A method for sampling and testing fruit pectin

Abstract

The present invention relates to the technical field of food component analysis and testing, and in particular to a method for sampling and testing fruit pectin, comprising: step 1: directional freezing and crushing, calculating a target freezing temperature based on the tissue freezing point and cell structure characteristics of the fruit to be tested; step 2: gradient centrifugation, mixing the crushed particles with a cryoprotectant buffer to maintain the temperature below the critical gelling temperature; performing a three-stage centrifugation, wherein the parameters of each stage are corrected in real time based on the physical properties of the preceding isolate; step 3: dynamic turbidity detection, mixing a pure pectin component with a reaction solution containing a chelating agent and cations; calculating the pectin content based on the geometric characteristic parameters of the characteristic inflection point, with the calculation model calibrated using a standard atlas library. This method can effectively address the problems of tissue damage and impurity interference faced by berries and seed-containing fruits, ensuring the accuracy and reliability of pectin detection, and has important technical advantages and application prospects.

Description

Technical Field

[0001] The invention relates to the technical field of food component analysis and testing, in particular to a fruit pectin sampling and testing method. Background Art

[0002] As a key indicator of fruit processing quality, accurate detection of pectin is crucial for quality control of berries (strawberries, blueberries, raspberries, etc.) and seed-containing fruits (kiwi, passion fruit, etc.). However, the unique tissue structure of these fruits leads to systematic defects in traditional detection methods:

[0003] 1. Pectin loss caused by tissue damage;

[0004] The thin-walled cells in berry fruits account for more than 60%, and the cell wall thickness is generally ≤8μm (strawberries are only 3-5μm). The shear force generated by traditional mechanical crushing will cause:

[0005] Structural collapse of the cell wall: pectin seeps out with the juice, and electron microscopy shows a loss rate of >30%;

[0006] Pectin molecular chain breakage: Especially for high molecular weight pectin (MW>150kDa), the fragmentation process can cause its degradation rate to be as high as 25%.

[0007] 2. The problem of impurities interfering with seed-containing fruits;

[0008] The seeds of seed-containing fruits have overlapping physical and chemical properties with pectin:

[0009] Density similarity: The difference between kiwifruit seed density (1.28 g / cm³) and pectin precipitation density (1.25-1.30 g / cm³) is less than 4%;

[0010] Chemical binding effect: The hemicellulose on the surface of the seeds is bonded to the pectin through hydrogen bonds, which cannot be separated by conventional centrifugation, causing the impurity content of the extract to increase by 3-5 times.

[0011] Therefore, a method for sampling and testing fruit pectin is urgently needed to solve the above problems. Summary of the Invention

[0012] Based on the above objectives, the present invention provides a fruit pectin sampling and testing method, comprising:

[0013] Step 1: Directional freezing and crushing, calculate the target freezing temperature based on the freezing point temperature and cell structure characteristics of the fruit to be tested;

[0014] Perform step freezing at the target freezing temperature, and dynamically control the temperature change rate to make the sample reach a brittle fracture state;

[0015] Vibration crushing of frozen samples, adjusting the crushing intensity to the target particle size range based on real-time particle size feedback;

[0016] Step 2: Gradient centrifugation, mixing the broken particles with cryoprotective buffer, maintaining the temperature below the critical gelation temperature;

[0017] Perform a three-stage centrifugation:

[0018] The first stage: removing soluble sugars at the first separation temperature and the first centrifugal force;

[0019] The second stage: precipitation of the pectin complex at a second separation temperature and a second centrifugal force;

[0020] The third stage: separating the pure pectin component at the third separation temperature and the third centrifugal force;

[0021] The parameters of each stage are modified in real time according to the physical properties of the previous separation;

[0022] Step 3: Dynamic turbidity detection, mixing the pure pectin component with the reaction solution containing the chelating agent and cations;

[0023] Continuously collect turbidity data during programmed temperature rise and identify the characteristic inflection point of the turbidity-temperature curve;

[0024] The pectin content was calculated based on the geometric characteristic parameters of the characteristic inflection point, and the calculation model was calibrated with the standard atlas library.

[0025] Beneficial effects of the present invention:

[0026] 1. This invention utilizes low-shear gentle crushing technology and an optimized cryopreservation method to effectively reduce damage to the cell walls of berry fruits during mechanical crushing. This low-temperature crushing technology allows for minimal pectin loss during cell wall destruction, maintaining the integrity of the cell structure and effectively preventing pectin from seeping into the juice.

[0027] 2. The present invention utilizes precise temperature control and a low-speed crushing process during the pectin extraction process, reducing the degradation rate of pectin molecular chains, particularly during the extraction of high-molecular-weight pectins. In particular, for pectin molecules with a molecular weight greater than 150 kDa, the present invention maintains their molecular integrity, avoiding the 25% degradation rate that can occur in traditional methods, thereby ensuring pectin quality.

[0028] 3. This invention utilizes an optimized separation process, employing a layered centrifugation method with varying density gradients, to effectively overcome the similar density problem of seeds and pectin precipitates in seed-containing fruits such as kiwifruit. This process allows for more precise separation of seeds and pectin, avoiding the separation difficulties associated with similar densities in traditional methods.

[0029] 4. By introducing a specific enzymatic hydrolysis step and a suitable buffer formulation, the present invention can dissolve the hydrogen bonds between hemicellulose and pectin on the seed surface, significantly reducing the binding between the seed and pectin during conventional centrifugation. This improved method effectively reduces the impurity content in the extract, reducing impurities and improving the purity of the pectin extracted. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0031] Figure 1 is a flow chart of the steps of the method of the present invention;

[0032] Figure 2 Flow chart of the steps for determining the target particle size range in the method of the present invention;

[0033] Figure 3 This is a flow chart of the steps of the method for identifying the characteristic inflection point in the method of the present invention. DETAILED DESCRIPTION

[0034] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0035] See Figure 1-Figure 3 The present invention provides a method for sampling and testing fruit pectin. First, an appropriate target freezing temperature is calculated based on the freezing point and cell structure characteristics of the fruit to be tested. At this target freezing temperature, the fruit sample is gradually cooled at different temperatures through a step-freezing method. During the freezing process, the temperature change rate is dynamically controlled to break the cellular structure of the fruit sample when it reaches a brittle fracture state. This step effectively reduces cell wall damage and prevents pectin loss.

[0036] After crushing, the samples are processed using a vibration-based crushing technique at low temperatures. During the crushing process, particle size changes are monitored in real time, and the crushing intensity is adjusted based on the feedback to ensure that the crushed particles reach the desired target size range. This not only ensures efficient pectin extraction, but also avoids excessive cell wall damage, ensuring the integrity of the pectin molecules and minimizing pectin degradation.

[0037] The crushed sample is mixed with a cryoprotectant buffer to ensure that the temperature remains below the critical gelling temperature of pectin during the separation process, thus preventing pectin degradation during the separation process. A three-stage gradient centrifugation separation separates the crushed particles into different components, gradually removing impurities.

[0038] The first stage: By setting the first separation temperature and the first centrifugal force, soluble sugars are removed to ensure that the sugars do not interfere with the subsequent pectin extraction.

[0039] The second stage: at the set second separation temperature and second centrifugal force, the pectin complex is precipitated to further concentrate the pectin.

[0040] Phase 3: In the third separation phase, pure pectin fractions are isolated by precisely controlling the third separation temperature and the third centrifugal force. The parameters of each separation phase are adjusted in real time based on the physical properties determined in the previous phase, ensuring the accuracy and efficiency of the separation process.

[0041] A reaction solution containing a chelating agent and cations is added to the isolated pure pectin fraction. During a temperature programming process, turbidity data from the reaction system is continuously collected. By analyzing the turbidity-temperature curve, characteristic inflection points are identified, and the pectin content is calculated based on the geometric characteristic parameters of the inflection points. To improve detection accuracy, the calculation model is calibrated using a standard spectral library, making the determination of pectin content more accurate and reliable.

[0042] The combination of directional freeze-fragmentation, gradient centrifugation, and dynamic turbidity detection effectively overcomes the pectin loss and impurity interference issues encountered in traditional methods. Freeze-fragmentation minimizes cell wall damage, preventing pectin loss and degradation. Gradient centrifugation, through precise control of temperature and centrifugal force, isolates pectin of higher purity and reduces impurity interference. Dynamic turbidity detection, through real-time monitoring during temperature rise, precisely identifies pectin content, improving measurement precision and accuracy. This method provides more reliable fruit pectin detection results and is adaptable to the pectin extraction needs of different fruit types, possessing significant application value.

[0043] In one possible implementation, a differential scanning calorimeter (DSC) is used to scan a sample of the fruit tissue being tested. The sample is then slowly cooled, and the heat flow curve is recorded. The temperature at which latent heat release begins is identified, which is the freezing point of the tissue for that type of fruit. This temperature reflects the point at which the intercellular fluid in the sample begins to freeze and serves as the basis for setting target freezing conditions.

[0044] Fruit tissues were cryosectioned to prepare ultrathin sections approximately 0.5 to 2 µm thick and stained with cellulose- or polysaccharide-specific dyes. High-resolution microscopy was used for imaging analysis. Cell wall thickness of intact cells was measured in each of at least 30 randomly selected fields of view within the image, and the data were recorded and analyzed. Finally, a histogram of the cell wall thickness distribution was plotted, and the peak area was extracted as representative data representing the main wall thickness range of the tissue structure.

[0045] Based on the peak range of the wall thickness distribution, the corresponding critical size for ice crystal growth is selected. This size refers to the diameter of the ice crystal sufficient to disrupt the cell structure without causing pectin degradation. Research has shown that this critical size is positively correlated with cell wall thickness; the thicker the wall, the larger the required ice crystal size. Combining the ice crystal growth kinetics model, the critical size parameters are input and the temperature drop threshold required to achieve this ice crystal size is calculated, that is, the specific degree of temperature drop from the freezing point.

[0046] The ultimate target freezing temperature is "tissue freezing point - temperature drop threshold", which can effectively induce the cell structure to reach a brittle fracture state, while avoiding component degradation or non-target breakage caused by excessively low temperature.

[0047] In one possible implementation, a highly sensitive acoustic emission sensor is placed during the freezing process, placed in close proximity to the surface of the fruit sample or a supporting device. The sensor collects the weak elastic wave signals generated by cell wall rupture in real time and converts them into electrical signals for spectral analysis. During the freezing-induced ice crystal growth phase, as stress accumulates within the tissue, the cell wall breaks due to brittle instability, releasing typical high-frequency pulsed acoustic waves.

[0048] When a dense pulse burst is detected within the 100-300kHz frequency range, with the time interval between adjacent pulses less than a preset threshold (e.g., less than 10ms), the sample is considered to have entered the stage of large-scale brittle fracture. This frequency range represents the characteristic spectrum of cell wall fracture, distinct from general physical disturbances or background noise, thus ensuring accurate determination.

[0049] Immediately after entering the brittle fracture state, the sample is subjected to low-temperature vibration crushing, which causes it to be cut in a frozen brittle state. The crushed product is then freeze-dried to remove moisture from the sample, making the fracture structure stable and suitable for further observation.

[0050] After drying, the cross-section of the sample is observed using a scanning electron microscope to obtain high-resolution images. Image processing algorithms are used to detect edges on the cross-section profile and extract the curvature distribution of the contour lines. The variance of all curvature values ​​is calculated and compared to a preset smoothness standard (e.g., if the variance is below a certain threshold). If the standard is met, the crushing process is considered sufficient and the fracture mode is primarily brittle, and the crushing process is terminated.

[0051] By introducing acoustic emission monitoring, non-contact identification of microscopic fracture behavior within fruit tissues is achieved, enabling real-time determination of whether the freeze-crushing process has entered a critical stage, significantly improving the controllability and reliability of the operation. Furthermore, by using cross-section smoothness as a termination criterion, combined with electron microscopy images and image processing algorithms, the subjectivity of manual judgment is effectively avoided, establishing a clear quantitative standard for the crushing endpoint. This method ensures the integrity and repeatability of the pectin sampling structure, making it suitable for sample pretreatment prior to high-precision pectin content analysis, and helping to improve the data consistency and scientific nature of the entire testing process.

[0052] In one possible embodiment, pectin is extracted from fruits of the same variety and processed into labeled samples using standard methods. During the preparation process, fluorescent molecular labeling technology is used to specifically fluorescently label the surface of the pectin molecules. This process makes the labeled pectin clearly visible under a microscope, facilitating subsequent testing. Fluorescent labeling can effectively distinguish pectin molecules from other non-pectin substances, providing an important basis for subsequent analysis.

[0053] Standard pectin samples are separated under centrifugal conditions at varying particle sizes. By varying the centrifugation speed and time, the size range of the pectin particles in the sample is adjusted. During centrifugation, larger pectin particles settle to the bottom, while smaller particles remain suspended in the upper layer. This process allows for the separation of pectin particles of varying sizes, ensuring that pectins of varying sizes can be effectively distinguished.

[0054] After centrifugation, pectin particles of varying particle sizes were observed under a fluorescence microscope to determine the percentage of pectin molecules within each particle. This was done by calculating the proportion of fluorescently labeled pectin molecules encapsulated within the particle and determining the distribution of pectin within each particle size range. This method allows for precise determination of the distribution of pectin molecules within particles of varying particle sizes.

[0055] The statistical results were plotted as a curve showing the relationship between particle size and pectin encapsulation rate. This curve reflects the variation in pectin encapsulation rate at different particle sizes. By comparison, it was determined that within a certain particle size range, the pectin encapsulation rate was low and stable, thus providing an ideal particle size range for subsequent pectin content analysis.

[0056] Based on the particle size-pectin encapsulation rate curve, the lower limit of the particle size range corresponding to an encapsulation rate of ≤5% was selected as the minimum target particle size. This indicates that the pectin encapsulation rate within this particle size range is very low, facilitating subsequent separation and analysis. Next, the upper limit of the particle size range corresponding to a centrifugal sedimentation time of ≤10 minutes was selected as the maximum target particle size. This ensures that the appropriate particle size range is achieved within a short centrifugation time, facilitating efficient processing and analysis.

[0057] This method accurately determines the target particle size range for fruit pectin samples, effectively avoiding sample inhomogeneity or detection errors caused by inappropriate particle size. The application of fluorescent labeling technology not only enhances the visibility of pectin molecules but also greatly improves the accuracy of pectin encapsulation rate determination. Furthermore, the establishment of a particle size-pectin encapsulation rate relationship curve makes the selection of particle size range more scientific and reasonable. Ultimately, this ensures the reliability, accuracy, and repeatability of the test results, facilitating the assessment and analysis of fruit pectin quality.

[0058] In one possible embodiment, pectin is extracted from fruit and combined with proteins in the fruit to form a pectin-protein complex. During the extraction process, the pectin-protein complex is dispersed into a suspension by disrupting the fruit tissue and extracting it using a solution. The particle size and distribution of these complexes in the suspension directly impact the effectiveness of subsequent separation processes.

[0059] The pectin-protein complex suspension was analyzed using dynamic light scattering. Dynamic light scattering accurately calculates the hydrodynamic radius distribution of the particles by measuring their Brownian motion in a liquid. The radius of the complex particles affects their sedimentation behavior during centrifugation, making this step crucial for determining the appropriate centrifugal force.

[0060] According to Stokes' sedimentation law, the formula F=k×r³×(ρ p -ρ m ) to infer the minimum separation force. Where k is the temperature-related correction factor, r is the median radius of the composite, and ρ p and ρ m are the densities of the composite and the medium, respectively. This formula can be used to calculate the minimum separation force, which is the basis for ensuring the effective separation of composite particles.

[0061] To ensure safety and accuracy during the separation process, the calculated minimum separation force is multiplied by a safety factor. This safety factor is calculated based on the particle size dispersion of the complex. A larger dispersion increases the safety factor to ensure that the centrifugal force can overcome aggregation and uneven distribution of the complex particles. The resulting second centrifugal force baseline value serves as a reference for subsequent centrifugation operations to ensure effective particle separation.

[0062] By accurately calculating the second centrifugal force, the intensity and application of the centrifugal force can be adjusted according to the particle size distribution and physical properties of the pectin-protein complex. By measuring the particle size distribution through dynamic light scattering, the size and distribution of the complex particles can be accurately understood, which helps to design a more sophisticated separation process based on the properties of different particles. Simultaneously, using Stokes' sedimentation law to infer the minimum separating force and combining it with a safety factor for correction can effectively avoid poor separation results or sample loss caused by excessive or insufficient centrifugal force. Therefore, the present invention not only improves the separation accuracy of fruit pectin-protein complexes, but also ensures safety and sample integrity during the experimental process, providing a more reliable fruit pectin sampling and testing method.

[0063] In one possible implementation, smoothing and denoising the collected raw turbidity-temperature data is the first step in identifying characteristic inflection points. Because the raw data may contain noise and fluctuations, directly analyzing the raw data can lead to misjudgment of the location of the characteristic inflection point. Therefore, a sliding window averaging method is used for denoising. This method reduces sudden noise interference by averaging each data point and the data within a certain range surrounding it. The window width is adaptively adjusted based on the rate of change of turbidity in the data: when turbidity changes steadily, the window width can be larger; when turbidity changes dramatically, the window width should be appropriately reduced to better track subtle changes in the data.

[0064] Calculating the first-order derivative of denoised data aims to capture trends in data change. The first-order derivative reflects the rate of change in the data and can help locate points of significant change. When data changes are relatively gradual, the derivative value is small; however, when data undergoes significant changes, the derivative value fluctuates significantly. By calculating a series of first-order derivatives, we can accurately identify potential inflection points in the data's evolution.

[0065] Within the calculated first-order derivative sequence, we search for extreme points where the absolute value of the derivative exceeds a noise threshold by three standard deviations. These extreme points represent inflection points in the data and typically occur when pectin properties undergo significant changes. Setting a noise threshold of three standard deviations ensures that extreme point identification is not affected by minor fluctuations or interference, focusing only on more obvious, real changes.

[0066] For the extreme value of the derivative, we further determine whether it is a valid characteristic inflection point. The specific judgment method is: when the sign of the derivative on either side of the extreme value changes from positive to negative, it indicates that the data curve has a clear inflection point, and the turbidity value at this point must be within the preset turbidity range. This method ensures that the identified characteristic inflection point is not only a turning point of the data curve, but also a physically meaningful change point within the preset turbidity range, avoiding incorrect inflection point judgments.

[0067] This method can effectively reduce misjudgments caused by noise by denoising and smoothing the original data, thereby improving the accuracy of identifying characteristic inflection points. The adaptive sliding window width adjustment ensures that the denoising process can flexibly adapt to and maintain the true fluctuations of the data under different turbidity change rates. By calculating the first-order derivative and combining it with the standard deviation threshold to locate the extreme points, it can not only capture the key nodes of data changes but also avoid the interference of small fluctuations. Finally, when judging the validity of the characteristic inflection point, the sign change of the data trend and the constraint of the turbidity range are combined to further ensure the scientific nature and validity of the characteristic inflection point. This method effectively improves the data analysis accuracy in the fruit pectin sampling and inspection process, providing reliable technical support for subsequent research and experiments.

[0068] In one possible embodiment, after identifying the characteristic inflection point, it is first necessary to determine the width of the platform area. The platform area refers to the area on both sides of the characteristic inflection point where the turbidity change rate is close to zero or the change amplitude is very small. Specifically, we determine the starting and ending positions of the platform area by searching for continuous temperature intervals with a turbidity change rate less than or equal to 0.5% / °C. The platform area usually indicates that the pectin exhibits a relatively stable state within a certain temperature range. The determination of this width can provide a stable benchmark for subsequent analysis. The larger the width of the platform area, the smaller the change in the pectin properties within the temperature range. Conversely, it may be an area where the pectin properties change significantly.

[0069] The curve integral area ratio is a key parameter for measuring the stages of turbidity change on either side of a characteristic inflection point. By calculating the ratio of the area under the curve for the 50% of the turbidity rise before and the 50% of the turbidity rise after the characteristic inflection point, a geometric characteristic reflecting the pattern of pectin change can be obtained. The specific calculation method is to fit the turbidity data before and after the characteristic inflection point into curves and integrate the areas under these two curves. Then, the ratio of the integral areas of the two curves is calculated. If the area in the first stage is larger, it means that the pectin shows relatively rapid changes during this stage, while a larger area in the second stage may indicate that the pectin is gradually stabilizing or changing slowly.

[0070] Finally, the pectin content is calculated using a linear regression model. Based on the experimental data of the standard pectin sample, the coefficients a, b, and c are determined through the multivariate linear regression method. These coefficients are related to the platform width and the curve area ratio. By combining these coefficients, the pectin content of the fruit can be accurately predicted. The specific formula is:

[0071] Pectin content = a × platform width + b × area ratio + c;

[0072] Where a, b, and c are the best-fit coefficients obtained through regression analysis. This method not only efficiently calculates pectin content but also compares changes in fruit samples with standard samples, further improving test accuracy.

[0073] By introducing two geometric characteristic parameters, the platform width and the curve integral area ratio, the changes in pectin properties near the characteristic inflection point can be more comprehensively described, providing a reliable mathematical basis for the prediction of pectin content. The platform width reflects the stability of pectin during temperature changes, while the curve integral area ratio reflects the characteristics of pectin during the rising phase. The combination of the two can more accurately reveal the relationship between pectin content and temperature changes. In addition, using a multivariate linear regression model and coefficients determined from standard pectin samples, the measured values ​​of different samples can be effectively matched to the standard values, enhancing the universality and reliability of the method. The combined application of these technical features not only improves the accuracy of the fruit pectin testing method, but also better adapts to the needs of different fruit samples and experimental environments.

[0074] In one possible embodiment, calcium ions are an important factor in cross-linking between pectin molecules, and their concentration directly affects the formation and stability of the pectin gel network. In order to avoid unexpected gelation or destruction of the gel structure during low-temperature treatment, the calcium ion concentration needs to be controlled between 85% and 95% of the critical gelation concentration. The critical gelation concentration is determined by measuring the pectin solution using a rheometer, specifically by gradually increasing the calcium ion concentration and recording the change in the storage modulus (G') in real time. The inflection point of its significant mutation is the critical gelation concentration. The actual use concentration is controlled at the lower limit of this value, which effectively reduces the risk of spontaneous gelation induced by temperature mutations, while maintaining moderate electrostatic cross-linking between pectin molecules.

[0075] The charge state of pectin molecules significantly influences their solubility and structural stability. Therefore, the buffer pH must be precisely controlled within ±0.3 of the pectin isoelectric point to reduce electrostatic repulsion between molecules and stabilize their conformation. Isoelectric point determination is performed using isoelectric focusing electrophoresis, which precisely determines the neutral charge point by monitoring the mobility of pectin at different pH levels. This pH control range helps minimize precipitation or denaturation of proteins or other coexisting substances, ensuring that the pectin composition remains stable and in its original state.

[0076] To prevent water crystallization and disruption of the pectin network during freezing, an osmotic protectant, a polyol (such as mannitol or sorbitol) with a molecular weight greater than the pectin network pore size, was added to the buffer. The pectin pore size was determined by cryo-electron microscopy of the freeze-dried pectin sample. This ensured that the selected protectant could not penetrate the pectin network, forming only a protective layer on the outside, thus providing an exogenous permeability barrier and preventing water molecules from directly interacting with the internal structure of the colloid. This strategy significantly mitigated the disruption of the pectin network caused by ice crystal formation, preserving its physical properties and mechanical integrity.

[0077] Through precise control of calcium ion concentration, pH value, and osmoprotectant, the thermal stability and low-temperature damage resistance of the pectin structure during sampling were significantly improved. The buffer not only effectively maintains the original configuration of the pectin molecules but also prevents potential gel destruction, protein precipitation, and pectin swelling during freezing, ensuring the accuracy and repeatability of subsequent testing. Furthermore, all parameters in the formula are supported by physical and chemical experimental data, ensuring its scientific validity and feasibility. This technology demonstrates great potential for application in scenarios such as high-sensitivity pectin detection and low-temperature storage and transportation sample processing, and possesses excellent practical value and promotion prospects.

[0078] In one possible implementation, the tissue characteristics of the applicable fruit are first defined to ensure the scientific suitability of the solution. Specifically, an optical microscope or scanning electron microscope is used to image and measure cross-sections of the fruit tissue, and statistical analysis of cell wall thickness is performed. Image processing software is used to measure the cell wall thickness per unit area, determining whether the proportion of thin-walled cells with a wall thickness of 8 μm or less reaches 60%. This criterion is based on the relatively loose cell tissue and low mechanical strength of berry fruits. This facilitates low-energy separation during the pectin extraction process and avoids damage or degradation of the pectin structure caused by excessive mechanical crushing.

[0079] Temperature control is crucial during the separation process, especially during the initial extraction phase, as the selected temperature directly impacts the dissolution efficiency and structural integrity of pectin. To maximize the preservation of the original configuration of native pectin, this method requires that the first-stage separation temperature be set 4 to 6°C below the gelation point of the fruit pectin. The gelation point of pectin is precisely determined using differential scanning calorimetry (DSC), which involves recording the heat flow curve of the pectin solution during heating, identifying characteristic endothermic and exothermic peaks, and determining its thermally induced phase transition point. A separation temperature below the gelation point effectively inhibits the spontaneous gelation of pectin molecules during the initial sampling phase, thereby improving the homogeneity and analyzability of the extract.

[0080] Through the dual optimization of structural determination and thermal control mechanism, the accuracy and repeatability of pectin extraction and analysis in berry fruits have been greatly improved. The determination criteria of parenchyma tissue ensure that the target sample has good tissue looseness, making the release of pectin more gentle and uniform under low temperature conditions, reducing the risk of structural damage caused by physical crushing. At the same time, the extraction temperature is precisely controlled to avoid the gelation temperature zone of pectin, effectively reducing heat-induced polymerization and gel formation, thereby preserving the original information of the pectin. This measure not only improves the accuracy of analysis, but also facilitates the smooth implementation of subsequent structural identification, viscoelasticity testing and other operations. The overall technical path is clear and the parameters are controllable, which is particularly suitable for wide application in pectin research or quality control scenarios such as high-fidelity sampling and fine grading detection.

[0081] In one possible embodiment, during the second stage of pectin extraction, the sample undergoes a preliminary centrifugation to separate the majority of the fruit solids from the liquid. Centrifugal force separates the pulp and other tissue components from heavier particles, such as seeds, paving the way for subsequent separation using density flotation.

[0082] Seeds are a key component in the separation process, and their density plays a decisive role in the effectiveness of flotation separation. The true density of seeds is determined using the helium pycnometer method. This method utilizes the properties of helium to accurately calculate the true density by measuring the volume of helium displaced by the seed. True density is the basis for subsequent calculation of apparent density.

[0083] The apparent density of a seed is calculated based on its true density and surface porosity. Porosity is the ratio of the volume of the seed's surface and internal voids to its total volume, and is determined using a mercury intrusion porosimeter. The mercury intrusion porosimeter measures the surface void structure of an object by intruding mercury, thereby obtaining an accurate porosity. The apparent density is calculated using the formula:

[0084] Apparent density = true density × (1 + surface porosity).

[0085] Based on the calculated apparent density, the flotation medium is formulated to match the apparent density of the seed. The medium density tolerance is controlled within ±0.5% to ensure effective separation of seed fragments during flotation. The matching flotation medium allows for a clear separation of seeds and other solid particles during flotation, accurately separating unwanted seed fragments. The supernatant is then collected to prepare a pure sample for further pectin extraction.

[0086] After the flotation operation is completed, the supernatant is collected and sent to the third stage of centrifugation. In this stage, the centrifugal force is used to further remove the remaining impurities, ultimately obtaining a pure pectin extract or other target components.

[0087] By accurately measuring the true density and apparent density of the seeds, the flotation method is used to separate the seed fragments, effectively improving the purity of the sample. Traditional separation methods often do not handle the seeds carefully enough, which can easily cause interference from seed fragments. However, this method improves the accuracy and efficiency of separation by carefully controlling the density stratification. By combining the helium pycnometer method with the mercury intrusion method, the apparent density of the seeds is accurately calculated, providing a scientific basis for matching the flotation medium and ensuring the efficiency and reliability of the flotation process. Finally, the supernatant after flotation is further centrifuged to effectively extract high-purity pectin, reducing the impact of impurities on the pectin analysis results and improving the accuracy and reliability of the test results.

[0088] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0089] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for sampling and testing fruit pectin, characterized in that: include: Step 1: Directional freezing and crushing, calculate the target freezing temperature based on the freezing point temperature and cell structure characteristics of the fruit to be tested; Perform step freezing at the target freezing temperature, and dynamically control the temperature change rate to make the sample reach a brittle fracture state; Vibration crushing of frozen samples, adjusting the crushing intensity to the target particle size range based on real-time particle size feedback; The target freezing temperature is obtained by: A differential scanning calorimeter is used to scan the fruit tissue sample to be tested; Slowly cool the sample and record the heat flow curve to identify the starting temperature of latent heat release, which is the tissue freezing point of the fruit. Draw a histogram of cell wall thickness distribution and extract its peak area as representative data characterizing the main wall thickness range of the tissue structure; According to the peak range of wall thickness distribution, the corresponding critical size of ice crystal growth is selected; Combined with the ice crystal growth kinetics model, the critical size parameter is input to calculate the temperature drop threshold required to achieve the ice crystal size, that is, the specific degree of temperature drop from the freezing point; The final target freezing temperature is "tissue freezing temperature - temperature drop threshold"; Step 2: Gradient centrifugation, mixing the broken particles with cryoprotective buffer, maintaining the temperature below the critical gelation temperature; Perform a three-stage centrifugation: The first stage: removing soluble sugars at the first separation temperature and the first centrifugal force; The second stage: precipitation of the pectin complex at a second separation temperature and a second centrifugal force; The third stage: separating the pure pectin component at the third separation temperature and the third centrifugal force; The parameters of each stage are modified in real time according to the physical properties of the previous separation; Step 3: Dynamic turbidity detection, mixing the pure pectin component with the reaction solution containing the chelating agent and cations; Continuously collect turbidity data during programmed temperature rise and identify the characteristic inflection point of the turbidity-temperature curve; The pectin content was calculated based on the geometric characteristic parameters of the characteristic inflection point, and the calculation model was calibrated with the standard atlas library.

2. A fruit pectin sampling and testing method according to claim 1, characterized in that: The calculation of the target freezing temperature includes: The freezing point temperature of the tissue of this type of fruit was measured by differential scanning calorimetry; Obtain cell wall thickness distribution data for the fruit: prepare ultrathin sections of the fruit tissue, randomly select no less than 30 fields of view from the microscopic image after staining, measure the wall thickness of intact cells in each field of view, and generate a thickness distribution histogram; The critical size of ice crystal growth is selected based on the peak range of thickness distribution, which is positively correlated with the wall thickness; The critical size is converted into the required temperature drop threshold through the ice crystal growth kinetics model; Target freezing temperature = tissue freezing temperature - temperature drop threshold.

3. A fruit pectin sampling and testing method according to claim 1, characterized in that: The confirmation of the brittle fracture state includes: The acoustic emission signal intensity of the samples was monitored during the freezing process; When the acoustic emission spectrum shows continuous pulse groups in the 100-300kHz frequency band and the pulse interval is less than the set threshold, it is determined to have reached a brittle fracture state; The termination condition of the vibration crushing is that after the crushed material is freeze-dried, the cross-section smoothness observed under a scanning electron microscope meets a preset standard, and the smoothness is determined by calculating the curvature variance of the cross-section profile using an image edge detection algorithm.

4. A fruit pectin sampling and testing method according to claim 1, characterized in that: Determination of the target particle size range includes: Prepare standard pectin-labeled samples of the same variety of fruit and fluorescently label the pectin molecules on their surface; Centrifugal separation was performed under different particle size conditions, and the proportion of pectin remaining inside the particles was counted using fluorescence microscopy to establish a particle size-pectin encapsulation rate relationship curve. The lower limit of the particle size interval corresponding to the encapsulation rate ≤ 5% was selected as the minimum target particle size, and the upper limit of the particle size interval corresponding to the centrifugal sedimentation time ≤ 10 minutes was selected as the maximum target particle size.

5. A fruit pectin sampling and testing method according to claim 1, characterized in that: The generation of the second centrifugal force comprises: Extract the broken pectin-protein complex suspension; The hydrodynamic radius distribution of the composites was measured by dynamic light scattering; According to Stokes' sedimentation law, the formula F=k×r 3 ×(ρ p -ρ m ) reversely calculate the minimum separation force, where k is the temperature-related correction coefficient, r is the median radius of the composite, and ρ p and ρ m are the densities of the composite and medium, respectively; The minimum separation force is multiplied by a safety factor as the second centrifugal force reference value, and the safety factor is calculated based on the particle size dispersion of the composite.

6. A fruit pectin sampling and testing method according to claim 1, characterized in that: The identification method of the characteristic inflection point includes: The collected turbidity-temperature raw data were smoothed and denoised using a sliding window averaging method, with the window width adaptively adjusted according to the turbidity change rate. Calculate the first-order derivative sequence of the denoised data and locate the extreme point where the absolute value of the derivative exceeds 3 times the standard deviation of the noise threshold; When the derivative signs on both sides of the extreme point change from positive to negative, and the turbidity value of the point is within the preset range, it is determined to be a valid characteristic inflection point.

7. A fruit pectin sampling and testing method according to claim 1, characterized in that: The geometric characteristic parameters of the characteristic inflection point include: Platform width: the length of the continuous temperature interval on both sides of the characteristic inflection point where the turbidity change rate is ≤0.5% / °C; Curve integral area ratio: calculate the ratio of the area under the curve of the 50% turbidity rising stage before the characteristic inflection point to the 50% rising stage after the characteristic inflection point; Pectin content = a × platform width + b × area ratio + c, where coefficients a, b, and c were determined by multiple linear regression of standard pectin samples.

8. A fruit pectin sampling and testing method according to claim 1, characterized in that: The preparation of the low temperature protection buffer comprises: The calcium ion concentration is set at 85%-95% of the critical gelling concentration, and the critical concentration is determined by measuring the storage modulus mutation point of the pectin solution by rheometer; The pH value of the buffer was controlled within ±0.3 of the isoelectric point of pectin, and the isoelectric point was determined by isoelectric focusing electrophoresis; The osmotic protectant is a polyol having a molecular weight greater than the pore size of the pectin network. The pore size is obtained by measuring the porous structure of freeze-dried pectin using a cryogenic electron microscope.

9. A method for sampling and testing fruit pectin according to any one of claims 1 to 8, characterized in that: Applicable to berry fruits, the judgment criteria are: The proportion of parenchyma tissue with cell wall thickness ≤8μm is ≥60%; In step 2, the first stage separation temperature is 4-6°C lower than the gel point of the fruit pectin, as determined by differential scanning calorimetry.

10. A fruit pectin sampling and testing method according to any one of claims 1 to 8, characterized in that: For fruit with seeds, in step 2 also include: After the second centrifugation stage, the seed fragments are separated by density flotation: Determination of true density of seeds: measured by helium pycnometer method; Calculate the apparent density of the seeds: true density × (1 + surface porosity), where porosity is measured by mercury intrusion porosimetry; Prepare flotation media that matches the apparent density, and control the medium density error within ±0.5%; After flotation, the supernatant was collected and subjected to the third stage of centrifugation.

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