Mathematical Modeling of Drying Kinetics and Technological and Chemical Properties of Pereskia sp. Leaf Powders

Abstract

This study aimed to assess the effects of convective drying at different temperatures (50, 60, and 70 °C) on the technological and chemical properties of Pereskia sp. leaf powders and to identify the most accurate mathematical model for describing their drying kinetics. Drying kinetics were modeled using four mathematical models: Henderson and Pabis, Lewis, Logarithmic, and Page. The Page and Logarithmic models provided the best fit for the drying kinetics of both species, with high coefficients of determination (R² > 0.98) and low MSE and χ² values, indicating their suitability for describing the drying behavior of Pereskia leaves. Enthalpy and entropy decreased with increasing temperature, while Gibbs free energy increased, and effective diffusivity was not affected by temperature. These changes directly affected the powders’ color, density, compressibility, wettability, water activity, chlorophyll, and bioactive components, including carotenoids, proteins, and phenolics. Notably, P. grandifolia powders retained higher levels of ash, protein, and lipids, indicating greater nutritional value, while P. aculeata powders exhibited higher solubility and lower water activity, suggesting superior technological properties for industrial applications. The findings highlight the potential of Pereskia species as functional ingredients in food products, with implications for optimizing drying processes to enhance both nutritional and industrial value.

1. Introduction

Plants from the genus Pereskia of the Cactaceae family are relatively understudied and less characterized compared to other genera. Commonly found in countries like Brazil and Mexico, Pereskia species are woody vine shrubs with mucilaginous leaves, varying in diameter, thorn arrangement, and flower colors [1]. Beyond their culinary applications, these plants are also used in traditional medicine to treat various ailments [2].

Pereskia aculeata, popularly known as “ora-pro-nóbis”, is a non-conventional plant rich in protein, earning it the nickname “poor man’s meat”. Its nutritional profile, which includes minerals, proteins, essential amino acids, fibers, and pigments, positions it as a promising candidate for human nutrition [3]. The use of non-conventional plants can lead to the development of new biofunctional foods with health benefits [4].

The hydrocolloids formed by polysaccharides and proteins in Pereskias leaves can provide desirable characteristics when applied in food preparation, including stability, emulsification, nanoemulsification, and the formation of biodegradable films [5]. These powders have also been used in the preparation of cakes, breads, and pasta, expanding their possible applications in the food industry [6]. Additionally, Pereskias powders act as functional ingredients in food formulations, enhancing nutritional value while contributing to preservation properties [7].

Fresh leafy vegetables are highly susceptible to post-harvest deterioration, especially those with high water content, which provides ideal conditions for microbial growth and accelerates degradation and senescence. Post-harvest conservation techniques, such as drying, have long been utilized to increase the physicochemical stability of foods by concentrating nutrients and removing free water through thermodynamic processes and unit operations [8]. Dehydrated products not only contribute to product stability but also facilitate logistical operations by yielding lighter, easily transportable, and storable items [9].

The control of thermodynamic processes and unit operations during the dehydration stage directly influences the quality of the final product, determining its shape, size, density, porosity, yield, mass recovery, pigmentation, and bioactive component retention [10]. In this context, selecting the appropriate mathematical model to describe the drying kinetics is essential for predicting the drying behavior and ensuring the quality of the dried product [11]. Specifically, this study employed the Henderson and Pabis, Lewis, Logarithmic, and Page models with the hypothesis that at least one of them would provide a superior description of the drying kinetics at the temperatures studied. Additionally, it is crucial to understand how different drying temperatures affect the preservation of bioactive compounds, such as carotenoids and phenolics, as well as the overall physicochemical properties of the final product. Thus, we expect that convective drying temperature will significantly influence the preservation of these compounds and properties in P. aculeata and P. grandifolia leaf powders.

The present study aimed to evaluate the effects of convective drying at different temperatures (50, 60, and 70 °C) on the technological and chemical properties of Pereskia sp. leaf powders and to identify the most accurate mathematical model for describing their drying kinetics.

2. Materials and Methods

2.1. Plant Material

The leaves of Pereskia aculeata and Pereskia grandifolia were collected from an experimental area at the Instituto Nacional do Semiárido (INSA), Campina Grande, Paraíba, Brazil. The samples were collected early in the morning, stored in thermal containers, and transported to INSA’s Plant Production Laboratory. The leaves were washed under running water and sanitized in a 50 ppm sodium hypochlorite solution for 15 min, followed by rinsing with potable water. Afterward, the leaves were placed on paper towels to completely remove surface water.

2.2. Convective Drying and Experimental Design

The leaves of each species were placed in rectangular stainless steel trays (12.7 × 8.3 cm) separately, forming a layer 0.4 cm in height. Drying was carried out in an oven with forced air circulation (MA 035/3IN250 Marconi, Piracicaba, Brazil). The trays were weighed on a semi-analytical scale at intervals of 5 min during the first 30 min and then every 10, 15, 30, 60, and 120 min until a constant weight was reached.

A completely randomized design with six treatments and three replications was applied. The treatments combined three drying temperatures (50, 60, and 70 °C) and two types of plant material (P. aculeata and P. grandifolia leaves). Drying temperatures were selected based on the commonly used ranges for agricultural products. Each replication consisted of a tray containing 4 g of fresh leaf. After drying, each sample was crushed in a ball mill (Retsch MM200). The powders obtained were standardized in terms of particle size (60 mesh) and packaged in laminated packaging until the moment of analysis.

2.3. Moisture Ratio

The final moisture content was determined in an oven at 105 °C for 24 h [12]. The moisture ratio (RX) was obtained by converting water loss data into a dimensionless parameter using the equation:

(1)$\mathrm{RX}={\displaystyle \frac{\mathrm{X}-{\mathrm{X}}_{\mathrm{e}}}{{\mathrm{X}}_0-{\mathrm{X}}_{\mathrm{e}}}}$

where RX is the moisture content ratio of the product (dimensionless), X_e is the equilibrium moisture content (dry basis), X is the moisture content (dry basis), and X₀ is the initial moisture content (dry basis) [12,13].

2.4. Mathematical Models

Four empirical equations were selected with up to three adjustable parameters. The equations used in this study are listed in Table 1. The empirical models were fitted to the experimental data using the R software (version 4.4.1) [14].

The criteria for evaluating the best model fit included the coefficient of determination (R²), mean squared error (MSE), and chi-square (χ²).

2.5. Effective Diffusivity

The effective diffusivity or effective diffusion coefficient was determined based on the theory of liquid diffusion in accordance with Fick’s law, considering a geometric shape similar to an infinite flat plate, and using an analytical solution with approximately 9 terms, as demonstrated in Equation (2) [15].

(2)$\frac{8}{{\mathsf{\pi }}^2}{\displaystyle \sum }_{\mathrm{n}=0}^{\infty }\frac{1}{\left(2\mathrm{n}+1\right)}\exp \left[-{\left(2\mathrm{n}+1\right)}^2{\mathsf{\pi }}^2\frac{{\mathrm{D}}_{\mathrm{eff}}}{{\mathrm{L}}^2}\mathrm{t}\right]$

where RX is the moisture content ratio of the product (dimensionless), D_eff is the effective diffusivity (m² s⁻¹), n is the number of terms in the equation, L is the plate thickness (m), and t is the time (s).

The effect of drying temperature on effective diffusivity was evaluated using an Arrhenius-type equation (Equation (3)), which describes the relationship between activation energy and the speed at which the reaction occurs.

(3)${\mathrm{D}}_{\mathrm{eff}}{=\mathrm{D}}_0\exp \left(\frac{{\mathrm{E}}_{\mathrm{a}}}{\mathrm{RT}}\right)$

where D_eff is the effective diffusivity (m² s⁻¹), D₀ is the pre-exponential factor (m² s⁻¹), E_a is the activation energy (J mol⁻¹), R is the universal gas constant (8.314 J mol⁻¹ K⁻¹), and T is the absolute temperature (K).

2.6. Thermodynamic Properties

The thermodynamic properties of the drying process: enthalpy (ΔH), entropy (ΔS), and Gibbs free energy (ΔG) were quantified using Equations (4)–(6), respectively.

(4)${\Delta \mathrm{H}=\mathrm{E}}_{\mathrm{a}}-\mathrm{RT}$

(5)$\Delta \mathrm{S}=\mathrm{R}[\ln ({\mathrm{D}}_0)-\ln (\frac{{\mathrm{K}}_{\mathrm{b}}}{{\mathrm{h}}_{\mathrm{p}}})-\mathrm{lnT}]$

(6)$\Delta \mathrm{G}=\Delta \mathrm{H}-\mathrm{T}\Delta \mathrm{S}$

where ΔH is the enthalpy (J mol⁻¹), E_a is the activation energy (kJ mol⁻¹), R is the universal gas constant (0.008314 kJ mol⁻¹ K⁻¹), T is the absolute temperature (K), ΔS is the entropy (J mol⁻¹), D₀ is the pre-exponential factor (m² s⁻¹), K_b is the Boltzmann constant (1.38 × 10₋₂₃ J K⁻¹), h_p is Planck’s constant (6626 × 10⁻³⁴ J s⁻¹), and ΔG is the Gibbs free energy (J mol⁻¹).

2.7. Physical and Physicochemical Characteristics of Powders

The powders were characterized for parameters such as color, apparent density, compacted density, compressibility index, Hausner ratio, solubility, wettability, water content, water activity, ash content, pH, titratable acidity, chlorophyll, carotenoids, proteins, lipids, phenolics, and antioxidant activity.

The color was determined by directly reading the powders using a spectrophotometer. The determined coordinates were: L* which represents the luminosity, transition from white (0) to black (100), a* which represents the transition from green (−a*) to red (+a*), and b* the transition from the blue color (−b*) to the yellow color (+b*). With the coloring parameters a, b, and L, the darkening index was determined [16].

The apparent density was determined by weighing 6 g of the powder sample in a 10 mL graduated cylinder, without compaction, to determine the total volume occupied by the solid [17,18]. The apparent density was calculated according to Equation (7):

(7)${\mathsf{\rho }}_{\mathrm{a}}={\displaystyle \frac{{\mathrm{M}}_{\mathrm{s}}}{{\mathrm{V}}_{\mathrm{t}}}}$

where ${\mathsf{\rho }}_{\mathrm{a}}$ is the apparent density (g cm⁻³), ${\mathrm{M}}_{\mathrm{s}}$ is the mass of the solid (g), and ${\mathrm{V}}_{\mathrm{t}}$ is the total volume (cm³).

For the determination of compacted density, a sample mass was weighed until a 10 cm³ graduated cylinder was filled. The compacted density was determined from the mass of powder contained in the cylinder after being manually tapped 50 times on a bench surface from a height of 10 cm [19] and calculated according to Equation (8):

(8)${\mathsf{\rho }}_{\mathrm{c}}={\displaystyle \frac{{\mathrm{M}}_{\mathrm{s}}}{{\mathrm{V}}_{\mathrm{c}}}}$

where ${\mathsf{\rho }}_{\mathrm{c}}$ is the compacted density (g cm⁻³), ${\mathrm{M}}_{\mathrm{s}}$ is the mass of the solid (g), and ${\mathrm{V}}_{\mathrm{c}}$ is the volume of the solid after compaction (cm³).

The compressibility index was calculated by comparing the apparent density (${\mathsf{\rho }}_{\mathrm{a}}$) and the compacted density (${\mathsf{\rho }}_{\mathrm{c}}$) of the powder, according to Equation (9) [20]:

(9)$\mathrm{CI}={\displaystyle \frac{{\mathsf{\rho }}_{\mathrm{c}}-{\mathsf{\rho }}_{\mathrm{a}}}{{\mathsf{\rho }}_{\mathrm{c}}}}$

where CI is the compressibility index, ${\mathsf{\rho }}_{\mathrm{c}}$ is the compacted density, and ${\mathsf{\rho }}_{\mathrm{a}}$ is the apparent density.

The Hausner ratio is used to indirectly evaluate the flow properties of powders. From the apparent density (${\mathsf{\rho }}_{\mathrm{a}}$) and the compacted density (${\mathsf{\rho }}_{\mathrm{c}}$), the Hausner ratio was determined [21,22] according to Equation (10):

(10)$\mathrm{HR}={\displaystyle \frac{{\mathsf{\rho }}_{\mathrm{c}}}{{\mathsf{\rho }}_{\mathrm{a}}}}$

where HR is the Hausner ratio (dimensionless), ${\mathsf{\rho }}_{\mathrm{c}}$ is the compacted density (g cm⁻³), and ${\mathsf{\rho }}_{\mathrm{a}}$ is the apparent density (g cm⁻³).

Solubility was determined by adding 0.5 g of the sample to a container containing 50 mL of distilled water, under magnetic stirring at 1000 rpm for 5 min, followed by centrifugation at 2600 rpm for 5 min. A 12.5 mL aliquot of the supernatant was transferred to a pre-weighed Petri dish and subjected to drying in an oven at 105 °C for 24 h [23,24]. Solubility was calculated according to Equation (11):

(11)$\mathrm{S}=\left[\left(\frac{{\mathrm{M}}_{\mathrm{s}}}{{\mathrm{M}}_{\mathrm{a}}}\right)\times 4\right]\times 100$

where S is the solubility (%), ${\mathrm{M}}_{\mathrm{s}}$ is the mass of the solids dissolved in the supernatant (g), and ${\mathrm{M}}_{\mathrm{a}}$ is the mass of the sample (g).

Wettability was determined according to the static wettability method [25]. This method involves gently placing 1 g of sample onto 100 mL of distilled water at 25 °C and visually determining the time required for all particles to become wetted, recording the time with the aid of a stopwatch. Wettability was calculated according to Equation (12):

(12)$\mathrm{W}={\displaystyle \frac{\mathsf{\eta }}{\mathrm{t}}}$

where W is the wettability (g s⁻¹), η is the sample mass (g), and t is the time (s).

The water content was determined by drying the samples in an oven at 105 °C until constant weight [12]. Water activity was determined using the LabMaster equipment with ±0.003 aw accuracy at 25 °C (Novasina, BR). Ash content was determined by incinerating the sample in a muffle furnace heated to 550 °C until a residue free of carbon with a grayish-white color was obtained [12].

Titratable acidity was determined by the titrimetric method, using a 1.0 g aliquot of the sample to which 49.0 mL of distilled water and 3 drops of 1% alcoholic phenolphthalein were added, using 0.1 N sodium hydroxide (NaOH), standardized with potassium biphthalate, as the titrant [12]. The pH was determined using a pH meter model mPA-210P (Tecnopon, Piracicaba–São Paulo, Brazil) [12].

Total chlorophylls and carotenoids were determined by spectrophotometry [26]. A 0.5 g sample was weighed and ground in a mortar with 0.2 g of calcium carbonate and 5 mL of 80% acetone. The extract was transferred to Falcon tubes and centrifuged at 10 °C and 3000 rpm for 10 min. Aliquots of the supernatant were placed in cuvettes, and readings were taken using a spectrophotometer at absorbances of 470, 646, and 663 nm for the determination of carotenoids, chlorophyll a, and b, respectively. All procedures were performed in a dark environment. The contents of chlorophyll and carotenoids were expressed in mg 100 g⁻¹.

The total nitrogen content of the samples was determined by the Kjeldahl method, using a generic conversion factor of 6.25 to convert the quantified content to protein [12]. Lipid quantification was determined using a Soxhlet extractor [27]. The flasks were previously dried in an oven for one hour at 105 °C and cooled in a desiccator. Cartridges were prepared so that 0.5 g of sample were weighed on an analytical balance. Extraction was done using a Soxhlet extractor with petroleum ether. Results were expressed as percentages of lipids.

Total phenolic compounds were estimated using the Folin-Ciocalteu method [28]. About 0.1 g of flour was ground in 3 mL of distilled water, and the volume was adjusted to 100 mL. The extract was filtered through filter paper, and a 500 μL aliquot of the powder extract with 1000 μL of distilled water and 125 μL of Folin-Ciocalteu was mixed and allowed to stand for 5 min. The standard curve was prepared with gallic acid, and readings were taken with a spectrophotometer (model SP-1105) at 765 nm, with results expressed as mg of gallic acid equivalent per 100 g. ABTS radical scavenging activity was determined by the rate of decay in absorbance at 754 nm.

In this procedure, the ABTS solution (2,2-azino-bis(3-ethylbenzthiazoline-6-sulfonic) acid) was diluted with ethanol to an absorbance of 0.700 ± 0.030 [29]. For each sample, the absorbance of the ABTS solution (940 µL) was measured at time t = 0 min.

2.8. Statistical Analysis

The data were subjected to analysis of variance, and the means were compared using the Sidak test, with a significance level of up to 5%. All analyses were performed using the statistical software R 4.4.1 [14].

3. Results and Discussion

3.1. Drying Kinetics

Table 2 presents the data obtained for the parameters of the mathematical models fitted to the drying kinetics of Pereskias species at temperatures of 50, 60, and 70 °C. At the three temperatures studied for the two species of Pereskias, the four applied models showed a satisfactory fit to the experimental data, with coefficients of determination (R²) above 0.98 and low values for the mean squared error (MSE) and chi-square (χ²). However, the Page model demonstrated the best fit to the experimental data at different drying temperatures, with lower values of χ² and MSE compared to the other models. The Page model, which extends the Lewis model by adding an extra parameter (n), provided a more precise adjustment to the drying rate.

In the literature, several studies corroborate the effectiveness of the Page model in describing the drying kinetics of various types of plant materials. For instance, Patel and Panwar [30] reported that the Page model provided the best fit for the drying kinetics of Cucumis callosus, showing high R² values similar to those observed in the study of Pereskia sp. This model was also effective in predicting the drying of avocado skins, as described by Razola-Díaz et al. [31], where the model maintained low χ² and MSE values, which are critical criteria for selecting models that minimize the error between observed and predicted data. Therefore, the Page model, like others, is more suitable for describing the drying behavior of various vegetables [32].

The drying constant (k) was positive across all models, indicating that the leaf temperature during dehydration increased over time relative to the ambient temperature. Except for the Henderson and Pabis model, the others had a higher rate of heat transferred to the leaves at 60 °C, with an increased water migration rate from the interior to the surface. In the drying kinetics of mandacaru, there was an increase in heat transferred to the material [33].

Figure 1 and Figure 2 present the graphs of the Pereskia leaf drying kinetics at temperatures of 50, 60, and 70 °C for the best-studied mathematical models: Henderson and Pabis, Lewis, Page, and Logarithmic.

The models demonstrated suitability in modeling with predicted and observed moisture rates in agreement. Patel and Panwar (2022) [30] highlight that such fitting occurs because the model predicts data that generally cluster around the line, thus allowing for the description of drying kinetics behavior. The authors had a better fit for the drying kinetics of Cucumis callosus (melon) using the Page model. In the studies by Masud et al. [34] on potato drying, the Logarithmic and Page models also had good fits for temperatures of 50 and 60 °C, with values R² > 0.99. Therefore, it is observed that both models can be used to understand the behavior of this process not only in vegetables but also in other plants.

3.2. Effective Diffusivity

Table 3 presents the effective diffusivity (D_eff) values for P. aculeata and P. grandifolia at different drying temperatures. Both species exhibited increased diffusivity as the drying temperature rose. This may be due to the decrease in water viscosity, which makes water flow easier through its chemical potential, driving the simultaneous transport of water through the deformations generated in the plant tissue [35].

The effective diffusivity was related to the drying temperature of Pereskias using an Arrhenius-type equation, from which the activation energy (E_a) was determined. E_a is the energy required to initiate water diffusion. In this study, E_a was found to be 107.90 kJ mol⁻¹ and 103.58 kJ mol⁻¹ for Pereskia aculeata and Pereskia grandifolia, respectively. The values for activation energy vary according to the composition of the product, with lower E_a indicating that the process is easier to occur, meaning less energy is required for the physical process. Thus, Pereskia grandifolia indicated a lower energy requirement to initiate the drying process [11,36].

3.3. Thermodynamic Properties

Table 4 presents the results obtained for the thermodynamic properties in P. aculeata and P. grandifolia leaves.

It was observed that the increase in drying temperature resulted in a slight reduction in enthalpy in Pereskias. Enthalpy (ΔH) showed positive values indicating an endothermic system, which is typical of drying processes where energy is transferred from the air to the product in the form of heat [33]. Entropy showed negative values, progressively decreasing with the increase in temperature. According to Almeida et al. (2016) [37], the decrease in ΔS occurred due to the reduction in the product’s water content during drying, thus restricting the movement of molecules.

Gibbs free energy (ΔG) values increased with the rise in drying temperature of Pereskias and are positive, indicating a non-spontaneous process. This suggests that energy from the surrounding air is required to reduce the water content. There was an increase in ΔG of approximately 2.4% for temperatures ranging from 50 to 70 °C.

3.4. Technological and Chemical Properties of Pereskia sp. Powders

Figure 3 shows the powders of Pereskia leaves at different drying temperatures, with the powders of P. aculeata (Figure 3a) and P. grandifolia (Figure 3b). The powders dried at 50 °C exhibited a more intense green color in the visible light spectrum, and there was a change in reflectance with increasing temperature, with the green color shifting towards the green/brown color scale.

The leaves predominantly contain chlorophyll; however, drying may have induced degradation of the green pigmentation, resulting in the appearance of other degradation-derived components and color changes. According to Xu et al. (2020) [38], the thermal drying process induces browning reactions as well as degradation of pigments during drying.

The influence of drying temperature on most physical properties of Pereskia powders was observed as shown in Figure 3. Color parameters were affected by the drying process, as well as between Pereskia species (Figure 4). The powders exhibited low brightness values for both species. Powders obtained at 50 and 60 °C for P. grandifolia and at 60 and 70 °C for P. aculeata showed better brightness among the species. The negative a* and positive b* values of the powders indicated that drying caused a color shift from green to yellow, with high color intensity and slightly low saturation in the powders (Figure 4B,C).

The browning index was higher in the powders dried at 70 °C for P. aculeata and at 60 and 70 °C for P. grandifolia, showing greater sensitivity in optical parameters during drying. The drying process can affect pigment degradation as well as browning reactions due to the degradation of components such as proteins [39,40].

The apparent and compacted density of the powders were influenced by the temperature in both Pereskia species. At 70 °C, P. aculeata and P. grandifolia exhibited the highest bulk density among the powders dried at different temperatures (Figure 4G,H). The tapped density was highest for the 70 °C powder of P. aculeata and the 50 °C powder of P. grandifolia. The density of powders affects flowability and fluidity in mixtures, compaction, processing, and storage.

The standardization of powder particles is crucial for the precise determination of material density, as the particle size and distribution directly influence this property. The mechanical properties of powders, such as compressive strength, flowability, and cohesion, are closely linked to the mass, volume, and specific surface area of the particles. These properties, in turn, are directly derived from the intrinsic physical characteristics of the product, such as particle shape, size, and distribution [41]. By understanding and controlling these properties, it is possible to conduct a detailed market study of the product, assessing its feasibility for commercialization based on its performance and behavior in industrial and application processes.

The compressibility index of the powders revealed poor flowability with values between 20–35%, a parameter influenced by temperature. An inverse behavior was observed between the Pereskias, as the 50 °C powder of P. aculeata exhibited higher flowability, while the 50 °C powder of P. grandifolia showed lower flowability. The moisture content in the powders affects compressibility because the lower the moisture, the more difficult it is for particles to agglomerate in the empty spaces. Moisture content influences the static friction angle of the container with the powder, weakening the friction force and the compressibility index with low moisture levels, as higher moisture facilitates compression [42]. The Hausner ratio values for all powders indicated low cohesiveness. The cohesiveness index can also be used to determine the flowability of the powders.

The solubility of Pereskias was high, over 90%, with P. aculeata exhibiting higher solubility compared to P. grandifolia. This difference may be explained by the higher lipid content in P. grandifolia, which contains nonpolar components. Solubility is also correlated with the protein content, which is water-soluble, and is crucial for incorporation into food products [43]. According to Shahbal et al. [44], low water solubility of proteins in many vegetables limits their use in the food market, which was not observed in the Pereskia powders, making them viable for food preparation.

The wettability of the powders increased with temperature, except for the 70 °C powder of P. aculeata, which decreased. The best wettability was observed at 60 °C for P. aculeata and at 70 °C for P. grandifolia, with very similar values. Low wettability can hinder rapid rehydration and use in other applications, especially in the food sector. During the wetting stage, the liquid penetrates the particles, increasing their weight and immersion, as reconstitution replaces the air/solid interface with a liquid/solid interface [45].

With initial moisture contents around 90%, the Pereskia leaves reduced their water content similarly for both species, except for the 70 °C powder of P. aculeata, which had lower moisture content. The deformation caused by the drying process allows for the migration of intracellular water and an increase in temperature, resulting in lower moisture and a faster dehydration rate.

As the temperature rises, it can induce or break the microstructure of thermosensitive components, causing degradation [46] and thus inducing the removal of bound water that was in their structure. Factors such as uneven volume reductions can influence cell rupture and hinder water transport during drying in conventional media [47].

The water activity of the Pereskias was low, with P. aculeata having the lowest content. The reduction of free water activity in the powders provides greater stability during storage, reducing enzymatic or chemical reactions and extending shelf life. The stability of powdered food depends on storage conditions and environmental control, as it may tend to have a higher moisture sorption capacity from the surrounding environment or container [48].

Pereskias had a good percentage of minerals, with a higher content in P. grandifolia. Typically, the mineral value expressed in the leaves reflects the mineral content in the plant [49]. High ash content indicates inorganic matter, as well as quality required for nutritional value.

The pH values of the powders decreased with increasing drying temperature, while the titratable acidity values increased for both species. However, variations in pH and acidity were greater in P. aculeata powders than in P. grandifolia powders. Thus, the P. aculeata powder that had a higher pH than the P. grandifolia powder when dried at 50 °C recorded lower values when dried at 70 °C.

The reduction in pH has an inverse behavior to acidity, which increased up to 70 °C for both species, showing similar final acidity content. With pH values closer to neutrality and low acidity, the powders indicate high contents of amino acids, lipids, and sugars. Low acidity can cause more changes in powder coloration, making them more sensitive to the drying process, as conditions where the pH is closer to neutrality accelerate non-enzymatic browning reactions. The powders may have their flavor influenced by the reduction in pH, which can affect sensory acceptance when consumed in human food, as it may impact the functional, organoleptic, and nutritional properties of the food [50].

Total chlorophyll compounds in the powders from drying temperatures of 50 and 60 °C were more stable during the drying of Pereskias (Figure 5). Powders dried at 70 °C exhibited greater degradation of apolar esterified phytol (chlorophyll a and b), which are responsible for the intense green and bright color of vegetables [51]. The chlorophyll structure undergoes modifications when vegetables are subjected to thermal treatments at higher temperatures due to its heat sensitivity [51], leading to thermal destruction.

The protein content in Pereskias was influenced by temperature, showing a decrease compared to the 50 °C temperature. For both species, this temperature had the highest protein content for the powders of P. aculeata. Interestingly, P. grandifolia showed a significant increase in protein content from 60 °C to 70 °C. This behavior may be related to the reduced exposure time of the leaves to heat, even with higher heat application, leading to a shorter drying process and reduced protein hydrolysis due to faster moisture removal.

P. grandifolia had the highest protein content in all powders compared to P. aculeata. Proteins can act as emulsifiers in food preparations due to their chemical properties that spread well at fluid interfaces such as oil and water. The rheological properties of protein interfaces influence viscoelasticity, stability, and dispersion in fluids [52]. In addition to their solubility in polar and nonpolar solvents, proteins have foam-forming and gelation capabilities [43].

Pereskias had a high lipid content in the leaves, which is not typical for conventional leafy vegetables. Oils in vegetables can be used for human consumption as well as energy reserves and for the absorption of fat-soluble vitamins in the body. According to Masiero et al. [53], vegetable oils are used not only for food but also for pharmaceuticals and cosmetics. The classification of vegetable oils depends on their chemical composition, such as phenolics, fatty acids, and carotenoids.

The carotenoid values in the powders were low, as expected, given the intense green color of the powders. Carotenoids are pigments responsible for yellow, orange, and red hues in many fruits and vegetables, and their presence in green leafy vegetables is often masked by chlorophyll [54]. Despite their known susceptibility to degradation under thermal treatments [55], the powders from P. grandifolia exhibited greater stability and preservation of carotenoid content during drying at different temperatures. This stability suggests that P. grandifolia may possess structural or biochemical factors that help mitigate carotenoid degradation, making it a valuable source of these bioactive compounds even after processing [55].

The total phenolic content was higher in the P. grandifolia powders, especially in the powder obtained at 50 °C. Phenolic compounds are a diverse group of phytochemicals known for their potent antioxidant properties, which play a critical role in neutralizing oxidative stress in the body [54]. Oxidative stress is linked to various chronic diseases, including cardiovascular diseases, cancer, and neurodegenerative disorders. Therefore, foods rich in phenolics are highly valued for their potential to contribute to health and wellness [56]. The consumption of such foods can help alleviate oxidative damage by scavenging free radicals [29]. The retention of phenolic compounds at lower drying temperatures, as observed in P. grandifolia, suggests that careful control of drying processes can enhance the antioxidant potential of the final product, contributing to its functional food properties.

According to the antioxidant activity measured by the ABTS method, the Pereskia powders did not show significant differences between species or drying temperatures. This indicates a level of resilience in the antioxidant components of Pereskia, which may include not only phenolics and carotenoids but also other compounds that contribute to the overall antioxidant capacity. The stability observed suggests that Pereskia species could serve as robust sources of antioxidants in various food products, retaining their bioactive properties even after processing [57]. Such stability is crucial for the development of functional foods, as it ensures that the health-promoting qualities of the ingredients are preserved [39].

4. Conclusions

This study evaluated the effects of convective drying at different temperatures (50, 60, and 70 °C) on the physical, physicochemical, and bioactive properties of leaf powders from P. aculeata and P. grandifolia. The Page and Logarithmic models were found to be the most effective in accurately describing the drying behavior of both Pereskia species, with high coefficients of determination and low error metrics. These models are particularly useful for predicting drying kinetics, which is crucial for optimizing drying processes to ensure product quality.

The results confirm that convective drying temperature significantly influences the preservation of bioactive compounds and physicochemical properties in P. aculeata and P. grandifolia leaf powders, supporting the study’s initial hypothesis. It was demonstrated that higher drying temperatures (70 °C) led to a greater degradation of sensitive compounds like chlorophyll and phenolics, particularly in P. aculeata, which aligns with the expectation that bioactive compounds are susceptible to thermal degradation. Conversely, P. grandifolia exhibited greater stability in carotenoid and phenolic content, making it a more robust candidate for nutritional applications under varying drying conditions.

Overall, this study highlights the importance of carefully selecting drying conditions and appropriate modeling techniques to preserve the quality and enhance the utility of dried Pereskia leaf powders in food and nutraceutical applications. Future research should explore the specific mechanisms behind the stability of bioactive compounds in P. grandifolia and assess the scalability of these findings in industrial drying processes.

Author Contributions

C.M.d.A.: conceptualization, methodology, investigation, validation, formal analysis, writing—original draft; I.d.S.M.: methodology, investigation, validation, formal analysis, writing—review and editing; M.T.C.: project administration, supervision and funding acquisition; R.P.L.: methodology, data curation, software, writing—review and editing; H.V.M.: formal analysis, writing—review and editing; R.d.S.N.: formal analysis; C.A.L.C.: conceptualization, methodology, investigation; J.J.A.M.: writing—review and editing; F.R.d.C.B.: project administration, supervision and funding acquisition; E.M.P.: project administration, supervision and funding acquisition. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The authors declare that the data supporting the conclusions of this study are available within the article. If any raw data files are needed in another format, they will be available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

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Figures and Tables

Figure 1. Observed and estimated values by the Henderson and Pabis and Lewis equations fitted to the experimental data of Pereskia leaf drying: (a) fit of the Henderson and Pabis model data for P. aculeata; (b) fit of the Lewis model data for P. aculeata; (c) fit of the Henderson and Pabis model data for P. grandifolia; and (d) fit of the Lewis model data for P. grandifolia.

Figure 2. Observed and estimated values using the Logarithmic and Page equations fitted to the experimental data of Pereskia leaf drying: (a) fitting of Logarithmic model data for P. aculeata; (b) fitting of Page model data for P. aculeata; (c) fitting of Logarithmic model data for P. grandifolia; and (d) fitting of Page model data for P. grandifolia.

Figure 3. Visual appearance of Pereskia sp. powders obtained at different drying temperatures (50, 60, and 70 °C). (a) P. aculeata and (b) P. grandifolia.

Figure 4. Physical quality of P. aculeata and P. grandifolia powders at different drying temperatures (50, 60, 70 °C). Luminosity (A), a*—red intensity (B), b*—yellow intensity (C), C—color saturation (D), H°—hue angle (E), IE—browning index (F), bulk density (G), tapped density (H), compressibility index (I), Hausner ratio (J), solubility (K), wettability (L), moisture (M), water activity (N), ash (O), pH (P), and acidity (Q). Different lowercase letters indicate significant differences between species within the same temperature level according to the 5% probability t-test. Different uppercase letters indicate significant differences between temperature levels for each species according to the 5% probability Sidak test. “ns” Indicates no significant difference according to the 5% probability F-test. IE: color index; AD: apparent density; CD: packed density; IC: compressibility index; HR: Hausner ratio; Aw: water activity. Significant differences within species at the same temperature level are indicated by lowercase letters (t-test, 5% probability). Significant differences across temperature levels for each species are indicated by uppercase letters (Sidak test, 5% probability).

Figure 5. Physical-Chemical Quality of Powders from Non-Conventional Plants (P. aculeata and P. grandifolia) at Different Drying Temperatures (50, 60, 70 °C). Chlorophyll a (A), Chlorophyll b (B), Total Chlorophyll (C), Carotenoids (D), Proteins (E), Lipids (F), TPC—Total Phenolic Compounds (G), and ABTS+—Antioxidant Activity (H). Different lowercase letters indicate significant differences between species within the same temperature level, according to the t-test at 5% probability. Different uppercase letters indicate significant differences between temperature levels for each species, according to the Sidak test at 5% probability. * Indicates significant difference according to the F-test at 5% probability. “ns” Not significant according to the F-test at 5% probability.

Table 1

Empirical models used to predict the drying phenomenon of P. grandifolia and P. aculeata leaves.

Models	Equations
Henderson and Pabis	RX = $\mathrm{a}·\exp (-\mathrm{kt})$
Lewis	RX = $\exp (-\mathrm{kt})$
Logaritmo	RX = $\mathrm{a}·\exp (-\mathrm{kt})$ + c
Page	RX = $\exp (-{\mathrm{kt}}^{\mathrm{n}})$

RX = moisture content ratio of the product (dimensionless); t = drying time (min); k = drying constant, a, c, and n = model coefficient (dimensionless).

Table 2

Parameters of the mathematical models fitted to the drying data of Pereskia leaves with coefficients of determination (R²), mean squared errors (MSE), and chi-square (χ²).

Model	Species	T (°C)	a	c	k	n	R2	MSE	χ2
Henderson and Pabis	P. grandifolia	50	1.0053	-	0.0031	-	0.9876	0.036	0.0281
		60	0.9883	-	0.0267	-	0.993	0.0261	0.0207
		70	1.0596	-	0.0268	-	0.9931	0.03	0.0241
	P. aculeata	50	1.0156	-	0.006	-	0.9928	0.03	0.0228
		60	1.0561	-	0.017	-	0.9959	0.0226	0.0182
		70	1.0633	-	0.0135	-	0.9904	0.035	0.0277
Lewis	P. grandifolia	50	-	-	0.0031	-	0.9875	0.0354	0.028
		60	-	-	0.0271	-	0.9929	0.0257	0.0204
		70	-	-	0.025	-	0.9891	0.0366	0.0314
	P. aculeata	50	-	-	0.0059	-	0.9924	0.0303	0.023
		60	-	-	0.0159	-	0.9924	0.0301	0.0243
		70	-	-	0.0125	-	0.9839	0.044	0.0372
Logarítmico	P. grandifolia	50	1.0846	0.0919	0.0026	-	0.9902	0.0326	0.0254
		60	0.9776	−0.0234	0.0289	-	0.9953	0.022	0.0184
		70	1.0875	0.0415	0.0242	-	0.9956	0.0247	0.019
	P. aculeata	50	1.0117	−0.0051	0.0061	-	0.9928	0.0305	0.023
		60	1.0465	−0.0149	0.0177	-	0.9964	0.0217	0.0161
		70	1.1469	0.1046	0.011	-	0.9962	0.0225	0.0158
Page	P. grandifolia	50	-	-	0.0028	1.016	0.9876	0.036	0.028
		60	-	-	0.0394	0.896	0.9956	0.0208	0.0142
		70	-	-	0.0109	1.2242	0.9986	0.0082	0.0135
	P. aculeata	50	-	-	0.0061	0.9909	0.9924	0.034	0.0229
		60	-	-	0.0089	1.1437	0.996	0.0224	0.0181
		70	-	-	0.004	1.2647	0.9984	0.014	0.0103

T = temperature (°C); k = drying constant; a, c, and n = model coefficients (dimensionless).

Table 3

Effective diffusivity of Pereskia aculeata and Pereskia grandifolia leaves at different drying temperatures.

Specie	T (°C)	Effective Diffusivity (m2 s−1)	R2
P. aculeata	50	3.51 × 10−11	0.9424
	60	3.26 × 10−10	0.9753
	70	3.57 × 10−10	0.9535
P. grandifolia	50	5.72 × 10−11	0.9694
	60	3.97 × 10−10	0.9839
	70	5.33 × 10−10	0.9713

Table 4

Values obtained for enthalpy (ΔH), entropy (ΔS), and Gibbs free energy (ΔG) of Pereskia aculeata and Pereskia grandifolia leaves subjected to drying temperatures of 50 to 70 °C.

Specie	T (°C)	ΔH (kJ mol−1)	ΔS (kJ mol−1 K−1)	ΔG (kJ mol−1)
P. aculeata	50	105.21	−0.1090	140.4493
	60	105.13	−0.1093	141.5409
	70	105.05	−0.1095	142.6351
P. grandifolia	50	100.90	−0.1190	139.35
	60	100.81	−0.1192	140.54
	70	100.73	−0.1195	141.74

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