Optimizing Texture and Drying Behavior of Squid (Todarodes pacificus) for Elder-Friendly Applications Using Alkaline Pretreatment and Intermittent Drying: An Experimental and Numerical Study

Abstract

This study addresses the increasing demand for texture-modified seafood products suitable for elderly consumers by focusing on dried squid, a popular protein source. The aim was to optimize the softening and drying procedures to produce a dried squid product with improved chewability and quality. Fresh squid was pretreated using sodium bicarbonate or potassium carbonate solutions (0, 0.3, 0.6, and 0.9 mol/kg) and dried at 40 °C using either continuous (CD) or intermittent drying (ID) until the final moisture content reached 18.34 ± 0.44%. Hardness generally increased with higher alkaline concentrations, with the potassium carbonate-treated samples showing better softening effects. Based on standards for elderly-friendly foods targeting chewable hardness (10,000–50,000 N/m²), low water activity (<0.58), and limited color change (ΔE = 14.32), the optimal result was achieved with 0.3 mol/kg potassium carbonate and ID. Among the thin-layer drying models, the Midilli–Kucuk model showed the best fit, with the highest average R² (0.9974), and lowest SSE (0.0481) and RMSE (0.1688), effectively capturing the drying kinetics. Scanning electron microscopy (SEM) revealed smoother surfaces and consistent porosity in samples dried intermittently, indicating less structural degradation. Finite element analysis showed that ID improved internal moisture distribution, reduced surface crusting, and alleviated internal stresses. These results support mild alkaline soaking combined with ID as an effective strategy for enhancing dried squid quality for elderly individuals.

1. Introduction

Seafood is globally valued for its high-quality proteins, essential amino acids, and bioavailable micronutrients such as iodine, vitamin B12, selenium, and omega-3 fatty acids [1,2]. Among seafood, squid (Todarodes pacificus) is notable for its nutritional benefits and versatility in processed foods [3]. Its muscle composition of approximately 75–80% moisture, 15–18% protein, 1–2% fat, and 1–2% ash makes it a lean, protein-rich option [4,5]. However, high moisture and enzymatic activity make squid highly perishable, promoting microbial and biochemical spoilage [4]. Drying, particularly hot-air drying, is widely used to extend the shelf life by lowering water activity due to its simplicity and cost-effectiveness [5].

Nevertheless, continuous hot-air drying (CD) often causes surface hardening, shrinkage, and uneven internal moisture distribution, leading to poor texture and color quality [6,7,8]. These drawbacks are critical when aiming to produce squid snacks with improved softness and palatability. Traditional drying methods often yield tough textures unsuitable for consumers such as elderly individuals seeking more tender options. Age-related physiological changes, including reduced saliva production and muscle strength, can limit food intake and reduce the enjoyment and safety of meals [9,10,11]. As such, developing chewable yet softer-textured seafood snacks can improve the dietary experience for older adults, without necessarily targeting individuals with clinical dysphagia who require puréed or minced foods.

To improve the drying performance and texture, chemical pretreatments like alkaline soaking have gained interest. Soaking in sodium bicarbonate (NaHCO₃) or potassium carbonate (K₂CO₃) solutions increases the alkalinity of the soaking medium, which affects the muscle’s structural proteins, primarily myofibrillar proteins. The elevated pH leads to an increase in net negative charge on these proteins, causing electrostatic repulsion that relaxes muscle fiber packing and weakens the intermolecular bonds responsible for firmness and toughness [12,13,14]. This process enhances porosity, facilitates water migration, and promotes muscle network loosening, thereby softening the final product texture. Such structural modifications make the product more easily chewable and digestible, which is particularly important for elderly consumers facing reduced chewing efficiency and oral moisture [12,13]. For instance, Lin et al. [14] reported improved tenderness and moisture loss in abalone treated with NaHCO₃, while Liu et al. [15] demonstrated that alkaline treatment enhanced the drying rates and softened the texture by loosening the muscle structure and increasing water mobility. However, the specific application of alkaline pretreatment for squid to meet the texture requirements of elder-friendly foods remains underexplored and warrants further investigation.

In addition to pretreatments, modifying the drying strategy through intermittent drying (ID), which introduces rest periods during drying, has been shown to improve moisture redistribution, reduce shrinkage, and enhance texture [16,17]. While ID has been tested in plant and meat products, its application to seafood and in combination with alkaline soaking remains underexplored.

Given the complexity of moisture transport in biological tissues during drying, understanding the heat and mass transfer mechanisms is critical to optimizing the drying parameters. Numerical modeling offers an efficient approach to simulate internal transport phenomena, predict drying behaviors, and reduce trial and error in process development. Finite element modeling (FEM) has gained traction in simulating temperature and moisture profiles in porous food matrices. Soydan [18] used FEM to study the drying characteristics of lean and minced meat samples and validated the predicted moisture distributions with experimental data. Similarly, Sun and Hu [19] modeled simultaneous heat and mass transfer in meat during vacuum cooling and emphasized the importance of incorporating tissue-specific thermal properties to enhance model accuracy. These models provide critical insights into how the drying conditions and pretreatments influence the internal dynamics and guide process design for industrial applications [19]. Despite the increasing adoption of computational tools in drying research, there is limited integrative work combining simulation with experimental quality evaluation for squid or other soft-textured seafood products.

Therefore, this study addresses these knowledge gaps by exploring the potential of alkaline soaking and controlled intermittent drying to produce soft-dried squid suitable for the elderly or people with chewing problems. Specifically, this research aims to (1) evaluate the influence of sodium bicarbonate and potassium carbonate soaking on squid drying kinetics and texture properties, (2) compare continuous and intermittent hot-air drying regimes in terms of drying efficiency and product quality, (3) analyze microstructural behavior using scanning electron microscopy (SEM), and (4) develop and validate a finite element model to simulate heat and moisture transfer during drying. The findings will provide insights into how pre-drying treatments and drying regimes can be optimized to meet the textural and nutritional needs of elderly consumers, while enhancing the efficiency of seafood drying processes.

2. Materials and Methods

2.1. Sample Preparation

Fresh squid (Todarodes pacificus), averaging 18–20 cm in body length, were purchased from a local seafood market in Chuncheon, South Korea. Upon arrival, they were cleaned, gutted, and cut into uniform 5 cm × 5 cm sections with an average thickness of 0.5 ± 0.1 cm to ensure consistent heat and moisture transfer during drying and simulation. Surface moisture was removed using paper towels before treatment. Samples were soaked in two alkaline solutions: (a) sodium bicarbonate (BS; NaHCO₃) and (b) potassium carbonate (PC; K₂CO₃), prepared at concentrations of 0, 0.3, 0.6, and 0.9 mol/kg using deionized water. These concentrations were selected based on preliminary experiments balancing effective softening and product quality without compromising safety or sensory properties. The pH of these solutions ranged approximately from 8.3 (BS) to 11.4 (PC), providing mild to moderate alkalinity suitable for seafood muscle modification. Soaking was conducted for 60 min at room temperature (25 ± 2 °C) under mild agitation, based on preliminary tests. After soaking, samples were briefly rinsed with distilled water to remove surface alkali and blotted dry.

2.2. Drying Procedure

Drying was performed using a laboratory-scale hot-air oven (Model: NB-901M, N-BIOTEK, Bucheon, Gyeonggi, Republic of Korea) with chamber dimensions of 550 mm × 520 mm × 600 mm. The drying temperature was set at 40 °C, selected from preliminary trials at 40 °C and 50 °C. Among the tested conditions, 40 °C provided an optimal balance between drying efficiency, textural softening, and minimal thermal degradation of sensitive nutrients such as proteins, making it suitable for elder-friendly squid products [5]. Drying conditions included 10% relative humidity and 3 m/s inlet air velocity, measured using a digital hot-wire anemometer (Model: TES-1341N, TES Electrical Electronic Corp., Taipei, Taiwan, China).

Two drying regimes were used as follows: (1) continuous drying (CD), where samples were dried to a target moisture content of 18.34 ± 0.44% (wet basis); and (2) intermittent drying (ID), applying a 3:1 drying-to-rest ratio, i.e., 45 min of drying followed by 15 min of rest (no airflow, heater off), repeated until the same moisture target was reached.

The approximately 18% final moisture level ensured optimal softness (hardness: 10,000–50,000 N/m²) and water activity below 0.68, balancing chewability and microbial stability [11]. Under both regimes, five samples were arranged horizontally in the dryer at 300 mm intervals following the drying conditions in Park and Yoon [17] (Figure 1).

2.3. Moisture Content Determination and Drying Kinetic of Squid

The drying behavior of the squid samples was evaluated by determining the moisture ratio (MR) and drying rate at various time intervals. The moisture content on a wet basis (MC_wb), MR, and drying rate were calculated using the following equations [20,21]:

$${\mathrm{M}\mathrm{C}}_{\mathrm{w}\mathrm{b}}={\displaystyle \frac{{\mathrm{W}}_{\mathrm{w}}-{\mathrm{D}}_{\mathrm{w}}}{{\mathrm{W}}_{\mathrm{w}}}},$$

(1)

where MC_wb is the wet base moisture content (g of water/g of sample), W_w is the sample’s initial weight (g), and D_w is the final dry weight (g).

$$\mathrm{M}\mathrm{R}={\displaystyle \frac{{\mathrm{M}\mathrm{C}}_{\mathrm{w}\mathrm{b}}-{\mathrm{M}}_{\mathrm{e}}}{{\mathrm{M}}_{\mathrm{i}}-{\mathrm{M}}_{\mathrm{e}}}},$$

(2)

where MR denotes the moisture ratio (dimensionless), M_i is the initial moisture content (g of water/g of sample), and M_e represents the equilibrium moisture content (g of water/g of sample). The drying rate was assessed at different stages of the drying process using the following equations:

At the start (t = t₀),

$${\displaystyle \frac{\mathrm{d}\mathrm{M}}{\mathrm{d}\mathrm{t}}}={\displaystyle \frac{{\mathrm{M}}_{\mathrm{i}}-{\mathrm{M}}_0}{{\mathrm{t}}_1-{\mathrm{t}}_0}},$$

(3)

During drying (t = t_i i = 1, 2, …, n − 1),

$${\displaystyle \frac{\mathrm{d}\mathrm{M}}{\mathrm{d}\mathrm{t}}}={\displaystyle \frac{{\mathrm{M}}_{\mathrm{t}+1}-{\mathrm{M}}_{\mathrm{t}-1}}{{\mathrm{t}}_{\mathrm{i}+1}-{\mathrm{t}}_{\mathrm{i}-1}}},$$

(4)

At the final time point (t = t_n),

$${\displaystyle \frac{\mathrm{d}\mathrm{M}}{\mathrm{d}\mathrm{t}}}={\displaystyle \frac{{\mathrm{M}}_{\mathrm{n}}-{\mathrm{M}}_{\mathrm{n}-1}}{{\mathrm{t}}_{\mathrm{n}}-{\mathrm{t}}_{\mathrm{n}-1}}},$$

(5)

where ${\mathrm{M}}_0,{\mathrm{M}}_{\mathrm{i}},{\mathrm{M}}_{\mathrm{i}+1},{\mathrm{M}}_{\mathrm{i}-1},{\mathrm{M}}_{\mathrm{n}},{\mathrm{M}}_{\mathrm{n}-1}$ are the moisture content (g water/g sample) at corresponding drying time (min) points (${\mathrm{t}}_0,{\mathrm{t}}_{\mathrm{i}},{\mathrm{t}}_{\mathrm{i}+1},{\mathrm{t}}_{\mathrm{i}-1},{\mathrm{t}}_{\mathrm{n}},{\mathrm{t}}_{\mathrm{n}-1}$).

The experimental moisture loss data were fitted to the selected thin-layer models using MATLAB (R2024a, MathWorks Inc., Natick, MA, USA) (

Table 1. Thin-layer drying models for analyzing the drying characteristics of squid.

Model Name	Model Name	References
Page	$\mathrm{MR}={\mathrm{e}}^{{-\mathrm{k}\mathrm{t}}^{\mathrm{n}}}$	[22]
Newton	MR = e−kt	[23]
Midilli–Kucuk equation	$\mathrm{MR}=\mathrm{a}{\mathrm{e}}^{{-\mathrm{k}\mathrm{t}}^{\mathrm{n}}}+\mathrm{b}\mathrm{t}$	[24]
Henderson and Pabis	$\mathrm{MR}={\mathrm{a}\mathrm{e}}^{-\mathrm{k}\mathrm{t}}$	[25]

). Model performance was assessed by evaluating the R-squared (R²) values and Sum of Squared Errors (SSEs), with the model yielding the highest R² and the lowest SSE values considered the most appropriate. The model parameters were subsequently used to estimate the drying time required to reach an approximated target moisture content of 18%.

2.4. Texture Analysis

The textural properties of the dried squid samples were evaluated using a texture analyzer (Model TA.XT Plus, Stable Micro Systems, Surrey, UK). A cutting test was conducted with a flat blade (0.5 mm thick) at a constant speed of 1 mm/s to quantify resistance to deformation. Samples were consistently oriented on the platform to ensure uniform contact with the blade. The maximum cutting force required to shear each sample was recorded, and the cutting stress (N/m²) was calculated by dividing the peak force by the cross-sectional area in contact with the blade, allowing comparison across samples of varying thickness.

The cutting test provides insights into the material’s mechanical behavior, distinguishing between brittle and ductile characteristics. Brittle materials typically fracture suddenly with little deformation and exhibit sharp breaks, which may be perceived as hard or tough, potentially causing discomfort for consumers, especially elderly individuals with reduced chewing ability. In contrast, ductile materials deform plastically before breaking, resulting in a softer, more gradual failure that tends to be easier to chew and more acceptable to consumers seeking tender textures. Thus, understanding whether the dried squid exhibits brittle or ductile behavior is critical for assessing its suitability for elderly consumers who require foods that are easy to bite and chew without excessive effort or risk of injury. Each treatment was replicated seven times, and results were reported as mean ± standard deviation. All measurements were conducted at ambient temperature (25 ± 2 °C) and completed within 24 h post-drying to minimize the effect of ambient humidity on texture.

2.5. Scanning Electron Microscopy (SEM)

Microstructural changes from soaking and drying were analyzed using an ultrahigh-resolution scanning electron microscope (Model SU8600, Hitachi High-Tech Corporation, Tokyo, Japan), following the procedure of Deng et al. [26]. Dried squid pieces (~5 mm × 5 mm) were mounted on copper grids and sputter-coated with gold to enhance conductivity. Imaging was performed at 3000× magnification, 15 kV accelerating voltage, and 10.00 mm working distance. These settings enabled detailed visualization of surface features such as porosity, voids, and tissue collapse. Images were used to qualitatively evaluate the effects of alkaline treatments and drying methods on the internal structure. A 10 µm scale bar was included in all micrographs.

2.6. Color Measurement

The surface color of the dried squid samples was measured using a calibrated colorimeter (Model CR-300, Minolta, Osaka, Japan) with a standard white tile. Color parameters (lightness (L*), redness/greenness (a*), and yellowness/blueness (b*)) were recorded to evaluate visual changes from different treatments. Total color difference (ΔE) was calculated relative to fresh, untreated squid using the equation:

$$\Delta \mathrm{E}=\sqrt{{({\mathrm{L}}^{*}-{\mathrm{L}}_0^{*})}^2+{({\mathrm{a}}^{*}-{\mathrm{a}}_0^{*})}^2+{({\mathrm{b}}^{*}-{\mathrm{b}}_0^{*})}^2}$$

(6)

where ${\mathrm{L}}_0^{*}$, ${\mathrm{a}}_0^{*}$, and ${\mathrm{b}}_0^{*}$ represent the color values of the fresh sample. This provided an objective measure of color change due to soaking and drying.

2.7. Microbial Analysis of Dried Squid

Microbial stability of the dried squid samples was evaluated by measuring the total viable counts (TVCs), representing the total aerobic mesophilic bacteria, to assess general microbial load and storability under different temperatures. Samples were stored at 25 °C and 40 °C for two weeks, with microbial analysis conducted on days 0, 7, and 14. At each time point, 5 g of the sample was aseptically mixed with 45 mL sterile peptone water (0.1% w/v) and homogenized for 2 min. Serial dilutions were plated on Plate Count Agar (PCA) for TVC, incubated at 37 °C for 48 h. Results were expressed as log colony-forming units per gram (log CFU/g). Microbial changes were compared to evaluate the impact of the storage temperature on the shelf life and safety.

2.8. Water Activitiy Analysis of Dried Squid

Water activity (a_w) of the dried squid samples was measured to assess microbial stability and storage potential. A water activity meter (Model: AMITTARI WA-160A, Guangzhou Anmiao Instrument Co., Ltd., Guangzhou, China) calibrated with standard salt solutions was used. About 2–3 g of each sample was placed in the holder, and readings were taken at room temperature (25 ± 2 °C). Each sample was measured in triplicate, and the average value reported. Water activity values were compared across soaking treatments and storage temperatures to evaluate their impact on drying efficiency and product stability.

2.9. Numerical Simulation

2.9.1. Modeling of Heat and Moisture Transport in Squid Sample

The simulation was based on a 3D model constructed from the measured geometry of the dryer chamber and squid samples (Figure 1). Forced convection drying was applied with an air velocity of 3 m/s. While the airflow field was not explicitly simulated, the velocity was used to estimate surface convective heat and mass transfer coefficients, assuming turbulent flow over flat surfaces [27,28]. Local convective transfer coefficients were calculated using empirical correlations applicable to turbulent boundary layer flow over flat plates. To quantify convective heat and mass transfer, the Nusselt number (Nu) and Sherwood number (Sh) were computed through well-established empirical relations linked to forced convection drying processes. These dimensionless numbers correlate the convective transfer rates to the flow conditions and physical properties of the air and samples, facilitating accurate estimation of heat and moisture fluxes on the sample surfaces [29,30]:

$$\mathrm{N}\mathrm{u}=0.682·{\mathrm{R}\mathrm{e}}^{0.466}·{\mathrm{P}\mathrm{r}}^{0.333}$$

(7)

$$\mathrm{S}\mathrm{h}=0.683·{\mathrm{R}\mathrm{e}}^{0.466}·{\mathrm{S}\mathrm{c}}^{0.333}$$

(8)

The Reynolds number (Re), Prandtl number (Pr), and Schmidt number (Sc) were calculated from standard fluid mechanics relations:

$$\mathrm{R}\mathrm{e}={\displaystyle \frac{{\mathsf{\rho }}_{\mathrm{a}\mathrm{i}\mathrm{r}}·\mathrm{u}·\mathrm{L}}{{\mathsf{\mu }}_{\mathrm{a}\mathrm{i}\mathrm{r}}}}; \mathrm{P}\mathrm{r}={\displaystyle \frac{{\mathsf{\mu }}_{\mathrm{a}\mathrm{i}\mathrm{r}}·{\mathrm{C}}_{\mathrm{p},\mathrm{a}\mathrm{i}\mathrm{r}}}{{\mathrm{k}}_{\mathrm{T}}}}; \mathrm{S}\mathrm{c}={\displaystyle \frac{{\mathsf{\mu }}_{\mathrm{a}\mathrm{i}\mathrm{r}}}{{\mathsf{\rho }}_{\mathrm{a}\mathrm{i}\mathrm{r}}·\mathrm{D}}}$$

(9)

where ρ_air is the density of air (kg/m³), u is the drying air velocity (3 m/s), L is the characteristic length of the sample (0.05 m), μ_air is the dynamic viscosity (Pa·s), C_p,air is the specific heat capacity (J/kg·K), k_T is the thermal conductivity of air (W/m·K), and D is the binary diffusion coefficient of water vapor in air (m²/s). These air properties were evaluated at the film temperature T_f, defined as the average of the air temperature (T_∞) and the sample surface temperature (T_s):

$${\mathrm{T}}_{\mathrm{f}}={\displaystyle \frac{{\mathrm{T}}_{\mathrm{\infty }}+{\mathrm{T}}_{\mathrm{s}}}{2}}$$

(10)

The heat (h) and mass (h_m) transfer coefficients were subsequently calculated as follows:

$$\mathrm{h}={\displaystyle \frac{\mathrm{N}\mathrm{u}·{\mathrm{k}}_{\mathrm{T}}}{\mathrm{L}}}$$

(11)

$${\mathrm{h}}_{\mathrm{m}}={\displaystyle \frac{\mathrm{S}\mathrm{h}·\mathrm{D}}{\mathrm{L}}}$$

(12)

All thermo-physical air properties were temperature-dependent and implemented via interpolation in COMSOL Multiphysics (Version 5.1, COMSOL Inc., Burlington, MA, USA), using data from Park and Yoon [17] (

Table 2. Thermal and physical properties of squid, aluminum, and air.

Property	Density (ρ) (kg/m3)	Specific Heat (cp) (kJ/kg·K)	Thermal Conductivity (k) (W/m·K)	Reference
Control	1065 ± 5 cd	3.25 ± 0.03 d	0.48 ± 0.02 b
BS_ 0.3	1070 ± 4 cd	3.31 ± 0.04 c	0.49 ± 0.01 b
BS_ 0.6	1082 ± 5 b	3.38 ± 0.04 ab	0.52 ± 0.02 a
BS_ 0.9	1095 ± 6 a	3.42 ± 0.05 a	0.54 ± 0.02 a
PC_ 0.3	1073 ± 5 c	3.29 ± 0.03 cd	0.50 ± 0.02 ab
PC_ 0.6	1088 ± 6 b	3.35 ± 0.03 b	0.53 ± 0.02 a
PC_ 0.9	1100 ± 6 a	3.40 ± 0.04 a	0.55 ± 0.03 a
Aluminum	2700	0.9	232.4	COMSOL multiphysics (Version 5.1)
Air (at 40 °C)	Ideal gas model	6.10−7T2 − 4.10−5T + 1	7.10−5T + 0.0238	[17]

“BS” refers to sodium bicarbonate (NaHCO₃) treatment, and “PC” denotes potassium carbonate treatment (K₂CO₃). Concentrations (0.3, 0.6, and 0.9 mol/kg) refer to the soaking solutions applied prior to drying. Different superscript letters (a–d) within the same column indicate significant differences between treatment groups (p < 0.05).

). A free tetrahedral mesh was applied to the squid sample geometry with localized mesh refinement near the boundaries to enhance the resolution of the surface temperature and moisture gradients during the simulation.

2.9.2. Modeling of the Internal Temperature and Moisture Fields of the Sample

The internal heat and moisture transport in the squid sample was modeled by solving coupled transient heat and mass diffusion equations in 3D Cartesian coordinates. Sample shrinkage, internal heat generation, and radiation effects were neglected. The governing equations are as follows:

$${\displaystyle \frac{\partial \mathrm{T}}{\partial \mathrm{t}}}=\mathsf{\alpha }\left({\displaystyle \frac{{\partial }^2\mathrm{T}}{{\partial \mathrm{x}}^2}}+{\displaystyle \frac{{\partial }^2\mathrm{T}}{{\partial \mathrm{y}}^2}}+{\displaystyle \frac{{\partial }^2\mathrm{T}}{{\partial \mathrm{z}}^2}}\right)$$

(13)

$${\displaystyle \frac{\partial \mathrm{M}}{\partial \mathrm{t}}}={\mathrm{D}}_{\mathrm{e}\mathrm{f}\mathrm{f}}\left({\displaystyle \frac{{\partial }^2{\mathrm{M}}_{\mathrm{i}}}{{\partial \mathrm{x}}^2}}+{\displaystyle \frac{{\partial }^2{\mathrm{M}}_{\mathrm{i}}}{{\partial \mathrm{y}}^2}}+{\displaystyle \frac{{\partial }^2{\mathrm{M}}_{\mathrm{i}}}{{\partial \mathrm{z}}^2}}\right)$$

(14)

where T is the temperature (K), ${\mathrm{M}}_{\mathrm{i}}$ is the moisture content (g water/g dry matter), α is the thermal diffusivity of squid (m²/s), and D_eff is the effective moisture diffusivity (m²/s), assumed constant at each drying temperature and extracted from the literature values for aquatic-based biological materials under convective drying [31,32].

Initial conditions assumed uniform temperature and moisture:

$$\mathrm{T}\left(\mathrm{x},\mathrm{y},\mathrm{z},0\right)={\mathrm{T}}_{\mathrm{i}},\hspace{1em}\hspace{1em}\mathrm{M}\left(\mathrm{x},\mathrm{y},\mathrm{z},0\right)={\mathrm{M}}_{\mathrm{i}}$$

(15)

Boundary conditions on the sample surface (top, bottom, and lateral) incorporated convective transfer of heat and moisture:

$$-{\mathrm{k}}_{\mathrm{T}}·{\displaystyle \frac{\partial \mathrm{T}}{\partial \mathrm{n}}}=\mathrm{h}({\mathrm{T}}_{\mathrm{s}}-{\mathrm{T}}_{\mathrm{b}})$$

(16)

$$-{\mathrm{D}}_{\mathrm{e}\mathrm{f}\mathrm{f}}·{\displaystyle \frac{\partial \mathrm{M}}{\partial \mathrm{n}}}={\mathrm{h}}_{\mathrm{m}}({\mathrm{M}}_{\mathrm{s}}-{\mathrm{M}}_{\mathrm{e}})$$

(17)

where n represents the outward normal direction, T_b and M_e are the ambient air temperature and equilibrium moisture content, and T_s and M_s are the local surface temperature and moisture content of the squid, respectively.

2.10. Statistical Analysis

All experimental data were analyzed to determine the significance of differences among treatments using one-way analysis of variance (ANOVA), followed by Duncan’s post hoc test to identify statistically distinct groups using SPSS statistics software (Version 26.0, IBM Corp., Armonk, NY, USA). A significance level of p < 0.05 was applied. Results are presented as mean ± standard deviation, with different superscripts indicating significant differences. For the simulation, model accuracy was assessed by comparing the predicted and experimental moisture ratios, and the root mean square error (RMSE) was calculated as follows:

$${\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}}_{\mathrm{M}\mathrm{R}}=\sqrt{{\displaystyle \frac{1}{\mathrm{N}}}\sum _{\mathrm{i}=1}^{\mathrm{N}}{({\mathrm{M}\mathrm{R}}_{\mathrm{s}\mathrm{i}\mathrm{m},\mathrm{i}}-{\mathrm{M}\mathrm{R}}_{\mathrm{e}\mathrm{x}\mathrm{p},\mathrm{i}})}^2}$$

(18)

where MR = M/Mi is the moisture ratio, MR_sim is the simulation moisture ratio, MR_exp denotes the experimental moisture ratio, and N is the number of sampling points used for comparison.

3. Results

3.1. Drying Characteristics of Squid

The moisture ratio (MR) shown in Figure 2 illustrates the drying behavior of the squid samples under different drying regimes and alkaline pretreatments. The MR decreased steadily across all treatments, indicating progressive moisture loss through evaporation. However, notable differences were observed depending on the drying method and type of pretreatment. Intermittent drying (ID) resulted in a faster MR decline than conventional drying (CD), likely due to tempering periods that allowed internal moisture redistribution and reduced surface hardening (case hardening). This redistribution improved moisture migration from the core to the surface, maintaining a more uniform drying gradient [33].

Alkaline pretreatment further enhanced the drying efficiency. Both sodium bicarbonate (BS) and potassium carbonate (PC) lowered the MR compared to the untreated controls, with the PC-treated samples exhibiting the lowest values. This superior performance can be attributed to two main factors: (i) stronger alkalinity—PC has a higher pH (~11.4) than BS (~8.3), which promotes more extensive protein denaturation by disrupting hydrogen bonds and weakening electrostatic interactions in squid muscle, increasing porosity and enhancing water diffusion; and (ii) ion-specific effects—potassium ions (K⁺) have a smaller hydration radius (~3.31 Å) than sodium ions (Na⁺, ~3.58 Å), meaning K⁺ retains less bound water and can penetrate muscle microstructures more easily [34]. This deeper penetration raises the local osmotic pressure within cells, causing greater cell swelling, expansion of inter-fibrillar spaces, and loosening of the muscle matrix. Additionally, K⁺ ions have higher ionic mobility in aqueous environments, facilitating faster ionic exchange and structural modification compared to Na⁺. These combined effects promote more efficient moisture migration from the tissue core to the surface, accelerating the drying process and explaining why PC consistently outperforms BS in MR reduction.

Figure 3 displays the drying rate curves, showing an initial rising rate followed by a falling rate period, typical for biological materials. The initial stage corresponds to rapid surface evaporation, while the latter reflects slower internal moisture diffusion. The ID-treated samples consistently demonstrated higher drying rates, particularly in the early stage, supporting the idea that ID reduces surface crusting and improves internal water migration. Among the pretreatments, PC again outperformed BS and the control samples, especially at the 0.3 and 0.6 mol/kg concentrations, which sustained higher drying rates. These levels appear to achieve optimal structural loosening without compromising tissue integrity. In particular, 0.3 mol/kg exhibited superior performance compared with higher concentrations, likely because excessive alkalinity can lead to over-swelling or structural collapse, which hinders effective water transport [34,35]. In addition, high-pH conditions can alter protein–protein and protein–water interactions, promoting capillary water movement through structural weakening [34,35]. Thus, the combination of strong alkalinity and ID significantly improved moisture removal.

The drying kinetics were analyzed using four thin-layer models: Page, Newton, Henderson and Pabis, and Midilli–Kucuk (

Table 3. Fitted parameters of the thin-layer drying model for squid samples under different alkaline treatments and drying conditions and drying time to reach approximate 18% moisture content.

	Concentration	a	b	k	n	R-Square	SSE	RMSE	Required Drying Time (min)
	Cont_CD	0.9984	−0.0002	0.0207	0.6447	0.9999	8.3 × 10−5	0.0024	495
	Cont_ID	1.0020	−0.0004	0.0197	0.6364	0.9986	1.7 × 10−3	0.0105	461
	BS_CD_0.3	1.0040	−0.0001	0.0251	0.6213	0.9997	2.9 × 10−4	0.0044	478
	BS_CD_0.6	1.0030	−0.0001	0.0246	0.6296	0.9998	2.0 × 10−4	0.0037	482
	BS_CD_0.9	1.0030	−0.0001	0.0257	0.6278	0.9996	3.6 × 10−4	0.0049	491
	PC_CD_0.3	1.0080	−0.0004	0.0609	0.4651	0.9925	7.9 × 10−3	0.0229	477
Midilli–Kucuk equ	PC_CD_0.6	1.0080	−0.0004	0.0580	0.4557	0.9869	1.4 × 10−2	0.0301	483
	PC_CD_0.9	1.0020	−0.0003	0.0270	0.5627	0.9998	1.4 × 10−4	0.0030	489
	BS_ID_0.3	1.0100	−0.0002	0.0167	0.7089	0.9978	2.7 × 10−3	0.0134	431
	BS_ID_0.6	1.0070	0.0000	0.0193	0.7124	0.9978	2.6 × 10−3	0.0131	439
	BS_ID_0.9	1.0100	−0.0001	0.0214	0.6872	0.9961	4.6 × 10−3	0.0174	455
	PC_ID_0.3	0.9844	0.0000	0.0069	0.9385	0.9980	3.2 × 10−3	0.0146	421
	PC_ID_0.6	0.9856	−0.0001	0.0060	0.9936	0.9982	3.0 × 10−3	0.0142	428
	PC_ID_0.9	0.9883	−0.0001	0.0061	0.9883	0.9983	3.0 × 10−3	0.0141	435
	Cont_CD	0.9185		0.0030		0.9870	1.3 × 10−2	0.0277
	Cont_ID	0.9411		0.0033		0.9925	8.9 × 10−3	0.0229
	BS_CD_0.3	0.9035		0.0028		0.9745	2.4 × 10−2	0.0372
	BS_CD_0.6	0.9029		0.0029		0.9751	2.3 × 10−2	0.0372
	BS_CD_0.9	0.9025		0.0031		0.9774	2.3 × 10−2	0.0365
	PC_CD_0.3	0.8642		0.0035		0.9574	4.5 × 10−2	0.0514
Henderson and Pabis	PC_CD_0.6	0.8733		0.0033		0.9554	4.6 × 10−2	0.0521
	PC_CD_0.9	0.9135		0.0026		0.9834	1.5 × 10−2	0.0295
	BS_ID_0.3	0.9428		0.0034		0.9890	1.3 × 10−2	0.0280
	BS_ID_0.6	0.9190		0.0035		0.9792	2.4 × 10−2	0.0378
	BS_ID_0.9	0.9193		0.0035		0.9777	2.6 × 10−2	0.0391
	PC_ID_0.3	0.9659		0.0048		0.9971	4.6 × 10−3	0.0164
	PC_ID_0.6	0.9740		0.0053		0.9969	5.3 × 10−3	0.0176
	PC_ID_0.9	0.9757		0.0052		0.9971	4.9 × 10−3	0.0170
	Cont_CD			0.0154	0.7260	0.9988	1.2 × 10−3	0.0085
	Cont_ID			0.0100	0.8160	0.9948	6.2 × 10−3	0.0190
	BS_CD_0.3			0.0207	0.6683	0.9994	5.7 × 10−4	0.0058
	BS_CD_0.6			0.0210	0.6702	0.9996	4.2 × 10−4	0.0050
	BS_CD_0.9			0.0210	0.6794	0.9992	7.9 × 10−4	0.0068
	PC_CD_0.3			0.0344	0.6183	0.9864	1.4 × 10−2	0.0290
Page model	PC_CD_0.6			0.0301	0.6320	0.9797	2.1 × 10−2	0.0352
	PC_CD_0.9			0.0163	0.6986	0.9973	2.4 × 10−3	0.0119
	BS_ID_0.3			0.0115	0.7970	0.9970	3.6 × 10−3	0.0146
	BS_ID_0.6			0.0179	0.7255	0.9977	2.6 × 10−3	0.0125
	BS_ID_0.9			0.0177	0.7270	0.9959	4.8 × 10−3	0.0168
	PC_ID_0.3			0.0084	0.9022	0.9978	3.5 × 10−3	0.0144
	PC_ID_0.6			0.0086	0.9166	0.9978	3.7 × 10−3	0.0148
	PC_ID_0.9			0.0082	0.9203	0.9980	3.5 × 10−3	0.0143
	Cont_CD			0.0034		0.9595	4.1 × 10−2	0.0476
	Cont_ID			0.0036		0.9505	2.3 × 10−2	0.0358
	BS_CD_0.3			0.0033		0.9322	6.3 × 10−2	0.0590
	BS_CD_0.6			0.0033		0.9336	6.3 × 10−2	0.0591
	BS_CD_0.9			0.0036		0.9388	6.1 × 10−2	0.0584
	PC_CD_0.3			0.0043		0.8901	1.2 × 10−1	0.0803
Newton	PC_CD_0.6			0.0040		0.8939	1.1 × 10−1	0.0781
	PC_CD_0.9			0.0030		0.9469	4.7 × 10−2	0.0512
	BS_ID_0.3			0.0037		0.9783	2.6 × 10−2	0.0383
	BS_ID_0.6			0.0040		0.9573	5.0 × 10−2	0.0526
	BS_ID_0.9			0.0040		0.9560	5.1 × 10−2	0.0535
	PC_ID_0.3			0.0050		0.9945	8.7 × 10−3	0.0220
	PC_ID_0.6			0.0055		0.9956	7.6 × 10−3	0.0205
	PC_ID_0.9			0.0054		0.9959	6.9 × 10−3	0.0196

Variations in the drying constant (a, b, k, n) representing the effects of drying mode and alkaline pretreatment on the drying behavior of squid. “CD” refers to continuous drying, “ID” to intermittent drying, “BS” to sodium bicarbonate (NaHCO₃) treatment, and “PC” to potassium carbonate treatment (K₂CO₃). Concentrations (0.3, 0.6, and 0.9 mol/kg) refer to the soaking solutions applied prior to drying. Control samples (Cont_CD and Cont_ID) were not pretreated with any alkaline solution.

). Model performance was evaluated using three metrics: (i) R-squared (R²), indicating the proportion of variance in the experimental data explained by the model, with values closer to 1 representing better fit, (ii) Sum of Squared Errors (SSEs), measuring the total squared deviation between the predicted and observed values; a lower SSE indicates better accuracy, and (iii) root mean square error (RMSE), providing the average magnitude of the prediction error, with a lower RMSE signifying more precise model predictions. Based on these criteria, the Midilli–Kucuk model showed superior performance, with the highest average R² (0.9974) and the lowest SSE (0.0481) and RMSE (0.1688), effectively capturing both exponential and linear moisture loss behaviors. The model constants offered further insights. The parameter a, ideally near 1, reflects model alignment with the experimental data; most samples showed values close to 1.000, confirming accuracy.

The drying constant k reflects the drying rate, with higher values observed in the PC-treated samples, particularly PC_ID_0.3 (0.0609) and PC_ID_0.6 (0.0580), indicating enhanced internal diffusivity. The exponent n reflects the drying behavior throughout the process. Values around 1.3–1.5 in the ID samples suggest a more gradual drying curve after initial evaporation, consistent with internal redistribution facilitated by both ID and alkaline-induced porosity [23]. The linear term b accounts for residual moisture loss during the slower final phase. Although typically small, b was higher in the CD samples, reflecting slower final drying due to crust formation. In contrast, the PC and ID treatments had lower b values, indicating less resistance to internal moisture flow. Altogether, the constants confirm that the Midilli–Kucuk model accurately describes the drying dynamics and the synergistic effect of the PC pretreatment and intermittent drying.

The drying time required to reach ~18% moisture varied significantly. The ID treatments shortened the drying time compared to CD. PC_ID_0.3 achieved the shortest drying time (421 min), followed by PC_ID_0.6 (428 min) and BS_ID_0.3 (431 min), while the control CD sample required the longest (495 min). These findings demonstrate that the PC pretreatment, especially when paired with ID, enhances dehydration by improving porosity, moisture diffusion, and minimizing structural damage.

Overall, the combined effects of the alkaline pretreatment and drying regime critically influence the drying kinetics and product quality. The Midilli–Kucuk model, due to its robust predictive capability, is recommended for simulating and optimizing squid drying processes under such variable conditions.

3.2. Texture Characteristics of Dried Squid

Figure 4 presents the hardness values of the dried squid samples, expressed as stress (N/m²). The control samples under CD and ID exhibited the highest values, 56,408 N/m² and 49,325 N/m², respectively. These elevated values suggest surface hardening, typically caused by rapid surface dehydration during CD, which forms a crust and restricts internal moisture movement [33]. ID, by incorporating rest periods, mitigated this effect, lowering hardness and promoting uniform internal moisture redistribution.

The alkaline-treated samples showed markedly lower hardness across all concentrations and drying modes. Notably, PC_ID_0.3 mol/kg recorded the lowest value (15,870 N/m²), followed by PC_ID at 0.6 mol/kg (22,364 N/m²), indicating that PC was more effective than BS in softening squid tissue. This effectiveness is linked to PC’s higher alkalinity, which facilitates protein unfolding, collagen solubilization, and weakening of the muscle matrix [36,37]. Alkaline soaking may also induce partial proteolysis and electrostatic repulsion among myofibrillar proteins, further improving softness [38].

From a mechanical perspective, the cutting test results suggest that the control samples exhibit a more brittle texture, characterized by abrupt fracture and resistance to deformation, which may result in a tough, less palatable mouthfeel. In contrast, the alkaline-treated samples, especially those soaked with potassium carbonate and dried intermittently, exhibited more ductile behavior with gradual deformation before fracture, leading to a softer, easier-to-chew texture. This ductile-like behavior is particularly important for elderly consumers, who often experience reduced chewing strength and saliva production, making brittle or hard textures difficult and potentially unsafe to consume.

Practically, hardness values between 10,000 and 50,000 N/m² are considered appropriate for easier mastication in older adults [11]. All the alkaline-treated samples, especially those with potassium carbonate under intermittent drying, met this criterion, supporting their suitability for inclusion in age-targeted dried seafood products. These findings align with previous research where Harikedua and Mireles [39] reported significant softening effects in fish fillets pretreated with alkaline salts. Additionally, Pacheco-Aguilar and Mazorra-Manzano [40] found that proteolytic activity enhanced by high-pH treatments reduces the mechanical resistance of seafood muscle during mastication, improving its appropriateness for individuals with chewing difficulties.

Overall, alkaline soaking, particularly with potassium carbonate, coupled with intermittent drying is a promising strategy for producing dried squid with a significantly improved, ductile-like texture, offering potential for inclusion in elderly food systems.

3.3. Color Changes in Dried Squid

The color parameters (L*, a*, b*) of and total color difference (ΔE) in the dried squid samples are shown in

Table 4. Color parameters (L*, a*, b*, and ΔE) of dried squid samples as influenced by alkaline treatment and drying method.

Sample	L*	a*	b*	ΔE
Fresh	58.21 ± 0.09 a	1.91 ± 0.07 m	4.32 ± 0.05 o	—
Control_CD	30.99 ± 0.01 m	7.27 ± 0.02 e	6.23 ± 0.01 n	27.80
Control_ID	37.62 ± 0.02 h	7.38 ± 0.06 c	10.08 ± 0.04 a	22.07
BS_CD_0.3	34.17 ± 0.01 k	7.91 ± 0.01 b	7.88 ± 0.02 j	25.03
BS_CD_0.6	36.82 ± 0.01 j	7.12 ± 0.02 f	8.12 ± 0.01 h	22.34
BS_CD_0.9	40.83 ± 0.03 e	5.45 ± 0.02 k	8.84 ± 0.02 e	18.30
PC_CD_0.3	40.49 ± 0.01 f	7.34 ± 0.01 d	6.91 ± 0.01 m	18.71
PC_CD_0.6	37.35 ± 0.03 i	7.01 ± 0.02 g	8.54 ± 0.02 f	21.88
PC_CD_0.9	33.41 ± 0.03 l	7.02 ± 0.02 g	7.45 ± 0.01 l	25.51
BS_ID_0.3	40.79 ± 0.03 e	7.12 ± 0.02 f	9.11 ± 0.02 c	18.81
BS_ID_0.6	41.95 ± 0.03 c	4.68 ± 0.02 l	8.08 ± 0.01 i	16.92
BS_ID_0.9	41.43 ± 0.02 d	5.93 ± 0.02 j	8.99 ± 0.01 d	17.87
PC_ID_0.3	45.18 ± 0.10 b	6.71 ± 0.01 i	7.81 ± 0.01 k	14.32
PC_ID_0.6	39.68 ± 0.01 g	8.04 ± 0.02 a	9.16 ± 0.01 b	20.11
PC_ID_0.9	36.82 ± 0.01 j	6.94 ± 0.03 h	8.43 ± 0.01 g	22.35

L*: Lightness (0 = black, 100 = white); a*: red/green axis (+ = red, − = green); b*: yellow/blue axis (+ = yellow, − = blue); ΔE: total color difference compared to fresh sample. Different superscript letters (a–o) within the same column indicate significant differences between treatment groups (p < 0.05).

. Fresh squid had a light, slightly yellow color with a high L* (58.21), low a* (1.91), and moderate b* (4.32). All drying treatments reduced L* and increased a* and b*, indicating darkening and browning due to Maillard reactions, pigment oxidation, and protein denaturation [41].

The control samples showed a higher ΔE under CD (27.80) than ID (22.07), suggesting that intermittent drying reduces color degradation by moderating the temperature and heat exposure. Pretreatment with BS or PC improved color retention, especially at higher concentrations (0.9 mol/kg), which helped preserve the L* values. Among all samples, PC_ID_0.3 had the lowest ΔE (14.32), highest L* (45.18), and moderate a* (6.71) and b* (7.81), indicating reduced browning and better visual quality.

PC’s superior performance, particularly under ID, may result from its alkalinity and mineral content, which stabilize pigments and inhibit non-enzymatic browning [41]. ID likely enhances this by reducing thermal stress and allowing moisture redistribution. This aligns with Fathi et al. [42], who reported that intermittent drying preserves color in heat-sensitive foods. The BS-treated samples showed greater variation (ΔE = 16.92–25.03) and did not demonstrate consistent improvements across concentrations. Overall, PC_ID_0.3 provided the best color retention in dried squid, effectively preserving the visual appearance.

3.4. Water Activity of Dried Squid

The water activity (a_w) of the dried squid samples is shown in Figure 5. Water activity critically influences microbial stability, chemical reactivity, and overall shelf life. For dried seafood, a a_w below 0.6 is generally considered effective for inhibiting microbial growth [43]. In this study, the control samples had the highest a_w (CD: 0.67, ID: 0.66), exceeding the safe threshold. This indicates that drying alone, without pretreatment, was insufficient to reduce the moisture-binding capacity to desirable levels.

In contrast, the alkali-treated samples, particularly those dried under intermittent conditions (ID), showed a significantly lower a_w. The lowest value was observed in PC_0.9_ID (a_w = 0.50), followed by PC_0.6_ID (a_w = 0.53), both well below 0.6, suggesting improved shelf stability. This reduction is linked to chemical modifications of squid proteins. Both PC and BS disrupt ionic bonds and unfold myofibrillar proteins, facilitating greater moisture release during drying [34]. PC’s higher alkalinity caused more extensive protein swelling and matrix loosening, explaining its stronger effect compared to BS at equivalent concentrations.

Additionally, ID outperformed CD in lowering a_w, especially at higher alkaline concentrations. The tempering phases in ID reduce surface crusting, enabling more uniform internal moisture migration [24]. This improves deep moisture removal and contributes to lower final a_w values.

Overall, alkaline soaking, especially with PC and intermittent drying, substantially improved moisture diffusivity and reduced a_w, enhancing microbial safety and storability. These treatments are promising for producing elder-friendly dried squid products stable at ambient temperatures without refrigeration.

3.5. Scanning Electron Microscopy of Dried Squid

The SEM micrographs in Figure 6 show microstructural differences in squid muscle fibers affected by soaking agents (BS or PC), their concentrations (0.3, 0.6, 0.9 mol/kg), and drying methods (CD or ID). Major structural changes included fiber shrinkage, compaction, and inter-fiber space retention. The control samples showed highly compact, collapsed fibers, which are severe under CD and moderate under ID due to direct thermal stress and irreversible protein denaturation, contributing to increased hardness and discoloration, as discussed in Section 3.2 and Section 3.3.

Alkaline soaking reduced structural damage, but preservation depended on the alkaline type, concentration, and drying mode. The BS-treated samples, especially at 0.6–0.9 mol/kg, had dense, indistinct muscle fibers under CD. This likely resulted from over-swelling during soaking and collapse during drying, weakening connective tissues and reducing porosity. The compact matrix limited moisture removal and explained the higher hardness values.

In contrast, the PC-treated samples, particularly PC_ID_0.3, maintained porous, well-separated fibers, indicating lower thermal stress and better tissue preservation. Visible inter-fiber gaps and less shrinkage suggest potassium carbonate at 0.3 mol/kg provided optimal structural modification by increasing the pH and improving myofibrillar protein stability. The associated lower hardness and higher brightness support this, highlighting PC_ID_0.3’s superior microstructural integrity.

Overall, the SEM results confirm that intermittent drying combined with mild alkaline pretreatment (PC at 0.3 mol/kg) minimizes structural degradation and enhances the final product quality. This approach is promising for producing dried squid with improved texture and appearance, particularly for applications requiring softer, more manageable food structures.

3.6. Storability Analysis

The microbial stability of the dried squid samples was assessed during storage at 25 °C and 40 °C for 14 days (

Table 5. Microbial load (log CFU/g) of dried squid samples stored at 25 °C and 40 °C over 14 days.

	Storage Condition (°C)
		25 °C		40 °C
Sample	Initial (0 Day)	7 Days	14 Days	7 Days	14 Days
Control_CD	2.70 ± 0.11 a	2.72 ± 0.08 a	2.88 ± 0.05 ab	2.90 ± 0.13 a	2.92 ± 0.03 a
Control_ID	2.58 ± 0.03 ab	2.61 ± 0.01 a	2.87 ± 0.04 a	2.79 ± 0.15 ab	2.90 ± 0.05 a
BS_CD_0.3	2.59 ± 0.01 ab	2.60 ± 0.01 a	2.74 ± 0.05 abc	2.72 ± 0.08 ab	2.83 ± 0.09 a
BS_CD_0.6	2.58 ± 0.03 ab	2.59 ± 0.01 a	2.73 ± 0.09 abc	2.70 ± 0.11 ab	2.76 ± 0.02 a
BS_CD_0.9	2.60 ± 0.01 ab	2.61 ± 0.01 a	2.65 ± 0.06 c	2.72 ± 0.08 ab	2.86 ± 0.06 a
PC_CD_0.3	2.67 ± 0.15 a	2.70 ± 0.11 a	2.73 ± 0.06 c	2.71 ± 0.09 ab	2.81 ± 0.06 a
PC_CD_0.6	2.57 ± 0.04 ab	2.58 ± 0.03 a	2.61 ± 0.07 c	2.72 ± 0.08 ab	2.89 ± 0.07 a
PC_CD_0.9	2.68 ± 0.14 a	2.72 ± 0.08 a	2.74 ± 0.04 abc	2.71 ± 0.09 ab	2.81 ± 0.08 a
BS_ID_0.3	2.42 ± 0.16 b	2.58 ± 0.03 a	2.71 ± 0.03 c	2.62 ± 0.02 b	2.77 ± 0.01 a
BS_ID_0.6	2.71 ± 0.09 a	2.71 ± 0.09 a	2.72 ± 0.08 bc	2.80 ± 0.14 ab	2.85 ± 0.07 a
BS_ID_0.9	2.61 ± 0.01 ab	2.63 ± 0.04 a	2.65 ± 0.08 c	2.73 ± 0.06 ab	2.85 ± 0.07 a
PC_ID_0.3	2.59 ± 0.01 ab	2.61 ± 0.01 a	2.63 ± 0.06 c	2.71 ± 0.04 ab	2.89 ± 0.02 a
PC_ID_0.6	2.58 ± 0.03 ab	2.59 ± 0.01 a	2.62 ± 0.07 c	2.74 ± 0.05 ab	2.83 ± 0.07 a
PC_ID_0.9	2.61 ± 0.01 ab	2.71 ± 0.09 a	2.73 ± 0.05 abc	2.73 ± 0.07 ab	2.83 ± 0.05 a

Samples were treated with either sodium bicarbonate (BS) or potassium carbonate (PC) at concentrations of 0.3, 0.6, or 0.9 mol/kg and dried using continuous drying (CD) or intermittent drying (ID). Different superscript letters (a–c) within the same column indicate significant differences between treatment groups (p < 0.05).

). The initial microbial loads ranged from 2.42 to 2.71 log CFU/g, indicating effective microbial reduction during drying. At 25 °C, the microbial counts remained stable, with minimal increases (<0.1 log CFU/g), confirming that both the drying and alkaline treatments effectively inhibited microbial growth under ambient conditions.

At 40 °C, microbial loads increased slightly by day 7, reaching 2.62–2.80 log CFU/g. Notably, PC_ID_0.3, identified earlier for its soft texture and better appearance (Section 3.2 and Section 3.3), also showed relatively lower microbial counts (2.71 log CFU/g), suggesting enhanced preservation due to improved moisture removal. This supports findings that intermittent drying disrupts microbial cycles by extending thermal exposure [44].

The control samples (Control_CD and Control_ID) showed slightly higher growth at 40 °C, reaching up to 2.92 log CFU/g, highlighting the additional microbial suppression provided by alkaline soaking. Alkaline pretreatment is known to alter osmotic balance and damage microbial membranes [45].

The combination of alkali soaking and ID drying proved most effective in maintaining microbial stability. Although all samples remained below the spoilage limit of 5 log CFU/g [46], the observed increase at 40 °C underscores the importance of cool storage to extend the shelf life of dried squid products.

3.7. Numerical Simulation of Heat and Mass Transfer in Squid During Drying Proccess

The simulation of squid drying behavior was validated by comparing the predicted moisture ratio (MR) curves with the experimental data (Figure 7). The MR profiles generated from the mass transfer model aligned closely with the experimental values for both the continuous drying (CD) and intermittent drying (ID) conditions. The root mean square error (RMSE) was <0.068 for CD and <0.055 for ID, indicating high predictive accuracy. This confirms the model’s reliability in capturing squid drying kinetics under different regimes, consistent with prior seafood drying simulations [18,28].

The simulation enabled the accurate prediction of the drying time needed to reach the target moisture content, crucial for improving energy efficiency and product quality. MR trends varied between CD and ID: CD showed a steady MR decline due to continuous heat and mass transfer, while ID exhibited a stepwise MR decrease, resulting from tempering periods that facilitated internal moisture redistribution and reduced surface hardening [33]. Consequently, the ID samples generally retained lower moisture at equivalent times.

Figure 8 displays the spatial moisture distribution within the squid. The CD samples retained high core moisture due to crust formation, impeding internal moisture migration. In contrast, ID produced more uniform moisture profiles, attributed to rest intervals that allow equilibration. These results are consistent with reports that ID enhances internal moisture mobility and reduces stress gradients, leading to better texture [33].

Simulation also examined the influence of the soaking medium concentration. Higher concentrations led to a faster MR reduction, especially early in drying. This can be attributed to stronger osmotic gradients enhancing water loss and increasing porosity, which promotes internal moisture migration. Similar behavior has been reported in the osmotic dehydration of marine products [47].

The relationship between the moisture content and internal temperature distribution is further demonstrated in Figure 9. Under CD conditions, the sample exhibited a temperature gradient with lower temperatures at the center, especially during the early and middle drying stages (≤300 min). This is consistent with the high moisture content in the core, which absorbs latent heat during evaporation, thereby lowering the temperature. In contrast, the ID-treated samples exhibited more uniform temperature profiles across the tissue. This can be attributed to the intermittent resting periods, which allow heat conduction to equalize the internal temperature while moisture redistribution limits local evaporative cooling. Among the ID-treated samples, no significant variation in temperature profiles was observed across different soaking medium concentrations. Similarly, the CD-treated samples exhibited consistent temperature trends regardless of the soaking concentration. These results can be partly attributed to the thin geometry of the squid samples (0.5 ± 0.1 cm), which reduces the thermal and moisture gradients due to the small diffusion path and rapid equilibration across the thickness. This agrees with prior studies highlighting that sample geometry significantly influences the thermal and mass transfer behavior in drying processes [17].

These findings hold significant implications for the development of texture-modified seafood products suitable for elderly individuals and patients with dysphagia. The ID method’s ability to achieve uniform moisture and temperature distribution offers a controlled approach to softening the product structure, reducing toughness and enhancing the rehydration properties. This is crucial for producing safe-to-consume, easily chewable and swallowable dried squid products that align with the dietary guidelines for texture-modified diets [11]. The simulation model thus serves as a valuable tool not only for process optimization but also for designing personalized, elder-friendly foods with predictable and reproducible qualities tailored to the physiological needs of vulnerable populations.

4. Conclusions

This study developed an elder-friendly dried squid by optimizing softening and drying via alkaline soaking and intermittent drying (ID). Sodium bicarbonate (NaHCO₃) and potassium carbonate (K₂CO₃) were tested at 0, 0.3, 0.6, and 0.9 mol/kg, with drying by continuous (CD) or intermittent (ID) hot air at 40 °C targeting 18.34 ± 0.44% moisture. The optimum condition was squid soaked in 0.3 mol/kg K₂CO₃ and dried with ID, yielding low hardness suitable for elderly mastication, safe water activity, minimal color change, and microbial safety. The drying kinetics fit best with the Midilli–Kucuk model (R² = 0.9974), accurately describing both the rapid and slow drying phases. The drying constants (k, n, b) showed ID and K₂CO₃ improved the drying efficiency and reduced moisture resistance. SEM analysis indicated ID with 0.3 mol/kg K₂CO₃ increased porosity and reduced fiber collapse, enhancing moisture migration and surface quality. Numerical simulations matched the experiments (RMSE < 0.068 for CD, <0.055 for ID), showing ID improved moisture and temperature uniformity and lowered structural stress. Combining mild alkaline soaking with intermittent drying effectively produces dried squid with improved texture and stability. Predictive modeling is a valuable tool for optimizing the process and quality control of texture-modified seafood for aging consumers. Future work should include clinical validation, sensory evaluation, and consumer acceptance to ensure market readiness. These steps will facilitate industrial application and health-focused product development.