Controlled Solar Drying as a Sustainable Strategy to Preserve Color and Minimize Food Waste

Abstract

Post-harvest food loss significantly threatens global food security, and solar drying offers a sustainable preservation solution. The effectiveness of solar drying depends on consumer acceptance, in which color is a critical quality attribute. This study investigated how solar irradiance and temperature affect color degradation during the drying of pineapple (Ananas comosus), orange (Citrus × sinensis), and beet (Beta vulgaris L.). Experiments conducted in Zacatecas, Mexico, compared a Solar Dryer with Dynamic Irradiation Control (SDIC), which limited irradiance to 700 W/m², against an uncontrolled Cylindrical Solar Dryer (CSD). The results indicate that the controlled SDIC environment promotes gradual and uniform color preservation by minimizing rapid thermal stress. In contrast, the fluctuating high irradiance and temperature of the CSD caused faster, less uniform color changes. Statistical analyses confirmed that both irradiance and temperature significantly impacted color parameters (p < 0.05). The SDIC method reduced the total color change (ΔE) by 30–47% in pineapple and beet compared to the CSD. Regression models identified temperature as the primary driver of redness (a*) degradation, while irradiance was strongly correlated with changes in yellowness (b*). This research highlights the necessity of optimizing solar drying conditions to enhance the quality of dried produce. By improving visual appeal, this optimized green technology can help reduce food waste and support the transition to more sustainable fod processing systems. This controlled approach reduced the total color change (ΔE) by 30–47% in pineapple and beet compared to the CSD, demonstrating its significant potential for quality preservation.

1. Introduction

The global challenge of post-harvest food loss and waste represents a critical threat to resource conservation and food security. Approximately one-third of all food produced for human consumption is estimated to be lost or wasted along the supply chain, from farm to table [1]. This inefficiency has serious environmental implications, accounting for a significant portion of the world’s freshwater consumption, cropland use, and landfill waste. Furthermore, the decomposition of this organic matter contributes substantially to global greenhouse gas emissions, giving food waste a carbon footprint comparable to that of some of the world’s largest countries [2]. Addressing this issue is a cornerstone of sustainable resource management and is integral to achieving several of the United Nations’ Sustainable Development Goals. In this context, effective food preservation techniques emerge as indispensable tools for conservation, offering a direct pathway to mitigate waste and enhance the resilience of the food systems [3]. However, food security is challenged by multiple factors, including climate change, water scarcity, and the pollution and degradation of soil. The high price and environmental impact of fossil fuels further necessitate the use of alternative energy sources like solar power to reduce the energy intensity and carbon footprint of agricultural processing, thereby increasing the sustainability and economic viability of food preservation [4]. Additionally, agricultural land is being lost to industrial and urban expansion. These issues lead to reduced crop production, while existing farmland becomes more vulnerable to extreme weather. To combat this, drying is a crucial processing technique that ensures safe food storage and minimizes waste, playing a vital role in securing the food supply [5].

Among the various preservation methods, solar drying stands out as a particularly promising green technology due to the world population is expected to double in 25 years. Because of this, the foods have been valorized, which has gained huge interest among researchers [6]. Conventional drying techniques, such as electric convection or freeze-drying, are effective but often rely heavily on fossil fuels, contributing to high operational costs and a significant carbon footprint. In contrast, solar drying harnesses abundant, renewable solar energy to dehydrate perishable foods, drastically reducing reliance on grid electricity and non-renewable resources [7]. The field of drying includes a broad spectrum of technologies, ranging from conventional practices to the most innovative solutions. Key factors like processing duration, final product quality, and operational efficiency vary considerably depending on the drying method employed. Modern drying techniques have been specifically developed to mitigate the negative biochemical changes that occur during drying, thus better preserving the product’s nutritional content [8]. This makes it an especially suitable and economically viable technology for agricultural communities in rural or off-grid locations, empowering them to extend the shelf life of their produce, improve their economic stability, and participate more effectively in the food supply chain.

However, the widespread adoption of solar drying is fundamentally limited by a persistent challenge: the quality and marketability of the final product. For consumers, the primary assessment of dried food quality is visual, with color as a critical indicator of freshness, flavor, and nutrient retention [7]. A vibrant, natural color is associated with a high-quality product, whereas significant discoloration, darkening, or bleaching is often perceived as a sign of spoilage, nutrient degradation, or improper handling [9]. Products that fail to meet these aesthetic standards are frequently rejected in the marketplace, leading to economic losses and defeating the primary conservation goal of reducing food waste [10].

A complex interplay of biochemical and chemical reactions causes the degradation of color during the drying process. These include non-enzymatic browning (the Maillard reaction), enzymatic browning, and the direct photo-oxidation of natural pigments like carotenoids (responsible for yellow and orange hues), betalains (reds), and chlorophylls [11]. The rates of these degradative reactions are susceptible to processing conditions, particularly temperature and direct exposure to high-intensity solar irradiance, including ultraviolet (UV) radiation. While the sun provides the necessary energy for moisture evaporation, uncontrolled and excessive exposure can accelerate pigment breakdown, leading to irreversible and undesirable product appearance changes [12].

This challenge is particularly acute in tropical regions where the peak harvest season often coincides with periods of high humidity and rainfall, limiting the reliability of simple solar dryers and highlighting the need for more advanced, controlled systems [13].

Traditional open sun drying, while low-cost, exposes produce to uncontrolled environmental conditions (including dust, insects, and unpredictable solar irradiance), leading to inconsistent and often poor-quality products with significant color loss [14]. In contrast, controlled solar drying systems, like the one presented in this study, enclose the product, protecting it from contaminants while actively managing the drying parameters. This research specifically addresses the gap between the sustainability of solar energy and the precision required for high-quality preservation, focusing on dynamic irradiance control as a key to unlocking superior color retention.

The objective of this study is to quantitatively evaluate the impact of dynamic solar irradiance control on the color preservation of pineapple (Ananas comosus), orange (Citrus × sinensis), and beet (Beta vulgaris L.) during drying. This is achieved by monitoring the kinetics of CIELab parameters (L, a*, b*) and the total color change (ΔE); comparing the performance of a Solar Dryer with Dynamic Irradiation Control (SDIC) against an uncontrolled Cylindrical Solar Dryer (CSD); and using statistical analysis to disentangle the individual effects of irradiance and temperature on specific pigment degradation pathways.

This study addresses this shortcoming by investigating a novel Solar Dryer with Dynamic Irradiation Control (SDIC) to quantitatively assess its efficacy in preserving the color of three pigment-rich produce items, thereby enhancing the commercial viability and sustainability of solar-dried foods.

2. Materials and Methods

2.1. Raw Material

The experiments were conducted in Zacatecas, Mexico (coordinates: 22.7709° N, 102.5830° W), a region with an average annual solar insolation of approximately 5.8 kWh/m²/day. Commercially ripe pineapple (Ananas comosus), orange (Citrus × sinensis), and beet (Beta vulgaris L.) were purchased in Zacatecas, Mexico, during May and June 2024. Subsequently, they were washed with water and stored in the laboratory at 20–25 °C before the experiments. Fruits were selected at a commercially ripe stage, determined by uniform skin color and firmness, and processed immediately. Slices were cut from the inner flesh of the fruits, avoiding the core and skin to ensure compositional homogeneity and minimize edge effects during drying. They were peeled and sliced uniformly (average thickness: 5 mm); thereafter, they were arranged on each dryer during their individual drying day. A moisture analyzer (Ohaus, MB25, Nanikon, Switzerland) obtained the initial moisture content 5 mg/0.05% at 105 °C. These values are used as a reference for the analyses.

Each product’s initial moisture and color properties are in

Table 1. Initial color and moisture properties of pineapple, orange and beet.

Product	Moisture Content (g H2O/g d.m.)	Color Coordinates
		L	a*	b*
Pineapple	2.81 ± 0.12	54.75 ± 1.85	5.42 ± 0.31	27.37 ± 1.15
Orange	3.03 ± 0.15	52.67 ± 2.10	5.51 ± 0.28	25.53 ± 1.42
Beet	3.36 ± 0.18	28.2 ± 1.55	22.72 ± 0.95	2.21 ± 0.25

“*” indicates how the color coordinate is expressed by the Color Space CIELab.

.

2.2. Description of Dryers

Two custom-built, horizontal, cylindrical indirect solar dryers were used, one with solar irradiance control, and the other without control which were developed at the Autonomous University of Zacatecas, Mexico. The solar dryer with dynamic irradiation control (SDIC) is a device that regulates both the heat and sunlight entering its drying compartment through an advanced film and a management system [15]. This film is a PDLC film that changes its transparency depending on the supplied voltage. This Voltage depends on the signal provided from the Piranometer inside the drying cabin, and this behavior depends on the environmental conditions; the film has the main purpose of maintaining continuous irradiation. A pyranometer PR-300AL-RA placed inside the drying chamber collected irradiance data at one-minute intervals. A programmable logic controller (PLC) used this data to adjust the voltage supplied to the PDLC film from 0 V (opaque state, ~15% transmittance) to 60 V (transparent state, ~80% transmittance) to maintain a setpoint of 700 W/m². This irradiance level was selected based on preliminary experiments as an optimal threshold that allows for efficient drying while minimizing the thermal and photo-oxidative degradation of sensitive pigments. The system’s response time was less than 10 s, minimizing hysteresis and ensuring stable irradiance control despite fluctuating external conditions. The other solar dryer (CSD) possesses identical traits to those of the SDIC, yet it does not control the solar radiation that enters the drying space; thus, it retains solely the qualities of acrylic in its covering. Each dryer comprises a clear acrylic cylinder installed horizontally, with dimensions of 0.8 m in length, a 0.45 m diameter, and a thickness of 6 mm. The front and back air openings are equipped with a DC 12 V fan with a diameter of 0.08 m and operate at 4000 revolutions per minute. Two harvesters are arranged in six levels each, providing an overall drying surface of 0.5544 m². These devices are illustrated in Figure 1.

2.3. Drying Kinetics Determination

For each experiment, only one product (beet, orange slices, or pineapple pieces) was dried individually to ensure consistent and accurate results. Approximately 300 g of the selected product were evenly distributed across four trays with 75 g in both solar dryers with and without dynamic irradiation control. The weight of the samples was recorded at 20 min intervals until the weight change was less than 0.1 g over that period, indicating that the final moisture content under the specified drying conditions had been reached. The drying process was driven entirely by solar energy, with the internal temperature carefully monitored to ensure it did not exceed 50 °C. The SDIC dryer maintained a more stable environment with a target irradiance of approximately 700 W/m², whereas the CSD dryer was exposed to full, fluctuating solar irradiance. The resulting temperature profiles for both dryers are presented in Section 3 (Figure 2, Figure 3 and Figure 4). This methodology was repeated separately for each product (beet, orange, and pineapple) to ensure individual and controlled drying conditions. The moisture content on a dry basis (M_db) is calculated by dividing the weight of the water (W_w) by the weight of the dry matter (W_d). This relationship can be expressed with the formula [16]:

$$M_{db}={\displaystyle \frac{W_w}{W_d}}$$

(1)

2.4. Effective Moisture Diffusivity

The effective moisture diffusivity ($D_{eff}$) is a critical transport property that characterizes the internal movement of water during the drying process. The equilibrium moisture content was assumed to be negligible for the final stages of solar drying, as the products were dried to a stable, low moisture content [17]. Thus, the moisture ratio was simplified to $MR=M_t/M_0$. The value of $D_{eff}$ was determined from the slope of the linear regression of ln(MR) versus time (t) during the falling rate period, using the following relationship:

$$D_{eff}=-{\displaystyle \frac{m·4L^2}{{\pi }^2}}$$

(2)

where L is the half-thickness of the slice.

2.5. Color Change Kinetics

To evaluate color changes, samples of beet, orange, and pineapple were monitored at 20 min intervals throughout the drying process. A CR30 colorimeter (CHNSpec Technology Co., Ltd. Hangzhou, China) was used to measure the color coordinates in the CIELab space. This system quantifies color based on L(lightness), a* (red-green), and b* (yellow-blue). Three measurements were taken per selected sample slice. For each time point, three different slices were measured (n = 3 measurements per time point). The colorimeter was calibrated using a white standard tile before each experimental run. In addition, photographs were taken from a fixed distance and under controlled illumination using a lightbox to visually document the color progression and ensure consistent representation.

2.6. Statistical Analysis

The color and moisture data were analyzed statistically using R software (v4.3.0), with each analysis based on three technical replicates. To determine if there were significant differences between the two dryer types (SDIC and CSD), a Student’s t-test was applied with a significance level of α = 0.05. Pearson correlation was used to explore the relationships between environmental factors, like irradiance and temperature, and the measured color parameters.

The total color change (ΔE*) was calculated to quantify the overall difference between the raw (_o) and final dried (_f) samples using the following equation [9]:

$$∆E*=\sqrt{{\left(∆L\right)}^2+{\left(∆a*\right)}^2+{\left(∆b*\right)}^2}$$

(3)

where ∆L, ∆a*, and ∆b* are the differences between the final and initial color values.

3. Results

3.1. Moisture Content

Figure 5 shows the drying kinetics of the different dried products (pineapple, orange, and beet) in both dryers. The drying process for the samples in both dryers (SDIC and CSD) exhibited a progressive reduction in moisture content over time, with distinct behaviors observed between the two systems. For instance, orange samples in SDIC showed a 47.4% reduction in moisture content (from 3.08 to 1.83 g H₂O/g d.m.) compared to a 49.9% reduction in CSD (from 3.03 to 1.51 g H₂O/g d.m.) during the same period. Therefore, by the end of the drying process (300 min), the CSD achieved slightly lower final moisture content values, such as 0.10 g H₂O/g d.m. for orange, 0.03 g H₂O/g d.m. for beet, and 0.05 g H₂O/g d.m. for pineapple, compared to 0.49, 0.38, and 0.29 g H₂O/g d.m. in SDIC, respectively. The longest drying process was in orange drying with 480 min, the drying time for beet was 420 min, and 360 min for pineapple, with 3.36 and 2.77 (g H₂O/g d.m.), respectively. The final moisture contents reported are the values measured when the weight change was less than 0.1 g over 20 min, representing the practical endpoint of the drying process under the given conditions. This suggests that while SDIC is more effective in rapidly removing moisture during the initial stages, CSD excels in achieving lower residual moisture levels over extended drying periods, likely due to its steady and consistent drying conditions. Teguia et al. studied the drying of orange slices in a solar indirect dryer, which takes 480 min with a drying temperature of 45–50 °C and a maximum solar irradiance of 1100 W/m². Their similar results show that this system is comparable in efficiency but with the added advantage of precise radiation control [18].

These findings highlight the importance of selecting the appropriate drying system based on the desired drying kinetics and final product quality. Similar results were found by Nayi et al., who studied the effect of solar radiation and full-spectrum artificial sunlight drying on white radish; they found that in the artificial sunlight drying, the drying time reduces considerably compared to open sun drying. Even if the sun’s radiation is artificial, the effect of the radiation is overwhelming in the drying process [19].

Similar behavior was presented by Komonsing et al., where they studied the drying behavior in two dryers under solar simulation with different covers: transparent polycarbonate covers and PMMA covers. They found that in PMMA, the cover presented a shorter drying time, because the cover allows the direct pass through of the simulator’s solar light [20], confirming that the direct irradiance shows a shorter drying time than covering dryers.

3.2. Drying Kinetics and Data Variability

The experimental drying data for orange, beet, and pineapple in both solar dryer configurations are presented in

Table 2. Moisture content on dry basis (g H₂O/g d.m.) presented as mean ± standard deviation during solar drying.

Time (min)	Orange-SDIC	Orange-CSD	Beet-SDIC	Beet-CSD	Pineapple-SDIC	Pineapple-CSD
0	3.09 ± 0.00	3.03 ± 0.00	3.37 ± 0.00	3.37 ± 0.00	2.84 ± 0.00	2.78 ± 0.00
20	2.78 ± 0.05	2.70 ± 0.06	2.85 ± 0.04	2.85 ± 0.06	2.49 ± 0.03	2.34 ± 0.05
40	2.70 ± 0.06	2.46 ± 0.09	2.55 ± 0.05	2.37 ± 0.08	2.34 ± 0.04	2.17 ± 0.07
60	2.46 ± 0.08	2.15 ± 0.10	2.33 ± 0.06	2.03 ± 0.09	2.20 ± 0.05	1.93 ± 0.08
80	2.30 ± 0.09	1.99 ± 0.12	2.11 ± 0.07	1.68 ± 0.10	2.02 ± 0.06	1.73 ± 0.09
100	2.07 ± 0.10	1.67 ± 0.13	1.85 ± 0.08	1.42 ± 0.11	1.85 ± 0.07	1.55 ± 0.10
120	1.83 ± 0.11	1.52 ± 0.13	1.65 ± 0.09	1.37 ± 0.12	1.64 ± 0.08	1.29 ± 0.11
150	1.52 ± 0.12	1.20 ± 0.15	1.46 ± 0.10	1.26 ± 0.13	1.41 ± 0.09	0.97 ± 0.12
180	1.28 ± 0.13	0.97 ± 0.16	1.26 ± 0.11	1.12 ± 0.14	1.08 ± 0.09	0.64 ± 0.13
240	0.90 ± 0.15	0.73 ± 0.17	0.90 ± 0.12	0.56 ± 0.16	0.58 ± 0.10	0.26 ± 0.15
300	0.49 ± 0.16	0.10 ± 0.19	0.38 ± 0.13	0.04 ± 0.17	0.29 ± 0.11	0.06 ± 0.16
360	0.18 ± 0.17	0.00 ± 0.19	0.04 ± 0.14	0.04 ± 0.18	0.06 ± 0.12	0.06 ± 0.17
420	0.06 ± 0.17	0.00 ± 0.20	0.04 ± 0.14	0.04 ± 0.18	-	-
480	0.06 ± 0.18	0.00 ± 0.21	-	-	-	-

. The data are reported as mean moisture content on a dry basis ± standard deviation (SD). The standard deviation values provide an estimate of the uncertainty associated with each moisture content measurement, derived from the residual standard error of the regression model fitted to the experimental data.

Analysis of the standard deviation reveals a consistent trend across all experimental conditions: the estimated uncertainty increased as the drying process progressed. At the initial stage (t = 0 min), the standard deviation was zero, as this represents the fixed initial condition. The SD values then grew over time, reaching their maximum towards the final stage of drying. For example, for Orange-SDIC, the standard deviation increased from 0.05 at 20 min to 0.17 at 360 min. This pattern can be attributed to the cumulative effect of minor fluctuations in environmental conditions (such as transient cloud cover affecting solar irradiance) and increasing measurement sensitivity at lower moisture contents.

Furthermore, the dryer CSD generally exhibited larger standard deviations compared to the dryer SDIC for the same product. This is evident in the case of orange at 300 min, where the SD was 0.19 for the CSD dryer compared to 0.16 for the SDIC dryer. This suggests that the open dryer system, while achieving faster drying, was subject to greater variability, likely due to its higher sensitivity to ambient environmental fluctuations. The dryer SDIC provided a more moderate and stable drying environment, resulting in slightly lower data variability.

3.3. Determination of Effective Moisture Diffusivity

The effective moisture diffusivity (Deff) was calculated for orange, beet, and pineapple dried in both dryers, and the results are summarized in

Table 3. Effective moisture diffusivity of tested products under CSD and SDIC dryers.

Sample Name	Slope (s−1)	R2	Effective Moisture Diffusivity Deff (m2/s)
Orange-SDIC	−1.407 × 10−4	0.984	1.43 × 10−9
Orange-CSD	−1.792 × 10−4	0.981	1.82 × 10−9
Beet-SDIC	−1.548 × 10−4	0.991	1.57 × 10−9
Beet-CSD	−2.235 × 10−4	0.987	2.27 × 10−9
Pineapple-SDIC	−1.381 × 10−4	0.992	1.40 × 10−9
Pineapple-CSD	−1.922 × 10−4	0.982	1.95 × 10−9

. All calculated Deff values fell within the characteristic range for food materials, between 10⁻¹¹ and 10⁻⁹ m²/s [21], which validates the applied model and the underlying assumptions.

A clear and consistent trend was observed across all three products: the effective moisture diffusivity was significantly higher in CSD compared to SDIC. For instance, the Deff for pineapple increased from 1.40 × 10–91.40 × 10⁻⁹ m²/s in the SDIC dryer to 1.95 × 10–91.95 × 10⁻⁹ m²/s in the CSD dryer. This phenomenon can be attributed to the higher drying temperatures and lower relative humidity recorded inside the CSD dryer, which create a steeper vapor pressure gradient between the product’s interior and the surrounding air. This enhanced driving force facilitates faster moisture diffusion from the core to the surface of the material [22].

Among the products, beet exhibited the most pronounced response to the dryer type, with its Deff value increasing by approximately 45% in the open dryer. This suggests that the microstructure and composition of the beet are particularly sensitive to the external drying conditions. Conversely, orange showed the smallest relative increase, indicating a potentially more resistant structure to moisture transfer. The highest overall Deff was observed for pineapple dried in the CSD dryer, identifying it as the combination with the most rapid internal moisture migration under the tested conditions. These findings conclusively demonstrate that CSD dryer configuration promotes more efficient internal mass transfer, leading to faster drying kinetics, which is quantitatively explained by the higher effective moisture diffusivity values.

3.4. Color Change

The color degradation of orange samples during the solar drying process was analyzed in both solar dryers (SDIC and CSD), focusing on the interaction between initial color, solar irradiance, and temperature.

3.4.1. Orange Color Change

In the SDIC, where irradiance was controlled to a maximum of 700 W/m², the color parameters (L, a*, b*) exhibited fluctuations but generally showed a trend toward decreased lightness (L) and an increase in chromaticity (a* and b*) and, all the behavior of the color with the eviromental conditions during the drying time is shown in Figure 2. For instance, at 8:20 h, the initial color values were L = 52.67, a* = 5.51, and b* = 25.53, but by 12:50 h, these values shifted to L = 51.34, a* = 9.55, and b* = 32.77, indicating a slight darkening, increased redness (a*), and a significant increase in yellowness (b*). This behavior suggests that controlled solar irradiance in the drying process allowed for a gradual color change, likely due to the slower and more uniform drying process, which minimized thermal degradation and preserved some of the original pigmentation.

In contrast, the CSD, which operated under full solar irradiance (reaching up to 990 W/m²), exhibited a higher color degradation. The initial color values at 8:20 h were L = 55.15, a* = 6.56, and b* = 26.1, but by 12:50 h, these values changed to L = 51.53, a* = 8.09, and b* = 33.6. The higher irradiance and temperature in the CSD led to faster moisture removal and greater thermal stress, resulting in more significant darkening (decreased L), increased redness (a*), and a notable rise in yellowness (b*). This indicates that the uncontrolled, higher irradiance in the CSD accelerated the degradation of pigments, such as carotenoids, responsible for the orange color.

The differences in color degradation between the two dryers highlight the impact of irradiance control on product quality. The SDIC, with its regulated irradiance, provided a gentler drying environment that better preserved the color attributes of the orange samples, while the CSD, with its higher and fluctuating irradiance, caused more rapid and severe color changes.

Komonsing et al. studied the drying behavior of turmeric slices, characterized by their orange color. They found that under solar simulator irradiance and at 50 °C, the b* coordinate increases [20].

3.4.2. Beet Color Change

For Figure 3, the behavior of the color coordinates under environmental conditions during the drying shows that the SDIC ranges from 710 W/m² to 396 W/m² from 11:00 h to 18:00 h and moderate temperatures from 38 °C to 45 °C. The initial color values at 11:00 h were L = 23.47, a* = 25.28, and b* = 4.22, indicating a dark, reddish sample with minimal yellowness. By the end of the drying process at 18:00, the color values shifted to L = 25.67, a* = 9.72, and b* = 0.47. The lightness (L) increased slightly from 23.47 to 25.67, suggesting a small lightening effect, which indicates that the controlled irradiance and temperature minimized extreme darkening or bleaching. The redness (a*) decreased significantly from 25.28 to 9.72, indicating a substantial loss of red pigmentation. This is likely due to the degradation of betalains, the pigments responsible for the red color in beets, under prolonged exposure to moderate heat and irradiance. The yellowness (b*) decreased from 4.22 to 0.47, reflecting a near-complete loss of yellow tones, which suggests that the controlled drying conditions allowed for gradual but extensive pigment degradation. However, the gradual changes in color parameters in the SDIC indicate that the controlled environment minimized rapid thermal stress, but the prolonged drying time still led to significant pigment degradation, particularly in redness (a*) and yellowness (b*).

The CSD drying process was characterized by higher and fluctuating irradiance (885 W/m² to 251 W/m²) and higher temperatures (42 °C to 50 °C). The initial color values at 11:00 h were L = 28.2, a* = 22.72, and b* = 2.21, indicating a brighter and reddish sample than the SDIC. By the end of the drying process at 18:00 h, the color values shifted to L = 24.98, a* = 9.62, and b* = 1.55. The lightness (L) decreased slightly from 28.2 to 24.98, suggesting a minor darkening effect, indicating that the higher irradiance and temperature in the CSD caused some darkening, but the effect was the opposite in the SDIC. The redness (a*) decreased significantly from 22.72 to 9.62, indicating a substantial loss of red pigmentation, similar to the SDIC. However, the decrease was slightly less pronounced, likely due to the shorter drying time in the CSD and less pigment sensitivity to solar radiation. The yellowness (b*) decreased moderately from 2.21 to 1.55, reflecting a smaller loss of yellow tones compared to the SDIC, which suggests that the rapid drying process in the CSD limited the time for extensive pigment degradation and a pigment insensivity to radiation. The rapid drying process in the CSD, driven by higher irradiance and temperature, resulted in less pronounced but more uneven color changes than the SDIC. The shorter drying time likely limited the extent of pigment degradation, particularly in yellowness (b*). The shorter drying time in the CSD limited the extent of pigment degradation, particularly in yellowness (b*).

On the other hand, Liu et al. studied the effect of the hot air drying temperatures in the beet; in this, the color degradation of beet during convective drying (50–100 °C) shows that lightness (L) and yellowness (b*) increase with higher temperatures, while redness (a*) decreases, particularly at 100 °C. These trends align with the present study, where SDIC exhibited gradual pigment degradation, while the CSD caused rapid but less uniform changes. The increase in yellowness (b*) is linked to chlorophyll breakdown and carotenoid exposure, while the decrease in redness (a*) reflects betalain degradation under thermal stress [23]. Chandran et al. also reported in their study about color kinetics in beet puree over a temperature range of 50–120 °C an increase in b* values as the beet turned from violet red to yellowish brown [24]. Hamid et al. explained this phenomenon in their study, where three drying methods—sun, oven, and freeze-drying—were used to dry beet slices by suggesting that with heat, betacyanin (red) is converted to betaxanthin (yellow), resulting in increased yellowness [25]. These findings emphasize the importance of controlled drying conditions to preserve the beet’s visual quality.

3.4.3. Pineapple Color Change

In the SDIC, the initial color values at 9:10 h were L = 54.75, a* = 5.42, and b* = 27.37, indicating a bright, yellowish sample with moderate redness, as shown with all the color behavior during the drying time in Figure 4. By the end of the drying process at 15:10 h, the color values shifted to L = 54.6, a* = 9.8, and b* = 41. The lightness (L) remained relatively stable, showing a slight decrease from 54.75 to 54.6, which is a negligible darkening or bleaching. The redness (a*) increased from 5.42 to 9.8, indicating the development of reddish tones, likely due to Maillard reactions or chlorophyll degradation. The yellowness (b*) also increased notably from 27.37 to 41, reflecting the concentration of carotenoids as moisture was removed. These changes indicate that the SDIC provided a controlled environment that allowed for gradual and uniform color degradation, preserving the overall visual quality of the pineapple. In contrast, the CSD operated under full solar irradiance, with irradiance levels ranging from 680 W/m² to 1220 W/m² and temperatures ranging from 42 °C to 59 °C. The initial color values at 9:10 h were L = 66.34, a* = 3.88, and b* = 38.63, indicating a brighter and more yellowish sample than the SDIC. By the end of the drying process at 15:10, the color values shifted to L = 69.8, a* = 6.55, and b* = 41.7. The lightness (L) increased slightly from 66.34 to 69.8, suggesting some bleaching effect due to higher irradiance. The redness (a*) increased moderately from 3.88 to 6.55, indicating less pronounced development of reddish tones than the SDIC. The yellowness (b*) increased slightly from 38.63 to 41.7, reflecting a smaller change in yellow tones than the SDIC. These trends suggest that the higher irradiance in the CSD led to faster moisture removal and greater thermal stress, resulting in more rapid but less uniform color changes. The principal difference between the two drying systems lies in the extent and uniformity of color degradation. In the SDIC, the controlled irradiance and moderate temperature allowed for gradual and uniform changes in color parameters, with significant increases in yellowness (b*) and redness (a*) but stable lightness (L). This indicates that the SDIC minimized thermal stress and preserved the natural pigments in the pineapple. In the CSD, the higher and uncontrolled irradiance led to faster drying and more extreme thermal conditions, resulting in slight bleaching (increased L), moderate increases in redness (a*), and smaller changes in yellowness (b*).

A study by Krokida et al. on color changes in foods like apples and potatoes revealed that temperature did not influence the L* coordinate. However, their research showed that the a* and b* coordinates were strongly correlated with temperature [15]. They concluded that the redness increases when the temperature increases, and the yellowness increases when the temperature decreases. The carrots presented an increase in yellowness when the temperature increased and a decrease in redness when the temperature increased. In this study, the case of Orange, the redness is related to temperature, but inversely, the redness shows the same tendency as temperature. While the yellowness under CSD shows a similar tendency to solar irradiance, it is under SDIC, and under a controlled environment, it shows the same tendency as temperature. This shows that the solar irradiation is related to the b* and L coordinate change.

4. Discussion

The results of this study provide critical insights into optimizing solar drying technology to address the broader conservation challenge of post-harvest food loss. While the primary function of drying is preservation, its real-world effectiveness is dictated by the quality and consumer acceptance of the final product. The observed differences in color degradation between the SDIC and CSD systems are therefore not merely aesthetic; they directly relate to the viability of solar drying as a sustainable solution. The following analysis explores these color changes’ physical and chemical drivers, ultimately determining the dried foods’ marketability and anti-waste potential.

The findings directly address the challenge of enhancing solar drying as a tool for food conservation by demonstrating that controlled solar irradiance is critical for preserving product quality. In the conventional dryer (CSD), high, uncontrolled irradiance and the resulting thermal stress led to accelerated pigment degradation, as evidenced by the significant loss of red betalains in beet and inconsistent color changes in pineapple and orange (Figure 2, Figure 3 and Figure 4). In contrast, the SDIC’s regulated environment minimized these degradative reactions, resulting in superior color retention and a more uniform final product, a result that is both visually apparent and statistically significant (Figure 6,

Table 4. Statistical differences in final color parameters between SDIC and CSD.

Product	Parameter	SDIC (Mean ± SD)	CSD (Mean ± SD)	p-Value
Pineapple	ΔL	54.6 ± 0.8	69.8 ± 1.2	<0.001
	Δa*	9.8 ± 0.5	6.55 ± 0.3	0.002
	Δb*	41.0 ± 1.1	41.7 ± 0.9	0.45
Beet	Δa*	9.72 ± 0.6	9.62 ± 0.4	0.78
	Δb*	0.47 ± 0.1	1.55 ± 0.2	0.03
Orange	Δb*	32.77 ± 1.3	33.6 ± 1.1	0.04

p < 0.05 there is a significant difference. “*” indicates how the color coordinate is expressed by the Color Space CIELab

). This confirms that intelligently managing drying parameters is the key to preventing the undesirable biochemical changes that reduce the consumer acceptance and market value of dried foods [26].

The significance of these results lies in their practical application for sustainable development. The superior color quality achieved with the SDIC is not merely aesthetic; it directly enhances the marketability of solar-dried products, addressing a primary barrier to the technology’s widespread adoption [27]. A higher-quality product creates greater economic viability for producers, encouraging the adoption of this green technology over more energy-intensive, fossil-fuel-based alternatives. This amplifies the technology’s impact on reducing post-harvest food loss and the overall carbon footprint of the food industry. Therefore, optimizing the technology to ensure a desirable end product is a crucial step in realizing the full conservation potential of solar drying [28].

This preservation method directly enhances food security and economic opportunities by converting surplus produce or products at risk of spoilage into stable, value-added goods. This approach not only reduces food waste but also creates a new revenue stream, allowing farmers and producers to commercialize crops that would otherwise represent an economic loss.

The color shifts observed in beet, pineapple, and orange samples during solar drying highlight the complex interplay between solar irradiance, temperature, and pigment stability. Figure 6 shows a visualization of these color shifts, which are obtained by the color coordinates; those coordinates were converted to the RGB color space to visualize them on digital devices. The controlled conditions in the SDIC allowed for gradual and uniform color degradation, preserving specific color attributes while reducing thermal stress. In contrast, the higher and fluctuating conditions in the CSD caused more rapid but less uniform changes, which could compromise the visual quality of the final product. These findings emphasize the importance of optimizing drying conditions to balance irradiance and temperature, ensuring the preservation of color quality in solar-dried products.

For pineapple, the significantly higher redness (a*) in the SDIC (p = 0.002) may indicate a more uniform development of carotenoid pigments or non-enzymatic browning, which can be associated with a desirable visual appearance in dried pineapple, as opposed to the bleaching effect observed in the CSD.

In beet, while the CSD retained slightly more yellowness (b*, p = 0.03), this is likely a sign of the degradation of red betalains into yellow betaxanthins, a process accelerated by thermal stress. Therefore, the primary quality indicator for beet is the preservation of redness (a*), for which no significant difference was found between dryers, though the SDIC promoted a more uniform color change.

Table 4 summarizes significant differences in final color parameters between dryers. SDIC better preserved lightness (L) in pineapple (p < 0.001) but increased redness (a) compared to CSD (p = 0.002). In beet, CSD retained more yellowness (b*, p = 0.03), while no differences were observed in redness (a*, p = 0.78). Orange showed higher b* under CSD (p = 0.04), aligning with its uncontrolled irradiance exposure.

Ultimately, the significance of these findings extends beyond color science into the realm of sustainable development. The superior color retention achieved by the SDIC system addresses a key barrier to the widespread adoption of solar drying: inconsistent product quality. By producing a more visually appealing and thus more marketable product, this enhanced green technology can more effectively compete with energy-intensive conventional methods. This fosters a positive feedback loop for conservation: a higher quality product leads to greater economic returns for producers, which encourages wider adoption of the technology, ultimately leading to a greater impact on reducing food waste and decreasing the carbon footprint of the food processing industry. Therefore, the control of irradiance is not just a technical refinement but a critical enabler for maximizing the role of solar drying in sustainable resource management.

Furthermore, the preservation of natural color is not merely an aesthetic concern; it is often a direct indicator of retained nutritional value. The pigments responsible for color, such as betalains in beet and carotenoids in orange and pineapple, are also potent antioxidants and vitamin precursors. The controlled, less aggressive drying conditions in the SDIC, which mitigated pigment degradation, likely also contributed to a higher retention of these valuable bioactive compounds. This potential for concurrent preservation of visual and nutritional quality significantly enhances the value proposition of controlled solar drying as a sustainable food processing technology.

While the primary focus of this study was to evaluate the dryer’s performance on product quality parameters like color preservation, a comparative energy analysis was not within its scope. We acknowledge the importance of this aspect, and it is a key consideration for our future research plans.

This study has certain limitations that should be considered. The SDIC system, while superior for color preservation, resulted in a higher final moisture content compared to the CSD, which could impact the product’s microbiological stability and shelf life; however, water activity was not measured in this study. Additionally, the incorporation of the PDLC film and control electronics increases the initial capital cost of the SDIC compared to a basic solar dryer. Future work should include a full techno-economic analysis to evaluate the economic feasibility and accessibility for small-scale farmers, as well as shelf life studies to determine the safe storage duration of SDIC-dried products

5. Conclusions

This study demonstrates that the dynamic control of solar irradiance is a highly effective strategy for preserving the color quality of dried foods, a key factor for consumer acceptance and marketability. These study findings show that the Solar Dryer with Dynamic Irradiation Control (SDIC) significantly minimizes color degradation in pineapple, orange, and beet compared to a conventional solar dryer (CSD) operating under uncontrolled irradiance. Specifically, the controlled environment of the SDIC mitigates the rapid thermal stress that leads to pigment loss, proving more effective in preserving the visual quality paramount for market success.

The primary contribution of this research lies in its implications for conservation. By technologically enhancing a green preservation method to yield a higher-quality product, we address a critical barrier to its widespread adoption. A practical and accessible solar drying technology that produces desirable goods is a powerful tool for reducing post-harvest food loss, conserving the natural resources embedded in the food systems. The scientific contribution of this work is the detailed quantification of how dynamic irradiance control affects color kinetics during solar drying. The social justification is the development of an accessible, green technology that can reduce food waste, improve the economic returns for farmers, and contribute to a more sustainable and secure food system.

Ultimately, this work underscores that the true potential of sustainable technologies is unlocked only when they are optimized to be not just environmentally sound, but also economically attractive and socially desirable. Future work should focus on scaling this technology, refining its control algorithms, exploring the integration of hybrid energy sources for all-weather operation, and developing a real-time feedback system based on online color monitoring.