High-voltage pulsed electric field pretreatment-assisted freezing to improve the quality of blueberries after freezing and its influencing mechanisms

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Additionally, differential scanning calorimetry (DSC) experiments showed that PEF pretreatment altered the thermal stability of blueberries, causing them to enter the phase transition stage earlier and reducing the time required to pass through the maximum ice crystal formation zone. This study elucidates the mechanism by which high-voltage pulsed electric field (PEF) pretreatment enhances post-freezing quality of blueberries by accelerating freezing and regulating ice crystal formation. It provides theoretical support for the application of non-thermal processing technologies in the frozen fruit and vegetable industry, opening a new, green, and efficient pathway to address the widespread issue of quality degradation in frozen fruits. This research holds significant practical value for optimizing frozen fruit processing techniques, elevating the standards of the frozen fruit industry, and reducing energy consumption in traditional freezing processes. Blueberry Artificial neural network PEF Freezing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Blueberries are a widely consumed healthy fruit known as the “king of berries” and “queen of fruits”[1]. It is rich in a variety of bioactive compounds, such as anthocyanins, vitamin E, vitamin A, phenolic acids and flavonoids[2], which help to lower blood pressure, improve vision, prevent heart disease, protect the brain from neurological aging, strengthen the heart, soften blood vessels, and boost the body's immune system, thereby reducing the risk of cancer[3]. Because of their high water content and the hot, rainy summer months when they ripen, blueberries are more limited in their storage and transportation[4]. Freezing is currently the most extensive and effective means of extending the shelf life and maintaining the quality of food, but traditional freezing produces large and uneven ice crystals, which damage the organization of the food[5] and cause the loss of nutrients. As a result, new technologies to improve the quality of foods after freezing have received much attention. Pulsed electric fields (PEF) have emerged as one of the most promising technologies in recent years. Initially, this technology was primarily used for non-thermal sterilization, leveraging the electroporation effect to achieve microbial inactivation[6]. However, subsequent studies revealed that applying short bursts of high voltage through PEF to food created micropores in the cell membranes[7, 8]. And these micropores promote moisture transfer during the freezing process, leading to faster, more uniform freezing rates[9]. Several studies have been conducted to prove that PEF can indeed improve the freezing efficiency of food products and the quality of food products after freezing. In the case of plant products such as apples[10], strawberries[11], spinach leaves[12], carrots[13], etc., by setting different parameters, the PEF can shorten their freezing time and generate smaller and more homogeneous ice crystals, which in the end can better preserve the sample's appearance and shape. And in the freezing of animal products PEF also shows good results. During the freezing process of beef[14] and Atlantic salmon[15], PEF was able to maintain a good fiber structure and kept the water holding capacity of the samples. Therefore, this study aims to comprehensively investigate the impact of PEF pretreatment on the freezing process of blueberries, with a particular focus on its effects on cell membrane permeability, microstructure, water distribution, and thermal properties. By exploring these critical aspects, this research seeks to elucidate the mechanisms by which PEF enhances freezing efficiency and preserves fruit quality, ultimately providing a scientific basis for optimizing frozen fruit processing technologies. 2. Material and methods 2.1 Blueberries preparation Select fresh blueberries (variety: “Hokuriku,” purchased from Longxin Fruit Supermarket) that are uniformly ripe and weigh 1.5 ± 0.1 grams, and divide them into three groups—FG, CK, and EG—for subsequent processing. The FG group consists of fresh blueberries, the CK group consists of blueberries frozen at -20°C, and the EG group consists of blueberries frozen after PEF treatment. 2.2 PEF-assisted freezing Place the selected blueberries into the PEF (Pulsed Electric Fields) device, and obtain different treatment effects by adjusting the voltage, processing time, and stacking layers for subsequent single-factor experiments. 2.3 One-way experiment Voltage gradients (15, 25, 35, and 45 kV), treatment time gradients (10, 20, 30, and 40 minutes), and stacked layer gradients (1, 2, and 3 layers) were set up in a PEF (pulsed electric field) system for comparison with the CK group (conventional freezing group). The effect of these three factors on the freezing effectiveness of blueberries was examined based on the time it took for the blueberries to pass through the zone of maximum ice crystal formation during freezing. The blueberries that underwent treatment were first pre-cooled in a refrigerator set to 4°C for 1 hour. Subsequently, they were moved to another refrigerator maintained at -20°C for freezing. Prior to freezing, a thermometer probe was placed in the center of the blueberries. From an initial temperature of 4°C down to -18°C, the temperature was recorded every 5 seconds. This data was used to plot the freezing curve during the process and to ascertain the duration it took for the blueberries to traverse the zone where ice crystals form most abundantly. 2.4 Artificial Neural Network Parameter Optimization Genetic algorithms were executed using the MATLAB Genetic Algorithms Toolbox through genetic operators, selection, crossover and mutation for global multipoint optimization. Artificial neural network simulation was operated on three factors (voltage parameter, processing time, and number of stacked layers) for PEF pretreatment-assisted flash-frozen blueberries within their respective parameter ranges, and the output of the model was used to construct an individual fit function for overall optimization. For this study, 193 data sets were used, 70% for training, 15% for prediction, and 15% for validation. Figure 1 is a schematic of the PEF preprocessing-assisted frozen blueberry neural network model. 2.5 Microstructure observation Sample preparation Frozen sections were prepared using a Leica-CM1900 cryostat, and the samples were cut into thin slices with thicknesses ranging from 4 to 30 µm, and then the tissue structure of the samples was observed under an Olympus light microscope equipped with a homemade cryogenic freezing stage. In order to better observe the effect of PEF (pulsed electric field) treatment on the tissue structure of the blueberry samples, the microstructure of the control and experimental samples was examined using a S-4800 scanning electron microscope (SEM). The accelerating voltage of the SEM was 10 kV, and the magnification was 600 and 3000 times, respectively 2.6 Moisture status and distribution The method was slightly modified with reference to Wang[16] . The water distribution and moisture state in samples were measured by NMI 20 low-field NMR analyzer (Shanghai Niumai Electronic Technology Co., Ltd., China). The working frequency was 22.0 MHz, the magnet temperature was 32 ◦C, EchoCnt = 18,000, τ = 200.00 μs, NS = 8, repetition time = 9000 ms, SW = 100 kHz, P 90 = 14.00 μs, P180 = 28.00 μs, Td = 720,020. The internal water distribution was measured by low field nuclear magnetic resonance imaging system. Imaging test parameters were: TR = 1000 ms, TE = 1.52 ms, Average =3, image. The pseudo-color software was used to process the image of the sample. 2.7 Relative electrical conductivity The relative electrical conductivity was determined according to Wang[17] with slight modification. Ten blueberries were randomly selected from each treatment group and sliced into 0.5 cm thick sections. After rinsing with distilled water, 20 mL of distilled water was added, and the mixture was shaken for 20 minutes. The conductivity of the solution (P0) was measured using an RX-TDS 210 conductivity meter (Hangzhou Meichem Automation Technology Co., Ltd.). The solution was then extracted in a boiling water bath (HH-4 digital thermostat, Shanghai Lixinbangxi Instrumentation Co., Ltd.) for 15 minutes. After cooling to room temperature, the conductivity (P1) was measured again. The relative conductivity, which indicates cell membrane permeability, was calculated using the formula: Relative Conductivity (%) = (P0 / P1) × 100%. 2.8 MDA content The MDA content was determined using thiobarbituric acid[18]. Fresh and thawed samples, each weighing 1.0 g, were measured into centrifuge tubes, to which 5.0 mL of trichloroacetic acid solution (100 g L⁻¹ TCA) was added per tube. The tubes, containing the combined mixture, underwent centrifugation at 10,000 × g for 20 minutes at a temperature of 4 °C. Following centrifugation, 2.0 mL of the supernatant was obtained (for the control blank tube, 2.0 mL of TCA solution (100 g L⁻¹) was substituted in place of the extraction). Subsequently, 2.0 mL of 2-thiobarbituric acid (TBA) at a concentration of 0.67% was introduced to the supernatant, and the mixture was thoroughly blended. The tubes were then subjected to a boiling water bath for 20 minutes, after which they were taken out, allowed to cool, and centrifuged once more. Using an L9 Plus UV–visible spectrophotometer manufactured by INESA Analytical Instrument Co., Ltd. in Shanghai, China, the absorbance was measured at wavelengths of 450 nm, 532 nm, and 600 nm. The MDA content was determined using the subsequent equations: Where c (nmol L −1 ) is 6.45 × (OD 532 nm – OD 600 nm) – 0.56 × OD 450 nm is the MDA concentration, V (mL) is total volume of the sample extract, Vs (mL) is the volume of sample extract taken at the time of the assay, m (g) is sample content. 2.9 Thermal state and thermal stability 10 ± 0.01 mg of sample was weighed into a 50 μL aluminum dish and sealed with a lid. Nitrogen at a flow rate of 20mL/min was used as the carrier gas. The sample was cooled to -40°C at 10°C/min, equilibrated for 10 min, and then heated to 40°C at 10°C/min. 2.10 Statistical analysis The freezing curve and the assessment of the maximum ice crystal formation zone were replicated nine times, while the remaining measurements were repeated three times. The graphs were created utilizing Origin 9.1 software (Origin Lab Corp., USA). For statistical analysis, SPSS (version 26.0, IBM, Chicago, IL, USA) was employed to conduct an Analysis of Variance (ANOVA) among the different treatments, followed by a post-hoc analysis using Duncan's method. The level of statistical significance was established at P < 0.05. 3. Results and analysis 3.1 One-way experiment As shown in Fig 2abc, the freezing curves of blueberries were significantly different after treatment with different parameters. The total freezing time increased with the increase of voltage at 15~35kV, while there was no significant difference in the total freezing time when the voltage reached 45kV; the total freezing time was shorter the longer the treatment time was at 10~40min, but the total freezing time shortened more and more slowly when it was more than 20min; the number of stacked layers was contrary to the previous two parameters, and the total freezing time increased when the stacked layers were 1~3. The number of stacked layers was opposite to the previous two parameters, and the total freezing time increased with the increase of the number of stacked layers, which might be that the number of stacked layers affected the effect of PEF treatment. As shown in Figure 3abc, the time for blueberries to pass through the maximum ice crystal generation zone during freezing varied across the different parameter treatments. As the voltage increases, the time for blueberries to pass through the maximum ice crystal generation band becomes shorter and shorter; the increase of the treatment time has little effect on the time for blueberries to pass through the maximum ice crystal generation band; the increase of the number of stacked layers makes the time for blueberries to pass through the maximum ice crystal generation band longer. Consistent with the results for total freezing time, voltage and time can shorten the freezing process of blueberries to some extent, while the number of stacked layers can have a side effect. The best performing set of parameters in this one-factor experiment was voltage intensity 35 kV, treatment time 30 min, and stacking one layer. Under this parameter, the total freezing time of blueberries was 2810s, which was 28% lower compared to the total freezing time of 3905s in the CK group, and the time to pass through the maximum ice crystal generating zone was 1670s, which was 27% lower compared to the time to pass through the maximum ice crystal generating zone in the CK group, which was 2295s. It is certain that blueberries frozen after PEF treatment had significantly lower total freezing time and time to pass the maximum ice crystal generation zone than control blueberries. 3.2 Artificial Neural Network Parameter Optimization The BP artificial neural network model was operated based on the experimental conditions, with neural network statements programmed to execute the model's operations. The outcomes indicate that the performance of the trained network is depicted in Fig 4a. For this neural network model, the foundation for constructing an accurate prediction model lies in minimizing the correlation error between experimental and simulated data in each scenario. Upon reaching 8 Epochs, the training target converges with the optimal error, and the preset learning objective accepts the minimal error at this point, terminating the training process. Based on the model's design, MATLAB's diverrand function is utilized to randomly generate datasets for training, testing, and validation. The linear regression plots for these sets are then depicted in Fig 4b. At this juncture, the predictive power coefficients (R) of the regression models for the training, testing, and validation sets are 0.99345, 0.99429, and 0.99164, respectively, highlighting the model's optimal performance. Furthermore, the R value for the entire dataset is 0.99337, indicating that the Mean Squared Error (MSE) of the regression model is at its lowest, thereby signifying the best model performance. To further assess the forecasting capability of the model, 57 groups of predicted values were randomly selected for analysis and comparison, as illustrated in Figure 4c. Among these 57 groups, there is both overlap and deviation, but approximately 90% of the real values overlap with the predicted values. This demonstrates that the designed network model possesses the ability to predict the impact of various parameters on frozen blueberries, and the neural network's prediction accuracy is high. Subsequently, the true values were fitted and analyzed with the model predictions, with the results presented in Figure 4d. The findings reveal a high degree of fit between the actual values of frozen blueberries passing through the maximum ice crystal formation zone and the predicted values of the BP neural network model, with a coefficient of determination (R²) of 0.9918. Upon verification, it was concluded that the model's predictions are relatively accurate and acceptable. Therefore, the BP neural network can effectively predict the time taken by blueberries to traverse the maximum ice crystal formation zone during the freezing process, exhibiting a high level of prediction accuracy. The model predicted that the optimal parameters for PEF-assisted freezing of blueberries were voltage 34.2 kV, treatment time 23.7 min, and stacking of 1 layer, which were used to treat blueberries in all subsequent experiments. 3.3 Microstructure observation SEM is an instrumental tool for imaging and exploring the intangible domains of micro- and nanospace [19]. As can be seen in Fig 5a, the ice crystals produced during the freezing process of blueberries extruded the cellular tissue and eventually formed cavities. Compared to Fig 5b, blueberries frozen after PEF treatment produced a higher number of cavities, but all of them were smaller in size. This is due to the fact that blueberries have a shorter time to generate the largest regions through ice crystals during freezing, and the internal water has no time to migrate and redistribute. Fig 5c and Fig 5d are magnified microstructural images. Comparing the two sets of images we found that the ice crystals generated by the conventional freezing method cause some damage to the cell tissues, which are broken in many places in Fig. 5, whereas the cell tissues in Fig. 6 are much smoother and have no obvious damage. This could be attributed to the larger and more irregularly shaped ice crystals formed by the traditional freezing method, which result in cellular tissue extrusion and damage. 3.4 Water status and water distribution LF-NMR serves as a powerful, non-invasive testing method, enabling the visualization of changes in moisture levels and water distribution patterns in control and treated blueberry samples [20]. The three graphs in Figure 6abc show the bound, fixed, and free water of blueberries in each treatment group, respectively. As shown, there was a significant difference in the water migration of blueberries in each treatment group, and the free water content of blueberries in the EG group was considerably lower than that of blueberries in the CK group, which was not significantly different from that of fresh blueberries. This may be due to the fact that the ice crystals generated during the freezing process of blueberries damaged the cellular tissues, and the water flowed out of the vesicles[21] and was redistributed to all parts of the blueberries[22], whereas the PEF could make the ice crystals smaller in size and uniformly distributed, which played a protective role for the cellular tissues, and the content of free water was also reduced. The moisture distribution in blueberries, as measured by MRI and visualized through pseudo-color processing, is shown in Fig 7. In the image, different colors represent varying moisture levels within the blueberries. Higher moisture content corresponds to stronger signals, depicted by colors closer to red, while lower moisture content is indicated by colors shifting away from red. As illustrated in Figure 7, the CK group exhibited a larger red area, indicating higher moisture content, whereas the EG group showed a smaller red area, with results similar to the FG group. This difference can be attributed to the slow freezing process in the CK group, which formed large ice crystals that damaged the cellular water storage structures. Upon thawing, the cells were unable to reabsorb the released water, leading to uneven water distribution. In contrast, the EG group experienced faster freezing, which minimized ice crystal size by rapidly passing through the zone of maximum ice crystal formation. As a result, less cellular damage occurred, and the tissues required less water reabsorption after thawing, leading to a more uniform water distribution compared to the CK group. 3.5 Cell membrane permeability Relative conductivity is an important measure of cell membrane permeability in fruits and vegetables[23]. As depicted in Fig 8, the relative conductivities of the treatment groups are 37.7%, 76.45%, and 60.12%, respectively, with the FG group exhibiting the lowest and the CK group the highest. Notably, the relative conductivity of the EG group decreased significantly by 21.36% compared to the CK group. This is because in the CK group, larger ice crystals were formed, causing extensive damage to the cell membrane and reducing its integrity. Consequently, electrolytes leaked out of the cells, leading to a substantial increase in relative conductivity. Conversely, in the EG group, smaller ice crystals were formed, preserving the integrity of the cell membrane and minimizing the leakage of electrolytes, resulting in a lower relative conductivity. This suggests that PEF treatment safeguards the integrity of the cell membrane. 3.6 MDA content MDA, a major byproduct of membrane lipid peroxidation, accumulates in fruit tissues when low temperatures trigger higher free radical production and intensify lipid peroxidation processes[24]. As shown in Figure 9, PEF performed well in controlling MDA content. Compared with the CK group, the MDA content in the EG group decreased by 34.34%, even the MDA content in the EG group was not significantly different from that in the FG group. This indicates that PEF has a good role in protecting the cell membrane structure during the freezing process of blueberries, and a large amount of membrane lipid peroxidation did not occur at low temperature. 3.7 Thermal state and thermal stability DSC is a thermal analysis technique that examines the thermal behavior of a material as it undergoes temperature changes by measuring the differential heat flow between a sample and a reference. According to Table 1, with an equivalent amount of sample, a reduction in peak area and peak value signifies a weakened response of the sample to thermal effects, resulting in less heat absorption or release during the thermal process. Additionally, a decrease in Tr, Ts, as well as the initial and final temperatures, indicates that the sample undergoes transformations more readily, altering its thermal stability. Following PEF (Pulsed Electric Field) treatment, the thermal stability of the samples shifts, causing them to enter the phase transition stage earlier during freezing. Consequently, the duration for these samples to traverse the maximum ice crystal generation zone is shortened. Table1 Various parameters of the DSC experiment in the CK and EG groups Sample Peak area Peak value Tr Ts Initial temperature Termination temperature CK 231.1±14.5J/g 5.9±2℃ 11.5±1.6℃ 5.9±2.0℃ -2.7±0.5℃ 16.3±2.5℃ EG 222.9±25.3J/g 4.0±1℃ 9.7±0.5℃ 4.0±1.0℃ -2.7±0.5℃ 13.4±0.6℃ 4.Conclusion The application of high-voltage pulsed electric field pretreatment-assisted freezing technology in the blueberry freezing process resulted in a notable reduction in both freezing time and the duration through the maximum ice crystal generation zone. This led to decreased ice crystal damage to blueberry cells. Furthermore, the technology enhanced the microstructure of blueberries by preserving a more intact cellular morphology. It also optimized the water status and distribution, minimizing water crystallization and aggregation. In addition, the MDA (malondialdehyde) content of pretreated blueberries was significantly reduced during the freezing process, the degree of lipid peroxidation of cell membranes was reduced, and the relative conductivity remained stable, indicating that the permeability of cell membranes was not significantly affected. Thermal property analysis showed that the pretreated blueberries had higher thermal stability and lower thermal degradation temperature. In summary, the high-voltage pulsed electric field pretreatment-assisted freezing technology significantly improved the quality of blueberries after freezing through various improvements, and provided a new and effective way for the preservation of fruits and vegetables by freezing. Declarations CRediT authorship contribution statement Tianyuan Liang: Writing – original draft,Writing – review & editing, Data curation,Formal analysis, Conceptualization, Methodology.Bin Li: Resources, Methodology.Fuzhi Xuan: Investigation, Data curation, Visualization.Baoru Yang: Participate in the analysis of actual production issues.Xiaoru Liu: Participate in the analysis of actual production issues.Yan Ma: Participate in the analysis of actual production issues .Xianjun Meng: Conceptualization, Methodology, Validation, Funding acquisition.Yuehua Wang: Project administration, Conceptualization, Funding acquisition,Supervision, Writing – review & editing. Declaration of competing interest None. Acknowledgments This study was supported by grants from the China Postdoctoral Science Foundation.(2024M760337). References Reque, P.M., et al., Cold storage of blueberry (Vaccinium spp.) fruits and juice: Anthocyanin stability and antioxidant activity. 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Chemicals & Chemistry, 2015. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6807944","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467881047,"identity":"63ec7927-cb04-46f9-b488-c734b45ea89f","order_by":0,"name":"Tianyuan Liang","email":"","orcid":"","institution":"Shenyang Agriculture University","correspondingAuthor":false,"prefix":"","firstName":"Tianyuan","middleName":"","lastName":"Liang","suffix":""},{"id":467881049,"identity":"f56e6862-c851-4b33-a50d-763f576c9d2c","order_by":1,"name":"Bin Li","email":"","orcid":"","institution":"Shenyang Agriculture 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Wang","email":"data:image/png;base64,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","orcid":"","institution":"Shenyang Agriculture University","correspondingAuthor":true,"prefix":"","firstName":"Yuehua","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-06-03 07:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6807944/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6807944/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84220352,"identity":"607e91fb-3f46-4c28-aad1-659427d1d215","added_by":"auto","created_at":"2025-06-09 11:30:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57622,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of PEF pretreatment-assisted artificial neural network model for frozen blueberries.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6807944/v1/d541c2e70966751d0962a2a5.png"},{"id":84221235,"identity":"287af673-ad01-4e5f-b07e-56fc6d7f6522","added_by":"auto","created_at":"2025-06-09 11:46:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":82886,"visible":true,"origin":"","legend":"\u003cp\u003eFreezing curves of blueberries after treatment with different parameters\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6807944/v1/b188f43e4d8d2ef7001f4bb8.png"},{"id":84220353,"identity":"3d0f711c-d82e-41d0-aed1-3eaae0626a02","added_by":"auto","created_at":"2025-06-09 11:30:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":49645,"visible":true,"origin":"","legend":"\u003cp\u003eTime to pass the maximum ice crystal generation band during freezing of blueberries after treatment with different conditions\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6807944/v1/9739adf6e21f68e36e63bbc7.png"},{"id":84220819,"identity":"a4216704-d73f-4a98-a07c-3e1a1d766a69","added_by":"auto","created_at":"2025-06-09 11:38:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":156333,"visible":true,"origin":"","legend":"\u003cp\u003eArtificial Neural Network Results\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6807944/v1/0d6af771ad5c5615e4c82e8f.png"},{"id":84220825,"identity":"6afb8da1-470a-4c4f-9cb5-cced18ad1440","added_by":"auto","created_at":"2025-06-09 11:38:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":492108,"visible":true,"origin":"","legend":"\u003cp\u003eMicrostructure observation results\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6807944/v1/8051427ef6195c2172c03a1d.png"},{"id":84220820,"identity":"64cc6fae-2888-4468-a435-dddbdafee260","added_by":"auto","created_at":"2025-06-09 11:38:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":79955,"visible":true,"origin":"","legend":"\u003cp\u003eResults of water state detected by LF-NMR\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6807944/v1/0fea4ed27310574608a4f094.png"},{"id":84220382,"identity":"5646ba0e-659f-497a-b82c-f6029425f627","added_by":"auto","created_at":"2025-06-09 11:30:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":571736,"visible":true,"origin":"","legend":"\u003cp\u003eMoisture distribution results\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6807944/v1/0b59e9a768cf522554e1c013.png"},{"id":84220363,"identity":"41819c86-79d2-48af-aa89-045f5778a4a2","added_by":"auto","created_at":"2025-06-09 11:30:53","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":39399,"visible":true,"origin":"","legend":"\u003cp\u003eRelative conductivity of each group\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6807944/v1/b965d15c6ab4a7b0ad2e5915.png"},{"id":84220358,"identity":"aee3dfcf-c658-409e-ba34-fc960a1f0f60","added_by":"auto","created_at":"2025-06-09 11:30:52","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":33057,"visible":true,"origin":"","legend":"\u003cp\u003eMDA content of each group\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6807944/v1/c047e0842a62bf6349acb477.png"},{"id":84278631,"identity":"5703efa6-1f85-49f8-b2ee-707bf3e71784","added_by":"auto","created_at":"2025-06-10 06:09:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1840516,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6807944/v1/f78df042-876f-42c8-ac55-4faa010a44f1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"High-voltage pulsed electric field pretreatment-assisted freezing to improve the quality of blueberries after freezing and its influencing mechanisms","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBlueberries are a widely consumed healthy fruit known as the \u0026ldquo;king of berries\u0026rdquo; and \u0026ldquo;queen of fruits\u0026rdquo;[1]. It is rich in a variety of bioactive compounds, such as anthocyanins, vitamin E, vitamin A, phenolic acids and flavonoids[2], which help to lower blood pressure, improve vision, prevent heart disease, protect the brain from neurological aging, strengthen the heart, soften blood vessels, and boost the body\u0026apos;s immune system, thereby reducing the risk of cancer[3]. Because of their high water content and the hot, rainy summer months when they ripen, blueberries are more limited in their storage and transportation[4]. Freezing is currently the most extensive and effective means of extending the shelf life and maintaining the quality of food, but traditional freezing produces large and uneven ice crystals, which damage the organization of the food[5] and cause the loss of nutrients. As a result, new technologies to improve the quality of foods after freezing have received much attention.\u003c/p\u003e\n\u003cp\u003ePulsed electric fields (PEF) have emerged as one of the most promising technologies in recent years. Initially, this technology was primarily used for non-thermal sterilization, leveraging the electroporation effect to achieve microbial inactivation[6]. However, subsequent studies revealed that applying short bursts of high voltage through PEF to food created micropores in the cell membranes[7, 8]. And these micropores promote moisture transfer during the freezing process, leading to faster, more uniform freezing rates[9].\u003c/p\u003e\n\u003cp\u003eSeveral studies have been conducted to prove that PEF can indeed improve the freezing efficiency of food products and the quality of food products after freezing. In the case of plant products such as apples[10], strawberries[11], spinach leaves[12], carrots[13], etc., by setting different parameters, the PEF can shorten their freezing time and generate smaller and more homogeneous ice crystals, which in the end can better preserve the sample\u0026apos;s appearance and shape. And in the freezing of animal products PEF also shows good results. During the freezing process of beef[14] and Atlantic salmon[15], PEF was able to maintain a good fiber structure and kept the water holding capacity of the samples.\u003c/p\u003e\n\u003cp\u003eTherefore, this study aims to comprehensively investigate the impact of PEF pretreatment on the freezing process of blueberries, with a particular focus on its effects on cell membrane permeability, microstructure, water distribution, and thermal properties. By exploring these critical aspects, this research seeks to elucidate the mechanisms by which PEF enhances freezing efficiency and preserves fruit quality, ultimately providing a scientific basis for optimizing frozen fruit processing technologies.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cp\u003e2.1 Blueberries preparation\u003c/p\u003e\n\u003cp\u003eSelect fresh blueberries (variety: \u0026ldquo;Hokuriku,\u0026rdquo; purchased from Longxin Fruit Supermarket) that are uniformly ripe and weigh 1.5 \u0026plusmn; 0.1 grams, and divide them into three groups\u0026mdash;FG, CK, and EG\u0026mdash;for subsequent processing. The FG group consists of fresh blueberries, the CK group consists of blueberries frozen at -20\u0026deg;C, and the EG group consists of blueberries frozen after PEF treatment.\u003c/p\u003e\n\u003cp\u003e2.2 PEF-assisted freezing\u003c/p\u003e\n\u003cp\u003ePlace the selected blueberries into the PEF (Pulsed Electric Fields) device, and obtain different treatment effects by adjusting the voltage, processing time, and stacking layers for subsequent single-factor experiments.\u003c/p\u003e\n\u003cp\u003e2.3 One-way experiment\u003c/p\u003e\n\u003cp\u003eVoltage gradients (15, 25, 35, and 45 kV), treatment time gradients (10, 20, 30, and 40 minutes), and stacked layer gradients (1, 2, and 3 layers) were set up in a PEF (pulsed electric field) system for comparison with the CK group (conventional freezing group). The effect of these three factors on the freezing effectiveness of blueberries was examined based on the time it took for the blueberries to pass through the zone of maximum ice crystal formation during freezing.\u003c/p\u003e\n\u003cp\u003eThe blueberries that underwent treatment were first pre-cooled in a refrigerator set to 4\u0026deg;C for 1 hour. Subsequently, they were moved to another refrigerator maintained at -20\u0026deg;C for freezing. Prior to freezing, a thermometer probe was placed in the center of the blueberries. From an initial temperature of 4\u0026deg;C down to -18\u0026deg;C, the temperature was recorded every 5 seconds. This data was used to plot the freezing curve during the process and to ascertain the duration it took for the blueberries to traverse the zone where ice crystals form most abundantly.\u003c/p\u003e\n\u003cp\u003e2.4 Artificial Neural Network Parameter Optimization\u003c/p\u003e\n\u003cp\u003eGenetic algorithms were executed using the MATLAB Genetic Algorithms Toolbox through genetic operators, selection, crossover and mutation for global multipoint optimization. Artificial neural network simulation was operated on three factors (voltage parameter, processing time, and number of stacked layers) for PEF pretreatment-assisted flash-frozen blueberries within their respective parameter ranges, and the output of the model was used to construct an individual fit function for overall optimization. For this study, 193 data sets were used, 70% for training, 15% for prediction, and 15% for validation. Figure 1 is a schematic of the PEF preprocessing-assisted frozen blueberry neural network model.\u003c/p\u003e\n\u003cp\u003e\u003c!--[if !mso]--\u003e2.5 Microstructure observation\u003c/p\u003e\n\u003cp\u003eSample preparation Frozen sections were prepared using a Leica-CM1900 cryostat, and the samples were cut into thin slices with thicknesses ranging from 4 to 30 \u0026micro;m, and then the tissue structure of the samples was observed under an Olympus light microscope equipped with a homemade cryogenic freezing stage. In order to better observe the effect of PEF (pulsed electric field) treatment on the tissue structure of the blueberry samples, the microstructure of the control and experimental samples was examined using a S-4800 scanning electron microscope (SEM). The accelerating voltage of the SEM was 10 kV, and the magnification was 600 and 3000 times, respectively\u003c/p\u003e\n\u003cp\u003e2.6 Moisture status and distribution\u003c/p\u003e\n\u003cp\u003eThe method was slightly modified with reference to Wang[16] . The water distribution and moisture state in samples were measured by NMI 20 low-field NMR analyzer (Shanghai Niumai Electronic Technology Co., Ltd., China). The working frequency was 22.0 MHz, the magnet temperature was 32 ◦C, EchoCnt = 18,000, \u0026tau; = 200.00 \u0026mu;s, NS = 8, repetition time = 9000 ms, SW = 100 kHz, P 90 = 14.00 \u0026mu;s, P180 = 28.00 \u0026mu;s, Td = 720,020. The internal water distribution was measured by low field nuclear magnetic resonance imaging system. Imaging test parameters were: TR = 1000 ms, TE = 1.52 ms, Average =3, image. The pseudo-color software was used to process the image of the sample.\u003c/p\u003e\n\u003cp\u003e2.7 Relative electrical conductivity\u003c/p\u003e\n\u003cp\u003eThe relative electrical conductivity was determined according to Wang[17] with slight modification.\u0026nbsp;Ten blueberries were randomly selected from each treatment group and sliced into 0.5 cm thick sections. After rinsing with distilled water, 20 mL of distilled water was added, and the mixture was shaken for 20 minutes. The conductivity of the solution (P0) was measured using an RX-TDS 210 conductivity meter (Hangzhou Meichem Automation Technology Co., Ltd.). The solution was then extracted in a boiling water bath (HH-4 digital thermostat, Shanghai Lixinbangxi Instrumentation Co., Ltd.) for 15 minutes. After cooling to room temperature, the conductivity (P1) was measured again. The relative conductivity, which indicates cell membrane permeability, was calculated using the formula: Relative Conductivity (%) = (P0 / P1) \u0026times; 100%.\u003c/p\u003e\n\u003cp\u003e2.8 MDA content\u003c/p\u003e\n\u003cp\u003eThe MDA content was determined using thiobarbituric acid[18]. Fresh and thawed samples, each weighing 1.0 g, were measured into centrifuge tubes, to which 5.0 mL of trichloroacetic acid solution (100 g L⁻\u0026sup1; TCA) was added per tube. The tubes, containing the combined mixture, underwent centrifugation at 10,000 \u0026times; g for 20 minutes at a temperature of 4 \u0026deg;C. Following centrifugation, 2.0 mL of the supernatant was obtained (for the control blank tube, 2.0 mL of TCA solution (100 g L⁻\u0026sup1;) was substituted in place of the extraction). Subsequently, 2.0 mL of 2-thiobarbituric acid (TBA) at a concentration of 0.67% was introduced to the supernatant, and the mixture was thoroughly blended. The tubes were then subjected to a boiling water bath for 20 minutes, after which they were taken out, allowed to cool, and centrifuged once more. Using an L9 Plus UV\u0026ndash;visible spectrophotometer manufactured by INESA Analytical Instrument Co., Ltd. in Shanghai, China, the absorbance was measured at wavelengths of 450 nm, 532 nm, and 600 nm. The MDA content was determined using the subsequent equations:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"330\" height=\"73\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere c (nmol L\u003csup\u003e\u0026minus;1\u003c/sup\u003e) is 6.45 \u0026times; (OD\u003csub\u003e532\u003c/sub\u003e nm \u0026ndash; OD\u003csub\u003e600\u003c/sub\u003e nm) \u0026ndash; 0.56 \u0026times; OD\u003csub\u003e450\u003c/sub\u003e nm is the MDA concentration, V (mL) is total volume of the sample extract, Vs (mL) is the volume of sample extract taken at the time of the assay, m (g) is sample content.\u003c/p\u003e\n\u003cp\u003e2.9 Thermal state and thermal stability\u003c/p\u003e\n\u003cp\u003e10 \u0026plusmn; 0.01 mg of sample was weighed into a 50 \u0026mu;L aluminum dish and sealed with a lid. Nitrogen at a flow rate of 20mL/min was used as the carrier gas. The sample was cooled to -40\u0026deg;C at 10\u0026deg;C/min, equilibrated for 10 min, and then heated to 40\u0026deg;C at 10\u0026deg;C/min.\u003c/p\u003e\n\u003cp\u003e2.10 Statistical analysis\u003c/p\u003e\n\u003cp\u003eThe freezing curve and the assessment of the maximum ice crystal formation zone were replicated nine times, while the remaining measurements were repeated three times. The graphs were created utilizing Origin 9.1 software (Origin Lab Corp., USA). For statistical analysis, SPSS (version 26.0, IBM, Chicago, IL, USA) was employed to conduct an Analysis of Variance (ANOVA) among the different treatments, followed by a post-hoc analysis using Duncan\u0026apos;s method. The level of statistical significance was established at P \u0026lt; 0.05.\u003c/p\u003e"},{"header":"3. Results and analysis","content":"\u003cp\u003e3.1 One-way experiment\u003c/p\u003e\n\u003cp\u003eAs shown in Fig 2abc, the freezing curves of blueberries were significantly different after treatment with different parameters. The total freezing time increased with the increase of voltage at 15~35kV, while there was no significant difference in the total freezing time when the voltage reached 45kV; the total freezing time was shorter the longer the treatment time was at 10~40min, but the total freezing time shortened more and more slowly when it was more than 20min; the number of stacked layers was contrary to the previous two parameters, and the total freezing time increased when the stacked layers were 1~3. The number of stacked layers was opposite to the previous two parameters, and the total freezing time increased with the increase of the number of stacked layers, which might be that the number of stacked layers affected the effect of PEF treatment.\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 3abc, the time for blueberries to pass through the maximum ice crystal generation zone during freezing varied across the different parameter treatments. As the voltage increases, the time for blueberries to pass through the maximum ice crystal generation band becomes shorter and shorter; the increase of the treatment time has little effect on the time for blueberries to pass through the maximum ice crystal generation band; the increase of the number of stacked layers makes the time for blueberries to pass through the maximum ice crystal generation band longer. Consistent with the results for total freezing time, voltage and time can shorten the freezing process of blueberries to some extent, while the number of stacked layers can have a side effect. The best performing set of parameters in this one-factor experiment was voltage intensity 35 kV, treatment time 30 min, and stacking one layer. Under this parameter, the total freezing time of blueberries was 2810s, which was 28% lower compared to the total freezing time of 3905s in the CK group, and the time to pass through the maximum ice crystal generating zone was 1670s, which was 27% lower compared to the time to pass through the maximum ice crystal generating zone in the CK group, which was 2295s. It is certain that blueberries frozen after PEF treatment had significantly lower total freezing time and time to pass the maximum ice crystal generation zone than control blueberries.\u003c/p\u003e\n\u003cp\u003e3.2 Artificial Neural Network Parameter Optimization\u003c/p\u003e\n\u003cp\u003eThe BP artificial neural network model was operated based on the experimental conditions, with neural network statements programmed to execute the model\u0026apos;s operations. The outcomes indicate that the performance of the trained network is depicted in Fig 4a. For this neural network model, the foundation for constructing an accurate prediction model lies in minimizing the correlation error between experimental and simulated data in each scenario. Upon reaching 8 Epochs, the training target converges with the optimal error, and the preset learning objective accepts the minimal error at this point, terminating the training process.\u003c/p\u003e\n\u003cp\u003eBased on the model\u0026apos;s design, MATLAB\u0026apos;s diverrand function is utilized to randomly generate datasets for training, testing, and validation. The linear regression plots for these sets are then depicted in Fig 4b. At this juncture, the predictive power coefficients (R) of the regression models for the training, testing, and validation sets are 0.99345, 0.99429, and 0.99164, respectively, highlighting the model\u0026apos;s optimal performance. Furthermore, the R value for the entire dataset is 0.99337, indicating that the Mean Squared Error (MSE) of the regression model is at its lowest, thereby signifying the best model performance.\u003c/p\u003e\n\u003cp\u003eTo further assess the forecasting capability of the model, 57 groups of predicted values were randomly selected for analysis and comparison, as illustrated in Figure 4c. Among these 57 groups, there is both overlap and deviation, but approximately 90% of the real values overlap with the predicted values. This demonstrates that the designed network model possesses the ability to predict the impact of various parameters on frozen blueberries, and the neural network\u0026apos;s prediction accuracy is high.\u003c/p\u003e\n\u003cp\u003eSubsequently, the true values were fitted and analyzed with the model predictions, with the results presented in Figure 4d. The findings reveal a high degree of fit between the actual values of frozen blueberries passing through the maximum ice crystal formation zone and the predicted values of the BP neural network model, with a coefficient of determination (R\u0026sup2;) of 0.9918. Upon verification, it was concluded that the model\u0026apos;s predictions are relatively accurate and acceptable. Therefore, the BP neural network can effectively predict the time taken by blueberries to traverse the maximum ice crystal formation zone during the freezing process, exhibiting a high level of prediction accuracy. The model predicted that the optimal parameters for PEF-assisted freezing of blueberries were voltage 34.2 kV, treatment time 23.7 min, and stacking of 1 layer, which were used to treat blueberries in all subsequent experiments.\u003c/p\u003e\n\u003cp\u003e3.3 Microstructure observation\u003c/p\u003e\n\u003cp\u003eSEM is an instrumental tool for imaging and exploring the intangible domains of micro- and nanospace\u0026nbsp;[19]. As can be seen in Fig 5a, the ice crystals produced during the freezing process of blueberries extruded the cellular tissue and eventually formed cavities. Compared to Fig 5b, blueberries frozen after PEF treatment produced a higher number of cavities, but all of them were smaller in size. This is due to the fact that blueberries have a shorter time to generate the largest regions through ice crystals during freezing, and the internal water has no time to migrate and redistribute.\u003c/p\u003e\n\u003cp\u003eFig 5c and Fig 5d are magnified microstructural images. Comparing the two sets of images we found that the ice crystals generated by the conventional freezing method cause some damage to the cell tissues, which are broken in many places in Fig. 5, whereas the cell tissues in Fig. 6 are much smoother and have no obvious damage. This could be attributed to the larger and more irregularly shaped ice crystals formed by the traditional freezing method, which result in cellular tissue extrusion and damage.\u003c/p\u003e\n\u003cp\u003e3.4 Water status and water distribution\u003c/p\u003e\n\u003cp\u003eLF-NMR serves as a powerful, non-invasive testing method, enabling the visualization of changes in moisture levels and water distribution patterns in control and treated blueberry samples [20].\u0026nbsp;The three graphs in Figure 6abc show the bound, fixed, and free water of blueberries in each treatment group, respectively. As shown, there was a significant difference in the water migration of blueberries in each treatment group, and the free water content of blueberries in the EG group was considerably lower than that of blueberries in the CK group, which was not significantly different from that of fresh blueberries. This may be due to the fact that the ice crystals generated during the freezing process of blueberries damaged the cellular tissues, and the water flowed out of the vesicles[21]\u0026nbsp;and was redistributed to all parts of the blueberries[22], whereas the PEF could make the ice crystals smaller in size and uniformly distributed, which played a protective role for the cellular tissues, and the content of free water was also reduced.\u003c/p\u003e\n\u003cp\u003eThe moisture distribution in blueberries, as measured by MRI and visualized through pseudo-color processing, is shown in Fig 7. In the image, different colors represent varying moisture levels within the blueberries. Higher moisture content corresponds to stronger signals, depicted by colors closer to red, while lower moisture content is indicated by colors shifting away from red. As illustrated in Figure 7, the CK group exhibited a larger red area, indicating higher moisture content, whereas the EG group showed a smaller red area, with results similar to the FG group. This difference can be attributed to the slow freezing process in the CK group, which formed large ice crystals that damaged the cellular water storage structures. Upon thawing, the cells were unable to reabsorb the released water, leading to uneven water distribution. In contrast, the EG group experienced faster freezing, which minimized ice crystal size by rapidly passing through the zone of maximum ice crystal formation. As a result, less cellular damage occurred, and the tissues required less water reabsorption after thawing, leading to a more uniform water distribution compared to the CK group.\u003c/p\u003e\n\u003cp\u003e3.5 Cell membrane permeability\u003c/p\u003e\n\u003cp\u003eRelative conductivity is an important measure of cell membrane permeability in fruits and vegetables[23]. As depicted in Fig 8, the relative conductivities of the treatment groups are 37.7%, 76.45%, and 60.12%, respectively, with the FG group exhibiting the lowest and the CK group the highest. Notably, the relative conductivity of the EG group decreased significantly by 21.36% compared to the CK group. This is because in the CK group, larger ice crystals were formed, causing extensive damage to the cell membrane and reducing its integrity. Consequently, electrolytes leaked out of the cells, leading to a substantial increase in relative conductivity. Conversely, in the EG group, smaller ice crystals were formed, preserving the integrity of the cell membrane and minimizing the leakage of electrolytes, resulting in a lower relative conductivity. This suggests that PEF treatment safeguards the integrity of the cell membrane.\u003c/p\u003e\n\u003cp\u003e3.6 MDA content\u003c/p\u003e\n\u003cp\u003eMDA, a major byproduct of membrane lipid peroxidation, accumulates in fruit tissues when low temperatures trigger higher free radical production and intensify lipid peroxidation processes[24]. As shown in Figure 9, PEF performed well in controlling MDA content. Compared with the CK group, the MDA content in the EG group decreased by 34.34%, even the MDA content in the EG group was not significantly different from that in the FG group. This indicates that PEF has a good role in protecting the cell membrane structure during the freezing process of blueberries, and a large amount of membrane lipid peroxidation did not occur at low temperature.\u003c/p\u003e\n\u003cp\u003e3.7 Thermal state and thermal stability\u003c/p\u003e\n\u003cp\u003eDSC is a thermal analysis technique that examines the thermal behavior of a material as it undergoes temperature changes by measuring the differential heat flow between a sample and a reference. According to Table 1, with an equivalent amount of sample, a reduction in peak area and peak value signifies a weakened response of the sample to thermal effects, resulting in less heat absorption or release during the thermal process. Additionally, a decrease in Tr, Ts, as well as the initial and final temperatures, indicates that the sample undergoes transformations more readily, altering its thermal stability. Following PEF (Pulsed Electric Field) treatment, the thermal stability of the samples shifts, causing them to enter the phase transition stage earlier during freezing. Consequently, the duration for these samples to traverse the maximum ice crystal generation zone is shortened.\u003c/p\u003e\n\u003cp\u003eTable1 Various parameters of the DSC experiment in the CK and EG groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003ePeak area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePeak value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eTr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 74px;\"\u003e\n \u003cp\u003eTs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 81px;\"\u003e\n \u003cp\u003eInitial temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eTermination temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eCK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e231.1\u0026plusmn;14.5J/g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e5.9\u0026plusmn;2℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e11.5\u0026plusmn;1.6℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e5.9\u0026plusmn;2.0℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e-2.7\u0026plusmn;0.5℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e16.3\u0026plusmn;2.5℃\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eEG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e222.9\u0026plusmn;25.3J/g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4.0\u0026plusmn;1℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e9.7\u0026plusmn;0.5℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e4.0\u0026plusmn;1.0℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e-2.7\u0026plusmn;0.5℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e13.4\u0026plusmn;0.6℃\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4.Conclusion","content":"\u003cp\u003eThe application of high-voltage pulsed electric field pretreatment-assisted freezing technology in the blueberry freezing process resulted in a notable reduction in both freezing time and the duration through the maximum ice crystal generation zone. This led to decreased ice crystal damage to blueberry cells. Furthermore, the technology enhanced the microstructure of blueberries by preserving a more intact cellular morphology. It also optimized the water status and distribution, minimizing water crystallization and aggregation. In addition, the MDA (malondialdehyde) content of pretreated blueberries was significantly reduced during the freezing process, the degree of lipid peroxidation of cell membranes was reduced, and the relative conductivity remained stable, indicating that the permeability of cell membranes was not significantly affected. Thermal property analysis showed that the pretreated blueberries had higher thermal stability and lower thermal degradation temperature. In summary, the high-voltage pulsed electric field pretreatment-assisted freezing technology significantly improved the quality of blueberries after freezing through various improvements, and provided a new and effective way for the preservation of fruits and vegetables by freezing.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eCRediT authorship contribution statement\u003c/p\u003e\n\u003cp\u003eTianyuan Liang: Writing \u0026ndash; original draft,Writing \u0026ndash; review \u0026amp; editing, Data curation,Formal analysis, Conceptualization, Methodology.Bin Li: Resources, Methodology.Fuzhi Xuan: Investigation, Data curation, Visualization.Baoru Yang: Participate in the analysis of actual production issues.Xiaoru Liu: Participate in the analysis of actual production issues.Yan Ma: Participate in the analysis of actual production issues .Xianjun Meng: Conceptualization, Methodology, Validation, Funding acquisition.Yuehua Wang: Project administration, Conceptualization, Funding acquisition,Supervision, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eDeclaration of competing interest\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the China Postdoctoral Science Foundation.(2024M760337).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eReque, P.M., et al., Cold storage of blueberry (Vaccinium spp.) fruits and juice: Anthocyanin stability and antioxidant activity. Journal of Food Composition and Analysis, 2014. 33(1): p. 111-116.\u003c/li\u003e\n\u003cli\u003eNowak, K.W., M. Zielinska, and K.M. Waszkielis, The effect of ultrasound and freezing/thawing treatment on the physical properties of blueberries. Food Sci Biotechnol, 2019. 28(3): p. 741-749.\u003c/li\u003e\n\u003cli\u003eMartynenko, A. and Y. Chen, Degradation kinetics of total anthocyanins and formation of polymeric color in blueberry hydrothermodynamic (HTD) processing. Journal of Food Engineering, 2016. 171: p. 44-51.\u003c/li\u003e\n\u003cli\u003eLobos, G.A., P. Callow, and J.F. Hancock, The effect of delaying harvest date on fruit quality and storage of late highbush blueberry cultivars (Vaccinium corymbosum L.). Postharvest Biology and Technology, 2014. 87: p. 133-139.\u003c/li\u003e\n\u003cli\u003eTeng, X., et al., Effects of liquid nitrogen freezing at different temperatures on the quality and flavor of Pacific oyster (Crassostrea gigas). Food Chem, 2023. 422: p. 136162.\u003c/li\u003e\n\u003cli\u003eRaso, J., et al., Recommendations guidelines on the key information to be reported in studies of application of PEF technology in food and biotechnological processes. Innovative Food Science \u0026amp; Emerging Technologies, 2016. 37: p. 312-321.\u003c/li\u003e\n\u003cli\u003eBocker, R. and E.K. Silva, Pulsed electric field technology as a promising pre-treatment for enhancing orange agro-industrial waste biorefinery. RSC Adv, 2024. 14(3): p. 2116-2133.\u003c/li\u003e\n\u003cli\u003eGenovese, J., et al., PEF-treated plant and animal tissues: Insights by approaching with different electroporation assessment methods. Innovative Food Science \u0026amp; Emerging Technologies, 2021. 74.\u003c/li\u003e\n\u003cli\u003eMok, J.H., et al., Emerging pulsed electric field (PEF) and static magnetic field (SMF) combination technology for food freezing. International Journal of Refrigeration, 2015. 50: p. 137-145.\u003c/li\u003e\n\u003cli\u003eDorota, N. and J. Ewa, Effect of Pulsed Electric Field Pre-Treatment and the Freezing Methods on the Kinetics of the Freeze-Drying Process of Apple and Its Selected Physical Properties. Foods, 2022. 11(16): p. 2407-2407.\u003c/li\u003e\n\u003cli\u003eLammerskitten, A., et al., Pulsed electric field pre-treatment improves microstructure and crunchiness of freeze-dried plant materials: Case of strawberry. Lwt, 2020. 134.\u003c/li\u003e\n\u003cli\u003eDymek, K., et al., Influence of vacuum impregnation and pulsed electric field on the freezing temperature and ice propagation rates of spinach leaves. LWT - Food Science and Technology, 2015. 64(1): p. 497-502.\u003c/li\u003e\n\u003cli\u003eShayanfar, S., et al., Pulsed electric field treatment prior to freezing carrot discs significantly maintains their initial quality parameters after thawing. 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Food and Bioprocess Technology, 2011. 5(8): p. 3075-3089.\u003c/li\u003e\n\u003cli\u003eXuehui, C., et al., Effects of freezing conditions on quality changes in blueberries. Journal of the science of food and agriculture, 2018. 98(12): p. 4673-4679.\u003c/li\u003e\n\u003cli\u003eYılmaz, F.M. and S. Ersus Bilek, Natural colorant enrichment of apple tissue with black carrot concentrate using vacuum impregnation. International Journal of Food Science \u0026amp; Technology, 2017. 52(6): p. 1508-1516.\u003c/li\u003e\n\u003cli\u003eFood Science; Findings on Food Science Detailed by Investigators at Institute of Agriculture (Effects of High CO2 Levels on Fermentation, Peroxidation, and Cellular Water Stress in Fragaria vesca Stored at Low Temperature in Conditions of Unlimited O-2). Chemicals \u0026amp; Chemistry, 2015.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Blueberry, Artificial neural network, PEF, Freezing","lastPublishedDoi":"10.21203/rs.3.rs-6807944/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6807944/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTraditional freezing techniques damage blueberries by forming large ice crystals, thereby limiting their freshness.This study utilized an artificial neural network (ANN) to optimize the parameters of high-voltage pulsed electric field (PEF)-assisted freezing and compared it with traditional freezing methods.The results showed that pulsed electric field technology significantly reduced the time blueberries required to pass through the maximum ice crystal formation zone during freezing, resulting in smaller, more uniformly distributed ice crystals and more uniform moisture distribution.Compared to traditional freezing, PEF-assisted freezing reduced the relative electrical conductivity of blueberries by 21.36% and malondialdehyde (MDA) content by 34.34%, indicating a protective effect on cellular tissue during freezing. Additionally, differential scanning calorimetry (DSC) experiments showed that PEF pretreatment altered the thermal stability of blueberries, causing them to enter the phase transition stage earlier and reducing the time required to pass through the maximum ice crystal formation zone. This study elucidates the mechanism by which high-voltage pulsed electric field (PEF) pretreatment enhances post-freezing quality of blueberries by accelerating freezing and regulating ice crystal formation. It provides theoretical support for the application of non-thermal processing technologies in the frozen fruit and vegetable industry, opening a new, green, and efficient pathway to address the widespread issue of quality degradation in frozen fruits. This research holds significant practical value for optimizing frozen fruit processing techniques, elevating the standards of the frozen fruit industry, and reducing energy consumption in traditional freezing processes.\u003c/p\u003e","manuscriptTitle":"High-voltage pulsed electric field pretreatment-assisted freezing to improve the quality of blueberries after freezing and its influencing mechanisms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 11:30:48","doi":"10.21203/rs.3.rs-6807944/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bf61c6db-3789-48d4-9c8d-b1e4dc0837df","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-10T06:08:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-09 11:30:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6807944","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6807944","identity":"rs-6807944","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}