Introduction

The intake of edible ferns has increased significantly in everyday routine due to its wide range of beneficial properties. Dhekia (Diplazium esculentum), a wild edible rhizomatous fern from Athyriaceae family, is possibly the most consumed fern among tribal communities of Northeastern India. In a variety of local cuisines, the tender young shoots are used in stir-fries and salads (Junejo et al. 2018; Archana et al. 2012). Dhekia is the name locally used to refer Diplazium esculentum fiddleheads in Assam, Northeastern Region (NER) of India (Saikia et al. 2022a, b; Srivastava and Rao 2019). The fern is said to have a variety of therapeutic benefits like antioxidant, anti-inflammatory, anticarcinogenic, and hepatoprotective properties, as well as anti-diabetic and wound healing properties some of which have been scientifically proven (Koniyo et al. 2021). Wild leafy vegetables like Colocasia esculenta (L) Schott (family: Araceae), Derringia amaranthoides (Lamk) Merr (family: Amaranthaceae), and Talinum triangulare (Jacq.) Willd (family: Portulacaceae) are still underutilized due to a lack of awareness and engagement of technology aspects for their proper utilization (Nakhuru et al. 2021). Dhekia’s significant nutrient composition makes it a good choice for inclusion in food or nutritional supplement programs. Due to their high moisture level, most cash crops are highly perishable and cannot be preserved beyond their harvesting season. As a result, to extend the life of self-storage, drying is required (Nayak et al. 2018).

The process of reducing moisture in any substance until it reaches an acceptable moisture content level is termed as drying. It is a complicated process that involves uneven heat and mass transfer, along with physical and chemical transformations that may affect product quality (Arunsandeep et al. 2018). Sun drying has been used as a method of food preservation since ancient time. Unfortunately, it has various drawbacks, such as being subjected to rainfall and air, besides being contaminated and powdered by bugs or other creatures, leading in losses and produce of low-quality products (Vijayan et al. 2016).

Solar energy is ideal for drying of agricultural produce owing to its long-term sustainability especially for remote regions where power is limited or inconsistent (Lakshmi et al. 2018). Because of superior control over the quality of dried items, solar drying is preferable to traditional sun drying. In indirect solar dryers, solar radiation is employed to heat the air, thereby absorbing moisture from the materials being dried. An indirect solar dryer with good design does have a significant drying rate and produces excellent products. Development of such a dryer will cost more compared to regular solar dryers, but will cost less than electricity-powered convective dryers (El-Sebaii and Shalaby 2017).

Depending on continuous solar energy is not possible because of non-uniformity of solar radiation, and thus, surplus solar energy should be saved and used during evening hours adopting collecting approaches. Widely used sensible heat storage materials (SHM) include gravel, concrete, fire bricks, and calcareous stones. The high density and temperature variations of SHM make them unsuitable for use in solar dryers. Since, latent heat storage materials are commonly available and economical; they may be employed to store thermal energy efficiently (Sharma et al. 2009). In addition, latent heat storage materials have a wide range of applications in solar dryers for agro-product drying. The widely used storage material is paraffin wax; however, wax has a low thermal conductivity which is further resolved by employing wax in aluminum tubes (Krishnan and Sivaraman 2017). While drying of several food samples in a solar dryer employing phase changing material (PCM) as a storage medium, the drying chamber temperature was reported to reach 20–50% higher temperature than the ambient temperature. Also, at a constant airflow of 0.6 m/s, the PCM-assisted solar dryer is reported to discharge 23% higher time (Agarwal and Sarviya 2016). One study found that while drying of sliced black turmeric in a forced convection solar dryer (assisted with PCM), the drying time was shortened by 60 to 70 percent particularly in comparison to open sun drying (OSD) (Lakshmi et al. 2018).

Exergy study of a solar dryer offers extensive data on how thermal energy is used within the dryer and estimates the heat that is not used. A number of different approaches on the energetic and exergetic evaluations of solar drying of various farm produce were conducted, such as star fruit (Kondareddy et al. 2022), elephant apple (Kondareddy et al. 2022), rosemary leaves (Karami et al. 2021), valerian rhizomes (Bhardwaj et al. 2021), black pepper (Lakshmi et al. 2021), blood fruit (Kondareddy et al. 2021), stevia leaves (Lakshmi et al. 2019), black turmeric (Lakshmi et al. 2018), ghost chilli pepper and ginger (Rabha et al. 2017), Jackfruit leather (Chowdhury et al. 2011), and mulberry (Akbulut and Durmuş, 2010).

To acquire specific variables that affect the dried material, the drying kinetics must be carried out. The drying parameters, the type of solar dryers used, and the physical characteristics of the food samples all have an impact on the drying kinetics. It helps researchers find the best drying operational settings and situational factors. Previous studies offered different empirical models to select the most convenient model for specific food products such as two-term exponential (Krishnan et al. 2020; Özdemir and Devres 1999), modified page (White et al. 1981), Wang and Singh (Kaleta and Górnicki 2010), and Weibull (Puente-Díaz et al. 2013) methods. However, most exergetic analyses have focused on the drying of fresh produce, but so far, no extensive experimental study on drying of dhekia is reported. Therefore, the present study is focused on dhekia dried in a modified indirect thermal storage integrated solar dryer (SDTS) and exploring its drying behavior and kinetics.

The current research is focused on developing an economical thermal storage integrated solar dryer for drying seamlessly. Integration of thermal storage helps uniform drying of dhekia over sun drying. Furthermore, a comparison of the quality of dhekia (in terms of nutritional analysis, bioactive compounds, and rehydration study) dried in modified indirect thermal storage integrated solar dryer (SDTS), open sun drying (OSD), and tray drying (TD) was performed.

Materials and methods

Materials

Dhekia was procured from Kokrajhar’s local market (Assam, India). They were graded based on their size and color. For the drying study, dark green fiddleheads with more foliage were selected. For determining the moisture content (MC) of fresh samples, dhekia was dehydrated in a hot air oven for 3 hours at 105 °C (AOAC 1984). Wet basis moisture content (MC) of newly collected dhekia fern was found to be 80.6 ± 1.22%.

Solar dryer design

The designed SDTS is depicted schematically in Fig. 1. The experimental set up consists of four major components: (i) solar air collector, (ii) drying chamber (with two drying trays), (iii) PCM thermal storage unit for storing the solar energy throughout daytime so as to utilize it after sunset, and (iv) controller section chamber where all the control equipment was placed.

Fig. 1
figure 1

Schematic (with details specifications) of thermal storage integrated solar dryer (SDTS)

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Solar collector design

The solar collector is made up of a finned absorber (painted in black), a transparent glass, insulation, and a frame. Fresh air was routed through air duct under the absorber, which was formed of aluminum sheet. The detailed measurement specification of SDTS was shown in Fig. 1. The primary collector is 152.4 cm in length, 80 cm in breadth, and 10 cm in height. The basic structure includes galvanized iron (GI) sheets with a thickness of 2 mm and iron bars to support the collector. The base plate and a rectangular partition in the collecting chamber were made of 0.5-mm-thick aluminum sheet. An optimal air gap between the glass and base plate is 150 mm. To increase thermal efficiency, ambient air was drawn into the collector and routed along the zig-zag channel (Saikia et al. 2022a, b). A flange section was supplied at the collector’s top end (air output) to attach the flexible connector with nuts and bolts. The polystyrene of 2-cm thickness with thermal conductivity of 0.033 W/(mK) was used as a sealing material to make the air duct thermal leak-proof. Wire mesh was installed on the collector’s input side to keep insects out of the dryer. Since the collector was intended for adjustable inclination, a flexible connector built of special fiber-reinforced plastic material is provided to connect the collecting chamber to the thermal storage and plenum chamber (Pangavhane et al. 2002). To increase the connector’s durability, two layers of fiber-reinforced plastic material were attached among two rectangular cross-sectional ducts with outer flanges made for connectors’ inlet and outlet ends. The collector tilt angle is 21° toward the south from the horizontal stage to receive the optimal reception of solar radiation.

To obtain the optimal hot air flow rate of 0.65 m/s to the storage unit and dryer, a tiny fan (12 V blower fan) was installed in the solar collector’s outer duct. A lightweight EE671 air velocity probe with pre-installed cable was used to measure the airflow velocity in the collector. Two resistance temperature (RTD) sensors were placed both inlet (to measure ambinet temeperature) and outlet of the collector, respectively (Fig. 2). The ACE 2007, 24 V DC data acquisition system is a Wi-Fi current module (24 V DC Wi-Fi DAQ) that is capable of taking four analog inputs and control up to two actuators/analog output. The analog input–output current range is from 4 to 20 mA.

Fig. 2
figure 2

Different parts of the SDTS (loaded condition)

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Energy storage unit

Dryers utilizing heat storage substances are especially beneficial for drying fresh farm produce at relatively stable temperatures of around 40–60 °C. Paraffin (P48-50) is a phase-changing material that absorbs latent heat in its crystalline form (Saikia et al. 2022a, b; Mugi and Chandramohan 2021; Kondareddy et al. 2020). The paraffin wax accumulates heat throughout the day and releases it at night. It plays an important role toward energy sustainability by limiting the existing demand on the energy supply.

The wax used in this system has melting point of 58–60 °C, heat of fusion of 180 kJ/kg, thermal conductivity of 0.59 W/m K, volume expansion of 8–9%, and density of 900 kg/m3 with specific heat of 2.45 kJ/kg K. In this study, the thermal storage unit was attached just below the drying unit (60 cm width and 20 cm height) where paraffin wax is packed in 110 aluminum tubes (2-inch size with each capacity of 50 ml). Two RTD sensors were installed to monitor the temperature of the thermal storage unit and to record data.

Drying unit

The drying unit consists of three perforated stainless steel trays, each having a load capacity of 5.5 kg, with a vertical space of 15 cm separating three successive trays (Fig. 2). The hot humid air that was discharged during drying is vented outside via an air duct that was installed on top of the drying chamber measuring 20 cm in height and 15 cm in width. For measuring the weight of the dhekia sample, a load cell of range 0–20 kg with analog signal output 4–20 mA connected with a 24v SV8 digital meter was used. Humidity cum temperature transmitter (MASIBUS HT7S11; 0–100% RH; 0–50 °C) was fitted at the top the plenum chamber. Figure 2 depicts the various components of the designed SDTS dryer.

Experimental procedure

The tests started at 8 a.m. in the developed dryer. To attain stable equilibrium, the setup was run for an hour without load. After reaching a steady condition, the product was placed into the dryer. The SDTS was located in front of the PG Boy’s Hostel, CIT Kokrajhar, Assam, India, at 90°18′ east and 26°28′ north. The drying tests were performed in a fully sunny day during the final week of March 2021. Using a four-channel k-type thermocouple, input and output temperature of solar collector, and the drying chamber, was monitored during the experiments. Based on the measured temperatures of air (in wet basis and dry basis; sample data are calculated to the moisture condition of the fern after being allowed to air-dry at room condition humidity), the different psychrometric characteristics of air (enthalpy, relative humidity and specific humidity) were evaluated and listed in Table 1. A pyranometer (LPPYRA10, Delta ohm) was used for recording solar insolation on the collector’s surface. The flow rate of air at the solar collector intake was measured using an anemometer make Metravi AVM-10 (model), and the accuracy is ± 0.065 m/s). A data logger was employed to store and record all readings periodically using all of the thermocouples and the pyranometer. The collected results were then used for analysis of energy and exergy. In addition, with the thermal storage integrated solar dryer (SDTS), drying experiments were carried out with sun drying (OSD) and tray drying (TD), respectively. A detailed flowchart of experiments has been presented in Fig. 3.

Table 1 Psychrometric correlations for energy and exergy analysis
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Fig. 3
figure 3

Dhekia fern flowchart for various drying approaches

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Mathematical model of developed SDTS

Mathematical model for the dryer was developed by taking the collector and drying chamber into consideration (Vásquez et al. 2019). As shown in the collector schematic in Fig. 4, the dynamic characteristics of glass cover temperature \({T}_{g}\), absorber plate temp (at the base) \({T}_{b}\), and circulating air temp \({T}_{ca}\) were used to examine the heat transfer within the collector. The dynamics of the circulating air in collector \({T}_{ca}\) were by convective heat transfer between glass surface to air and bottom absorbing plate to air. The collector’s inner area is \({A}_{(b,g)}\), and the collector’s length from air intake to the outlet is x, which is designated by a longitudinal length of dx. The collector’s energy balance equation was represented in Eq. (1).

Fig. 4
figure 4

Diagram of energy flow and heat transfer from the collector to the plenum chamber

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$${\rho }_{ca}{A}_{g}{\delta }_{sp}{C}_{ca}\frac{{\partial T}_{ca,x}}{\partial t}+{\beta F}_{ca}{C}_{ca}\frac{{\partial T}_{ca}}{\partial x}={A}_{g}{h}_{c,g ca}\left({T}_{g}-{T}_{ca}\right)+{A}_{b,g}{h}_{c,b ca}({T}_{b}-{T}_{ca})$$
(1)

Equation (2) depicts the dynamic functioning of the thermal storage unit, which is regulated by variables such as circulating air temperature \({T}_{ca}\), glass cover temperature \({T}_{g}\), and paraffin wax temperature \({T}_{w}\). The temperature of the circulating air inside the thermal storage was taken into account in this equation using position index x, when x is unity, the \({T}_{\left(ca,0\right)}\) is equal to \({T}_{a}\). The temperature of the circulating air inside the storage unit was determined using Eq. (2) and \({h}_{c,pw f}\) is the coefficient of convective heat transfer from wax to the circulating air. Cylindrical tubes were filled with 100 g of paraffin wax.

$${\rho }_{ca}{A}_{g}{\delta }_{su}{C}_{ca}\frac{{\partial T}_{ca ,x}}{\partial t}={F}_{ca}{C}_{ca}\left({T}_{ca ,0}-{T}_{ca ,x}\right)+{A}_{g}{h}_{c ,g ca}\left({T}_{g}-{T}_{ca ,x}\right)+{A}_{b , su }{h}_{c ,b ca}\left({T}_{b}-{T}_{ca ,x}\right)+ {A}_{w}{h}_{c,pw f}({T}_{pw ,x}-{T}_{ca , x})$$
(2)

The drying chamber’s mathematical model explains the dynamics with known parameters values like inlet and outlet temperatures and humidity in the drying chamber (\({T}_{di}\),\({T}_{do}, {H}_{di}\), \({H}_{do,}\) respectively). The ideal drying chamber circulating air velocity is \({V}_{d}\), \(T_{\mathrm p'}\) is the dried dhekia temperature, and the coefficient of convective heat transfer to the dhekia form circulating hot air was \({h}_{(\mathrm{c},\mathrm{pf})}\) in the chamber, were all taken into consideration in Eq. (2) (Saikia et al. 2022a, b).

The temperature at drying chamber outlet was determined with the following equation (3) (Sami et al. 2011).

$$\rho_dV_d\left(C_d+C_vH_{do}\right)\frac{{dT}_{di}}{dt}=F\left(C_d+C_vH_{di}\right)T_{do}-F\left(C_d+C_vH_{do}\right)T_{di}+A_ph_{c,pf}(T_{di}-T_{p'})$$
(3)

The temperature in the drying chamber was determined using both experimental and simulated data (Zoukit et al. 2019).

SDTS energy analysis

The moisture from the fresh product is reduced during the solar drying process by using thermal energy supplied by the solar thermal collector. The collector functions as a heat exchanger absorbs solar energy and transferred this energy to the working fluid (ambient air). Equations (4), (5), and (6) were used to determine the amount of energy necessary to remove moisture from dhekia in the proposed SDTS. The mass balance of air in the drying chamber is calculated using Eq. (4).

$${\mathrm{\Sigma m}}_{ai}={\mathrm{\Sigma m}}_{ao}$$
(4)

The moisture mass balance while drying is expressed in Eq. (5).

$${\Sigma (\mathrm{m}}_{wi}+{\mathrm{m}}_{m})={\mathrm{\Sigma m}}_{wo}$$
(5)

The ambient air was considered to be similar to the air entering the solar collector. The difference in the amount of radiation received and the amount of thermal energy dissipated by the collector was used to quantify the energy utilized.

Equation (6) was used to calculate the quantity of thermal heat gained by the solar collector (Fig. 5) from the atmospheric air.

Fig. 5
figure 5

Schematic diagram for flow of energy in SDTS

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$${Q}_{u}={m}_{ai}{C}_{{p}_{ai}}({T}_{ao}-{T}_{amb})$$
(6)

Equation (7) was used to estimate the quantity of heat delivered to the product within the drying chamber.

$${Q}_{d}={m}_{a}({h}_{di}-{h}_{do})$$
(7)

SDTS exergy analysis

The quantity of energy accessible and converted into work is referred to as exergy. Exergy evaluation at the dryer inlet and exit, along with exergy loss, were obtained by second law of thermodynamics. The evaluation of exergy readings at different phases of the process is the focus of exergy analysis.

Input exergy, exergy loss (\({\mathrm{Ex}}_{\mathrm{loss}}\)) and output exergy and exergy efficiency are estimated using Eqs. (8) and (9), respectively.

$${Ex}_{loss}={Ex}_{in}-{Ex}_{out}$$
(8)
$${E}_{x,in}={m}_{ai}{C}_{pai}\left[\left({T}_{id}-{T}_{amb}\right)-{T}_{amb}ln\frac{{T}_{id}}{{T}_{amb}}\right]$$
(8a)
$${E}_{x,out}={m}_{ai}{C}_{Pai}\left[\left({T}_{od}-{T}_{amb}\right)-{T}_{amb}ln\frac{{T}_{od}}{{T}_{amb}}\right]$$
(8b)
$${Ex}_{eff}=1-\frac{{Ex}_{loss}}{{Ex}_{in}}$$
(9)

Mathematical modeling

The moisture levels of dhekia after procuring (Minitial) and after drying (Mfinal) are calculated on a dry basis (db).

$${M}_{initial}=\frac{{m}_{initial}-{m}_{d}}{{m}_{d}}; {M}_{final}=\frac{{m}_{sd}-{m}_{d}}{{m}_{d}}; {M}_{time}=\frac{{m}_{ts}-{m}_{d}}{{m}_{d}}$$
(10)

where md represents mass of dhekia while drying, msd represents mass of dhekia dried in SDTS, and Mtime represents the moisture level of dhekia at time ts.

Moisture ratio (MR) of dhekia was estimated using Eq. (11):

$$MR=\frac{\left({M}_{t}-{M}_{e}\right)}{\left({M}_{0}-{M}_{e}\right)}$$
(11)

The above equation applicable for uniform relative humidity condition, in case of solar drying relative humidity fluctuates over drying time. The dimensionless MR expressed for solar drying (Eq. (12)) is as follows (Lakshmi et al. 2018):

$$MR=\frac{\left({M}_{t}\right)}{\left({M}_{0}\right)}$$
(12)

Drying is a continuous mass and heat transfer process that takes place over time. Several quasi-theoretical and empirical drying kinetics models have been proposed in the previous studies to estimate the drying behavior of agriculture products (Karthikeyan and Murugavelh 2018; Nukulwar and Tungikar 2021). The optimal drying curve equations for all cases of dhekia were found considering ten different models (Table 2). The first eight models mentioned in Table 2 are widely used in the literature on food drying using solar (Kondareddy et al. 2020; Krishnan et al. 2020; Lakshmi et al. 2019; Kumar et al. 2017). The last two models (Nos. 9 and 10) are provided for the first time in this study as proposed model 1 and proposed model 2. In all of these approaches that include drying time as a variable, the influence of drying conditions is represented by the drying constants (n and a, b, and c). The rate constant (min−1) is measured by the coefficient k, while time is measured by t (min). MINITAB 20.2.0 was used for conducting regression analysis. To define finest kinetic model for drying process, R2, χ2, and RMSE values have been used and expressed in Eqs. (13), (14), and (15).

Table 2 Different kinetic models fitted in thin layer drying of dhekia fern
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$${x}^{2}=\frac{\sum_{i=1}^{N}{\left({MR}_{exp,i}-{MR}_{pr,i}\right)}^{2}}{N-n}$$
(13)
$${R}^{2}=1-\frac{\sum_{i=1}^{N}{\left({MR}_{exp,i}-{MR}_{pr,i}\right)}^{2}}{\sum_{i=1}^{N}{\left({MR}_{avg pr}-{MR}_{exp,i}\right)}^{2}}$$
(14)
$$RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}{\left({MR}_{exp,i}-{MR}_{pr,i}\right)}^{2}}$$
(15)

Quality analysis

The following processes and methods were used to determine the nutritional and biochemical characteristics of the fresh and dried samples. The tests were all performed in triplicate.

Estimation of protein

The protein analysis was carried out using the Kjeldahl method and represented as a percentage of gross nitrogen (AOAC 1984).

Estimation of lipid

The Soxhlet method was used to calculate lipid content and expressed in terms of percentage (Lakshmi et al. 2018).

Estimation of ash content

The AOAC (1984) technique was used to estimate the ash content. The ash’s weight was calculated as a percentage of its total weight.

Estimation of carbohydrate

Carbohydrate was estimated by hydrolysing it with sulfuric acid into simple sugars and then using the anthrone technique to demonstrate the resulting monosaccharides (Lakshmi et al. 2019).

Total phenolic content

Nayak et al. (2017) described the Folin–Ciocalteu technique for determining TPC.

Total flavonoid content

Nayak et al. (2018) proposed an approach to determine the flavonoid content of dried materials. Flavonoids for samples were measured in mg CE/ml of extract.

Antioxidant activity

Demir’s approach was used for determining antioxidant activity of dried dhekia (Saikia et al. 2022b; Demir and Korukluoglu 2020).

Rehydration experiments

Rehydration studies for dried materials (OSD, TD, and SDTS) were conducted in a distilled water bath at 40 °C (± 2 °C). In a 500-ml beaker, dried dhekia of 1 g was mixed to 200 ml distilled water for 5–15 min. Before weighing, the sample was carefully taken by blotting on tissue paper. The dried and rehydrated samples were weighed using a digital electronic balance with an accuracy of 0.001 g. The following equation was used to determine the rehydration ratio (RR) (Ranganna 1986).

$${R}_{R}= \frac{{W}_{1}}{{W}_{2}}$$
(16)

where W1 is the weight of the dehydrated dhekia fern taken for rehydration, g and W2 is the drained weight of the rehydrated dhekia fern, g.

The coefficient of rehydration was calculated using the following equation:

$$CR= \frac{{W}_{2}\times [100-{M}_{1}]}{\left[{W}_{1}-{M}_{f}\right]X100}$$
(17)

where Mf is the moisture content of dried dhekia before rehydration, g and M1 is the moisture content of fresh sample before drying percentage.

Uncertainty analysis

The devices used to assess temperature, air velocity, sun intensity, and sample weight all have their own inaccuracies, resulting in some uncertainty in the estimated parameters’ readings. The efficiency of the dryer and solar heater can be estimated using the method described below (Kline and McClintock 1953). Equation (18) is used to compute the overall uncertainty in the dryer efficiency.

$${n}_{IFSCD}=f\left({m}_{w,}I,{A}_{s,},t\right)$$
(18)
$$\Delta {n}_{IFSCD}=\pm {\left[{\left(\frac{\partial n}{\partial {m}_{\omega }}\Delta {m}_{w}\right)}^{2}+{\left(\frac{\partial n}{\partial I}\Delta I\right)}^{2}+{\left(\frac{\partial n}{\partial {A}_{s}}\Delta {A}_{s1}\right)}^{2}+{\left(\frac{\partial n}{\partial t}\Delta t\right)}^{2}\right]}^\frac{1}{2}$$
(19)

The maximum uncertainty was estimated to be ± 3.2% and ± 1.2% for the solar heater and dryer, respectively.

Statistical analysis

Following three repetitions of the drying tests and analytical investigations, the data was calculated by MS Excel and statistical software (SPSS 19.0 trial version). A one-way ANOVA test was conducted on the data, with a statistical significance value of p < 0.05.

Results and discussion

SDTS performance characteristics

On a sunny day of 27th March 2021, the experiments were conducted. The performance parameters heat transfer fluid temperature, atmospheric temperature, and air temperature inside the drying chamber were measured and depicted in Fig. 6. From 8 a.m. to 5 p.m., solar radiation levels ranged from 300 to 950 W/m2. A robust controller controls the airflow velocity inside drying chamber to monitor the temperature with a ± 1 °C precision range. With selected control action, the ideal range of circulating air velocity inside drying chamber has been kept at 0.5–0.7 m/s. The ambient temperature ranged from 28 to 31 °C. The temperature within the drying chamber rises from 10:00 to 14:00 h, then falls due to solar insolation depletion until 4.30 p.m., and from 4:30 p.m. to 8 p.m., thermal storage delivers 29.16% more stable process temperature in the dryer. At 20:00 h, the temperature within the solar dryer with thermal storage was 44.3 °C, while the temperature within the solar dryer without thermal storage was 32.5 °C, as shown in Fig. 6.

Fig. 6
figure 6

Performance parameters of solar dryer with and without thermal energy storage

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Energy analysis of SDTS

Figure 7 shows how energy utilization as a function of drying time varies within the solar collector and drying chamber. Based on collector’s inlet and outlet temperatures, the quantity of usable energy from the solar collector was calculated using Eq. (6). The amount of useable energy obtained by the collector from solar radiation ranged from 0.07 to 0.63 kW. This thermal energy was supplied to drying chamber and thermal storage. The energy supplied to the product with and without integration of PCM was depicted in Fig. 7. In comparison to a simple solar dryer without a thermal storage, the dryer equipped with a thermal storage constantly supplied heat energy to the product after sunset hours, as shown in Fig. 7.

Fig. 7
figure 7

Energy utilization of the SDTS

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Reflection loss accounted for around 7 percent of the input solar energy that was lost or wasted because of the glass cover. Furthermore, the glass cover absorbed 6 percent of the input energy, enabling the remaining energy to travel through the glass. Furthermore, the absorber plate reflected 5 ± 0.4 percent of the received solar radiation, while the absorber plate absorbed a large portion of the received solar radiation, around 85 percent.

Moisture content of dhekia inside the chamber decreased from 80.6 to 8.2% (wb) in 5.5 h, corresponding to energy usage ranges of 7.45–72.99 W. The rate of energy consumption varies during the day, increasing in the afternoon (about 2 PM) when the received radiation on the collector is at its peak. The temperature inside the collector fluctuated linearly with the intensity and exposure to solar radiation, since the inlet temperature is influenced by ambient temperature. In another study, Rani and Tripathy (2021) reached similar conclusions, establishing the performance evaluation of a solar concentrator with flat plate using the first and second laws of thermodynamics. This study demonstrates that solar radiation and design of the collector influence the performance of a solar collector.

Exergy analysis of SDTS

Exergy analysis helps to estimate the losses occurring in the solar dryer. The usable energy delivered from collecting chamber to the drying unit is around 62.89 MJ, with 0.221 MJ energy wasted. Circulating air inside the dryer consumes 2.29 kw more energy during the drying process from 08:00 to 20:00 h with 62.8 °C average temperature and 9.4% relative humidity. The exact rate of moisture extraction throughout the drying process was 0.419 kg/kWh. Figure 8 shows the exergy analysis of SDTS in respect to exergy efficiency and loss. Efficiency of the dryer from 10:00 to 17:00 h is around 66.2%.

Fig. 8
figure 8

Exergy efficiency and exergy loss of SDTS

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Drying tests were conducted using three different methods, specifically OSD, TD, and SDTS. The fern was dried from initial moisture content of 75.55 ± 2% to 3.80 ± 0.5% (Fig. 9). The moisture content was found to decrease gradually as the drying time increased, with SDTS requiring a much shorter duration of 330 min while also maintaining its quality. Weight loss in dhekia, while drying in SDTS was initially quicker owing to unbound moisture on the surface, however reduced subsequently due to internal moisture transfer from the interior to the surface, following a period of falling rate (Lakshmi et al. 2021). In the context of OSD, the drying rate was too slow on the second day, as predicted by Lakshmi et al. 2019. Rahman et al. (2016) and Zomorodian and Moradi (2010) reported comparable results while drying of mushroom and cumin using solar energy.

Fig. 9
figure 9

Moisture content (w.b.) of dhekia dried by varying methods as a function of drying time (min)

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Evaluation of drying models

The drying parameters and R2, χ2, and RMSE values of dhekia fern are shown in Table 3. When the results of dhekia dried in trays were analyzed, the two-term exponential model with regard to the two variables utilized in mathematical modeling provided the best match, with the greatest R2 (0.9991) and the lowest RMSE (0.0021). The top three models proposed model 1, proposed model 2, and two-term exponential model) actually have R2 values that are all more than 0.999, showing that the models are perfectly matched (Table 3). Meanwhile, the proposed model 2 had the greatest R2 (0.9996) and the lowest RMSE when the SDTS drying data of the fern were investigated (0.0019). In summary, the moisture ratio is greatly overestimated by this recently suggested model.

Table 3 Comparison of the best models (top 5) for various drying processes and drying coefficients (constants)
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Quality analysis of dried dhekia

Effect of different drying processes on the proximate profile

Proximate analysis was carried out for fresh, SDTS, TD, and OSD dried dhekia. The dried fern’s considerable drop in macronutrient content (Table 4) might be attributable to the stability of the macronutrient linkages. Because of the dryers’ efficiency, the drop in protein content was found to be proportional to the amount of heat used. The observed reduction might be related to the dryers’ capacity to concentrate energy, which could result in some protein denaturation in dried samples. The application of heat causes significant nutritional losses during drying, lowering the content of protein, fat, ash, and carbohydrate (Morris et al. 2004). The carbohydrate content ranged between 76.29 ± 6.11and 59.39 ± 5.22 with SDTS dried having the highest value. The decrease in carbohydrate value after drying might be attributed to microflora using the carbon skeleton for nutrition synthesis. Carbohydrates aid fat metabolism and provide an alternative energy source for humans (Gordon 2000). The apparent rise in fat and ash content found in this study might be attributed to the elimination of moisture, which tends to raise nutrient concentrations and increased nutritional density of products, with ash being one of the nutrients. Amaranth (Amin et al. 2021) and drumstick leaves (Deepa and Kattimani 2021) were dried with similar results.

Table 4 Proximate analysis of dhekia fern dried using various methods
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Effect of different drying processes on TPC, TFC, and antioxidant activity

Biochemical analysis was carried out on OSD, TD, and SDTS samples and were evaluated for their antioxidant activities and TPC and TFC values. Fresh samples had an antioxidant activity of 25.55 ± 3.12 µ mol of TE/gm of sample. In all cases, the antioxidant activity reduced considerably, and this was also noticed during drying of turmeric slices (Lakshmi et al. 2019). As demonstrated in Fig. 10, the TPC and TFC values decreased significantly during drying. This result could be attributed to bound phenolic compounds disengaging from cellular components and releasing free phenolic compounds (Saikia et al. 2022a; Krishnan et al. 2020).

Fig. 10
figure 10

TPC, TFC, and antioxidant activity as a function of drying techniques. Values with different letters in the (fresh samples to SDTS) (a–c) are significantly different (p < 0.05) from each other. Values with different letters in the same quality parameters (column) (p–r) are significantly different (p < 0.05) from each other. Results were presented as means ± standard deviation of three replicates

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Effect of drying on rehydration characteristics of dhekia

The rehydration characteristics of dhekia fern samples were analyzed in terms of their ability to regain original shape. This characteristic is expressed in the form of rehydration ratio (RR) and coefficient of rehydration (CR) in rehydrated samples. The rehydration ratio increased as the drying air temperature decreased (Table 5). The lower rehydration ratio in case of SDTS may probably be due to cellular breakdown of the fern during drying (Bishnoi et al. 2020). Rehydration ratio increased in the range of 6 to 9.5 percent with time of rehydration from 5 to 15 min for all the drying methods. A similar trend like RR was noticed with maximum CR (0.806) for TD. Thus, it clearly indicated that drying methods affects the rehydration ratio and coefficient of rehydration.

Table 5 Experimental data on rehydration ratio
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Conclusion

The current research focused on the development of low-cost thermal storage integrated solar dryer (SDTS). The following conclusions of the work has been drawn as follows:

  • The temperature inside the SDTS ranged from 44 to 73 ºC whereas temperature in the solar dryer without thermal storage ranged from 32 to 62 ºC, respectively.

  • The product moisture content was reduced from 75.55 ± 2.01 percent to 3.80 ± 0.51 in 5.5 h in solar dryer with TES and in solar dryer without TES in 7.5 h drying time.

  • The developed drying kinetic models suits best for dhekia dried in SDTS.

  • Exergy efficiency of the SDTS was estimated to be ranging between 35 and 75%, respectively.

  • Quality analysis was performed for fresh dhekia and dried dhekia. Antioxidant activity for the fresh samples, samples dried in SDTS and OSD were 25.55, 19.55, and 10.99 µ mol of TE/gm of sample, respectively.

  • Developed solar dryer with TES benefits rural community of North Eastern India.