Dynamic Modeling of Convective Drying of Pineapple Peels: Bioactive, Physical, and Thermal Properties

Abstract

The fruit processing agroindustry generates waste, mainly composed of peels, which are often discarded but can be utilized as ingredients for developing new food products. However, their high perishability requires the application of preservation techniques, such as drying, which not only extends shelf life but also adds value and enables their conversion into flour, expanding their applications. This study evaluated the convective drying of pineapple peels for flour production, analyzing bioactive, physical, and thermal properties. Moisture was reduced by 91%, reaching a hygroscopic equilibrium of 6.84%. The Two-Term model provided the best fit for the data, with an R² above 0.9997. Effective diffusivity increased with temperature, ranging from 2.83 × 10⁻¹⁰ m²/s to 7.96 × 10⁻¹⁰ m²/s, with an activation energy of 47.90 kJ/mol, as described by the Arrhenius equation. Thermodynamic properties indicated an endothermic, non-spontaneous process, with reductions in enthalpy (45.21; 45.04 kJ/mol) and entropy (−0.2797; −0.2802 kJ/mol·K) and an increase in Gibbs free energy (135.60–141.20 kJ/mol) at higher temperatures. Fresh peels contained high levels of bioactive compounds, such as phenolics (1740.90 mg GAE/100 g d.b.) and tannins (613.42 mg TAE/100 g d.b.), which were best preserved at 70 °C. Drying altered the physical properties of the flour, resulting in higher absolute, apparent and compact densities, lower porosity (75.81%), and a reduced angle of repose (21.22°) suggesting greater material stability. Thermal analysis identified five mass loss events related to the degradation of water, carbohydrates, proteins, and fibers. Differential scanning calorimetry confirmed the thermal stability of the treatments. Thus, the study highlights pineapple peels as a promising raw material for producing nutrient-rich functional flour, with a drying temperature being a crucial factor in preserving bioactive compounds and achieving desirable product characteristics.

1. Introduction

Tropical fruits differ sensorially from temperate fruits by generally exhibiting more intense flavors and aromas, characteristics that in many species extend to their peels. They are natural sources of nutrients, phytochemicals, and bioactive compounds, thus possessing a high antioxidant potential associated with the prevention of diseases related to oxidative stress, such as Parkinson’s and Alzheimer’s, aging, and chronic inflammation [1,2]. According to FAO (2020) [3], pineapple stands out among these agricultural products of high value and are appreciated worldwide.

From production to processing and/or consumption, nutritious parts of fruits are often discarded because they are not typically consumable in their raw form or do not fit established eating habits. As a result, the fruit processing industry uses, on average, only 45% of the total mass of the fruit, discarding the other 55%, referred to as ‘waste’ [4]. Of this 55%, about 27% corresponds to peels, while the remaining 28% includes pulps, seeds, stems, and other less significant parts [5]. Discarding organic matter in the environment, regardless of its origin, poses inherent risks related to the proliferation of pathogenic microorganisms.

However, the utilization of residual parts is becoming a focal point in modern discussions. Waste such as tropical fruit peels, including pineapple, are rich in bioactive compounds like flavonoids and carotenoids, as well as oils essential for human health, showing great potential for developing functional food products [6]. Proper processing of these residues can yield value-added products rich in minerals, vitamins, and bioactive compounds, with the necessary microbiological and physicochemical stability for a wide range of applications. Abbas et al. [7] evaluated the extraction of bromelain isolated from different parts of the pineapple, such as the peel, crown, fruit, and stem, quantifying the highest proteolytic activity in the pineapple peel, equivalent to 3.417 U/μg, followed by the fruit with 2.556 U/μg. Misran et al. [8] also reported a bromelain content extracted from the peel with higher proteolytic activity (229.64 CDU/mL), followed by the fruit (219.46 CDU/mL).

Fruit peels, like pulps, have a high water content, making them perishable; thus, drying is the most suitable method for their utilization [9]. Drying is a widely used preservation technique for fruits and vegetables. It causes physical and chemical changes in the products, reduces water activity, and prevents deterioration related to microbial and enzymatic activities. The drying process involves applying heat to a product, resulting in the transfer of water from inside the material to its surface and the removal of this water to the surrounding atmosphere [10]. In addition to providing a longer shelf life, it substantially reduces mass and volume, minimizing packaging, storage, and transportation costs.

Studies on drying kinetics combined with mathematical modeling and evaluation of thermodynamic properties deserve researchers’ attention concerning various products, given the diversity of biological structures involved in heating and mass transfer and the effects observed in each material [11]. By studying effective diffusivity in the drying process, it is possible to gain insights into how the food structure affects the mass transfer rate. Thermodynamic parameters play a crucial role in process analysis: enthalpy indicates whether a process is endothermic or exothermic, Gibbs free energy determines spontaneity, and entropy reflects the level of disorder within the system. The pursuit of greater energy efficiency has become increasingly important due to its wide range of applications, driving researchers to prioritize both efficiency and product quality. In convective dryers, efficiency tends to be lower at low temperatures and higher at high temperatures, which may lead to additional costs [12].

In light of the above and considering the importance of studies that contribute to reducing waste from fruit processing, this study aimed to investigate the production of flour from pineapple peels after drying at temperatures of 50, 60, and 70 °C to adjust mathematical models to the drying kinetics, determine effective diffusivities and thermodynamic properties of the process, and evaluate the physicochemical composition, bioactive compounds, and physical and thermal properties of the obtained flours.

2. Materials and Methods

2.1. Raw Materials and Processing

The raw materials used were pineapple peels (Ananas comosus L. Merryl) of the ‘pearl’ variety, sourced from a fruit processing agroindustry located in Campina Grande, PB, Brazil. In the laboratory, the peels were selected, washed, and sanitized in a sodium hypochlorite solution (100 ppm) for 15 min, in accordance with Anvisa RDC n° 275/2002 [13], rinsed in running water, and placed on trays to allow surface water to evaporate. They were then vacuum-packed in low-density polyethylene bags and stored in a freezer at −18 °C until the time of the experiments.

2.1.1. Drying

The pineapple peels (PP) were cut into flat plates measuring 5 cm in length and 2 cm in width. They were placed in mesh baskets and subjected to drying in a convective dryer at temperatures of 50, 60, and 70 °C with an air velocity of 0.5 m/s until reaching hygroscopic equilibrium while monitoring the drying kinetics.

2.1.2. Drying Kinetics

The drying kinetics were determined in triplicate by weighing the baskets with the samples at regular intervals of 5, 10, 20, 30 and 60 min until reaching the equilibrium moisture content. The dry masses were then determined according to the Adolfo Lutz Institute [14], and the moisture content ratios were calculated (Equation (1)).

$$\mathrm{R}\mathrm{X}={\displaystyle \frac{\mathrm{X}-{\mathrm{X}}_{\mathrm{e}}}{{\mathrm{X}}_{\mathrm{i}}-{\mathrm{X}}_{\mathrm{e}}}}$$

(1)

where: RX—moisture content ratio of the product (dimensionless); X—moisture content at time t (% d.b.); X_i—initial moisture content of the product (% d.b.); X_e—equilibrium moisture content of the product (% d.b.).

The mathematical models in

Table 1. Mathematical models were used to fit the drying kinetics data of pineapple residues.

Model	Equation	References
Newton	$\mathrm{R}\mathrm{X}=\mathrm{e}\mathrm{x}\mathrm{p}\left(-\mathrm{k}\mathrm{t}\right)$	[15]
Page	$\mathrm{R}\mathrm{X}=\mathrm{e}\mathrm{x}\mathrm{p}\left(-{\mathrm{k}\mathrm{t}}^{\mathrm{n}}\right)$	[16]
Henderson and Pabis	$\mathrm{R}\mathrm{X}=\mathrm{a}\mathrm{e}\mathrm{x}\mathrm{p}\left(-\mathrm{k}\mathrm{t}\right)$	[17]
Henderson and Pabis modified	$\mathrm{R}\mathrm{X}=\mathrm{a}\mathrm{e}\mathrm{x}\mathrm{p}\left(-\mathrm{k}\mathrm{t}\right)+\mathrm{b}\mathrm{e}\mathrm{x}\mathrm{p}\left(-{\mathrm{k}}_0\mathrm{t}\right)+\mathrm{c}\mathrm{e}\mathrm{x}\mathrm{p}\left(-{\mathrm{k}}_1\mathrm{t}\right)$	[18]
Thompson	$\mathrm{R}\mathrm{X}=\mathrm{e}\mathrm{x}\mathrm{p}\left({\displaystyle \frac{-\mathrm{a}-\sqrt{{\mathrm{a}}^2+4\mathrm{b}\mathrm{t}}}{2\mathrm{b}}}\right)$	[19]
Two terms	$\mathrm{R}\mathrm{X}=\mathrm{a}\mathrm{e}\mathrm{x}\mathrm{p}\left(-{\mathrm{k}}_0\mathrm{t}\right)+\mathrm{b}\mathrm{e}\mathrm{x}\mathrm{p}\left(-{\mathrm{k}}_1\mathrm{t}\right)$	[20]
Midilli	$\mathrm{R}\mathrm{X}=\mathrm{a}\mathrm{e}\mathrm{x}\mathrm{p}\left(-{\mathrm{k}\mathrm{t}}^{\mathrm{n}}\right)+\mathrm{b}\mathrm{t}$	[21]
Diffusion approximation	$\mathrm{RX}=\mathrm{aexp}\left(-\mathrm{kt}\right)+\left(1-\mathrm{a}\right)\exp \left(-\mathrm{kbt}\right)$	[22]
Two-Term exponential	$\mathrm{R}\mathrm{X}=\mathrm{a}\mathrm{e}\mathrm{x}\mathrm{p}\left(-\mathrm{k}\mathrm{t}\right)+\left(1-\mathrm{a}\right)\mathrm{e}\mathrm{x}\mathrm{p}\left(-\mathrm{k}\mathrm{a}\mathrm{t}\right)$	[23]

Where: RX—moisture content ratio, dimensionless; a, b, c, k, k₀, k₁, n—model constants; t—drying time.

were fitted to the experimental data of the drying kinetics of the peels using the statistical software Statistica version 7.0, employing nonlinear regression with the Quasi-Newton method. To select the models that best represented the drying kinetics, the following criteria were used: the highest coefficient of determination (R²), the lowest chi-square (χ²), and the root mean square error (RMSE).

2.1.3. Effective Diffusivity

To determine the effective diffusivity, the liquid diffusion mathematical model was applied to the experimental data, considering a uniform initial water distribution and the absence of thermal resistance. Equation (2) is the analytical solution of Fick’s second law, assuming the geometric shape of the product as an infinite flat plate.

$$\mathrm{R}\mathrm{X}={\displaystyle \frac{\mathrm{X}-{\mathrm{X}}_{\mathrm{e}}}{{\mathrm{X}}_0-{\mathrm{X}}_{\mathrm{e}}}}={\displaystyle \frac{8}{{\mathsf{\pi }}^2}}{\sum }_{\mathrm{n}=0}^{\mathrm{\infty }}{\displaystyle \frac{1}{{\left(2\mathrm{n}+1\right)}^2}}\mathrm{e}\mathrm{x}\mathrm{p}\left[-{\left(2\mathrm{n}+1\right)}^2{\displaystyle \frac{{\mathsf{\pi }}^2{\mathrm{D}}_{\mathrm{e}\mathrm{f}}\mathrm{t}}{{\mathrm{L}}^2}}\right]$$

(2)

where: RX—moisture content ratio of the product (dimensionless); D_ef—effective diffusivity (m²/s); n—number of terms; L—thickness of the plate (m); t—time (s).

Since Equation (2) represents an infinite series of terms, it is necessary to use statistical calculation software to determine the effective diffusivity. In this case, Statistica 7.0 was used, employing nonlinear regression with the Quasi-Newton estimation method, approximating to 5 terms, beyond which no variation in the value of D_ef was observed.

The influence of temperature on effective diffusivity was assessed using the Arrhenius-type equation (Equation (3)), which represents the relationship between activation energy and the rate at which the reaction occurs.

$${\mathrm{D}}_{\mathrm{e}\mathrm{f}}={\mathrm{D}}_0\mathrm{e}\mathrm{x}\mathrm{p}\left({\displaystyle \frac{{\mathrm{E}}_{\mathrm{a}}}{\mathrm{R}\mathrm{T}}}\right)$$

(3)

where: D_ef—effective diffusivity (m²/s); D₀—pre-exponential factor (m²/s); E_a—activation energy (J/mol); R—universal gas constant (8.314 J/mol K); T—absolute temperature (K).

2.1.4. Thermodynamic Properties

The thermodynamic properties of enthalpy, entropy, and Gibbs free energy related to the drying process of the peels were calculated according to Equations (4)–(6), respectively.

$$\Delta \mathrm{H}={\mathrm{E}}_{\mathrm{a}}-\mathrm{R}\mathrm{T}$$

(4)

$$\Delta \mathrm{S}=\mathrm{R}\left[\mathrm{l}\mathrm{n}\left({\mathrm{D}}_0\right)-\mathrm{l}\mathrm{n}\left({\displaystyle \frac{{\mathrm{K}}_{\mathrm{B}}}{{\mathrm{h}}_{\mathrm{p}}}}\right)-\mathrm{l}\mathrm{n}\left(\mathrm{T}\right)\right]$$

(5)

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

(6)

where: ΔH—enthalpy (J/mol); ΔS—entropy (J/mol K); ΔG—Gibbs free energy (J/mol); E_a—activation energy (J/mol); R—universal gas constant (8.314 J/mol K); K_B—Boltzmann constant (1.38 × 10⁻²³ J/K); h_p—Planck constant (6.626 × 10⁻³⁴ J s); T—absolute temperature (K).

2.1.5. Preparation of Pineapple Peel Flour

After the convective drying of the pineapple peels, the samples were ground for 5 min in a household processor (Philips Walita^®, São Paulo, Brazil), and the flour granulation was standardized using a 16 mesh stainless steel sieve. All the flours were stored in a freezer at −18 °C until the analyses were conducted. The flours obtained from the convective drying were coded as PP 50, PP 60 and PP 70.

2.1.6. Characterization of Fresh and Flour Pineapple Peels

The fresh pineapple peels (PP) and their flours were characterized in quadruplicate regarding bioactive compounds and physical parameters described below.

2.2. Ascorbic Acid

The ascorbic acid content was determined by titration with 2.6 dichlorophenolindophenol sodium (DCFI) until the light pink, persistent color was obtained, using oxalic acid as an extracting solution following the AOAC (2009) procedure [24].

2.2.1. Total Phenolic Compounds

To determine the total phenolic content, extracts were obtained by macerating 1.0 g of the sample with 10 mL of 80% ethanol, followed by 60 min of sonication in an ultrasonic bath. The total phenolic content was measured using the Folin-Ciocalteu method as described by Waterhouse [25]. A standard curve was prepared using gallic acid, water, Folin-Ciocalteu reagent, and sodium carbonate. Absorbance readings for the standard curve and samples were conducted with a spectrophotometer (Agilent Cary, Agilent Technologies, Santa Clara, CA, USA) at 765 nm. The results were expressed as mg gallic acid equivalent (GAE) per 100 g of material (mg GAE/100 g d.b.).

2.2.2. Total Tannins and Flavonoids

Total tannins were determined according to the methodology described by Goldstein [26], which uses a tannic acid standard curve, with readings performed on a spectrophotometer (Agilent Cary, Agilent Technologies, Santa Clara, CA, USA) at a wavelength of 725 nm. The results were expressed in mg of tannic acid equivalent (TAE) per 100 g of material (mg TAE/100 g d.b.). Total flavonoids were determined according to the methodology described by Francis [27], which uses a solution of ethanol and hydrochloric acid in a ratio of 85:15 (v/v). Readings were performed on a spectrophotometer (Agilent Cary, Agilent Technologies, Santa Clara, USA) at a wavelength of 374 nm, and the results were expressed in mg of flavonoids per 100 g of material (mg/100 g d.b.).

2.2.3. Total Carotenoids

Total carotenoids were quantified according to the methodology described by Higby [28], in which 1 g of the sample was macerated using hexane for dilution, and the absorbances of the samples were performed in spectrophotometry at a wavelength of 450 nm (Agilent Cary, Agilent Technologies, USA). The results were expressed in mg of carotenoid per 100 g of material (mg/100 g d.b.).

2.3. Physical Characterization of Flours

2.3.1. Absolute, Apparent, and Compact Density

The absolute density of the flour was determined using the liquid displacement method, as described by Pragati et al. [29]. The apparent density was obtained from the ratio between the known mass of the flour and the volume occupied in a graduated cylinder, following the methodology proposed by Politi [30]. The compacted density was determined according to the methodology of Tonon [31].

2.3.2. Hausner Ratio and Carr Index

The Hausner ratio was determined by the ratio between the compacted density and the apparent density, according to the methodology described by Hausner [32]. The Carr index, which corresponds to the compressibility index, was determined according to the methodology described by Carr [33]. The fluidity and cohesiveness of the flours were evaluated in terms of the Carr Index (CI) and Hausner Ratio (HR), respectively. The fluidity indicators are very good (43%), and the cohesiveness indicators are low (1.4).

2.3.3. Porosity and Angle of Repose

The porosity of the flour was calculated as the ratio between the apparent density and the absolute density, according to the methodology described by Syamaladevi et al. [34], as shown in Equation (7).

$$\mathsf{\epsilon }=1-{\displaystyle \frac{{\mathsf{\rho }}_{\mathrm{a}\mathrm{p}}}{{\mathsf{\rho }}_{\mathrm{a}\mathrm{b}\mathrm{s}}\times 100}}$$

(7)

where: ε—intragranular porosity (dimensionless); ρ_ap—apparent density (g/cm³); ρ_abs—absolute density (g/cm³).

The static angle of repose was obtained from the geometry of the cone formed on a flat surface by a mass of 30 g of the sample flowing from a funnel, according to the methodology of Bhandari et al. [35], and calculated using Equation (8).

$$\mathsf{\theta }=\mathrm{a}\mathrm{r}\mathrm{c}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{g}{\displaystyle \frac{2\mathrm{h}}{\mathrm{D}}}$$

(8)

where θ—angle of repose (°); h—height of the cone (cm); D—diameter of the cone (cm).

2.4. Thermal Analyses

2.4.1. Differential Scanning Calorimetry and Thermogravimetry

The TGA and DSC curves were obtained using a STA 449 F3 Jupiter (Netzsch, Selb, Germany) at a heating rate of 10 °C/min over a temperature range of 30–600 °C, with a nitrogen flow rate maintained at 120 mL/min. A sample weight of 10 mg was used in aluminum pans.

2.4.2. Fourier Transform Infrared Spectroscopy (FT-IR)

The infrared spectra of all samples were measured using a FT-IR spectrometer at ambient temperature (Spectrum 400, Perkin Elmer, Tokyo, Japan) in the range of 500–4000 cm⁻¹, following the methodology of Xiang et al. [36].

2.5. Statistical Analysis

The experimental data obtained from a completely randomized design were subjected to analysis of variance (ANOVA) using the F-test, and differences between means were compared using the Tukey test at a 5% significance level, with the aid of Assistat software version 7.0 Beta [37].

3. Results and Discussion

3.1. Drying Kinetics of Pineapple Peels

The pineapple peels (PP) had an average initial moisture content of 81.94% (wet basis) and reached a hygroscopic equilibrium with an average moisture content of 6.84% (wet basis), representing a reduction of about 91% from the initial value. A gradual reduction in drying times and final moisture content was observed as the drying temperature increased. The shortest drying time and moisture content were achieved at 70 °C in 660 min, with a moisture content of 5.92 ± 0.50% (dry basis). For drying conducted at 50 °C and 60 °C, drying times of 1380 min and 780 min were required, resulting in moisture contents of 8.76 ± 0.8% and 7.40 ± 0.29% (dry basis), respectively. The shorter drying times with increased temperature reflect a greater driving force for mass transfer and an increase in vapor pressure within the sample, accelerating the removal of water from the interior to the surface resulting in reduced drying times [38].

3.2. Mathematical Modeling

Table 2. Adjustment parameters, coefficients of determination (R²), mean square deviations (MSD), and chi-square (χ²) of the mathematical models fitted to the experimental data of the drying kinetics of pineapple peels at temperatures of 50, 60, and 70 °C.

Model	Parameters
Newton	k						R2	MSD	χ2 (×10−1)
50	0.0076						0.9960	0.0309	0.0098
60	0.0147						0.9993	0.0132	0.0018
70	0.0202						0.9997	0.0084	0.0007
Page	k	n					R2	DQM	χ2 (×10−1)
50	0.0181	0.8155					0.9998	0.0074	0.0006
60	0.0193	0.9327					0.9997	0.0084	0.0008
70	0.0193	1.0116					0.9997	0.0082	0.0007
Henderson e Pabis	k	a					R2	DQM	χ2 (×10−1)
50	0.0068	0.9421					0.9979	0.0225	0.0054
60	0.9881	0.0144					0.9993	0.0125	0.0017
70	0.0204	1.0089					0.9997	0.0078	0.0007
Henderson e Pabis modificado	a	k	B	k0	C	k1	R2	DQM	χ2 (×10−1)
50	−0.0436	0.0068	1.0294	0.0068	−0.0436	0.0068	0.9979	0.0225	0.0061
60	0.2461	0.0345	0.3204	0.0117	0.4470	0.0118	0.9999	0.0156	0.0034
70	0.0512	0.0404	0.4598	0.0199	0.5010	0.0199	0.9997	0.0137	0.0026
Thompson	a	b					R2	DQM	χ2 (×10−1)
50	−10.3192	0.3212					0.9993	0.0129	0.0018
60	−19.6820	0.5620					0.9998	0.0069	0.0005
70	−1872.48	6.1474					0.9997	0.0084	0.0008
Two-Term	a	k0	B	k1			R2	DQM	χ2 (×10−1)
50	0.2338	0.0336	0.7719	0.00053			0.9999	0.0029	0.0001
60	0.2460	0.0345	0.7675	0.0118			0.9999	0.0056	0.0004
70	0.9608	0.0199	0.0511	0.0404			0.9997	0.0077	0.0007
Midilli	a	k	N	b (×10−2)			R2	DQM	χ2 (×10−1)
50	1.0109	0.0202	0.7954	-0.0005			0.9998	0.0066	0.0005
60	1.0169	0.0218	0.9085	0.0001			0.9998	0.0073	0.0006
70	1.0101	0.0208	0.9962	0.0000			0.9997	0.0078	0.0007
Diffusion approximation	a	k	B				R2	DQM	χ2 (×10−1)
50	0.2331	0.0318	0.1675				0.9999	0.0031	0.0001
60	0.3053	0.0275	0.4122				0.9998	0.0066	0.0005
70	−0.0151	0.6600	0.0312				0.9998	0.0075	0.0006
Two-Term exponential	a	k					R2	DQM	χ2 (×10−1)
50	0.1825	0.0320					0.9997	0.0084	0.0007
60	0.0197	0.7226					0.9994	0.0121	0.0016
70	0.0038	5.2396					0.9997	0.0089	0.0009

presents the parameters, coefficients of determination (R²), mean square deviations (MSD), and chi-square (χ²) obtained for each mathematical model fitted to the experimental data of the drying kinetics of pineapple peels (PP) at temperatures of 50, 60, and 70 °C.

It was observed that all models applied to the drying kinetics of PP fit the experimental data satisfactorily, showing R² ≥ 0.9910, MSD ≤ 0.0043, and χ² ≤ 0.00098. Therefore, all models can be used to describe the behavior of the samples subjected to drying at temperatures ranging from 50 to 70 °C. The best set of adjustment coefficients for the drying kinetics of PP was obtained with the Two-Term model, with R² ≥ 0.9997, MSD ≤ 0.0077, and χ² ≤ 0.0007 × 10⁻¹.

Dhara et al. [39], when fitting seven mathematical models to the experimental data of the drying kinetics of starfruit (Averrhoa carambola L.) peels, found that the model that best fit was the Two-Term model. Alibas et al. [40] found that the modified Henderson and Pabis model provided the best fit for the drying of green apple peels in microwaves at powers of 600 and 800 W.

The parameter k from the evaluated models, which represents the drying rate constant and reflects the relationship between effective diffusivity and the diffusion process [41], increased with the rise in drying temperature from 50 to 70 °C for the Newton, Page, Henderson and Pabis, Midilli, Diffusion Approximation, and Two-Term Exponential models for the pineapple peels (PP).

Figure 1 shows the experimental values and those estimated by the Two-Term model for the ratio of moisture content as a function of drying time for the pineapple peels.

The drying curves of pineapple peels at temperatures of 50, 60, and 70 °C showed behaviors commonly observed in agricultural products, exhibiting a more rapid loss of water during the initial drying period, which tends to decrease until the product reaches equilibrium moisture content, as noted by Zeng et al. [42] in their studies on the effects of drying on orange peels. It was found that for a constant drying, there is a reduction in the moisture content ratio with increasing temperature in both samples, indicating an increase in the drying rate, corroborating the behavior observed by Cavalcanti-Mata et al. [43] for industrial pineapple waste.

3.3. Effective Diffusivity

Table 3. Effective diffusivities (D_ef) obtained from the drying kinetics of pineapple peels at temperatures of 50, 60 and 70 °C.

Residue	T (°C)	Def (m2/s)	R2
Pineapple peels	50	2.83 × 10−10	0.9974
	60	5.64 × 10−10	0.9926
	70	7.96 × 10−10	0.9898

R²—coefficient of determination.

presents the effective diffusivities (D_ef) obtained from drying pineapple peels at temperatures of 50, 60 and 70 °C. Diffusivities increased by 181% between drying temperatures of 50 and 70 °C for pineapple samples. An increase in effective diffusivity with increasing drying temperature was reported by [44] for drying pineapple waste. All D_ef values are within the range mentioned by Kaveh et al. [45], who stated that values for food products vary between 10⁻¹² and 10⁻⁸ m²/s. The coefficients of determination (R²) were greater than 0.9790, demonstrating that the Fick model adjusted satisfactorily to the experimental data. Deng et al. [46] reported effective diffusivity values ranging from 7.89 × 10⁻¹⁰ to 16 × 10⁻¹⁰ m²/s for a range of orange peel drying temperatures between 50 and 70 °C.

Table 4. Activation energy and fitting parameter of the Arrhenius-type equation for the drying of pineapple peels.

Residue	D0 (m2/s)	Ea (kJ/mol)	R2
Pineapple peels	1.65 × 10−2	47.90	0.9705

E_a—activation energy; D₀—pre-exponential factor; R²—coefficient of determination.

presents the adjustment parameters of the Arrhenius-type equation correlating the effective diffusivity data with the drying temperature of the pineapple peels, which allowed the study of the activation energy of the process.

Activation energy (E_a) is the energy required to initiate the diffusion process in a given matrix. In the present study, an E_a of 47.90 kJ/mol was obtained for pineapple peel. This property is influenced by the hygroscopicity and other material properties, such as chemical and morphological characteristics. A similar E_a value was obtained by Lehmad et al. [47] in a kinetic study applied to black soldier fly larvae (Hermetia illucens).

The E_a obtained in our study is within the parameters set for food products, as determined by Zogzas et al. [48].

3.4. Thermodynamic Properties

Table 5. Thermodynamic properties associated with the drying of pineapple peels at temperatures of 50, 60, and 70 °C.

Residue	T (°C)	ΔH (kJ/mol)	ΔS (kJ/mol)	ΔG (kJ/mol)
Pineapple peels	50	45.21	−0.2797	135.60
	60	45.13	−0.2800	138.40
	70	45.04	−0.2802	141.20

ΔH—enthalpy; ΔS—entropy; ΔG—Gibbs free energy.

presents the thermodynamic properties of enthalpy (ΔH), entropy (ΔS), and Gibbs free energy (ΔG) determined from the drying of pineapple peels at temperatures of 50, 60, and 70 °C, which provide important information about the interaction between water molecules and the product’s components.

The increase in drying temperature resulted in a decrease in the enthalpy value of the sample, meaning that, with the rise in temperature, the energy required to remove the water from the product was reduced. Enthalpy is a measure of a system’s internal energy, which, in the context of drying, is related to the energy needed to separate the water from the attractive forces with the material. Lower enthalpy values were obtained by Junqueira et al. [49] for Bocaiuva (Acrocomia aculeata) slices subjected to infrared drying. Positive enthalpy indicates that the drying process is endergonic, meaning that it does not occur spontaneously and requires the application of energy to take place. In the case of drying at higher temperatures, although the energy required to remove the water decreases, the process still demands the introduction of heat, characterizing it as endothermic. This behavior is consistent with thermodynamic theory, which suggests that increasing the temperature facilitates the breaking of the bonding forces between the water and the material, which occurs more easily under high heat conditions [50].

Entropy is a measure of disorder or randomness within a system. During the drying process, there was a slight reduction in entropy as the drying temperature increased. This confirms that the drying process is endothermic, meaning that it requires the absorption of heat to occur. This is expected, as reducing the moisture content (removing water) restricts the movement of water molecules, thereby decreasing the disorder of the system (entropy) [51].

The positive values of ∆G observed reinforce that the drying process is endergonic. This means that the process does not occur spontaneously. For water removal to take place, energy must be supplied to the system in the form of heat. This behavior is characteristic of endothermic processes, where heat transfer is necessary to overcome the attractive forces between the water molecules and the material’s molecules (in this case, pineapple waste). Therefore, the increase in ∆G with the rise in temperature indicates that drying is not spontaneous and requires an external energy input (heat) for water to be removed from the product. Increases in ∆G with the elevation of drying temperature are also reported by Moura et al. [52] in the drying of trapiá (Crataeva tapia L.) residues.

3.5. Bioactive Compounds

The results obtained for the levels of bioactive compounds are presented in

Table 6. Characterization of total bioactive compounds in fresh pineapple peels and in the flours.

Parameters	Drying Temperature (°C)
	Fresh	50	60	70
Ascorbic acid (mg/100 g d.b.)	99.48 ± 0.74 a	68.55 ± 1.43 bc	61.58 ± 0.63 c	72.29 ± 5.70 b
Total phenolic compound (mg GAE/100 g d.b.)	1740.90 ± 5.02 a	1114.86 ± 0.67 c	1153.68 ± 0.61 b	1147.45 ± 0.44 b
Total tannins (mg TAE/100 g d.b.)	613.42 ± 0.38 d	626.97 ± 1.69 c	698.96 ± 0.47 b	720.14 ± 0.49 a
Total flavonoids (mg/100 g d.b.)	75.96 ± 0.06 a	13.44 ± 0.06 d	14.47 ± 0.02 c	16.01 ± 0.04 b
Total anthocyanins (mg/100 g d.b.)	8.10 ± 0.02 a	3.09 ± 0.005 d	3.52 ± 0.003 c	6.11 ± 0.014 b
Total carotenoids (mg/100 g d.b.)	0.955 ± 0.015 a	0.253 ± 0.0004 d	0.386 ± 0.002 b	0.339 ± 0.003 c

Means followed by the same lowercase letter in the rows do not differ statistically by Tukey’s test at 5% probability.

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Fresh pineapple peels had a higher ascorbic acid content, but during the dehydration process, a significant reduction of this compound was observed, which was more pronounced at 60 °C. This decrease reinforces the thermosensitive nature of ascorbic acid, which degrades when exposed to heat. On the other hand, in the samples dried at 70 °C, a higher concentration of ascorbic acid was preserved. This suggests that as the drying temperature increases, the exposure time to heat decreases, favoring the retention of the compound.

Fresh pineapple peels exhibited significant levels of phenolic compounds. The amount of bioactive compounds present in fruits and vegetables is subject to various variations due to factors such as maturation stage, species, variety, geographical origin, and climatic conditions during cultivation and harvest [53]. The temperatures of 60 °C and 70 °C resulted in the highest total phenolic compound contents, ranging from 1153.68 to 1147.45 mg GAE/100 g d.b.. These results highlight the positive impact of higher temperatures on the concentration of the aforementioned organic compounds, proving that increased temperature can favor the protection and retention of these bioactive compounds, as observed in studies on the effect of heat on phenolic-rich foods. This behavior is also observed in fiber-rich materials, such as pineapple peels, where phenolics can be linked to polysaccharides in the cell wall.

Tannins are phenolic in nature and possess antioxidant properties attributed to the phenolic rings present in their structure, which can act as electron scavengers, capturing ions and radicals [54]. However, the behavior of the total tannin levels was the opposite, with the fresh pineapple residue showing the lowest concentration. Nevertheless, the effect of temperature on the compound exhibited a similar trend, with an increase in concentration as the temperature rose. Flavonoids are present in significant amounts in fresh peels but were strongly affected by drying, reducing by an average of 81% in pineapple samples. Among the flours, as the drying temperature increased, the flavonoid content was better preserved. Deng et al. [55], while working with the drying of orange peels (Citrus sinensis L.) at temperatures of 50, 55, 60, 65, and 70 °C, reported reductions in total flavonoid levels ranging from 19.9% to 23.4%, and Gondim et al. [56], working with dehydrated mandacaru peels (Cereus jamacaru P. DC.) in a forced-air oven at temperatures of 50, 60, and 70 °C, observed reductions in flavonoids of about 57%, but without a significant effect between the temperatures.

The levels of flavonoids, anthocyanins, and carotenoids were higher in the fresh peels but experienced a significant reduction after the drying process. Among these compounds, flavonoids showed losses of up to 80% of their initial concentration due to the high temperatures applied during drying. The greatest reduction was observed in the flour dried at 50 °C, with a final value of 13.44 mg/100 g d.b. Regarding anthocyanins and total chlorophylls, it was found that these compounds are present in smaller amounts compared to the others, highlighting their greater sensitivity to thermal conditions. Higher total tannin values were observed by Albuquerque et al. [57] in mango peel flours.

3.6. Physical Properties

The results of the physical characterization of the flours obtained after drying the pineapple peels are presented in

Table 7. Physical characterization of pineapple peel flours.

Parâmetro	Drying Temperature (°C)
	50	60	70
BApparent density (g/cm3)	0.574 ± 0.01 b	0.580 ± 0.01 b	0.622 ± 0.01 a
Compacted density (g/cm3)	0.624 ± 0.02 c	0.665 ± 0.01 b	0.701 ± 0.01 a
Absolute density (g/cm3)	2.43 ± 0.17 a	2.51 ± 0.01 a	2.57 ± 0.05 a
Carr Index (%)	8.00 ± 2.00 b	12.67 ± 1.15 a	11.33 ± 2.31 b
Hausner Ratio	1.09 ± 0.02 b	1.15 ± 0.02 a	1.13 ± 0.0 ab
Angle of repose (°)	25.67 ± 1.45 a	21.18 ± 1.45 b	21.22 ± 0.38 b

Means followed by the same lowercase letter in the rows do not differ statistically by Tukey’s test at 5% probability.

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For bulk and tapped densities, the flours obtained at the highest temperature used in the study showed higher values (0.622 and 0.701 g/cm³) (p < 0.05). However, this effect was expected since higher temperatures tend to reduce the water absorption capacity of the material. Lower results were reported by Kaushik et al. [58] for flour from Indian brown millet cultivars (Brachiaria ramosa) for bulk density, with values ranging from 0.429 to 0.435 g/cm³, although similar data were observed for tapped density. For absolute density, no significant difference was found among the samples analyzed. According to Seerangurayar et al. [59], these values assist in predicting flour characteristics such as moisture content, yield, flowability, and product shelf life.

The Carr index and Hausner ratio showed variations in their values, with emphasis on the flours obtained at 70 °C. Compressibility Index (CI) values between 15% and 20% indicate good flowability, while values between 20% and 35% suggest low flowability, 35% to 45% very low flowability, and above 45% indicate extremely low flowability. For the Hausner ratio (HR), values above 1.4 characterize highly cohesive powders, whereas values below 1.4 indicate low cohesion [60]. Regarding the angle of repose, despite significant differences among the samples—where the flour obtained at 50 °C showed the highest angle of 25.67° (p < 0.05) the samples can still be classified as free-flowing, as established by Bhandari et al. [35], where powders with angles of repose below 45° exhibit free flow, while angles above 50° indicate cohesion or flow difficulty, corroborating the CI and HR values.

3.7. Thermal Properties and Spectrometry

3.7.1. Differential Scanning Calorimetry and Thermogravimetry

Thermogravimetric analysis (TGA) and Differential Scanning Calorimetry (DSC) provide a comprehensive view of the thermal behavior of pineapple peel flour. While TGA provides data on degradation and mass loss, DSC reveals how the components react to heat in terms of phase transitions and thermal stability. This enables process optimization and ensures higher quality in industrial applications. Figure 2 shows the thermograms from the thermogravimetric (TG) and differential scanning calorimetry (DSC) analyses.

Table 8. Thermogravimetric parameters: mass loss and degradation temperature.

Sample		TGA
	Event	Δm (%)	ΔT (°C)
PP 50 °C	1°	5.84	87.11–160.59
	2°	16.90	160.59–219.85
	3°	17.24	219.85–294.95
	4°	10.42	294.95–340.00
	5°	15.12	340.00–584.37
PP 60 °C	1°	3.88	68.34–154.68
	2°	15.68	154.68–234.73
	3°	12.19	234.73–279.99
	4°	14.62	279.99–354.82
	5°	15.26	354.82–584.42
PP 70 °C	1°	1.52	82.44–137.69
	2°	9.10	137.69–205.66
	3°	12.08	205.66–280.02
	4°	8.47	280.02–349.83
	5°	7.56	349.83–584.27

shows that for all drying temperatures, five main mass loss events were observed. These events may be associated with dehydration, degradation of oils, carbohydrates, proteins and fibers, as well as the oxidation of organic matter. As we can see, the first event, between the temperature range of 87.11–160.59 °C, corresponds to the evaporation of water, which is the least thermally stable compound [61], as well as the evaporation of some volatiles and hydrocarbons [62]. Tsai et al. observed a decline in the range between 25 and 200 °C, equivalent to the removal of bound water and other volatiles. It is also observed that, as the drying temperature increased, the mass loss of the sample decreased.

Fruit flours are generally rich in proteins, lipids, carbohydrates, and fibers, which makes it difficult to determine the exact temperature at which mass loss occurs for each event, as mass loss happens simultaneously for all fractions. The second and third events, between 137.69 and 294.95 °C, were associated with the decomposition of these components [63]. Hemicellulose generally decomposes within temperature ranges of 190–300 °C, with a peak around 250 °C, while cellulose tends to thermally degrade between 250 and 350 °C, with a sharp peak around 330 °C. Lignin, being a complex compound, typically degrades alongside hemicellulose and cellulose in the range of 200–550 °C [64]. The fifth and final event observed in all samples can be attributed to carbonization and the formation of mineral residues from organic matter [65]. It was observed that all flour samples exhibited a significant amount of final residue, ranging from 34.48% to 61.27%. These results indicate that the samples contain high amounts of fibers.

DSC analysis represents the endothermic and exothermic effects of the raw material during thermal treatment and the variation of heat flow with temperature. For all samples, exothermic peaks were observed in the temperature range of 100–180 °C (peak at 137.67 °C). This peak suggests that water volatilization occurred around 100 °C, a result corroborated by TGA analysis, where water loss and evaporation of volatile compounds [66] initially occur at this first peak. Differential scanning calorimetry of the flours shows that all of them exhibit a gelatinization transition with onset (Ti) and peak (Tp) temperatures, respectively, indicating no significant variations in the melting temperature as this parameter was increased.

3.7.2. Fourier Transform Infrared Spectroscopy (FT-IR)

Fourier-transform infrared (FTIR) spectroscopy has become one of the most important techniques in food science, particularly when applied to detect adulteration, quality control, and the analysis of food properties. This technique allows for extremely fast, non-destructive, and highly accurate determination of a wide range of compounds found in food [67]. Figure 3 shows the Fourier Transform Infrared spectrometry profile of pineapple waste flours.

From the FTIR spectrum obtained, the main peaks were observed at 3414 cm⁻¹, 2937 cm⁻¹, 1635 cm⁻¹, and 1047 cm⁻¹, corresponding to specific functional groups. The peak at 3414 cm⁻¹ can be attributed to O–H or N–H stretching vibrations, suggesting the presence of hydroxyl groups or primary amines. Its decreasing intensity with increasing temperature may indicate the loss of physically adsorbed water or changes in intermolecular interactions. The peak at 2937 cm⁻¹ corresponds to C–H stretching vibrations of aliphatic groups, often associated with methyl (-CH₃) or methylene (-CH₂-) groups. Meanwhile, the peak at 1635 cm⁻¹ arises from C=O (carbonyl) stretching or O–H bending in water. The peak at 1047 cm⁻¹ is attributed to C–O stretching, indicating the presence of ethers, alcohols, or esters. Changes in the intensity of these peaks at higher temperatures reflect processes such as dehydration, structural rearrangement, and/or thermal degradation of the sample, evidencing chemical or physical changes in the functional groups under investigation. A similar spectrometric profile behavior was observed by Kaushal et al. [68] in pineapple peels treated with microwaves for bromelain extraction. Yanuhar et al. [69]. The same behavior was observed in the use of pineapple peel residues as reinforcement for ZnO nanoparticles.

4. Conclusions

The pineapple peels reached hygroscopic equilibrium with an average moisture content of 6.84% w.b. All models applied to the drying kinetics of the pineapple peels fitted the experimental data satisfactorily; however, the best-fit coefficient was obtained for the Two-Term model, with R² ≥ 0.9997, DQM ≤ 0.0077, and χ² ≤ 0.0007 × 10⁻¹.

Gradual increases in effective diffusivity were observed as the drying temperature rose, with its relationship to temperature being adequately represented by the Arrhenius model equation. Thermodynamic properties suggest that the drying process of the peels is endothermic and does not occur spontaneously.

The fresh peels showed high levels of bioactive compounds, and a reduction was observed during the drying process, with 70 °C being the temperature that best preserved the compounds.

Drying temperature significantly impacts the physical properties of the material. As the drying temperature increases, there is an observed rise in densities (bulk, compacted, and absolute), indicating greater compaction and reduced porosity. Additionally, both the Carr index and the Hausner ratio increased, suggesting a decrease in material flowability, making it denser and more prone to compaction. On the other hand, the angle of repose decreased, indicating that the material settles more stably and is less likely to spill at higher temperatures. In summary, higher drying temperatures result in a denser and less flowable material with greater packing stability.

This study presents relevant contributions to the understanding of the pineapple waste drying process, offering subsidies for the optimization of operations in the fruit processing industry. The results highlight the potential for the development of more efficient and sustainable strategies in the management and valorization of agro-industrial waste, favoring environmental sustainability and the rational use of resources.

Future research may focus on optimizing drying conditions to maximize the retention of bioactive compounds, evaluating the stability of the flour over time, and testing its application in different food formulations. Furthermore, comparing various drying techniques could provide a more comprehensive understanding of the best strategy for efficiently and sustainably processing agro-industrial waste.