Quality variation analysis and rehydration kinetics modeling of yuba subjecting to three different drying process

Abstract

This study investigated the effects of hot air drying (HAD), microwave vacuum drying (MVD), and vacuum frying drying (VFD) on the quality of yuba and utilized mathematical models (Peleg model, Weibull model, first-order kinetic model, and exponential association model) to explore the rehydration kinetics of yuba. The results indicated that, compared to HAD, MVD significantly increased yuba's L* value and sensory score, while VFD notably reduced the cooking loss and enhanced moisture retention. Significant changes were observed in the texture of yuba before and after cooking, with distinct differences noted under microscopic observation, and a pronounced change in protein secondary structure. The Peleg model can effectively fit the rehydration processes of HAD and VFD yuba, while the first-order kinetic model can effectively fit the rehydration processes of MVD yuba. This study provides a valuable reference for yuba's drying methods and rehydration kinetics model.

Highlights

• •The effect of microwave vacuum drying on yuba was studied.
• •The effect of low temperature vacuum frying as a drying method on yuba was studied.
• •The effect of drying method on the rehydration performance of yuba was studied.
• •The dynamic model of rehydration process of yuba was established.

1. Introduction

Yuba is a food product formed by picking and drying the edible film that forms on soy milk's surface when heated (Zhang et al., 2018). It is rich in nutrients such as protein (52 %–57 %) and lipids (24 %–26 %) (Kong et al., 2023) and has a high digestibility rate and the properties of a meat substitute (Lee et al., 2020). The film-forming mechanism of yuba is as follows: Heating soy milk induces the thermal denaturation of β-conglycinin and globulin. The thermal denaturation of β-conglycinin dissociates into α', α, and β subunits, with hydrophobic groups partially exposed, leading to aggregation driven by hydrophobic interactions. The thermal denaturation of globulin dissociates into acidic A peptide chains and basic B peptide chains, where B peptide chains aggregate due to hydrophobic interactions, while the hydrophilic groups of A peptide chains lack sufficient repulsion to prevent other groups from approaching, leading to globulin aggregation. During the individual or joint thermal denaturation of β-conglycinin and globulin, protein-lipid membranes are formed (Li, 2023).

Drying is one of the most effective ways to extend shelf life by reducing water activity and inhibiting microbial growth through dehydration. The impact of temperature, time, and process on the product structure during drying is irreversible. Using different drying technologies to dry the product can effectively reduce water activity, inhibit microbes, and improve texture. With the industrial production of yuba, hot air drying (HAD) remains the most commonly used and widespread drying method. The HAD process requires relatively low temperatures, which results in a long drying cycle for yuba (Deng, 2011). Additionally, the uncontrollable tightness of the curling in yuba leads to uneven quality during the HAD process, and excessive tightness in curling causes prolonged water absorption time during the rehydration process. Therefore, it is necessary to explore the impact of different drying methods on the drying characteristics and quality of yuba, and different drying techniques present different effects (Table 1). Microwave vacuum drying (MVD) utilizes high-frequency electromagnetic fields to induce molecular motion and friction, generating heat and expansion to achieve drying. Consequently, this method ensures uniform drying, rapid processing, and preservation of the nutritional components and flavor of the food (Sun et al., 2023; Zhang et al., 2020). Research by Deng (2011) found that yuba treated with MVD has a fragrant aroma, good integrity, and a significantly higher rehydration ratio compared to yuba dried by HAD. Vacuum frying drying (VFD) combines vacuum technology with frying dehydration techniques, promoting dehydration of the material, resulting in high product expansion and better texture while suppressing oxidation. Compared with HAD, the total phenol of jujube slices significantly increased after VFD (Hu, 2023).

Table 1.

Characteristics of different drying methods.

Name	Principle	Moisture treatment	Quality differences	Energy consumption and efficiency	Equipment requirements	Applications
HAD	Circulating hot air transfers heat to the material, facilitating moisture evaporation	Surface moisture is removed quickly, while internal moisture migration is slow	Nutrient loss, darkening of color, and rough texture.	Low efficiency and high energy consumption	The equipment is simple, with a relatively low investment	Ginger(Sun et al., 2022) Jujube (Hu, 2023)
MVD	Microwaves act on the internal moisture of the material, causing moisture evaporation, while the vacuum environment accelerates drying.	Internal moisture is removed uniformly and at a rapid rate	Maintains good nutritional content, color, and texture.	High efficiency and high energy consumption.	The equipment is relatively complex, with moderate investment costs	Takifugu obscurus (Li et al., 2023) Flammulina velutipes (Sun et al., 2023)
VFD	Heated oil transfers heat to the material, while the vacuum environment accelerates drying.	The rate of moisture removal is fast	The texture is crispy, and the color is vibrant.	High efficiency and high energy consumption.	The equipment is complex, with high investment and maintenance costs.	Jujube (Hu, 2023) Tofu Puffs (Li et al., 2018)

Dried ingredients require rehydration before cooking to restore their fresh state(Sun et al., 2022). Rehydration is a multifactorial process, and different drying methods have different effects on the rehydration performance of the product. The rehydration time and the quality of the yuba after rehydration are among the primary factors affecting the quality of dishes, making it crucial to develop methods that can either accelerate the rate of water diffusion during rehydration or visualize the rehydration process of the yuba. Aksoy et al. (2019) discovered that freeze drying offers the best rehydration properties for minced meat compared to ultrasonic-assisted vacuum drying and vacuum drying. Similarly, Bhat et al. (2022) reported that the kiwifruit exhibits the highest rehydration capacity when mixed drying methods are used compared to convective and microwave drying. Rehydration is a dynamic process; as time progresses, the moisture content of the product after rehydration gradually approaches an equilibrium value. Dried products exhibit different rehydration rates at different rehydration temperatures. Thus, it is necessary to apply mathematical models to study changes in the absorption rate during the yuba rehydration process and to establish different kinetic models to explain these changes. Peng, Lu, et al. (2022) utilized four models to study the rehydration kinetics of wheat gluten tissue at different rehydration temperatures, and the Weibull model could well describe the rehydration process. Demiray and Tulek, (2017) studied the rehydration kinetics of red pepper at different temperatures, finding that the Weibull model effectively fitted the rehydration process under all experimental conditions. Increasing the rehydration temperature significantly reduced the time required to rehydrate the dried product (Demiray & Tulek, 2017). Sun et al. (2022) reported that increasing the temperature could improve the rehydration rate of dried ginger, reduce the time needed to reach moisture equilibrium and increase the final equilibrium moisture content of the samples. Moreover, freeze drying ginger slices exhibit a higher rehydration rate (Sun et al., 2022).

This study aims to investigate the effects of various drying technologies, including HAD, VFD, and MVD, on the quality of yuba. The goal is to identify suitable drying methods for the yuba industry to enhance drying efficiency and quality. Additionally, dynamic rehydration experiments were conducted on yuba dried by different methods at different temperatures (25 °C, 35 °C, and 45 °C), and four models were applied to describe the rehydration kinetics of yuba. This examination of the impact of drying methods on yuba rehydration aims to provide a basis for evaluating, predicting, and regulating yuba rehydration characteristics.

2. Materials and methods

2.1. Materials and reagents

Fresh yuba was obtained from Henan Province Xiangshang Food Co., Ltd. (Henan, China) and stored at room temperature for drying on the same day. The initial moisture content was 48.70 % ± 2.17 % (wet basis). All yuba samples were produced from the same batch of soybeans on the same day. KBr was purchased from Shandong Keyuan Biochemical Co., Ltd. (Shandong, China), while the other chemical reagents were obtained from China National Pharmaceutical Group Reagent Co., Ltd. (Shanghai, China).

2.2. Drying methods

According to GB/T 22106–2008 “National Standard of the People's Republic of China: Non-Fermented Soy Products,” the moisture content of yuba after drying must not exceed 12 % (dry basis). Fresh yuba was dried using HAD, MVD, and VFD methods, and the drying procedures are detailed in Table 2.

Table 2.

Drying procedures of yuba.

Drying method	Main Instrument	Processing	Drying time	Moisture content / (%)
HAD	Electrothermal constant temperature drying oven (DHG-9140 A Shanghai Sanfa Scientific Instrument Co., Ltd., China).	Dry at 60 °C.	10 h	7.71 ± 0.25 %
MVD	Microwave vacuum drying equipment (ZY-48ZK, Guangzhou Zhiya Industrial Microwave Equipment Co., LTD., China).	The upper temperature is 80 °C, the lower limit is 65 °C, and the vacuum degree is 0.098 MPa.	70 min	5.92 ± 0.14 %
VFD	Experimental automatic vacuum frying machine (JS-05, Shanghai Jinsen Light Industrial Machinery Co., LTD., China).	At 95 °C ± 5 °C, 0.086 MPa vacuum drying, deoiling frequency of 60 HZ, 200 s.	25 min	5.20 ± 0.04 %

Note: The data are the mean ± standard deviation of three repeated measurements.

2.3. Moisture content

According to GB 5009.3–2016 “National Food Safety Standard: Determination of Moisture in Foods,” the moisture content of the yuba was measured using the direct drying method.

2.4. Color measurement

The color of the yuba was measured following the method described by Li (2023) The yuba was ground into a fine powder passing through a 50-mesh sieve for color measurement. The yuba powder was evenly spread out, and a colourimeter (CR-10 Plus, KONICA MINOLTA, Japan) was used to randomly measure the L*, a*, and b* values. The average of these measurements was taken as the color value of the yuba.

2.5. Water holding capacity (WHC) analysis

WHC was determined using the method described by Qiu et al. (2022). A cloth was placed at the bottom of a 50 mL centrifuge tube, and the rehydrated yuba was placed in the tube, which was then centrifuged at 5000 rpm for 20 min. The WHC was calculated as follows (Eq. (1)):

$$WHC=\frac{W_o-W_t}{W_o}·100\%$$ (1)

where W_o (g) represents the weight of the yuba before centrifugation; W_t (g) represents the weight of the water lost from the yuba after centrifugation.

2.6. Cooking loss

Following the method described by Zhao et al. (2023). The dried yuba is weighed and then boiled in an 85 °C water bath for 15 min, drained for 5 min, and dried at 105 °C until constant weight is achieved. The cooking loss is calculated as the percentage of the mass difference of yuba before and after boiling relative to its mass before boiling.

2.7. Sensory evaluation

Yuba cut to 6–8 cm specifications was prepared, and sensory evaluation standards were established according to GB/T 22106–2008 “National Standards for Non-Fermented Soybean Products.” The sensory evaluation panel, comprising 10 students from the food science field, had studied sensory evaluation principles and was familiar with yuba's aroma and quality characteristics before the assessment. The yuba samples, subjected to three different drying methods, were evaluated based on color, appearance, aroma, and chewiness. Scores ranged from 1 to 25, with 1–9 indicating dislike, 10–17 indicating acceptability, and 18–25 indicating preference. The sensory analysis used in this study complies with sensory ethics standards; all participants are voluntary and consent to the trial, and anonymity is maintained during the experiment, ensuring complete protection of participants' rights and privacy. All participants consented to use sensory evaluation data from this experiment for publication in the research.

2.8. Texture analysis

Texture analysis was conducted concerning the methods by Guo (2023). The texture analyzer (TA-XT plus, Stable Micro Systems, UK) was used to test yuba, which was fully rehydrated at room temperature and boiled in hot water for 90 s after rehydration. The yuba was cut into 2 cm × 2 cm pieces. The testing parameters were as follows: P/36R cylindrical probe, TPA mode, force range of 50 N, pre-test speed 1.0 mm/s, test speed 0.5 mm/s, post-test speed 1.0 mm/s, strain 65 %, interval time 5 s, return distance 50 mm, return speed 10 mm/s. The samples' hardness, springiness, gumminess, and chewiness were measured, with 5 repetitions conducted for each sample.

2.9. Scanning electron microscopy (SEM)

Following the method of Wang et al. (2024), the yuba samples were cut into 5 mm × 5 mm slices and gold-coated. Scanning electron microscopy (Sigma 300, ZEISS, Germany) was used to observe the samples at magnifications of 1000× and 5000×.

2.10. Fourier transform infrared spectroscopy (FT-IR)

Yuba was ground into powder. A 1 mg: 100 mg ratio of yuba powder to KBr is placed in an agate mortar, ground, and then an appropriate amount is pressed into tablets using a tablet press. Scanning is performed within the 400–4000 cm⁻¹ range using a fourier transform infrared spectrometer (TENSOR 27, BRUKER, Germany).

2.11. Rehydration experiment

Yuba treated with different drying processes requires varying rehydration times, and the water temperature positively influences the rehydration rate. Therefore, specific rehydration times were set for yuba treated with different drying methods.

HAD: The dried yuba was soaked in 250 mL of distilled water in a water bath at 25 °C, 35 °C, and 45 °C for rehydration. The rehydration time points were controlled as follows: between 1 and 5 min, samples were taken out at 2.5-min intervals; between 5 and 30 min, samples were taken out at 5-min intervals; after 30 min, samples were taken out at 10-min intervals; after 80 min, samples were taken out at 20-min intervals; and after 120 min, samples were taken out at 30-min intervals. The surface water droplets on the yuba were wiped off, and the samples were weighed.

MVD: The dried yuba was soaked in 250 mL of distilled water in a water bath at 25 °C, 35 °C, and 45 °C for rehydration. The rehydration time points were controlled as follows: between 1 and 5 min, samples were taken out at 1-min intervals; between 5 and 30 min, samples were taken out at 5-min intervals; after 30 min, samples were taken out at 10-min intervals; and after 60 min, samples were taken out at 10-min intervals. The surface water droplets on the yuba were wiped off, and the samples were weighed.

VFD: The dried yuba was soaked in 250 mL of distilled water in a water bath at 25 °C, 35 °C, and 45 °C for rehydration. The rehydration time points were controlled as follows: between 1 and 5 min, samples were taken out at 1-min intervals; between 5 and 30 min, samples were taken out at 5-min intervals; after 30 min, samples were taken out at 10-min intervals; and after 60 min, samples were taken out at 20-min intervals. The surface water droplets on the yuba were wiped off, and the samples were weighed.

All experiments were repeated six times, and the average values were taken. The moisture content (g·g⁻¹) was calculated using (Eq. (2)):

$$Mc=\frac{W_t-W_o}{W_o}$$ (2)

where W_o (g) is the weight of the sample before rehydration; W_t (g) is the weight of the sample after rehydration.

2.12. Rehydration kinetics modeling

Four models were chosen to describe the rehydration kinetics of yuba subjected to different drying methods: the Peleg model (Eq. (3)), the Weibull model (Eq. (4)), the first-order kinetic model (Eq. (5)), and the exponential association model (Eq. (6)).

$$M={\mathit{\text{M}}}_o+\frac{t}{K_{\mathit{1}}+K_{\mathit{2}}t}$$ (3)

Peleg model, where M is the moisture content at time t (min) (g·g⁻¹); M_o is the initial moisture content, expressed on a dry basis (g·g⁻¹); t (min) is the rehydration time; K₁ and K₂ represent the Peleg rate constant (min / g·g⁻¹) and the Peleg capacity constant (g·g⁻¹), respectively.

$$\frac{M\mathit{-}{M}_{\mathrm{e}}}{M_{\mathit{o}}-M_e}=\mathit{exp}.\left[-{\left(\frac{t}{\beta }\right)}^{\alpha }\right]$$ (4)

Weibull model, where M is the moisture content at time t (min) (g·g⁻¹); M_o is the initial moisture content (g·g⁻¹); M_e is the equilibrium moisture content at t = ∞ (g·g⁻¹); α is the shape parameter, and β is the scale parameter.

$$\frac{M-M_e}{M_o-M_e}=\mathit{exp}.\left(-K_t\right)$$ (5)

First-order kinetic model, where M is the moisture content at time t (min) (g·g⁻¹); M_o is the initial moisture content (g·g⁻¹); M_e is the equilibrium moisture content at t = ∞ (g·g⁻¹); K is the rehydration kinetics constant (min⁻¹).

$$\frac{M}{M_e}=\mathit{1}-\mathit{exp}\left(-H_t\right)$$ (6)

Exponential association model, where M is the moisture content at time t (min) (g·g⁻¹); M_e is the equilibrium moisture content at t = ∞ (g·g⁻¹); H is the rehydration kinetics constant (min⁻¹).

2.13. Statistical analysis

Statistical analysis of experimental data was conducted using IBM SPSS Statistics 27 software (IBM, Armonk, NY, USA). A p-value of less than 0.05 was considered statistically significant. The figures were generated using Origin 2023b (OriginLab Corporation, Northampton, MA, USA). Nonlinear least squares regression analysis was employed to evaluate the model parameters. The goodness of fit between the experimental data and the model was assessed using the coefficient of determination (R²), standard error (SE), and root mean square error (RMSE).

3. Results and discussion

3.1. Color

The appearance of food is one of the criteria for consumer selection (Geng et al., 2023), and the color parameters of yuba dried by different methods are shown in Table 3. During the drying process, food composition may undergo certain physical or chemical changes, such as enzymatic browning, caramelization, and ascorbic acid browning, which can result in color changes (Chu et al., 2023). The L* value of yuba dried by VFD is significantly lower than that of the other groups, which may be attributed to the Maillard reaction between amino acids and reducing sugars at high temperatures, resulting in a decrease in the brightness of the yuba. The L* value of yuba dried by MVD is significantly higher than that of the other groups, which may be due to a lower degree of protein denaturation in the yuba (Li et al., 2023), or because MVD reduced the exposure of yuba components to oxygen during drying, thus preventing oxidative reactions. This demonstrates that MVD has a protective effect on the color of yuba. The L* value can be used to assess the quality of yuba, and changes in the L* value influence consumer choices. Variations in the L* value are related to protein denaturation, aggregation, and surface glossiness (Bu et al., 2023). The L* value of MVD yuba is significantly lower than that of the other groups, possibly due to reduced color changes of yuba in a low-oxygen, low-temperature environment. The b* value is related to lipid oxidation during drying (Bu et al., 2023). The b* value of VFD yuba is significantly higher than that of the other groups, which may be because VFD operates at a higher drying temperature than other methods, resulting in more severe oxidation of lipids in yuba at high temperatures during vacuum frying, thereby affecting the color of the yuba (Li et al., 2018). The b* value of yuba dried by HAD is significantly the lowest, possibly due to the low-temperature drying method of HAD reducing lipid oxidation in yuba, thus minimizing color changes in yuba during the drying process.

Table 3.

Effects of different drying methods on the color yuba.

	HAD	MVD	VFD
Image
L*	88.11 ± 0.55b	89.37 ± 0.42a	83.50 ± 0.37c
a*	4.50 ± 0.22b	2.55 ± 0.39c	5.41 ± 0.39a
b*	30.53 ± 0.52c	32.12 ± 0.63b	39.33 ± 1.00a

Note: The data are the mean ± standard deviation of three repeated measurements; different letters in the same line have significant differences (P < 0.05).

3.2. Cook loss and WHC

The cooking loss indicates the suitability of the drying method, as illustrated in Fig. 1(a), and significant differences are observed in the cooking loss of yuba processed by the three drying methods. The cooking loss in the MVD group is significantly higher than that of the other groups, which may be attributed to the principle of microwave vacuum drying, where microwaves directly act on the material, causing the internal moisture of the material to evaporate rapidly (Sun et al., 2024). As a result, the spatial structure of the MVD group has larger voids, leading to a larger surface area in contact with water, which causes the structure to be damaged by hot air, resulting in an increased cooking loss. The cooking rate in the VFD group is significantly lower than that of the other groups, which is because, during the vacuum frying process, moisture rapidly migrates from the interior to the surface of the yuba and evaporates, leading to product shrinkage (Jia & Wu, 2023), thereby reducing the cooking loss compared to the control group (HAD). Therefore, MVD yuba is unsuitable for heat treatment cooking methods, while VFD yuba demonstrates a certain degree of cookability.

Fig. 1.

Effects of different drying methods on the quality of yuba. (a) WHC and Cook loss; (b) Sensory quality; (c) Yuba after rehydration; (d) Boiled yuba after rehydration.

WHC was based on the number of polar sites interacting with water; this characteristic is influenced by factors such as protein conformation and hydrophilic groups (Peng, Zhang, et al., 2022). The proteins in yuba are mainly 7S and 11S proteins, and the drying methods can affect the denaturation or cross-linking of these proteins. Changes in some hydrophilic and hydrophobic amino acids may occur. Fig. 1(a) shows the differences in WHC of yuba due to the drying methods. Compared to HAD, the WHC of MVD is significantly lower, which may be attributed to the weakening of the protein network structure in MVD yuba, leading to the loss of internal moisture after centrifugation. Thus, it cannot achieve high water-holding capacity. Compared to HAD, the change in WHC for VFD is not significant. This may be due to protein denaturation during VFD drying, exposing more hydrophilic groups on the molecular surface (Peng, Zhang, et al., 2022). The dense internal network structure of yuba allows for high water retention capacity. Additionally, a comparison of WHC and cooking loss reveals an inverse relationship between the two, indicating that an increase in cooking loss is associated with poorer WHC, which underscores the impact of drying methods on the structural integrity of yuba and its cooking suitability. Therefore, yuba processed by MVD may not be suitable for prolonged heat treatment methods.

3.3. Sensory evaluation

As shown in Fig. 1(b), the color, shape, flavor, and chewiness of yuba were significantly affected by different drying methods. Compared to HAD, the color acceptability of yuba dried by MVD was significantly improved, while the color acceptability of VFD yuba decreased, indicating that MVD can enhance the color of yuba while VFD retained oils on the surface of the yuba, resulting in poor color. In terms of shape, the acceptability of MVD yuba was the highest, which may be due to MVD improving the fluffiness of the yuba, whereas VFD yuba exhibited uneven bubbling on the surface. Compared to the other two groups, the flavor acceptability of MVD yuba was the highest, indicating that MVD can effectively preserve and enhance the flavor of yuba (Deng, 2011). The chewiness of HAD yuba was the highest, which is attributed to the evaporation of moisture during drying, causing the yuba to shrink from the outside, resulting in a harder texture (Zhang et al., 2020). In contrast, MVD and VFD processing increased the fluffiness of the yuba. Overall, considering the scores of the first four items, the acceptability of MVD yuba was the highest, making it a novel method for drying yuba.

3.4. Texture analysis

The changes in the texture of yuba before and after cooking were evaluated in terms of hardness, springiness, gumminess, and chewiness. The effects of different drying methods on the texture of yuba are shown in Fig. 1(c) and (d). Before cooking, the samples treated with VFD had the lowest hardness, with significant differences (P < 0.05). After cooking, the MVD samples had the lowest hardness, which may be due to the loose and porous internal structure of MVD yuba, making it more susceptible to damage by hot water. This also explains why the cooking loss of MVD is the highest. The springiness of MVD was significantly lower than that of the other two groups both before and after cooking (P < 0.05). After cooking, the springiness of the yuba dried by the three methods did not show significant changes before and after cooking. The gumminess of HAD yuba was significantly higher than that of the other two groups (P < 0.05). It is speculated that the larger voids in the internal structure of MVD and VFD yuba reduce gumminess, resulting in significantly lower hardness and gumminess compared to HAD (P < 0.05). The chewiness of HAD is significantly higher than that of MVD and VFD (P < 0.05), which may be attributed to the longer drying period of HAD yuba, resulting in slower moisture evaporation, leading to the contraction and aggregation of the internal structure of the yuba, thereby increasing its density (Zhang et al., 2023). SEM images reveal the surface shrinkage of HAD yuba. After cooking, the chewiness of HAD and VFD yuba is significantly higher than that of MVD yuba due to the fluffiness of the internal structure of MVD yuba, resulting in a decrease in chewiness after cooking.

3.5. SEM

The effects of different drying methods on the surface structure of yuba were observed using SEM (Fig. 2). As shown in Fig. 2(a) and (d), HAD yuba exhibits localized shrinkage and depressions, which is due to the sequential evaporation of moisture from the outside to the inside during drying, and the prolonged HAD time, resulting in surface shrinkage and particle aggregation caused by polysaccharides and proteins, leading to a rough surface of the yuba (Sun et al., 2023), which exhibits greater hardness. As shown in Fig. 2(b) and (e), MVD yuba appears locally smooth and fluffy, which may be due to the simultaneous movement of molecules induced by the electromagnetic field during microwave heating, creating a small temperature difference between the interior and surface of the yuba, promoting uniform moisture migration within the yuba (An et al., 2022), which alters the brittleness and hardness of the yuba. As shown in Fig. 2(c) and (f), VFD yuba's surface displays irregular circular bumps and depressions during frying under low-temperature vacuum conditions, resulting from the heating of the yuba's structure. Compared to HAD, MVD improves the organizational structure of the yuba, enhancing the surface smoothness of the yuba, while VFD causes the yuba surface to exhibit localized bumps, which reduces the surface smoothness of the yuba. Different drying methods have a significant impact on the organizational structure of yuba.

Fig. 2.

SEM images of the surface microstructure of yuba with different drying methods. (a) (d) HAD 1000 and 5000 magnifications respectively; (b) (e) MVD 1000 and 5000 magnifications, respectively; (c) (f) VFD 1000 and 5000 magnifications, respectively.

3.6. FT-IR

FT-IR reflect the chemical bonds in the sample, allowing for the determination of molecular structures and the identification of chemical groups. As shown in Fig. 3(a), a prominent broad peak appears at Amide A, attributed to the characteristic absorption from the expansion vibrations of O—H. The peak in the Amide B range corresponds to CH₂ stretching vibrations and C—H bending vibrations. Amide I (1600–1700 cm⁻¹) results from coupling C O stretching and N—H bending vibrations (Zhang et al., 2020), making it a key region for studying protein secondary structures. Analysis reveals that MVD and VFD significantly affect the secondary structure of yuba proteins; using HAD as a control, the amide band peak for MVD and VFD shift to higher wavenumbers, which may be attributed to the enhanced protein cross-linking and network structure in yuba due to MVD and VFD(Wang et al., 2024).

Fig. 3.

FT-IR of yuba by different drying methods. (a) Spectrum; (b) Relative percentage of protein secondary structure.

The relative percentage content of protein secondary structures was calculated based on the Amide I region of the FT-IR (Fig. 3b). The spectral regions for protein secondary structures are β-sheet (1600–1640 cm⁻¹), random coil (1640–1650 cm⁻¹), α-helix (1650–1660 cm⁻¹), and β-turn (1660–1700 cm⁻¹) (Ji et al., 2022). Significant differences are observed in the relative content of protein secondary structures among the three drying methods. Compared to HAD, MVD and VFD show decreased contents of β-sheet and random coil structures, while α-helix and β-turn contents increase. The drying process causes protein rearrangement, and random coils are less likely to convert to other secondary structures during drying. Extended drying times may lead to the unfolding of α-helices, with some converting to β-sheets. Consequently, HAD yuba has significantly lower α-helix and higher β-sheet contents than the other two methods. Microwave power and radiation energy can influence the appearance of non-helical phenomena in yuba proteins (Su et al., 2023), destabilizing the α-helix and unfolding the molecular structure, which alters the ratios. VFD can lead to changes and conversions in protein secondary structures (Wang et al., 2022), relatively preserving more α-helix, attributed to lower temperatures and vacuum conditions reducing protein thermal denaturation and oxidation.

The differences in drying methods show differences in the intensity and position of the absorption peaks in the FT-IR of the yuba, which may be attributed to variations in the yuba's intrinsic components. Compared to HAD, the other two methods have a smaller impact on the protein secondary structure ratios in yuba and could be considered alternative drying methods.

3.7. Rehydration properties

3.7.1. Effect of water temperature on yuba dried by different methods

The rehydration curves of the yuba dried by different methods at different temperatures are shown in Fig. 4. During the soaking process, water migrates from the outside to the inside of the yuba, with the rate initially fast and then slowing down. Different rehydration temperatures can enhance the rehydration rate (Hu et al., 2021), rehydration experiments were conducted on yuba dried by different methods at 25 °C, 35 °C, and 45 °C.

Fig. 4.

Rehydration curves of yuba dried by different methods. (a) Rehydration curves of HAD yuba at different water temperatures; (b) Rehydration curves of MVD yuba at different water temperatures; (c) Rehydration curves of VFD yuba at different water temperatures; (d) Rehydration curves of yuba under different drying methods.

The rehydration curves of yuba dried by different methods showed an upward trend with increasing temperature. HAD yuba exhibited a significant upward change in rehydration at the three rehydration temperatures (Fig. 4a). A slower rate of reaching the maximum equilibrium moisture content was observed for HAD yuba at 45 °C. Therefore, increasing the temperature can enhance HAD yuba's maximum equilibrium moisture content. MVD yuba showed an increase in both the water absorption rate and the maximum equilibrium moisture content with increasing rehydration temperature (Fig. 4b). Compared to 25 °C and 35 °C, 45 °C exhibited a greater change in rehydration rate during the initial stage of rehydration. VFD yuba showed significant differences in water absorption rate and maximum equilibrium moisture content at different water temperatures (Fig. 4c). With increasing temperature, VFD yuba reached the maximum equilibrium moisture content faster at 45 °C. Interestingly, this is contrary to the change observed for HAD yuba at 45 °C, which may be attributed to the altered yuba tissue structure due to the different drying methods. These results indicate that the rehydration rate of yuba dried by different methods is influenced by different rehydration temperatures, which is due to the effective diffusion rate of water being related to temperature. The water diffusion rate increases as the temperature increases (Goula & Adamopoulos, 2009), leading to a higher effective diffusion rate and a faster rehydration rate. In the initial stage of rehydration, the water absorption rate of the yuba showed a sharp increase, which may be due to the dry outer surface and the larger internal cavities of the yuba (Demiray & Tulek, 2017). Therefore, yuba's rehydration rate and maximum equilibrium moisture content are closely related to the rehydration temperature.

3.7.2. Effect of different drying methods on yuba rehydration properties

The rehydration curves of yuba dried by different methods exhibited significant differences (Fig. 4d), in comparing the rehydration curves of yuba dried by different methods at a water temperature of 35 °C. During the initial 20 min of rehydration, MVD yuba and VFD yuba showed higher water absorption rates. This change can be explained by the higher moisture content gradient (difference between the current moisture content and the equilibrium moisture content) in samples with lower initial moisture content (Muñoz et al., 2012). Subsequently, the water absorption rate of VFD yuba tended to reach equilibrium, which may be attributed to the excessive oil retained during the VFD processing. MVD yuba, however, maintained a higher water absorption rate, likely due to the larger internal space structure and voids in MVD yuba. After the rehydration of the yuba tissue, the internal voids of MVD yuba could still absorb water. HAD yuba exhibited a slower water absorption rate, reaching equilibrium after 80 min, which may be due to the internal space of HAD yuba being more compact. These results indicate that different drying methods significantly impact the rehydration efficiency of yuba. MVD and VFD can influence the effective diffusion rate of water during the rehydration process and accelerate the rehydration process of yuba. Therefore, MVD and VFD can be effective methods for shortening the rehydration time of yuba.

3.8. Fitting of the rehydration kinetics model

Four kinetic models were used to fit the rehydration curves of yuba. The parameters of the four rehydration kinetic models varied at different temperatures (25 °C, 35 °C, and 45 °C) can be observed in Fig. 4 and Table 4. The closer the R² value of the model fitting data is to 1, the closer the relationship between the experimental and predicted values, and the smaller the SE and RMSE values, indicating a better fit of the model (Górnicki et al., 2020).

Table 4.

Fitting parameters of rehydration models for yuba dried by different methods.

Drying method	Model	Temperatures/°C	Parameter		R2	SE	RMSE
HAD			K1	K2
	Peleg model	25	13.6533	0.6732	0.9981	0.2348	0.0628
		35	10.7926	0.6583	0.9914	0.3999	0.1069
		45	8.8475	0.5317	0.9973	0.1844	0.0493
			α	β
	Weibull model	25	0.9096	22.7485	0.9813	0.5061	0.1404
		35	0.9697	21.8838	0.9800	0.6140	0.1703
		45	0.8786	23.5668	0.9898	0.3949	0.1095
			K
	First-order kinetic model	25	0.0418		0.9930	0.1304	0.0348
		35	0.0403		0.9953	0.1105	0.0295
		45	0.0399		0.9945	0.1170	0.0313
			H
	Exponential association model	25	0.0454		0.9855	0.1772	0.0473
		35	0.0431		0.9900	0.1517	0.0405
		45	0.0423		0.9893	0.1572	0.0420
MVD			K1	K2
	Peleg model	25	6.0999	0.7102	0.9481	1.4163	0.4088
		35	5.3880	0.5833	0.9772	0.8163	0.2356
		45	4.3629	0.3954	0.9964	0.2612	0.0754
			α	β
	Weibull model	25	0.9756	15.1285	0.9769	0.6874	0.2072
		35	0.8985	15.5785	0.9938	0.3245	0.0978
		45	0.9384	12.3188	0.9954	0.2933	0.0884
			K
	First-order kinetic model	25	0.0585		0.9969	0.1131	0.0327
		35	0.0614		0.9980	0.0880	0.0254
		45	0.0784		0.9982	0.0773	0.0223
			H
	Exponential association model	25	0.0616		0.9952	0.1364	0.0394
		35	0.0645		0.9957	0.1261	0.0365
		45	0.0819		0.9966	0.1054	0.0304
VFD			K1	K2
	Peleg model	25	4.1470	0.9299	0.9891	0.4375	0.1213
		35	3.3551	0.7174	0.9881	0.3703	0.1027
		45	2.5193	0.5841	0.9974	0.1280	0.0370
			α	β
	Weibull model	25	0.7121	9.6553	0.9873	0.4003	0.1156
		35	0.7837	8.1048	0.9821	0.5247	0.1515
		45	0.6997	6.4459	0.9846	0.4342	0.1253
			K
	First-order kinetic model	25	0.1040		0.9653	0.2943	0.0816
		35	0.1265		0.9729	0.2494	0.0691
		45	0.1758		0.9796	0.1942	0.0539
			H
	Exponential association model	25	0.1123		0.9447	0.3040	0.0877
		35	0.1371		0.9665	0.2672	0.0742
		45	0.1867		0.9758	0.2042	0.0567

Note: R², determination coefficient; SE, standard error; RMSE, root mean square error; K₁, K₂, α, β, K, H, model parameters.

In the Peleg model, the fitted constant value K₁ is related to the initial mass transfer rate (t = 0). A smaller K₁ indicates a higher initial rehydration efficiency. The fitted constant value K₂ is related to the maximum water absorption (t = 0). A smaller K₂ indicates a higher moisture content at equilibrium during rehydration (Muñoz et al., 2012).

In the Weibull model, the α value is influenced by the shape of the sample and experimental conditions, with a larger α value indicating a smaller initial water absorption rate and the β value is the rate constant of the system response, representing the time required to complete 63 % of the rehydration (Muñoz et al., 2012).

In the first-order kinetic model, the constant K represents the rehydration rate of the sample. A larger K indicates a faster rehydration rate in the same group of samples (Gowen et al., 2007).

In the exponential association model, the constant K may be related to the water absorption rate (Cox et al., 2012).

The fitting parameters of the rehydration models for HAD, MVD, and VFD yuba are shown in Table 4. For HAD yuba, the Peleg model parameters, K₁ and K₂, showed a decreasing trend with the temperature increase. The same trend can be observed by Cox et al. (2012), who suggested that K₂ is related to the water absorption capacity of the dried product. Increasing the rehydration temperature can enhance the water absorption capacity of the dried product, which means that the rehydration temperature can increase the maximum equilibrium moisture content of the yuba. The R² for the Peleg model ranged from 0.9914 to 0.9981, and the RMSE was equal to or less than 0.1069, indicating that the Peleg model effectively fitted the experimental data for HAD yuba at different temperatures. In the Weibull model, the β value initially decreases and then increases with rising rehydration temperature, while the α value first increases and then decreases, which may be due to the increase in maximum moisture content affecting the rehydration rate, and this does not match the actual rehydration curve. The R² range is 0.9800 to 0.9898, with an RMSE equal to or less than 0.1703, thus, the Weibull model is not suitable as the rehydration model for HAD yuba. In the first-order kinetic model and exponential association models, the model parameters decreased with increasing temperature, which does not match the rehydration curve of HAD yuba. The R² ranged from 0.9930 to 0.9953 and 0.9855 to 0.9900, respectively. These results indicate that the Peleg model has a high degree of fit for the rehydration characteristics of HAD yuba and can effectively predict the rehydration kinetics of HAD yuba at different rehydration temperatures.

For MVD yuba, both K₁ and K₂ in the Peleg model decreased with increasing temperature, indicating that increasing the rehydration temperature can enhance the rehydration rate and the maximum equilibrium moisture content of yuba. This trend is consistent with the findings by Li et al. (2020). The R² ranged from 0.9481 to 0.9964, indicating that the Peleg model effectively fitted the experimental data for MVD yuba at different rehydration temperatures. In the Weibull model, with the increase in temperature, the α value shows a trend of first decreasing and then increasing, while the β parameter shows a trend of first increasing and then decreasing. This trend of variation was also observed in the study by Pashazadeh et al. (2020), but the trends of parameters α and β with temperature do not match the experimental results in Fig. 4(b). Therefore, the Weibull model is not suitable as the rehydration model for MVD yuba. In the first-order kinetic model, K increased with increasing temperature. A larger K indicates a faster rehydration rate for the sample. Therefore, increasing the temperature can significantly improve the rehydration efficiency of MVD yuba. The R² value for this model ranged from 0.9969 to 0.9982, the maximum SE value was 0.1131, and the maximum RMSE value was 0.0327. In the exponential association model, H increased with increasing temperature. The R² value ranged from 0.9952 to 0.9966, the maximum SE value was 0.1364, and the maximum RMSE value was 0.0394. Therefore, the first-order kinetic model and exponential association model can effectively fit the experimental data for MVD yuba at different rehydration temperatures. These results indicate that the Peleg model, first-order kinetic model, and exponential association model all have good fitting degrees for the rehydration characteristics of MVD yuba and can predict the rehydration kinetics of MVD yuba at different rehydration temperatures.

In the Peleg model for VFD yuba, the parameters K₁ and K₂ show a decreasing trend, indicating that increasing the rehydration temperature can enhance the initial rehydration efficiency and maximum equilibrium moisture content of VFD yuba. The coefficient of determination R² ranges from 0.9881 to 0.9974, demonstrating that the Peleg model fits the experimental data of VFD yuba well at different rehydration temperatures. In the Weibull model, as the temperature increases, the parameter α shows a trend of first increasing and then decreasing, while the parameter β shows a decreasing trend, which does not match the actual rehydration curve of VFD yuba in Fig. 4(c). Therefore, this model is not suitable for describing the rehydration experimental data of VFD yuba. In the first-order kinetic model and exponential association models, the parameters K and H increase with rising temperature, with R² values ranging from 0.9653 to 0.9796 and 0.9447 to 0.9758, respectively, indicating that these two models can fit the experimental data changes of MVD yuba at different rehydration temperatures. The results show that the Peleg model, first-order kinetic model, and exponential association model are suitable for describing the rehydration changes of VFD yuba at different water temperatures.

Therefore, the results show that the fitting of the four rehydration kinetic models to the rehydration curves of yuba dried by different methods is as follows: The Peleg model can effectively simulate the rehydration kinetics of HAD yuba at different rehydration temperatures. The peleg, first-order kinetic, and exponential association models can effectively simulate the rehydration kinetics of MVD and VFD yuba at different rehydration temperatures.

4. Conclusions

This study examined the color, cooking loss, WHC, texture, sensory evaluation, protein secondary structure, and microscopic structure of yuba treated with different drying processes and employed four models to model the rehydration kinetics for each drying method individually. The results showed that MVD improved yuba's L* value and sensory score, while VFD reduced the cooking loss and increased WHC. Significant differences were observed in the SEM images of yuba, and a clear distinction in protein structure was found. Yuba dried using different methods showed significant variations in rehydration performance, with MVD yuba having a markedly higher maximum equilibrium moisture content than the other two groups. At different rehydration temperatures (25 °C, 35 °C, and 45 °C), the rehydration rate of yuba initially increased rapidly and then slowed, eventually approaching equilibrium; higher rehydration temperatures resulted in a higher maximum equilibrium moisture content. The Peleg model fit the rehydration changes of HAD yuba at different temperatures well, with the model parameters K₁ and K₂ decreasing with increasing temperature, indicating that higher rehydration temperatures can accelerate the initial water absorption rate and maximum equilibrium moisture content of yuba from different drying methods. The Peleg, first-order kinetic, and exponential association models effectively fit the rehydration changes of MVD and VFD yuba at different temperatures. Additionally, changes in model parameters indicate that increasing water temperature positively impacts the rehydration rate and time of yuba from different drying methods. MVD and VFD can alter the effective moisture diffusion rate during the rehydration process, shortening the rehydration time of yuba.

Therefore, MVD and VFD can be chosen as drying methods for rapidly rehydrating yuba products. Future research could focus on the rehydration kinetics of yuba under varying temperatures, exploring changes in the rehydration process and the application of various and combined drying methods in yuba drying.

Ethical statement

This study employs sensory analysis that adheres to ethical standards; all participants are professionally trained, and their rights and privacy are fully respected and protected during the experiment. The study's objectives and risks have been thoroughly communicated to all participants, and their consent has been obtained. All participants are voluntary and can withdraw from the experiment at any time.

CRediT authorship contribution statement

Jicai Bi: Writing – original draft, Methodology, Funding acquisition. Junyang Zhang: Validation, Formal analysis. Zhuo Chen: Investigation. Yunbo Li: Methodology, Data curation. Mohammed Obadi: Writing – review & editing. Wenhao Liu: Project administration. Renbing Qin: Validation. Lingwen Zhang: Software. Hongju He: Writing – review & editing.

Declaration of 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.

Data availability

Data will be made available on request.