energy was less in microwave-blanched samples. The retention of ascorbic acid and total sugar was also more in microwave-blanched samples. The microwave blanched and dehydrated sweet corn at 70 °C obtained higher sensory score after rehydration, indicating the suitability of dehydrated sweet corn for enhancing the storage life of sweet corn followed by steam blanching and dehydration at 70 °C.

Abbreviations

χ²

Chi-square

CRD

Completely randomized design

D_eff

Effective diffusivity, m²/s

Ea

Activation energy, kJ/mol

L_o

Half of drying thickness, m

M

Moisture contents at time t on dry basis

Me

Equilibrium moisture contents on dry basis

Mo

Initial moisture contents on dry basis

MR

Moisture ratio

R

Gas constant, kJ/mol K

R²

Coefficient of determination

RMSE

Root mean square error

Author contributions

NK–Planning of experiment, data interpretation, analysis, writing, overall guidance. KS–Execution of experiments, data collection, writing. NS–Helped conducting the experimentation.

Funding

Authors are thankful to the Anand Agricultural University, Anand for providing facilities to conduct the research.

Availability of data and material

The data and other material are available on the request from other researchers.

Code availability

Not applicable.

Declarations

Conflict of interest

Authors declare that there is no conflict of interest or competing interests for the submitted research article.

Consent to participate

All authors are agreed for the participation in the article.

Consent for publication

Not applicable (All the data, tables and figures are original).

Ethical approval

Approval for publishing the manuscript has been accorded from the competent authority.