Method for characterizing the dominant forces of protein aggregation in flour based on particle size

Abstract

The application discloses a method for characterizing the dominant force of flour protein aggregation based on particle size, relates to the flour processing quality detection technical field, and aims to realize the type of protein aggregation force characterized by a physical method, and comprises the following steps: dispersing a flour sample in deionized water; sequentially implementing a stirring stage and a stirring ultrasonic stage, and respectively measuring the average particle size at predetermined time intervals until equilibrium; respectively adopting a first-order kinetic model to fit the particle size-time data of the two stages, and obtaining k 搅拌 and k 搅拌超声 ; calculating the value of Δk, and determining that electrostatic interaction or hydrogen bond and hydrophobic interaction are dominant through the interval of Δk. The application has the advantages of small sample consumption, close-to-in-situ detection, no need of chemical reagents, and the constructed Δk without internal reference index can quantitatively distinguish the contribution of weak and strong non-covalent interaction.

Description

Technical Field

[0001] This invention relates to the field of flour processing quality testing technology. More specifically, this invention relates to a method for characterizing the dominant forces of protein aggregation in flour based on particle size. Background Technology

[0002] Flour is a powdery material obtained by milling wheat. Its protein content and quality are key factors determining the quality of processed flour products. The formation, extensibility, gas retention, and textural properties of dough are all closely related to the aggregation behavior of proteins in flour. Wheat proteins mainly form complex and ordered aggregated network structures through disulfide bonds, hydrogen bonds, hydrophobic interactions, and electrostatic interactions (including salt bridges mediated by metal ions such as calcium ions). Among these, disulfide bonds endow the gluten network with strong elasticity and toughness, hydrogen bonds and hydrophobic interactions mainly affect the viscoelasticity and plasticity of dough, while electrostatic interactions play an indispensable regulatory role in the assembly and aggregation of protein subunits. In-depth analysis of the dominant forces in flour protein aggregation and their contribution is of great significance for quality breeding, flour blending, and precise control of flour product processing technology.

[0003] Currently, the main method for characterizing the aggregation behavior of flour proteins is through indirect characterization using changes in the solubility of the sample in reagents that disrupt interactions. For example, changes in solubility in sodium chloride solution reflect the proportion of electrostatic interactions in the sample; changes in solubility in urea solution reflect the proportion of hydrogen bonds; and changes in solubility in sodium dodecyl sulfate solution reflect the proportion of hydrophobic interactions. These methods generally have the following drawbacks: the characterization process requires the use of multiple chemical reagents, making the operation cumbersome and the detection cycle long; multiple measurements of protein content are needed to calculate solubility, leading to accumulated errors and limited accuracy; and the addition of reagents themselves alters the state of the proteins, causing the measured results to deviate from the actual aggregation behavior of flour during processing (such as dough mixing). Summary of the Invention

[0004] The purpose of this invention is to provide a method for characterizing the dominant forces of protein aggregation in flour based on particle size, so as to achieve the characterization of the type of protein aggregation forces using physical methods.

[0005] This invention provides a method for characterizing the dominant force of protein aggregation in flour based on particle size, comprising: S1: dispersing a flour sample in deionized water; S2: implementing a stirring stage, stirring the flour dispersed in water obtained in S1, and simultaneously measuring the average particle size of the flour particles dispersed in water at predetermined time intervals until the particle size reaches equilibrium; S3: implementing a stirring-ultrasonic stage, superimposing ultrasonic treatment on the stirring treatment in S2, and continuing to measure the average particle size of the flour particles dispersed in water at predetermined time intervals until the particle size reaches equilibrium; S4: fitting the average particle size and corresponding measurement time measured in the stirring stage, and the average particle size and corresponding measurement time measured under the simultaneous stirring and ultrasonic stage, using a first-order kinetic model to obtain the rate constant k of the stirring stage. 搅拌 And the rate constant k during the ultrasonic stirring phase 搅拌超声 The first-order dynamic model is D(t) = D ∞ +(D0-D ∞ )×e -k(t-t0) Where D(t) is the average particle size at measurement time t, D ∞ S5: The average particle size at equilibrium, D0 is the average particle size at initial time t0, k is the rate constant, t is the measurement time, and t0 is the initial time of the stirring stage or the stage where ultrasound is applied simultaneously with stirring; S6: According to Δk=(k 搅拌超声 -k 搅拌 ) / (k 搅拌超声 +k 搅拌 Calculate the Δk value. If Δk is between -1 and -0.1, it is determined that the aggregation of flour proteins is dominated by electrostatic interactions; if Δk is between -0.1 and 1, it is determined that the aggregation of flour proteins is dominated by hydrogen bonds and hydrophobic interactions.

[0006] Furthermore, in S2 and S3, the average particle size of the flour particles dispersed in the water was determined using a laser particle size analyzer.

[0007] Furthermore, the predetermined time interval is 30 seconds.

[0008] Furthermore, the frequency of ultrasonic treatment in S3 is from 18 kHz to 22 kHz.

[0009] Furthermore, in S4, the average particle sizes measured during the stirring and ultrasonic stirring stages were fitted to the corresponding measurement times using both a first-order kinetic model and a zero-order kinetic model, respectively, to obtain the correction coefficient of determination R1 for the first-order kinetic model. 2 and the corrected coefficient of determination R0 of the zero-order dynamics model 2 When R1 2 Higher than R0 2 When, k is obtained by fitting with a first-order dynamic model. 搅拌 and k搅拌超声 Used for Δk calculation; when R1 2 Not higher than R0 2 Then, re-execute S2 and S3.

[0010] Furthermore, the average particle size D in equilibrium state ∞ The average particle size was determined as follows: During the stirring or ultrasonic stirring phase, starting from the first time the equilibrium state was reached, N measured average particle size values ​​were recorded, and the difference between the measured average particle size at adjacent time points did not exceed 1%. These N measured average particle size values ​​were sequentially recorded as D1, D2...D... N D N The particle size value at the last measurement time point; the average particle size D at equilibrium is calculated using the following formula. ∞ : Where α is the attenuation coefficient, with a value of 0.5-1.5, and i is an integer from 1 to N-1, representing the time sequence number of each measured average particle size value in the equilibrium stage.

[0011] Furthermore, the initial time t0 was determined as follows: starting from the moment the flour sample was dispersed in deionized water, the average particle size was continuously measured at predetermined time intervals, and the particle size change rate δ at each adjacent time point was calculated. j =[D(t j )−D(t j−1 )] / D(t j−1 ), where j is the sequence number of the measurement time point, δ j This represents the rate of change in particle size during the j-th time interval; When n consecutive rates of change δ first appear j When all values ​​are less than or equal to 0, the particle size is determined to have entered a continuous decreasing stage. The time point corresponding to the first rate of change in n consecutive rates of change is t0, which is the initial time of the stirring stage or the stirring and ultrasonic stage, where n≥2; the measured average particle size corresponding to the initial time t0 is D0.

[0012] Furthermore, in S5, when -1≤Δk<-0.1, the smaller the value of Δk, the greater the relative contribution of electrostatic interaction; when -0.1≤Δk≤1, the larger the value of Δk, the greater the relative contribution of hydrogen bonding and hydrophobic interaction; when Δk approaches 0, the relative contributions of electrostatic interaction are close to those of hydrogen bonding and hydrophobic interaction.

[0013] The present invention has at least the following beneficial effects: This invention achieves physical characterization of the dominant forces of protein aggregation solely by detecting and fitting the kinetic parameters of changes in the dispersed particle size of flour in deionized water. This requires no chemical reagents and directly reflects the dominant forces of flour protein aggregation. This invention introduces the dynamic change in particle size into a first-order kinetic model, and for the first time constructs the comprehensive physical quantity Δk. This index is independent of sample concentration and absolute particle size, possesses excellent internal reference characteristics, and can quantify the contribution ratio of electrostatic interactions (weak interactions) to hydrogen bonds and hydrophobic interactions (strong non-covalent interactions). This invention requires small sample amounts, has short testing time, and is simple to operate, making it suitable for screening early generations in breeding. The interval division of Δk can guide flour modification processes (such as enzyme treatment, emulsifier addition, or calcium ion regulation), providing a clear theoretical basis for the targeted regulation of flour product processing and effectively improving the stability of the final product quality.

[0014] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Attached Figure Description

[0015] Figure 1 This is a flowchart of one embodiment of this application.

[0016] Figure 2 This is a graph showing the average particle size test results during the stirring and ultrasonic stirring stages of one embodiment of this application. Detailed Implementation

[0017] The present invention will now be described in further detail so that those skilled in the art can implement it based on the description.

[0018] It should be understood that terms such as "having," "comprising," and "including" used in the embodiments of this application do not exclude the presence or addition of one or more other elements or combinations thereof. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationship and movement of components in a specific posture. If the specific posture changes, the directional indication will also change accordingly. When an element is referred to as "fixed to" or "set on" another element, it can be directly on the other element or may have an intervening element present. When an element is referred to as "connected to" another element, it can be directly connected to the other element or indirectly connected to the other element through an intervening element. Descriptions involving "first," "second," etc., in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features.

[0019] It should be noted that the technical solutions of the various embodiments of this application can be combined with each other, but only if they are based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by this application.

[0020] In one embodiment, the flour sample to be tested is first dispersed in deionized water. The conductivity of the deionized water can be controlled below 0.1 μS / cm. This avoids the shielding effect of external ions on the protein surface charge, ensuring that the subsequent particle size analysis results accurately reflect the aggregation and deaggregation behavior of the flour protein under different physical perturbations. The amount of flour sample used can be approximately 0.1 g, which is sufficient to generate a scattering signal that meets the light-shielding requirements of the laser particle size analyzer, while avoiding multiple scattering interference due to excessive concentration. After the flour is added to deionized water, the protein particles begin to hydrate and swell. At this time, various intermolecular interactions, including electrostatic interactions, hydrogen bonds, hydrophobic interactions, and disulfide bonds, work together to maintain the structural integrity of the aggregates.

[0021] The subsequent stirring stage involves agitating the flour sample dispersed in water. This stirring is performed using the stirring paddle unit integrated into the laser particle size analyzer. The shear force generated at this rotational speed is sufficient to disrupt the aggregated structure maintained by electrostatic interactions within the protein aggregates. During stirring, the average particle size of the flour particles dispersed in the water is measured at predetermined time intervals. This average particle size, denoted as D, refers to the volume-weighted average particle size measured by laser diffraction. Its change quantitatively reflects the degree of dispersion of the protein aggregates during hydration. Measurements are repeated until the difference between two consecutive average particle size measurements does not exceed 1%, at which point the particle size is considered to have reached equilibrium.

[0022] After completing the stirring stage, the ultrasonic stirring stage is implemented. This involves applying ultrasonic treatment to the flour sample while continuously stirring. The ultrasonic treatment can be performed using an immersion ultrasonic probe, and the ultrasonic frequency can be set from 18 kHz to 22 kHz. This frequency range falls within the low-frequency ultrasonic range, and the resulting cavitation effect can effectively disrupt strong non-covalent bonds such as hydrogen bonds and hydrophobic interactions. Similarly, the average particle size of the flour particles dispersed in water is measured at predetermined time intervals until the particle size reaches equilibrium when the difference between two consecutive measurements does not exceed 1%.

[0023] The average particle size at each time point during the stirring stage and the average particle size at each time point during the ultrasonic stirring stage were recorded. A first-order kinetic model was used to fit the data for both stages. The mathematical expression of this model is D(t) = D ∞ +(D0-D ∞ )×e -k(t-t0)Where D(t) represents the volume average particle size at measurement time t, D ∞ The average particle size limit under equilibrium conditions is represented by D0, the initial average particle size at initial time t0, and k is the rate constant characterizing how quickly the particle size approaches equilibrium. t is the measurement time, and t0 is the initial moment when the corresponding perturbation is applied at each stage. The rate constant for the stirring stage (denoted as k_stirring) and the rate constant for the stirring-ultrasonic stage (denoted as k_stirring-ultrasonic) can be obtained through fitting. Based on this, the formula Δk = (k_stirring-ultrasonic) is used. 搅拌超声 -k 搅拌 ) / (k 搅拌超声 +k 搅拌 The value of Δk is calculated. This value is a dimensionless normalization index, and its value ranges from -1 to 1.

[0024] If the calculated Δk is between -1 and -0.1, the protein aggregation in the flour sample is determined to be dominated by electrostatic interactions. Electrostatic interactions mainly refer to the salt bridging between amino acid residues with opposite charges and between phosphate groups and metal cations such as calcium ions. If Δk is between -0.1 and 1, the aggregation is determined to be dominated by hydrogen bonds and hydrophobic interactions. Hydrogen bonds mainly refer to the non-covalent bonds formed between peptide chains, while hydrophobic interactions refer to the driving force for nonpolar amino acid residues to aggregate in an aqueous environment to reduce the system's free energy.

[0025] In existing technologies, methods for characterizing the dominant forces in flour protein aggregation mainly rely on chemical reagent dissolution methods, which indirectly infer these forces by measuring changes in the solubility of the sample in reagents that selectively disrupt specific interactions. However, these methods require the use of multiple chemical reagents, are cumbersome, and have long detection cycles. Furthermore, multiple independent measurements of protein content are needed for conversion, leading to significant cumulative errors. In addition, the introduction of reagents alters the state of the proteins, making it difficult for the measurement results to accurately reflect the aggregation behavior of flour during actual processing (such as dough mixing). This embodiment uses a series of physical disturbances—stirring and ultrasonic stirring—to preferentially disrupt electrostatic interactions during the stirring stage, while hydrogen bonds and hydrophobic interactions are significantly disrupted only during the ultrasonic stage. The difference in particle size decay rates between the two stages is quantitatively analyzed using a first-order kinetic model, enabling effective separation of different types of non-covalent interactions at the kinetic level. Δk, as a normalized internal reference index, automatically eliminates the interference of sample concentration and initial particle size distribution on the judgment results, ensuring the objectivity and repeatability of comparisons between different batches.

[0026] In one embodiment, a laser particle size analyzer is used to perform particle size determination during the above-mentioned average particle size measurement process. The laser particle size analyzer can be a Malvern Mastersizer 2000 model, equipped with an automated sample dispersing system, Hydro2000MU, which integrates a stirring paddle unit and an ultrasonic probe unit, both of which can be independently controlled to turn on or off via software. During the stirring and ultrasonic dispersing stages, the laser particle size analyzer automatically records the volume average particle size value for each measurement. Data acquisition is automatically completed by the device's built-in computer program, and the measurement trigger condition is that a measurement is automatically initiated by the software at each predetermined time point. The light-blocking degree of the laser particle size analyzer can be controlled between 10% and 20% during measurement. This embodiment introduces the real-time online monitoring function of the laser particle size analyzer into the two continuous stages of stirring and ultrasonic dispersing, enabling the particle size evolution of the same flour sample under different physical disturbances to be continuously and objectively recorded. This dynamic monitoring method acquires far more information than static single-point measurement, providing sufficient density of time-series data to support subsequent kinetic model fitting.

[0027] In one embodiment, the predetermined time interval for measuring the average particle size during the stirring and ultrasonic stirring stages is 30 seconds. This time interval can be precisely controlled by a timer module built into the laser particle size analyzer. The timer module uses a quartz crystal oscillator as its clock reference, achieving a timing accuracy of ±1 millisecond. The 30-second interval is chosen based on the typical timescale of particle size decay of flour proteins in the aqueous phase. For most common wheat flour, the total time required for the particle size to approach its equilibrium value from its initial value under stirring shear force or ultrasonic action is typically in the range of 3-10 minutes. A 30-second sampling interval can collect 6-20 valid data points throughout the decay process, which is sufficient to meet the data requirements for nonlinear fitting of a first-order kinetic model. If the sampling interval is too long, such as greater than 60 seconds, only 3-5 data points can be collected throughout the decay process, and the statistical reliability of the fitting results will decrease significantly.

[0028] In one embodiment, the ultrasonic treatment frequency applied during the above-mentioned stirring and ultrasonication stage is 18 kHz to 22 kHz. This frequency band belongs to the low-frequency ultrasonic range and can generate a strong cavitation effect when propagating in a liquid medium. That is, the rarefied phase of the ultrasound forms tiny bubble nuclei in the liquid. These bubble nuclei are rapidly compressed to collapse in the subsequent compression phase. The collapse instantaneously generates extremely high temperatures and pressures locally. These extreme conditions are sufficient to break strong non-covalent bonds such as hydrogen bonds and hydrophobic interactions. However, because the action time is extremely short and the range is limited to the micrometer scale, it will not cause a significant increase in the temperature of the entire dispersion system, thereby avoiding protein thermal denaturation. The ultrasonic frequency can be selected from 18 kHz, 19 kHz, 20 kHz, 21 kHz, or 22 kHz. For example, 20 kHz can be used with an ultrasonic power supply with an output power of 60 W to 80 W. The lower the frequency, the larger the maximum radius that the cavitation bubbles can reach during the expansion phase, and the stronger the energy released during collapse. Therefore, for samples with a high proportion of hydrophobic interactions, a frequency of 18 kHz to 19 kHz can be preferentially selected. Higher frequencies increase the cavitation threshold and decrease the density of cavitation events. For samples that can be dispersed with gentle ultrasound, frequencies of 21 kHz to 22 kHz can be selected. Ultrasonic treatment can be applied in continuous or pulsed modes. Pulsed mode, for example, involves ultrasound for 3 seconds followed by a 2-second pause. This mode can effectively transfer ultrasonic energy while avoiding overheating of the ultrasound probe tip due to continuous vibration.

[0029] In one embodiment, during the kinetic model fitting process for the particle size data described above, the average particle size measured during the stirring and ultrasonic stirring stages is first fitted to the corresponding time data using both a first-order kinetic model and a zero-order kinetic model. The first-order kinetic model assumes that the rate at which the particle size approaches equilibrium is proportional to the difference between the current particle size and the equilibrium value; that is, the larger the particle size, the faster the decay, and the decay gradually slows down as the particle size approaches the equilibrium value, exhibiting an exponential decay characteristic. The zero-order kinetic model is expressed as D(t) = D0 - k0 × t, where k0 is the zero-order rate constant. It assumes that the particle size decreases linearly with time, meaning the decay rate is a constant and does not change with particle size. After fitting, the corrected coefficients of determination for both models are calculated. The formula for calculating the corrected coefficients of determination is R0. 2 =1-(1-R 2 )×(a-1) / (ab-1), where R 2Let R1 be the coefficient of determination, a be the number of data points, and b be the number of parameters in the model. The first-order kinetic model has four parameters, while the zero-order kinetic model has two. Due to the different number of parameters, a modified coefficient of determination (RCD) is used instead of the ordinary coefficient of determination (OCD) to fairly compare the goodness of fit between the two models, as the modified RCD penalizes the model with more parameters. The modified RCD for the first-order kinetic model is denoted as R1. 2 The corrected coefficient of determination for the zero-order dynamics model is denoted as R0. 2 When R1 2 Higher than R0 2 When the particle size decay process is determined to better conform to first-order kinetic characteristics, the k obtained by fitting the first-order kinetic model is used. 搅拌 and k 搅拌超声 Used for subsequent Δk calculations. When R1 2 Not higher than R0 2 If the data does not meet the first-order kinetic decay law, possible reasons include abnormal agglomeration of the flour sample during the measurement process, slippage of the instrument's stirring paddle causing the actual stirring speed to deviate from the set value, or the presence of large-sized bran particles in the sample causing abnormal fluctuations in the particle size measurement. In this case, the stirring and ultrasonic stirring stages need to be repeated. This model screening mechanism can exclude datasets that do not conform to the expected kinetic behavior during the data analysis stage, ensuring that the rate constants involved in the Δk calculation come from valid and reliable fitting results, thereby avoiding incorrect conclusions about the aggregation force based on erroneous data.

[0030] This embodiment introduces a modified coefficient of determination comparison mechanism, which ensures that the selection of the model has an objective statistical basis rather than being arbitrarily specified. This screening mechanism can effectively identify and eliminate abnormal test data, improve the reliability of the final judgment result, and avoid misjudgment caused by poor data quality.

[0031] In one embodiment, the average particle size D of the above equilibrium state ∞ The average particle size is determined as follows: During the stirring or ultrasonic stirring phase, starting from the first time the equilibrium state is reached, N measured average particle size values ​​are recorded. The first time the equilibrium state is reached means that the difference between the measured particle size value at a certain time point and the measured particle size value at the previous adjacent time point does not exceed 1% for the first time, and this 1% criterion serves as the entry condition for entering the equilibrium recording phase. Thereafter, measurements continue at predetermined time intervals, and the difference in the measured average particle size between adjacent time points remains below 1%. These N measured average particle size values ​​are sequentially recorded as D1, D2…D… N D N The value is the particle size at the last measurement time point. Then, the average particle size D at equilibrium is calculated using the following formula. ∞ : Where i is an integer from 1 to N-1, representing the chronological numbering of the measured average particle size values ​​during the equilibrium stage. The particle size value D measured in the early stage of the equilibrium stage... i Compared with the final measured value D N Differences exist between them, stemming from two main sources: firstly, the minute drift in particle size during the equilibrium phase before it fully stabilizes; and secondly, random measurement noise from the instrument. Typically, early D... i It will be slightly larger than D N However, D may also occur due to noise. i Less than D N In this case, the difference (D) i -D N α can be positive or negative. The specific value of α can be selected based on the time series characteristics of the particle size data during the equilibrium phase: when the particle size values ​​measured at each time point during the equilibrium phase fluctuate little and the data is relatively stable, a smaller α value, such as 0.5, can be selected. In this case, each early data point is assigned a more uniform weight, D ∞ The estimation fully utilizes the information from all N measured values, which helps improve the accuracy of the estimation; when the early data points in the equilibration phase are different from the final measured value D... N When the dispersion between data points is large and the data fluctuations are significant, a larger α value, such as 1.5, can be selected. In this case, the weight of the early data is rapidly decayed, and the data points that are closer to the stable state in the later stages dominate the estimation of D∞, which can effectively reduce the interference of large early fluctuations on the equilibrium particle size estimation. For most conventional flour samples, when the degree of data fluctuation is uncertain, a moderate α value, such as 1.0, can be selected as the default choice.

[0032] This calculation method differs from using D... N Directly as D ∞ The simplification lies in its utilization of the weighted average property of the exponent, incorporating information from all N measured values ​​during the equilibrium phase into D. ∞ This effectively reduces the impact of random errors from a single measurement on the accuracy of equilibrium particle size estimation.

[0033] This embodiment employs an exponentially decaying weighted method, which utilizes the information content of all equilibrium stage data and reasonably reflects the temporal correlation between early data points and the final stable state through exponential weights, thus enabling D... ∞ The estimation is more robust, which in turn improves the fitting accuracy of the rate constant k in the dynamic fitting.

[0034] In one embodiment, the initial time t0 is determined as follows: Starting from the moment the flour sample is dispersed in deionized water, i.e., the moment the flour sample is poured into the sample cup of the laser particle size analyzer, the time zero point is taken. The average particle size is continuously measured at predetermined time intervals, and the measurement is controlled by the automatic sample feeding program of the laser particle size analyzer. For the obtained particle size sequence changing over time, the particle size change rate δj at each adjacent time point is calculated, and the calculation formula is δ... j =[D(t j )−D(t j−1 )] / D(t j−1 ), where t j Let t represent the j-th measurement time point. j-1 This represents the (j-1)th measurement time point. Here, j is the sequence number of the measurement time point. A positive change rate δj indicates an increase in particle size compared to the previous time point, a negative value indicates a decrease in particle size, and zero indicates no change in particle size. When n consecutive change rates δj are all less than or equal to 0 for the first time, the particle size is determined to have entered a continuous decreasing stage. Here, n is the preset number of consecutive judgments, which can be 2, 3, or 4. The larger the value of n, the stricter the judgment standard, and the more reliably it can eliminate misjudgments caused by single random fluctuations. The time point corresponding to the first change rate among these n consecutive change rates is denoted as the initial time t0 of this stage, and the measured average particle size corresponding to this time point is D0. The necessity of this judgment method lies in the fact that flour particles do not immediately begin to decrease in particle size after adding deionized water. Instead, they first undergo a rapid hydration and swelling stage, during which protein particles absorb water and swell, leading to an increase in particle size. Only then, under the action of stirring shear force, do they begin to disperse and decrease in particle size. If the zero point of the sample addition time is directly taken as t0, the data segment of swelling increase will be incorrectly included in the subsequent kinetic decay fitting, leading to distortion of the fitting results. In one possible implementation, the value of n can be adjusted according to the characteristics of the sample. For high-protein flour with a long swelling stage, n=4 can be used to ensure that t0 actually falls at the starting point of the continuous decrease in particle size. For low-protein flour with a short swelling stage, n=2 can be used to capture the decay starting point earlier.

[0035] This embodiment uses a rate of change criterion to automatically identify t0 using an algorithm, eliminating the interference of human factors and significantly improving the objectivity and repeatability of the entire method. It also makes the automated high-throughput application of this method possible.

[0036] In one embodiment, after calculating the Δk value, the range of the proportion of the dominant protein aggregation force is further given based on the interval in which Δk falls. When Δk is between -1 and -0.1, the smaller the Δk value, the greater the relative contribution of electrostatic interactions. At this point, k... 搅拌 Significantly greater than k 搅拌超声This indicates that stirring shear force alone is sufficient to rapidly disaggregate most protein aggregates, and sonication can cause relatively little additional structural damage. Therefore, the aggregation network is mainly maintained by electrostatic interactions, a shear-sensitive force. When Δk approaches -1, the aggregation network relies almost entirely on electrostatic interactions, and stirring shear can almost completely disaggregate the aggregates. When Δk approaches 0, such as -0.1 ≤ Δk < 0.2, the relative contributions of electrostatic interactions, hydrogen bonds, and hydrophobic interactions are close. At this point, the rate constants of the two stages are relatively close, indicating that the contributions of electrostatic interactions, hydrogen bonds, and hydrophobic interactions in the aggregation network are roughly equivalent. When Δk is between -0.1 and 1, the larger the Δk value, the greater the relative contribution of hydrogen bonds and hydrophobic interactions. The particle size decay is slow during the stirring stage, i.e., k 搅拌 Smaller, but after ultrasound application, k 搅拌超声 The significant increase in Δk indicates that a large number of aggregates require cavitation energy provided by ultrasound to effectively deagglomerate. Therefore, the aggregation is mainly maintained by ultrasound-sensitive forces such as hydrogen bonds and hydrophobic interactions. When Δk approaches 1, the aggregation network almost entirely depends on hydrogen bonds and hydrophobic interactions, and stirring and shearing alone are almost insufficient to deagglomerate the aggregates. For this type of flour, in actual processing, ultrasonic-assisted treatment, emulsifier addition, or moderate enzymatic hydrolysis can be prioritized to effectively weaken the aggregation structure dominated by hydrogen bonds and hydrophobic interactions, improving the dough's extensibility and processing adaptability. When Δk approaches -1, mechanical shearing enhancement treatment or the addition of metal ion chelating agents should be prioritized to regulate the aggregation behavior dominated by electrostatic interactions. When Δk approaches 0, the contributions of the two types of forces are similar, the processing window is wide, and mild shearing or heat treatment can be used for flexible adjustment.

[0037] This embodiment can achieve semi-quantitative differentiation of different types of non-covalent interactions using a single index Δk, without the need for any chemical reagents. Furthermore, the flour sample remains in an aqueous environment throughout the test, maintaining the natural hydration state of the protein. The measurement results are closer to the actual behavior of flour in actual processing, such as dough mixing. At the same time, the division of the percentage range provides targeted quantitative guidance for subsequent operations such as enzyme treatment, emulsifier addition, or calcium ion regulation in flour modification processes.

[0038] The following is a description of a specific embodiment.

[0039] Example 1: Eight wheat flour samples with different protein compositions and physicochemical properties were selected and numbered S1 to S8. The dominant forces driving protein aggregation in each sample were determined using the method of this invention. The determination process is described in detail below using sample S1 as an example; the other samples were processed using the same procedure.

[0040] Weigh approximately 0.1 g of S1 flour sample and disperse it in deionized water in the dispersion cup of a laser particle size analyzer (Malvern Mastersizer 2000, equipped with a Hydro 2000MU automatic dispersion system). The volume of deionized water is 800 mL, and its conductivity is less than 0.1 μS / cm. Turn on the stirring function and set the stirring speed to 1000 r / min; do not use ultrasonication. The light-blocking degree of the laser particle size analyzer is controlled to approximately 12% by adjusting the amount of sample added. From the moment the flour is added to the deionized water, the instrument's built-in timer automatically measures the volume average particle size D of the flour particles every 30 seconds (see [reference]). Figure 2 During the measurement process, the particle size change rate δ at adjacent time points was calculated in real time. j =[D(t j )−D(t j−1 )] / D(t j−1 For sample S1, the particle size initially increases slightly and then decreases continuously. When three consecutive rates of change δj are all less than or equal to 0, the time point corresponding to the first rate of change is recorded as the initial time t0 of the stirring stage, and the measured particle size at this time is recorded as D0. Measurements continue until the difference between two adjacent average particle sizes does not exceed 1%, at which point equilibrium is reached, and the equilibrium recording stage begins. During the equilibrium recording stage, N measured average particle size values ​​are recorded at different time points, with the difference between adjacent time points not exceeding 1%. These values ​​are recorded in chronological order as D1, D2...D N An exponential weighting formula is used. Calculate the equilibrium average particle size D for this stage. ∞ Where α = 1.0. The particle size measurements at each time point during the stirring stage, along with the previously determined t0, D0, and D... ∞ Substituting the data into a first-order kinetic model and fitting it using the Levenberg-Marquardt nonlinear least squares method, the rate constant k during the stirring stage is obtained. 搅拌 =6.2×10 -3 s -1 Simultaneously, a zero-order kinetic model was used to fit the data set, and the corrected coefficient of determination R1 for the first-order kinetic model was calculated. 2 =0.982, the corrected coefficient of determination R0 for the zero-order dynamics model 2 =0.915, R1 2 Higher than R0 2 This indicates that the first-order dynamic model better matches the data characteristics, and the fitting results are effective.

[0041] After reaching equilibrium during the stirring phase, maintain the stirring state and initiate ultrasonic treatment. The ultrasonic frequency is set to 20 kHz, and the power to 80 W, applied in pulse mode (3 s action, 2 s interval). From the moment ultrasonic treatment is initiated, the average particle size is measured every 30 s (see [link to ultrasonic treatment]). Figure 2 The initial time t0' and equilibrium average particle size D of the ultrasonic stirring stage are determined using the same adaptive algorithm as the stirring stage. ∞ The particle size-time data measured during the ultrasonic stirring stage were fitted using the same first-order kinetic model, yielding the rate constant ksonic stirring for this stage as k = 4.0 × 10-1. -3 s -1 Similarly, a comparison was made between the first-level and zero-level models, and the adjusted coefficient of determination R1 for the first-level model was used. 2 =0.979, Level 0 R0 2 =0.908, confirming the validity of the first-level result.

[0042] According to the formula Δk=(k 搅拌超声 -k 搅拌 ) / (k 搅拌超声 +k 搅拌 The calculated Δk = (4.0 - 6.2) / (4.0 + 6.2) = -2.2 / 10.2 ≈ -0.22. This value falls within the range of -1 to -0.1, indicating that the protein aggregation in sample S1 is dominated by electrostatic interactions.

[0043] Samples S2 to S8 were measured and fitted using the same methods and parameters as described above. The rate constants and calculated Δk values ​​for each sample during the stirring and ultrasonic stirring stages are summarized in Table 1. To ensure clear and comparable data, Table 1 also lists the calcium ion content (unit: mg / kg) and disulfide bond content (unit: μmol / g) of each sample measured using traditional chemical analysis methods for reference.

[0044] Table 1. Protein aggregation-related indicators and diffusion rate parameters in flour samples. As can be seen, sample S1 has a Δk of -0.22, falling within the -1 to -0.1 range, indicating that it is dominated by electrostatic interactions. Its calcium ion content is as high as 7760 mg / kg, the highest among all samples. Calcium ions can mediate electrostatic aggregation between protein molecules through salt bridging, which is highly consistent with the judgment result. Its disulfide bond content is 2.7 μmol / g, which is at a moderate level, indicating that covalent crosslinking of disulfide bonds is not the main driving force for aggregation in this sample. Sample S3 has a Δk of 0.49, falling within the 0.2 to 1 range, indicating that it is dominated by hydrogen bonds and hydrophobic interactions. Its calcium ion content is 1745 mg / kg, and its disulfide bond content is 2.3 μmol / g, both the lowest among all samples. This indicates that neither electrostatic interactions (salt bridging) nor covalent disulfide bonds are dominant; aggregation is mainly maintained by hydrogen bonds and hydrophobic interactions, consistent with the judgment result.

[0045] The Δk values ​​of samples S2, S4, S5, S6, S7, and S8 range from -0.08 to 0.19, all approaching 0, indicating that the contributions of the two types of non-covalent interactions are similar. The calcium ion content of these samples ranges from 1796 to 2279 mg / kg, which is at a moderate level, while the disulfide bond content ranges from 3.0 to 3.8 μmol / g, which is at a relatively high level. This suggests that the relative contributions of electrostatic interactions, hydrogen bonds, and hydrophobic interactions in these samples are similar, consistent with the judgment that the contributions of the two types of forces are similar when Δk approaches 0.

[0046] Based on the above analysis, the Δk value obtained by the method of this invention can objectively and quantitatively distinguish the contribution of electrostatic interactions, hydrogen bonds, and hydrophobic interactions in flour protein aggregation. Moreover, the determination results show good logical consistency with traditional chemical indicators (calcium ion content, disulfide bond content), which confirms the effectiveness and reliability of the method.

[0047] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and embodiments shown and described herein.

Claims

1. A method for characterizing the dominant forces of protein aggregation in flour based on particle size, characterized in that, include: S1: Disperse the flour sample in deionized water; S2: The stirring stage is carried out. The flour dispersed in water obtained in S1 is stirred. At the same time, the average particle size of the flour particles dispersed in water is measured at predetermined time intervals until the particle size reaches equilibrium. S3: Implement the ultrasonic stirring stage. Based on the stirring treatment in S2, ultrasonic treatment is superimposed, and the average particle size of the flour particles dispersed in the water is measured at predetermined time intervals until the particle size reaches equilibrium. S4: The average particle size and corresponding measurement time measured during the stirring stage, and the average particle size and corresponding measurement time measured during the simultaneous stirring and ultrasonic application stage, were fitted using a first-order kinetic model to obtain the rate constant k for the stirring stage. 搅拌 And the rate constant k during the ultrasonic stirring phase 搅拌超声 The first-order dynamic model is D(t) = D ∞ +(D0-D ∞ )×e -k(t-t0) Where D(t) is the average particle size at measurement time t, D ∞ The average particle size in equilibrium is given by D0, the average particle size at initial time t0 is given by k, the rate constant is given by t, the measurement time is given by t0, and the initial time of the stirring stage or the ultrasonic stage applied simultaneously with stirring is given by t0. S5: According to Δk=(k 搅拌超声 -k 搅拌 ) / (k 搅拌超声 +k 搅拌 Calculate the Δk value. If Δk is between -1 and -0.1, then the aggregation of flour proteins is determined to be dominated by electrostatic interactions. If Δk is between -0.1 and 1, then the aggregation of flour proteins is determined to be dominated by hydrogen bonds and hydrophobic interactions.

2. The method for characterizing the dominant forces of protein aggregation in flour based on particle size as described in claim 1, characterized in that, In S2 and S3, the average particle size of flour particles dispersed in water was determined using a laser particle size analyzer.

3. The method for characterizing the dominant forces of flour protein aggregation based on particle size as described in claim 2, characterized in that, The scheduled time interval is 30 seconds.

4. The method for characterizing the dominant forces of protein aggregation in flour based on particle size as described in claim 1, characterized in that, The frequency of ultrasonic treatment in S3 is from 18 kHz to 22 kHz.

5. The method for characterizing the dominant forces of protein aggregation in flour based on particle size as described in claim 1, characterized in that, In S4, the average particle size measured during the stirring and ultrasonic stirring stages and the corresponding measurement time were fitted using a first-order kinetic model and a zero-order kinetic model, respectively, to obtain the correction coefficient of determination R1 for the first-order kinetic model. 2 and the corrected coefficient of determination R0 of the zero-order dynamics model 2 When R1 2 Higher than R0 2 When, k is obtained by fitting with a first-order dynamic model. 搅拌 and k 搅拌超声 Used for Δk calculation; when R1 2 Not higher than R0 2 Then, re-execute S2 and S3.

6. The method for characterizing the dominant forces of protein aggregation in flour based on particle size as described in claim 1, characterized in that, Average particle size D in equilibrium state ∞ Determined in the following ways: During the stirring or ultrasonic stirring phase, starting from the first time the equilibrium state is reached, a total of N measured average particle size values ​​are recorded, and the difference in measured average particle size between adjacent time points does not exceed 1%. These N measured average particle size values ​​are sequentially denoted as D1, D2...D... N D N This refers to the particle size value at the last measurement time point; The average particle size D at equilibrium is calculated using the following formula. ∞ : Where α is the attenuation coefficient, with a value of 0.5-1.5, and i is an integer from 1 to N-1, representing the time sequence number of each measured average particle size value in the equilibrium stage.

7. The method for characterizing the dominant forces of protein aggregation in flour based on particle size as described in claim 1, characterized in that, The initial time t0 is determined in the following way: Starting from the dispersion of flour samples in deionized water, the average particle size was continuously measured at predetermined time intervals, and the particle size change rate δ at each adjacent time point was calculated. j =[D(t j )−D(t j−1 )] / D(t j−1 ), where j is the sequence number of the measurement time point, δ j This represents the rate of change in particle size during the j-th time interval; When n consecutive rates of change δ first appear j When all values ​​are less than or equal to 0, the particle size is determined to have entered a continuous decreasing stage. The time point corresponding to the first rate of change in n consecutive rates of change is recorded as the initial time t0 of the stirring stage or the stirring and ultrasonic stage, where n≥2. The measured average particle size corresponding to the initial time t0 is D0.

8. The method for characterizing the dominant forces of protein aggregation in flour based on particle size as described in claim 1, characterized in that, In S5, when -1≤Δk<-0.1, the smaller the value of Δk, the greater the relative contribution of electrostatic interaction; When -0.1≤Δk≤1, the larger the value of Δk, the greater the relative contribution of hydrogen bonding and hydrophobic interactions; Furthermore, when Δk approaches 0, the relative contributions of electrostatic interactions are close to those of hydrogen bonds and hydrophobic interactions.

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