Exploring CO 2 -laser drilling: Potential for Enhanced Mass and Thermal Diffusion in Banana (Musa sapientum) Dehydration

doi:10.21203/rs.3.rs-5285110/v1

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Exploring CO 2 -laser drilling: Potential for Enhanced Mass and Thermal Diffusion in Banana (Musa sapientum) Dehydration

https://doi.org/10.21203/rs.3.rs-5285110/v1

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This study addresses the efficacy of CO₂-laser drilling as a pre-treatment method to enhance water diffusion in banana slices during air drying, aiming to mitigate the energy and time consumption associated with traditional hot air drying in the food industry. Drilling with CO₂-laser resulted in a higher rate of water diffusion, inferred from the higher values in the effective diffusivity coefficient in drilled samples (up to 1.7-fold), attributable to the increased surface area to volume ratio and energy absorption. Consequently, there was a significant reduction in dehydration time, up to 40% (from 169 min in control samples to 102 min in drilled samples). After dehydration process, banana slices drilled at the largest focal distance showed an increased stiffness according to the higher effective Young's modulus and maximum force observed attributable. Therefore, combining CO₂-laser drilling with air-drying will represent a promising strategy for reducing dehydration times in the food industry, providing a potential solution for food dehydration.

CO2-laser drilling

Dehydration

Mass diffusion

Musa sapientum

The use of dehydration technologies continues to be a profitable transformation process applied to various food materials to obtain shelf-stable products for extended periods, such as horticultural, meat, and dairy products. In practice, hot air drying is one of the common drying methods used in the food industry, although it is energy and time consuming. Around 35–45% of the energy supplied to the process of dehydration is wasted as reaction gases and GHG (Greenhouse Gases) emissions (Menon, Stojceska and Tassou, 2020), incurring in a high energy waste. Moreover, conventional drying can adversely impact product quality, leading to discoloring, aroma loss, textural changes, nutritive value, and changes in physical appearance and shape. This is due to the frequent need to apply high temperatures and/or prolonged drying periods to reach the desired moisture levels (Nguyen, Mondor and Ratti, 2018). In light of this, the food industry is continually searching for innovative drying technologies, such as hybrid drying systems (solar-assisted, infrared-assisted or microwave-assisted drying), heat pump drying, superheated steam drying, vacuum drying and microwave drying to not only improve the efficiency of the drying process by reducing energy consumption but also enhance product quality (Khaing Hnin et al., 2019; Menon et al., 2020).

One of the latest proposed techniques is CO₂-Laser microperforation of food products, which is attracting growing attention in recent years. CO₂–Laser operates with a mixture of carbon dioxide, nitrogen, and helium gas in the middle infrared spectral range (𝜆 = 10.6 µm), achieving efficiency up to 30%. The principle of operation is based on the generation of electron collisions that excite the meta-stable levels in the nitrogen molecules and transfer this energy to carbon dioxide. Nitrogen gas acts as an energetic buffer, keeping the average electron energy high in the gas discharge region (Silfvast, 2003). One of the many applications that CO₂-Laser can have in food processing is the acceleration of mass transfer operations such as infusion, diffusion, marinating, salting, and drying (Figueroa et al., 2020; Silva-Vera et al., 2020a; Puértolas, Pérez and Murgui, 2023). This promising technology and its application in food processing is due to its reliability and precision in creating a grid pattern of microperforations on the food surface prior to the mass transfer operation, allowing to reduce the processing time and/or increasing process yield (Puértolas et al., 2023). As for drying technology, CO₂-laser has been coupled to air dehydration, osmotic dehydration, and freeze-drying of fruits, such as blueberry, tomato, raspberry or apple, leading to a significant reduction of drying time in the range 20–60% (Chen et al., 2021; Munzenmayer et al., 2020; Araya et al., 2022; Deng et al., 2012). In addition to serving as a pre-treatment in mass transfer operations, some other potential applications of CO₂–laser in the food industry have been reported, including cooking, marking, cutting, peeling, laser sintering of foods, surface and packaging labeling or microbial decontamination (Sood et al., 2009; Puértolas et al., 2023; Silva-Vera et al., 2020a). These different applications of CO₂-laser in foods are determined by several laser processing parameters, such as laser wavelength and power, type of emission, laser scanning speed, frequency and duration of the pulse, material exposure time, and focal length (Puértolas et al., 2023).

When CO₂-laser is used as a non-contact drill for pretreatment in mass transfer operations like drying, the grid pattern, the number, diameter, and depth of microperforations, as well as the distance among them, are crucial in achieving significant reductions in mass transfer time (Puértolas et al., 2023). Generally, the greatest reductions have been achieved when pores pass completely through the food (Olivares et al., 2021). Significant reductions in apple drying time have been reported by increasing pore density and size when coupling CO₂-laser pretreatment with various drying technologies, such as osmotic dehydration or refractance window™ technology. This is attributed to the increase in the number of available routes for water diffusion, which leads to a significant increase in the effective diffusion coefficient (Araya et al., 2022; Veloso et al., 2021). However, to the best of our knowledge, CO₂-laser pretreatment has not been coupled with hot air-drying. In this study, slices of banana were subjected to CO₂-laser microperforation as a pretreatment for hot air-drying processing, using two different focal length lenses.

The aim of this study was to determine the effect of CO₂-laser microperforation on drying processing time and the physical properties of dehydrated bananas. The moisture content and effective moisture diffusivity were measured during air-drying process. Additionally, the color and mechanical properties of the resulting dehydrated bananas were evaluated.

The study was conducted under ambient conditions of temperature (24.0 ± 0.5°C) and relative humidity (45 ± 3% R.H.).

2.1. Materials

Approximately five kilograms of bananas (Musa sapientum) from Ecuador were purchased at a local market (Valparaíso, Chile). The samples were preserved at 4.0 ± 0.1°C and stored for no longer than two days before processing. The bananas were then sectioned into cylindrical slices, each measuring 5.0 ± 0.2 mm in thickness. Following the method employed by Taiwo and Adeyemi (2009) to prevent loss of product firmness and visual alterations, a thermal blanching pretreatment was applied at 60°C for 10 minutes.

2.2. Sorption Isotherms

Banana desorption isotherms were determined through direct measurement of the water activity (aw) and dry basis moisture content (X_ds) on sample sets, each undergoing drying processes in triplicate. This is justified as drying is the practical application and equilibrium during desorption is known to give the best representation according Quirijns, E. J., et al. (2005). The drying phase was conducted at two specific temperatures, 60 and 75°C, to estimate the net isosteric sorption heat, employing the methodology described by Vega-Gálvez et al. (2008). The water activity measurement was carried out using digital equipment (Rotronic, HygroPalm 23-AW-A, Bassersdorf, Suiza).

2.2.1. Sorption isotherm model

To model the relationship between equilibrium moisture content $\:{\text{X}}_{\text{d}\text{s}}$ (gwater/100 gds) and aw in the samples, the GAB model, as outlined by Caballero-Cerón et al. (2015), was utilized. This model was chosen because effectively captures both monolayer (first term in Eq. (1)) and multilayer sorption phenomena (second term in Eq. (1)). Key parameters such as the monolayer value (XGAB in g_water/100 g_ds), and dimensionless factors related to the sorption heat of the monolayer (c_GAB) and multilayer (K_GAB) regions relative to the bulk liquid were obtained via Non-linear least squares regression, using the Levenberg-Marquard algorithm, and employing multiple starting points with R-Studio open-source software.

$$\:\frac{{\text{X}}_{\text{d}\text{s}}}{{\text{X}}_{\text{G}\text{A}\text{B}}}=\frac{\left({\text{c}}_{\text{G}\text{A}\text{B}}-1\right){\text{K}}_{\text{G}\text{A}\text{B}}{\text{a}}_{\text{w}}}{\left(1-{\text{K}}_{\text{G}\text{A}\text{B}}{\text{a}}_{\text{w}}+{\text{c}}_{\text{G}\text{A}\text{B}}{\text{K}}_{\text{G}\text{A}\text{B}}{\text{a}}_{\text{w}}\right)}+\frac{{\text{K}}_{\text{G}\text{A}\text{B}}{\text{a}}_{\text{w}}}{\left(1-{\text{K}}_{\text{G}\text{A}\text{B}}{\text{a}}_{\text{w}}\right)}$$

According to literature, c_GAB parameter is defined as the ratio of the partition function of the first molecule sorbed on a site and the partition function of molecules sorbed beyond the first molecule in the multilayer. In fact, larger c_GAB values indicate stronger water bounded in the monolayer and the larger the difference in enthalpy between the monolayer and multilayer molecules. On the other hand, K_GAB represents the ratio of the partition function of molecules in bulk liquid and partition function of molecules sorbed in the multilayer. The more the sorbed molecules are structured in a multilayer, the lower the value for K_GAB (Quirijns, E. J., et al. 2005).

2.2.2. Net isosteric sorption heat (q_s)

The net isosteric sorption heat (q_s) represents the difference between the total sorption heat and the vaporization heat of pure water. Its value is useful in dehydration processes to identify the energy needed to break the bonds between water vapor molecules and the sorption surface (Vega-Gálvez et al., 2008). For a certain moisture content (X_ds) the net isosteric sorption heat may be determined by integrating the Clausius-Clapeyron equation (Eq. (2)) and using the sorption isotherm data.

$$\:{\left[\frac{\text{d}\:\left(\text{L}\text{n}\:{\text{a}}_{\text{w}}\right)}{\text{d}\left(\frac{1}{\text{T}}\right)}\right]}_{{\text{X}}_{\text{d}\text{s}}}=-\frac{{\text{q}}_{\text{s}}}{\text{R}}$$

In Eq. (2), q_s is the net isosteric sorption heat in kJ mol^− 1, T is the temperature in K and R is the ideal gas content equal to 8.314x10^− 3 kJ mol^− 1 K^− 1.

2.3. CO2–laser drilling

The banana slices were drilled through their thickness using a CO₂–laser (model: SYNRAD TI100-100 W, Mukilteo, USA) under controlled operating conditions. The system was operated in a continuous-wave mode with a power output of 10 W. Moreover, the spot laser duration, marking speed, and the pulse–width modulation was set to 1 ms, 762 mm/s, and 50 kHz, respectively. The parameters to be controlled were effective power of beam, pulse duration, and numbers of pulses. In addition, to achieve the minimum and maximum pore diameters, two different focal length lenses of 125 and 370 mm were used. Samples were drilled to achieve a uniform shape distribution of pores arranged in a hexagonal or “honeycomb” configuration (Chamberland, 2015) with pore densities of approximately 6, 11 and 24 pores/cm². After drilling, pore diameters were measured using an optical microscope (Hund 600/12, Wetzlar, Germany) connected to a PC with Imagen.Pro Plus software for calibrated image post-processing.

2.4. Air-drying

In brief, both drilled and non-drilled (control) samples were subjected to an air-drying process as a thermal treatment, with the samples being weighed every 15 minutes. Dehydration time for each sample was defined as the processing time to achieve the criteria to be considered as dehydrated food: a moisture content of 0.1765 (g_water/100 g_ds) according to FAO (Dauthy, 1995).

2.4.1. Air-Drying Process

The air-drying process was conducted at 60°C in a forced-convection air oven (Beschickung 100–800, Büchenbach, Germany). The temperature was selected in accordance with the recommendations set forth by Vilela et al. (2011), which ranged from 40 to 70°C. To ensure uniform exposure to the drying air, all samples were placed on a metal mesh.

2.4.1. Effective moisture diffusivity (D_eff)

Free Moisture content The variation of free moisture content X (g_water/100 g_ds) over time was calculated for each period using Eq. (3):

$$\:\text{X}={\text{X}}_{\text{t}}-{\text{X}}_{\text{e}}=\:\left[\frac{{\text{w}}_{\text{t}}-{\text{w}}_{\text{d}.\text{m}}}{{\text{w}}_{\text{d}.\text{m}}}\right]-\:{\text{X}}_{\text{e}}$$

Where, w_t is its total weight at time t, w_d.m is the sample dry mass, and X_e is the equilibrium moisture content (EMC).

Moisture Ratio The moisture ratio (MR) at time t was calculated using Eq. (4):

$$\:{\text{M}\text{R}}_{\text{t}}=\:\frac{\text{X}-{\text{X}}_{\text{e}}}{{\text{X}}_{0}-{\text{X}}_{\text{e}}}$$

Where, X₀ represents the initial moisture content (dry basis), while X_e denotes the equilibrium moisture content (dry basis) as determined by analysis of the a_w data (GAB model) under conditions of constant operational temperature.

Effective Moisture Diffusivity The moisture gradient is the driving force for water loss from the food matrix during the air-drying process. Fick’s Second Law describes this phenomenon as follows:

$\:\frac{\partial\:\text{X}}{\partial\:\text{t}}=\:\nabla\:\bullet\:\left({\text{D}}_{\text{e}\text{f}\text{f}}\nabla\:\text{X}\right)$

(5)

Where, D_eff is the effective diffusivity in m²s^− 1. This parameter represents the observed moisture diffusivity for the process, averaging the changes in material properties during drying. Eq. (5) can be solved for one-dimensional diffusion (infinite slab), assuming homogeneous initial concentration, symmetry, and surface concentration as boundary conditions. The solution for the average moisture is given by Eq. (6) (Simpson et al., 2013):

$$\:{\text{M}\text{R}}_{\text{t}}=\:\frac{8}{{{\pi\:}}^{2}}\sum\:_{\text{n}=0}^{{\infty\:}}\frac{1}{{\left(2\text{n}+1\right)}^{2}}\bullet\:{\text{e}}^{\left(-{\left(2\text{n}+1\right)}^{2}\bullet\:\frac{{{\pi\:}}^{2}\bullet\:{\text{D}}_{\text{e}\text{f}\text{f}}}{{\text{L}}^{2}}\bullet\:\text{t}\right)}$$

Where, L represents the thickness of the sample. For extended drying times (MR_t < 0.6), the Eq. (6) can be bounded by the first term (n = 0). However, to achieve the most precise values of D_eff, the first three terms (n = 0,1,2) were considered in this research.

2.5 Physical and Mechanical parameters

Physical parameters, including the axial face surface (A) and chromatic characteristic according to CIELab (L*, a*, b*, and ΔE*), were measured over time due to their importance in rate of water vapor transfer and quality standard (Prawiranto et al., 2019; Mahiuddin et al., 2018).

2.5.1. Surface variation

An imaging system was established to capture the upper surface of bananas during the drying process. Prior to use, the system was calibrated, and images were processed with ImageJ software (Schneider, Rasband and Eliceiri, 2012). The procedure for image analysis was similar to that reported by Silva-Vera, W. et al (2020b), wherein edge tracking over time was the primary objective, and then converted to surface variation (ΔA_t) using Eq. (7). For each treatment, data samples (n = 5) were acquired every 15 min for the first 2 h and then every 1 h until the end of processing.

$$\:\varDelta\:{\text{A}}_{\text{t}}\left(\text{\%}\right)=\left[\frac{{\text{A}}_{0}-{\text{A}}_{\text{t}}}{{\text{A}}_{0}}\right]\bullet\:100$$

Where, A₀ is the initial surface value and A_t is the surface value at time t.

2.5.2. Color analysis

Color measurement was conducted using a portable digital Colorimeter (Chroma meter CR-400, Konica Minolta, Tokyo, Japan) on randomly selected spots orthogonal to the axial surface exposed to the hot air. The instrument averaged the readings for the parameters L* (0 to + 100), a* (-120 to + 120), and b* (-120 to + 120). As a reference, the chromatic parameters for a fresh banana were measured (Degwale et al., 2022). Then, the overall colorimetric differences (ΔE*) for each sample (n = 5) and treatment were calculated, as follows:

$$\:\varDelta\:{\text{E}}_{\text{t}}^{\text{*}}=\sqrt{{{\left({\varDelta\:\text{L}}_{\text{t}}^{\text{*}}\right)}^{2}+\left({\varDelta\:\text{a}}_{\text{t}}^{\text{*}}\right)}^{2}+{\left({\varDelta\:\text{b}}_{\text{t}}^{\text{*}}\right)}^{2}}$$

The range of positive and negative values for lightness ($\:{{\Delta\:}\text{L}}_{\text{t}}^{\text{*}}$ ), red/green ($\:{{\Delta\:}\text{a}}_{\text{t}}^{\text{*}}$), and blue/yellow ($\:{{\Delta\:}\text{b}}_{\text{t}}^{\text{*}}$) variations were used in this context.

2.5.3. Mechanical properties

Mechanical properties of each dehydrated sample were evaluated using the Three Point Bend Testing Method in a Texture Analyser. The effective Young’s modulus E_eff (Pa), work per thickness W_T (J/m), and maximum force F_max (N) on first fracture were estimated using a texture analyzer (Brookfield Engineering, Model CT3-50 K, UK) with the probe TA3/100 over an element TA-RT-KI. This system uses TexturePro CT v1.2 software to regulate operational conditions as previously reported by Silva-Vera et al. (2020a). In summary, the probe velocity was 0.5 mm/s, the active load was 0.07 N, and displacement was 10 mm. Eeff was calculated using equation ( 9).

$$\:{\text{E}}_{\text{e}\text{f}\text{f}}=\frac{{\text{a}}^{3}}{4\bullet\:\text{d}\bullet\:\text{b}\bullet\:{\text{h}}^{3}}\bullet\:\text{F}$$

Where F represents the load (N), a corresponds to the length of the support span (m), d denotes the displacement (bend) of the solid (m), b stands for the width (m), and h indicates the thickness (m) of sample. All measurements were conducted in triplicate.

3.1. Sorption Isotherms

Figure 1 presents the sorption isotherm of bananas determined at 60 and 75 °C for an a_w range of 0.20-0.98. The experimental data were fitted to the GAB model, and the parameters of monolayer moisture (X_GAB) and entropic accommodation factors (K_GAB, c_GAB) were estimated (see Table 1).

Finally, net isosteric sorption heat (q_s) was determined using the Clausius–Clapeyron equation (Alamri et al., 2018; Talla et al., 2005; Al-Muhtaseb, McMinn and Magee, 2002; Vega-Gálvez et al., 2008) and plotted as a function of the equilibrium moisture content in Fig. 2.

Results exhibited a Type III isotherm, commonly referred to as the Flory-Huggins isotherm. There was an increase in equilibrium moisture content with a decrease in temperature at a constant a_w (see Fig. 1), as similarly reported in the literature (Yan, Sousa-Gallagher and Oliveira, 2008; Al-Muhtaseb et al., 2002). This observation suggests that the banana sample became more hygroscopic at the lower temperature. Such phenomena are commonly observed in materials with abundant hydroxyl groups, like starch (a primary component of bananas) and other polar groups on their surface, as noted by Alamri et al. (2018). Moreover, this result can be explained because of the binding energy between water molecules present in the food decrease as temperature increased, reducing the attractive forces between the water molecules and sorption sites (Ouafi, N., et al. 2015).

Significant differences in equilibrium moisture content were observed at a_w > 0.8. At higher water activity levels, there is, by definition, a higher water vapor pressure in the food sample at constant temperature. This condition accelerates water transfer between the phases involved and facilitates the dissolution of simple sugars, as outlined by Caballero-Cerón et al. (2018). The experimental data showed a strong dependence of a_w on temperature at constant moisture content, with higher values of temperature increasing a_w. This elevation in the activity would correspondingly increase both microbial and enzymatic activity (Caballero-Cerón et al., 2018).

An analysis of the GAB equation components (Table 1) reveals a clear trend for X_GAB and c_GAB values with changes in temperature. Conversely, the K_GAB value remained constant across the temperatures tested, consistently staying below 1, indicating a tendency towards no distinction between multilayer and liquid molecules. The c_GAB parameter exhibited an increase with increasing temperature consistently staying between 0 and 2. This supports the Type III result depicted in Fig. 1 (Al-Muhtaseb et al., 2002). Furthermore, the tendency shown by c_GAB indicates that higher temperature increases the difference of enthalpy between the monolayer and multilayer molecules. Conversely, the X_GAB constant, associated with the moisture content of the monomolecular layer covering the entire surface of the food sample, showed a slight tendency to decrease with an increase in temperature. The X_GAB parameter in the GAB model indicates the amount of water that is strongly adsorbed on the food surface, and in this study, it was demonstrated that the water adsorbed at specific binding sites on the surface of the food material decreased at higher temperatures, as reported in the literature (Talla et al., 2005; Caballero-Cerón et al., 2018).

Table 1

GAB model parameters XGAB, KGAB, and cGAB estimated for banana by NLS-algorithm.
Parameter	Temperature (°C)
	60	75
X_GAB (g_water/100 g_ds)	0.175 ± 0.0474	0.119 ± 0.0195
K_GAB (-)	0.975 ± 0.0123	0.976 ± 0.0072
c_GAB (-)	0.277 ± 0.1437	0.422 ± 0.1938

Figure 2 illustrates the relationship between the isosteric sorption heat (q_s) and moisture content (X_ds) based on the assumption that q_s is invariant with temperature. As result, it can be observed that the isosteric heat of sorption decreases with increasing moisture content, displaying a maximum value of 2340 (kJ/mol) at 0.23 (g_water/100 g_ds) and a minimum value of 774 (kJ/mol) at 3.47 (g_water/100 g_ds), exhibiting a clear tendency towards the heat of vaporization of pure water with an increased X_ds. These values obtained suggest a strong bond between the adsorbate (water) and the adsorbent (food matrix) at low moisture content and an exponential decrease with increasing moisture content. This can be attributed to the dissolution of sugars and macromolecules of the biopolymers, as well as capillary condensation as mentioned by Tsami (1991).

3.2. CO2–laser drilling

As detailed in Table 2, the total energy output of the CO2–Laser was influenced by the operating conditions during the laser drilling process, which included the number of pores created. At a focal distance of 125 mm, energy outputs ranged from 2269.2 to 9077.0 J/cm². In contrast, at 370 mm, energy outputs varied from 590.6 to 2362.3 J/cm². The occupancy percentage of the banana surface by the pores varied accordingly, from 0.084 to 0.375% at 125 mm and from 0.327 to 1.313% at 370 mm focal distance. In line with these occupancy percentages, the mass removed from the food was between 9.0⋅10^− 3 and 0.039 g at 125 mm and between 0.037 and 0.150 g at 370 mm focal distance, respectively. Mass removed values were estimated according to the expression reported by Yilbas and Aleem (2004).

Table 2

Processing parameters obtained for CO2-laser micro drilled dehydrated banana.
Diameter (µm)	Pore density (ε) (pores/cm²)	Time dehydration (min)	S/V (m^− 1)	Occupied surface (%)	Mass removed (g)	Energy output (J/cm²)	Q_abs (kW/kg_ds)
220.89 ± 14.15	5.95	186.2 ± 11.0^A,x,*	6.07 ± 0.025^A,x	0.084 ± 0.001^A,x	0.009 ± 0.001^A,x	2269.2 ± 275.2^A,x	7.71 ± 0.19^A,x,*
	10.84	141.1 ± 7.8^A,y	6.86 ± 0.034^A,y	0.196 ± 0.002^A,y	0.019 ± 0.002^A,y	4538.5 ± 550.4^A,y	8.09 ± 1.37^A,x,*
	23.79	126.3 ± 4.7^A,y	8.88 ± 0.34^A,z	0.375 ± 0.027^A,z	0.039 ± 0.005^A,z	9077.0 ± 1100.8^A,z	8.10 ± 0.12^A,x,*
431.96 ± 19.92	5.95	132.0 ± 1.1^B,x	6.79 ± 0.061^B,x	0.327 ± 0.008^B,x	0.037 ± 0.003^B,x	590.5 ± 57.4^B,x	8.06 ± 0.13^A,x,*
	10.84	102.8 ± 4.8^B,yz	9.94 ± 0.08^B,y	0.604 ± 0.012^B,y	0.075 ± 0.006^B,y	1181.1 ± 114.8^B,x	8.37 ± 0.11^A,x,*
	23.79	114.6 ± 0.1^A,xz	11.53 ± 0.37^B,z	1.313 ± 0.064^B,z	0.150 ± 0.013^B,z	2362.3 ± 229.6^B,y	8.71 ± 0.28^A,x,*
Control	-	169.2 ± 15.2^*	5.31 ± 7.4e-6^*	-	-	-	7.86 ± 0.31^*
Values are expressed as means ± standard deviation. Different capital letters (A, B) in the same column indicates significant differences (p ≤ 0.05) between samples with the same pore density. Different lowercase letters (x, y, z) in the same column indicates significant differences (p ≤ 0.05) among samples with the same pore diameter. *Drilled samples with this superscript in each column were statistically similar (p > 0.05) to the control non – drilled samples.

Drilled samples exhibited surface area to volume ratios (S/V) values between 1.14 and 2.17 times greater than those of the control sample, increasing alongside pore density for both sets of samples. Largest values were observed when the largest pore diameter was considered in conjunction with an increase in the number of pores. Moreover, samples exhibiting higher surface area to volume ratios showed an enhanced capacity for energy absorption (Q_ABS) when compared to the control sample, suggesting a more efficient use of the heat supplied by the hot air as banana is drilled. Notably, this relationship persisted despite the statistical similarities (p > 0.05) across all samples, including the control. These findings underscore that the specific conditions of the CO₂-laser drilling operation, including the focal distance and the pore density, significantly influence the changes in surface area to volume ratio (S/V), percentage of occupied surface area, and mass removal.

A larger surface to volume ratio (S/V) should enhance the mechanisms related to mass diffusion and heat transfer, as extensively documented in the literature (Guinee and Sutherland, 2016; Lewis, M. 2023). Hence, a preliminary hypothesis of this study proposed an improved use of energy along with a decrease in expected dehydration times with increasing surface area to volume ratios, accompanied by a reduction in peak energy density (energy output).

3.3. Air drying

To evaluate the effect of the combination of pore diameter and pore density on the dehydration time of bananas, representative curves for air drying process (Fig. 4.A) and surface variation (Fig. 4.B) over time were plotted as functions of pore diameter and pore density.

The dimensionless mass loss over time showed a decreasing trend for all banana samples, regardless of the treatment applied, as depicted in Fig. 4.A. Notably, a significant reduction in water content was observed across all treatments, with a notable tendency for this process to accelerate as pore density increases. Furthermore, as shown in Fig. 4.B, the surface variation increased over time with an average between 28% and 43% upon reaching the steady state, with the greatest variation occurring for increases in both pore density and pore diameter. It is widely reported in the literature that the rate of moisture removal during drying may be governed either by the capacity of moisture diffusion within the food matrix (intrinsic food properties) or by moisture evaporation from the food surface to the drying medium (air properties and velocity) (Sabarez, 2021). Additionally, liquid water migration within the food could occur by different ways, such as capillary flow, surface diffusion, and liquid diffusion; while water vapor could experience Knudsen diffusion, mutual diffusion, Steffan diffusion, Poiseuille flow and condensation–evaporation, among others (Chen et al., 2020). Consequently, most of these theories (macroscopic and microscopic) are mainly associated with bulk materials, implying that any change in this aspect may directly impact water migration. In this study, the macroscopic modification applied was the controlled CO₂–laser drilling, whose effects were primarily observed in terms of surface variation (Fig. 4.B) and drying rate values (Fig. 4.C), thereby artificially enhancing the material’s porosity.

Increased material porosity leads to deformations in food products directly impacting volume, bulk density, and porosity, as reported by Khalloufi et al. (2015). Initially, this increase in porosity will likely induce a shrinkage phenomenon as both pore density and diameter increase, potentially causing the “collapse” of the food structure under the current operating conditions. Although shrinkage is usually found in food dehydration, the presence of such artificially created pores in the food would result in a notable difference in bulk volume compared to the non-drilled sample, particularly at early stages of processing. This difference may cause the microstructure of the food to compact over time due to faster water migration through these “artificial channels”, leading to the eventual collapse of the pores. Furthermore, Mugi and Chandramohan (2021) observed a direct relationship between the volume of water removed and the volume shrinkage during the drying process, mainly related to the rubbery state of the food in the early stages. This compaction phenomenon was evident on the drying rate behavior for each condition tested as, a function of a surface variation over time (Fig. 4.B), since the drying rate was calculated at each time point considering the varying surface area, rather than assuming a constant surface area. However, in the final stages of the drying process, the outer surface of the food changes to a glassy state and becomes a rigid shell with a porous structure, which probably indicates a deceleration of the external volume change but without stopping the shrinkage of the internal structure. A comprehensive analysis for shrinkage, heat and mass transfer coefficient, and effective diffusion coefficient and their relationship with air velocity, temperature, and solar method has been described in the literature (Mugi and Chandramohan, 2021; Mahiuddin et al., 2018; Purlis, Cevoli and Fabbri, 2021).

Figure 4.C shows the water removal rate per surface, highlighting that the highest drying rate values were observed at the outset for samples with the largest pore density and diameter. Initially, the drying rates for drilled samples were 1.7 to 2 times higher than those for non-drilled samples. However, as the processing time progressed, there were no significant differences in surface variation and drying rate between the drilled and non-drilled samples (p > 0.05). Notably, among the drilled samples, clear differences in surface variation were observed within the first 3 h of processing. This might indicate that the modification of the geometric parameters (S/V) had no significant effect on the mass water diffusion among the dehydrated banana samples at longer processing times.

As a result of CO2–laser drilling, a significant reduction in dehydration times was observed in drilled samples when compared to the non-drilled control sample (p ≤ 0.05), as detailed in Table 2. The results indicated that dehydration times were reduced by up to 40% across various tested configurations, with the most considerable reduction observed in samples featuring a pore diameter of 431.96 ± 19.92 µm and a pore density of 10.84 pores/cm², aiming to achieve a final moisture content of 0.1765 (g_water/100 g_ds).

Generally, the results indicated that drilled samples with larger pore sizes (431.96 ± 19.92 µm) exhibited shorter dehydration times than those with smaller pores (220.89 ± 14.15 µm). A corresponding increase in pore density was associated with further reductions in dehydration times, especially pronounced in samples with smaller pore diameters. Within this framework, the introduction of these "artificial pores" throughout the food significantly enhanced water diffusion, despite the noticeable surface variation depicted in Fig. 4.B. These pores effectively facilitated water diffusion, thereby proving to be advantageous in accelerating the dehydration process for both sample groups.

3.3.1. Effective diffusivity coefficient (D_eff)

In this study, the effective diffusivity coefficient (D_eff) is related to the diffusion of water from the product’s inner to its outer surface and is especially significant during the falling drying rate period. This mechanism involves the presence of water, either in its liquid or vapor state, with D_eff values influenced by temperature, pressure, and moisture content of the product (Boudhrioua, 2004; Mugi and Chandramohan, 2021). Figure 5 depicts the variation of D_eff over time (Fig. 5.A) and in relation to varying moisture content (Fig. 5.B) for samples with different pore diameters and densities.

The impact of CO₂-laser drilling on D_eff value was estimated using the mathematical model previously outlined for this geometry (Eq. (4)), accounting for surface variability. It is widely recognized in the literature that D_eff values offer pertinent insights into the rate at which moisture is transferred within the food structure at different stages during the processing time, as well as the dynamic behavior of this value during dehydration processes (Rani and Tripathy, 2021). In the isothermal drying of bananas with varying pore density, diameter and a variable food surface exposed to hot air, the effective diffusivity - estimated using Fick’s model - increased in earlier stages of processing across all samples, regardless of the type of pretreatment applied, as shown in Fig. 5.A. Remarkably, D_eff values were significantly higher with increased pore density and diameter, indicating a proportional relationship between D_eff and pore configuration. Indeed, D_eff values in the drilled samples were about 1.7 times higher than those in the non-drilled samples, demonstrating an enhanced diffusion of water throughout the food matrix. Additionally, at lower moisture levels in bananas, an increase in pore density positively affected D_eff with values ranging from 4.7⋅10^− 10 to 1.1⋅10^− 9 (m²/s), as depicted in Fig. 5.B. This observation aligns with the findings of Djebli et al. (2019) and Rani and Tripathy (2021), who noted an increase in D_eff values with decreasing water content in food products when employing a mixed-mode solar dryer.

Given that CO₂-laser drilled samples exhibited higher values of D_eff, the pretreatment proposed in this study is anticipated to reduce the processing times needed to achieve a desired moisture content. However, after 2.5 hours of processing, D_eff values began to decline, possibly due to variations in both geometric and apparent density. The latter is supported by the work of Aversa et al. (2011), who noted a significant drop in the apparent density of eggplant at lower moisture content (X/X₀ < 1), attributing this mainly to the creation of pores within the solid matrix. Although CO₂–laser drilling as a pre-treatment for banana dehydration shows promising results in increasing D_eff values for the conditions tested, their behavior was similar regardless of their density and pore size. This is consistent with the findings of Thuwapanichayanan et al. (2011), who reported an exponential decline in D_eff during the convective drying of undrilled bananas.

3.4. Color

Table 3 details the chromatic parameters L*, a* and b* values together with ΔE* of dehydrated banana with and without CO2–laser drilling. The L* values, as a measure of the lightness of the product, for all dehydrated samples ranged from 57.6 to 61.0, significantly lower than those for fresh banana (67.8 ± 0.51). Thus, the drying process, with and without CO₂-laser pre-treatment, caused darkening of banana slices due to both enzymatic and non-enzymatic browning reactions (Nagvanshi, Venkata and Goswami, 2021; Krokida et al., 2000). The nondrilled samples (Control) displayed the highest L* values (61.0), which were similar (p > 0.05) to the samples with the smallest pore diameter. In drilled samples, it was observed that the lightness tended to decrease with increasing pore density at both pore diameters due to the destruction of the tissue surface and the formation of darkness around the emerging pore. This Heat Affected Zone (HAZ) identified in these samples may further accelerate browning reactions, as reported by Jiang, Duan, Qu and Zheng (2016).

Table 3

Color parameters L*, a*, b*, and ∆E* for control and CO2-laser drilled dehydrated bananas.
Diameter (µm)	Pore density (ε) (pores/cm²)	L*	a*	b*	∆E*
220.89 ± 14.15	5.95	60.7 ± 4.28^A,x,*	3.15 ± 1.10^A,y	20.2 ± 4.74^A,x	9.53 ± 5.13^A,x,*
	10.84	58.4 ± 3.82^A,y	2.05 ± 0.86^A,x,*	23.6 ± 5.98^A,y,*	9.68 ± 4.75^A,x,*
	23.79	59.4 ± 4.31^A,y	3.32 ± 0.93^A,y	21.7 ± 2.87^A,z	8.74 ± 3.60^A,x,*
431.96 ± 19.92	5.95	60.0 ± 4.16^A,x,*	2.51 ± 0.71^B,y	22.2 ± 4.10^B,y	5.89 ± 3.93^B,x
	10.84	57.6 ± 4.61^A,y	3.16 ± 1.37^B,x	25.6 ± 4.03^B,x	10.7 ± 5.99^A,y
	23.79	57.4 ± 3.69^B,y	2.33 ± 1.13^B,y,*	23.6 ± 4.44^B,y,*	8.38 ± 4.21^A,z,*
Control	-	61.0 ± 3.98^*	2.11 ± 0.81^*	24.6 ± 5.05^*	8.99 ± 4.37^*
Values are expressed as means ± standard deviation. Different capital letters (A, B) in the same column indicates significant differences (p ≤ 0.05) between samples with the same pore density. Different lowercase letters (x, y, z) in the same column indicates significant differences (p ≤ 0.05) among samples with the same pore diameter. *Drilled samples with this superscript in each column were statistically similar (p > 0.05) to the control non – drilled samples.

All dehydrated samples exhibited a* values within the positive range of 2.05–3.32, which were significantly higher (p ≤ 0.05) than the a* value of fresh banana (0.64 ± 0.12), indicating a color shift towards red due to browning reactions occurring during the drying process (Krokida et al., 2000). Although the drilled samples generally tended to exhibit slightly higher a* values compared to the non-drilled samples, significant differences (p ≤ 0.05) were only observed in isolated cases across the samples.

A comparison of the b* values revealed that the drying process did not significantly impact the distinctive yellow color of the fresh bananas, showing that the outcomes are independent of whether CO₂–laser pre-treatment was applied or not. Furthermore, all the dehydrated samples displayed b* values within the range of 20.2 to 25.6, closely mirroring those of the fresh bananas (23.2 ± 2.3). However, variations in the b* parameter of banana slices subjected to different drying processes have been documented in the literature. Specifically, Nagvanshi et al. (2021) reported a decrease in yellow pigmentation of banana slices exposed to microwave drying, attributing this change to the degradation of carotenoids. Conversely, Krokida et al. (2000), among others, reported an increase in the intensity of the yellow color in banana slices dried via air or microwave methods.

Finally, the dehydrated samples of banana exhibited ΔE* values ranging from 5.89 to 10.7. Notably, among all evaluated samples, those displaying the lowest ΔE* values, and thus the least color shift relative to fresh bananas, were specifically the drilled samples with a pore diameter of 431.96 µm and a pore density of 5.95 pores/cm². According to Mokrzycki and Tatol (2011), a standard observer can detect color differences with a ΔE* value greater than 5. Given this threshold, the color changes between fresh and dehydrated bananas are indeed perceptible by human vision, irrespective of drilling or the CO₂-laser configuration. Consequently, all dehydrated products exhibited noticeable color differences when compared to fresh bananas, as perceived by the human eye in practical terms.

3.5. Mechanical Properties

Mechanical properties, total compression work per unit thickness (W_T), maximum force (F_max), and effective Young’s modulus (E_eff) for both the control and CO₂–laser drilled dehydrated banana slices are shown in Table 4. The W_T represents the energy applied to the food product to cause deformations, changes, or breakdown in its structure (Lu, 2013). W_T values for drilled samples with pore diameters of 220.89 µm ranged from 1.71 to 3.15 J/m, and for those with pore diameters of 431.96 µm ranged from 0.46 to 1.99 J/m. Consequently, the drilled samples with the largest pore diameter exhibited significantly (p ≤ 0.05) lower WT values than those with the smallest pore diameter. The findings in CO₂-laser drilled tomato skin (Silva-Vera et al., 2020b), as well as in other studies on nonfood materials such as steel, aluminum plate, and plastic films (Formisano and Lombardi, 2016; Winotapun et al., 2015) support this result. This structural effect indicates that a high number of large-diameter pores might weaken the structure of dehydrated banana samples, leading to a non-uniform structure prone to fracture. This is also supported by the results shown for the dehydrated control sample (non-drilled).

Table 4

Mechanical parameters WT, Fmax, and Eeff for control and CO2-laser drilled dehydrated bananas.
Diameter (µm)	Pore density (ε) (pores/cm²)	W_T (J/m)	F_max (N)	E_eff (Pa ∙ 10⁷)
220.89 ± 14.15	5.95	3.15 ± 0.61^A,x	2.91 ± 0.3^A,x,*	1.19 ± 0.30^A,x,*
	10.84	1.71 ± 1.07^A,yz,*	3.71 ± 2.09^A,x,*	3.05 ± 1.91^A,x,*
	23.79	2.38 ± 1.01^A,xz,*	2.66 ± 0.85^A,x,*	2.01 ± 0.67^A,x,*
431.96 ± 19.92	5.95	0.56 ± 0.19^B,x,*	4.66 ± 2.31^A,x	6.95 ± 0.61^B,x
	10.84	0.46 ± 0.03^B,x,*	4.02 ± 0.34^A,x	7.38 ± 3.28^B,x
	23.79	1.99 ± 0.73^A,y,*	2.48 ± 0.29^A,x,*	5.80 ± 4.21^A,x
Control	-	1.54 ± 0.58^*	1.79 ± 0.32^*	1.16 ± 0.89^*
Values are expressed as means ± standard deviation. Different capital letters (A, B) in the same column indicates significant differences (p ≤ 0.05) between samples with the same pore density. Different lowercase letters (x, y, z) in the same column indicates significant differences (p ≤ 0.05) among samples with the same pore diameter. *Drilled samples with this superscript in each column were statistically similar (p > 0.05) to the control non – drilled samples.

Regarding the maximum force (F_max) observed, the drilled samples exhibited a range of values from 2.48 to 4.66 N, with no significant differences (p > 0.05) found due to variations in pore diameter or pore density. However, samples featuring a larger pore diameter (431.96 µm) exhibited F_max values significantly higher (p ≤ 0.05) than those of the non-drilled (control) sample, across pore densities of 5.95 and 10.84 pores/cm². This suggests that the dehydration process, facilitated by the presence of pores, potentially leads to an increase in stiffness in the dehydrated product, as it allows for a potential isotropic enhancement of water diffusion within the banana, as illustrated in Fig. 5.

Finally, the E_eff values for drilled samples ranged between 1.19⋅10⁷ and 7.38⋅10⁷ Pa, indicating magnitudes between 1.02 and 6.4 times larger that of the non-drilled control sample. A trend was observed where E_eff values increased as the pore diameter increased, regardless of pore density (p ≤ 0.05). This phenomenon has been reported in laser drilled materials like tomato skin (Silva-Vera et al., 2020b) and poly(3 hydroxybutyrate) films (Volova et al., 2015). Pore density, however, did not influence the Eeff values, and samples with identical pore diameter showed no significant differences (p > 0.05) in Eeff values. Literature suggests that the variation of E_eff in dehydrated materials is exclusively contingent upon their moisture content (Mayor, Cunha and Sereno, 2007; Sahputra, Alexiadis and Adams, 2019; Mvondo et al., 2017). In this context, drilling led to a more uniform and facilitated water removal in the banana. Hence, samples with larger pore diameters typically exhibited higher E_eff values than the non-drilled (control) samples. Another consideration is the proximity between pores and the presence of HAZ. The formation of a crater around a hole is often seen on surfaces with organic properties. Excessive heat can induce melting, leading to a localized increase in mechanical resistance and hardness (Majumdar, Nath and Manna, 2004).

Though some specific differences were noted in the mechanical properties between drilled and non-drilled samples, statistical analysis revealed that under most of the tested operating conditions, drilled banana slices were comparable to the control sample in terms of W_T and F_max. Nevertheless, Eeff showed significant variations when banana slices were drilled with the largest pore diameter, likely due to the induced structural changes within the food.

Banana slices were pre-treated with a CO₂–laser to drill them before air-drying. The effects of drilling on dehydration time, the effective diffusivity coefficient, color, and mechanical properties of the dehydrated products were evaluated. The surface area to volume ratio and absorbed energy showed a positive correlation with reduced dehydration time, indicating a more efficient use of energy from the hot air that resulted in a reduction of up to 40% in dehydration time. The effective diffusivity displayed dynamic behavior over time, closely related to changes in pore density and diameter. Moreover, the D_eff of drilled samples was at most 1.7 times higher than that of the non-drilled samples. While all dehydrated banana slices retained the characteristic yellow color of fresh bananas, the laser-drilled slices appeared slightly darker and shifted towards the red spectrum, suggesting a higher degree of browning, particularly around the pores. Banana slices drilled at the largest focal distance displayed higher E_eff (effective Young’s modulus) and F_max (maximum force) values compared to the control samples. This is attributed to their larger pore diameter. In conclusion, the CO₂–laser drilling pretreatment offers a promising approach to accelerate the water remotion from banana slices by using drying process in conventional air-drying equipment.

FUNDING

This work is the result of the research project funded by FONDECYT-POSTDOCTORADO N° 3180342 and FONDECYT Regular N° 1181270, Chile.

COMPETING INTEREST

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

CRediT authorship contribution statement

Silva-Vera. Wladimir: Project administration, Conceptualization, Writing - Review & Editing.

Giménez Begoña: Visualization, Writing - Original Draft.

Xiaojing Tian: Data Curation, Writing - Original Draft.

Abarca O. Romina: Data Curation.

Almonacid A. Sergio: Supervision, Resources.

Simpson R. Ricardo: Supervision, Resources.

Sandoval-Hevia. Gabriela.: Data Curation, Writing - Original Draft.

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No competing interests reported.

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Exploring CO 2 -laser drilling: Potential for Enhanced Mass and Thermal Diffusion in Banana (Musa sapientum) Dehydration

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Version 1

Abstract

Figures

1. INTRODUCTION

2. MATERIALS AND METHODS

2.1. Materials

2.2. Sorption Isotherms

2.2.1. Sorption isotherm model

2.2.2. Net isosteric sorption heat (q_s)

2.3. CO2–laser drilling

2.4. Air-drying

2.4.1. Air-Drying Process

2.4.1. Effective moisture diffusivity (D_eff)

2.5 Physical and Mechanical parameters

2.5.1. Surface variation

2.5.2. Color analysis

2.5.3. Mechanical properties

3. RESULTS AND DISCUSSION

3.1. Sorption Isotherms

3.2. CO2–laser drilling

3.3. Air drying

3.3.1. Effective diffusivity coefficient (D_eff)

3.4. Color

3.5. Mechanical Properties

4. CONCLUSIONS

Declarations

References

Additional Declarations

Status:

Version 1

Exploring CO 2 -laser drilling: Potential for Enhanced Mass and Thermal Diffusion in Banana (Musa sapientum) Dehydration

Status:

Version 1

Abstract

Figures

1. INTRODUCTION

2. MATERIALS AND METHODS

2.1. Materials

2.2. Sorption Isotherms

2.2.1. Sorption isotherm model

2.2.2. Net isosteric sorption heat (qs)

2.3. CO2–laser drilling

2.4. Air-drying

2.4.1. Air-Drying Process

2.4.1. Effective moisture diffusivity (Deff)

2.5 Physical and Mechanical parameters

2.5.1. Surface variation

2.5.2. Color analysis

2.5.3. Mechanical properties

3. RESULTS AND DISCUSSION

3.1. Sorption Isotherms

3.2. CO2–laser drilling

3.3. Air drying

3.3.1. Effective diffusivity coefficient (Deff)

3.4. Color

3.5. Mechanical Properties

4. CONCLUSIONS

Declarations

References

Additional Declarations

Status:

Version 1

2.2.2. Net isosteric sorption heat (q_s)

2.4.1. Effective moisture diffusivity (D_eff)

3.3.1. Effective diffusivity coefficient (D_eff)