Detection system and method for quality marker of soft-shelled turtle by-product active peptide

Abstract

The invention relates to the technical field of soft-shelled turtle by-product quality detection, and discloses a detection system and method for a quality marker of soft-shelled turtle by-product active peptides, and the method comprises the following steps: extracting and treating soft-shelled turtle by-products to obtain active peptide components; carrying out identification and quantitative analysis on a specific mass marker in the active peptide based on biochemical or molecular detection means; evaluating the quality, activity and stability of the active peptide based on the detected quality marker data; and generating a standardized quality model based on multi-dimensional data fusion, and determining the quality of the source active peptide in soft-shelled turtle byproduct extraction according to the standardized quality model and the quality, activity and stability of the active peptide. The method effectively realizes systematized and standardized detection of the soft-shelled turtle byproduct active peptide, improves the accuracy of quality, activity and stability evaluation, and ensures consistent and reliable quality of the source active peptide.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of quality detection of soft-shelled turtle by-products, in particular to a detection system and method for quality markers of active peptides from soft-shelled turtle by-products. BACKGROUND

[0002] In the development of functional foods and biological medicines, active peptides have attracted widespread attention due to their various physiological regulation effects. Soft-shelled turtle by-products are rich in various bioactive peptides, which have potential functional values such as antioxidant and immunomodulatory effects.

[0003] However, existing researches are mostly limited to crude extraction and basic function verification, and there are still deficiencies in systematic and standardized evaluation of the quality of active peptides, which makes it difficult to effectively demonstrate their application value. Current detection methods are mainly based on conventional physicochemical indicators or partial bioactivity experiments, which cannot fully reveal the composition differences and functional characteristics of active peptides from soft-shelled turtle by-products. In particular, there is a lack of unified evaluation standards for the quality, activity and stability of active peptides, and a quantitative detection system based on quality markers has not been established, which makes it difficult to control the differences between different batches of raw materials and affects the consistency and reliability of the products.

[0004] Therefore, there is an urgent need for a technical solution that can combine biochemical or molecular detection methods, identify and quantitatively analyze specific quality markers, and form a standardized quality model through multi-dimensional data fusion, in order to objectively evaluate and effectively control the quality of active peptides from soft-shelled turtle by-products, and thus promote their application and industrialization in the fields of food and medicine. SUMMARY

[0005] In view of this, the present application proposes a detection system and method for quality markers of active peptides from soft-shelled turtle by-products, aiming to solve the problem that the existing detection methods lack a systematic and standardized evaluation system based on quality markers, making it difficult to accurately evaluate the quality, activity and stability of active peptides from soft-shelled turtle by-products, thereby leading to insufficient product consistency and application value.

[0006] The present application proposes a detection method for quality markers of active peptides from soft-shelled turtle by-products, comprising: extracting and processing soft-shelled turtle by-products to obtain active peptide components; identifying and quantitatively analyzing specific quality markers in the active peptides based on biochemical or molecular detection methods; evaluating the quality, activity and stability of the active peptides based on the detected quality marker data; generating a standardized quality model based on multi-dimensional data fusion, and determining the quality of the active peptides from the soft-shelled turtle by-products based on the standardized quality model and the quality, activity and stability of the active peptides.

[0007] Further, when extracting and processing the fishery by-product to obtain active peptide components, the method comprises: mechanically crushing the fishery by-product and then adding a complex enzymatic solution for enzymatic hydrolysis, wherein the complex enzymatic solution is obtained by mixing a protease and a buffer solution according to a preset ratio; stirring and reacting under constant temperature conditions, and adjusting the reaction time according to the type of the fishery by-product; after the enzymatic hydrolysis is completed, removing residues based on centrifugal separation, and collecting supernatant as a crude extract; filtering the crude extract through an ultrafiltration membrane and desalting to obtain active peptide components with uniform molecular weight distribution, and freeze-drying the active peptide components to obtain a powdered sample.

[0008] Further, when identifying and quantitatively analyzing specific quality markers in the active peptide using biochemical or molecular detection methods, the method comprises: separating the active peptide components based on a high-performance liquid chromatography system, wherein the mobile phase is composed of an aqueous phase and an organic phase, and a gradient elution program is optimized according to the polarity characteristics of the target markers; qualitatively analyzing the separated components based on a mass spectrometry technique, wherein the mass spectrometer adopts an electrospray ionization mode, and the scanning range covers the molecular weight interval of the target markers; selecting an internal standard substance as a reference in the quantitative analysis stage, establishing a standard curve, and calculating the concentration of the target markers, and for some markers that are difficult to directly detect, designing specific antibodies based on immunological detection methods for indirect determination.

[0009] Further, when evaluating the quality, activity, and stability of the active peptide based on the detected quality marker data, the method comprises: inputting the detected quality marker data into a preset multidimensional data analysis model, and using principal component analysis to reduce dimensions and extract key features; classifying the active peptide samples based on a clustering algorithm, and evaluating their quality grades according to the classification results; introducing a cell culture model and an in vitro enzymatic reaction model in the activity evaluation stage to verify the functional characteristics of the markers; conducting an accelerated aging test on the active peptide samples in the stability evaluation stage, recording the changes in the marker content at different time points, drawing a stability curve, and predicting the shelf life.

[0010] Further, when generating a standardized quality model by fusing multidimensional data, the method comprises: correlating and analyzing the quality marker data with other auxiliary information to construct a multidimensional data matrix, and the auxiliary information includes extraction process parameters and environmental conditions; The data matrix is trained based on a machine learning algorithm, and a support vector machine or a random forest algorithm is selected as the model, and cross-validation is used to optimize the model parameters during the training process; In the model output stage, a standardized quality score function is generated, wherein the score function includes marker concentration, activity index and stability index.

[0011] Further, according to the standardized quality model and the quality, activity and stability of the active peptide, the quality of the source active peptide extracted from the soft-shelled turtle byproduct is determined, including: The concentration value, activity value and stability value of the extracted active peptide are obtained, and the quality score of the source active peptide is determined according to the relationship between the concentration value, activity value and stability value of the extracted active peptide and the marker concentration, activity index and stability index; According to the quality score, it is determined whether the quality of the source active peptide is qualified.

[0012] Further, according to the relationship between the concentration value, activity value and stability value of the extracted active peptide and the marker concentration, activity index and stability index, the quality score of the source active peptide is determined, including: The concentration difference value between the concentration value of the extracted active peptide and the marker concentration is obtained; The activity difference value between the activity value of the extracted active peptide and the activity index is obtained; The stability value difference between the stability value of the extracted active peptide and the stability index is obtained; The concentration difference value, activity difference value and stability value difference value are normalized respectively, and the quality score of the source active peptide is determined by weighted fusion according to the normalized concentration difference value, activity difference value and stability value difference value.

[0013] Further, according to the quality score, it is determined whether the quality of the source active peptide is qualified, including: According to the relationship between the quality score of the source active peptide and the configured preset quality score, it is determined whether the quality of the source active peptide is qualified: When the quality score is lower than the preset quality score, it is determined that the quality of the source active peptide is unqualified; When the quality score is higher than or equal to the preset quality score, it is determined that the quality of the source active peptide is qualified.

[0014] Compared with the prior art, the beneficial effects of the present application are that: by extracting and processing the soft-shelled turtle by-products, the active peptide components therein can be efficiently obtained, raw material waste is avoided, and the comprehensive utilization value of the by-products is improved. Secondly, by introducing biochemical or molecular detection means to identify and quantitatively analyze specific quality markers, the authenticity and functionality of the active peptides can be accurately determined, overcoming the shortcomings of traditional physical and chemical indicators and single activity experimental methods in terms of detection sensitivity and accuracy. In the quality evaluation link, based on the quality marker data, the quality, activity and stability of the active peptides are comprehensively evaluated to ensure that the detection results are more objective and scientific, and to provide a reliable basis for subsequent functional verification and product development. Finally, by multi-dimensional data fusion to generate a standardized quality model, a unified quality evaluation standard can be realized in different batches and different sources of soft-shelled turtle by-products, effectively reducing the influence of batch differences on application results, thereby improving the consistency and industrialization feasibility of the source active peptide products.

[0015] In another aspect, the present application also provides a quality marker detection system for soft-shelled turtle by-product source active peptides, comprising: an extraction unit configured to extract and process the soft-shelled turtle by-products to obtain active peptide components; a detection unit configured to identify and quantitatively analyze specific quality markers in the active peptides using biochemical or molecular detection means; an evaluation unit configured to evaluate the quality, activity and stability of the active peptides based on the detected quality marker data; a modeling unit configured to generate a standardized quality model through multi-dimensional data fusion; a monitoring unit configured to realize real-time monitoring of the active peptide products in combination with the rapid detection module.

[0016] Further, the monitoring unit is also connected to the cloud database based on a wireless network, and the monitoring unit is further configured to transmit the quality data of the active peptide products to the cloud database.

[0017] It can be understood that the quality marker detection system and method for soft-shelled turtle by-product source active peptides in each of the above embodiments of the present application have the same beneficial effects, which will not be repeated. BRIEF DESCRIPTION OF DRAWINGS

[0018] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The accompanying drawings are included to provide a description of the preferred embodiments and are not meant to limit the present application. Moreover, the same reference numerals in the attached drawings indicate the same or similar components. In the drawings: Figure 1A flow chart of a quality marker detection method of an active peptide from soft-shelled turtle by-product provided by an embodiment of the present application is shown in the figure. Figure 2 A flow chart of a quality marker detection method of an active peptide from soft-shelled turtle by-product provided by an embodiment of the present application is shown in the figure. Figure 3 A functional block diagram of a quality marker detection system of an active peptide from soft-shelled turtle by-product provided by an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0019] Exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly understood, and the scope of the present disclosure can be accurately conveyed to those skilled in the art. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0020] As shown in the figure, in some embodiments of the present application, the present embodiment provides a quality marker detection method of an active peptide from soft-shelled turtle by-product, which comprises: Figures 1-2 Step S100, extracting and processing the soft-shelled turtle by-product to obtain an active peptide component.

[0021] Specifically, when the soft-shelled turtle by-product is extracted and processed to obtain an active peptide component, the soft-shelled turtle by-product is mechanically broken and then added to a complex enzymatic solution for enzymolysis, the complex enzymatic solution is mixed by a protease and a buffer solution according to a preset ratio; stirring reaction under constant temperature condition, the reaction time is dynamically adjusted according to the type of soft-shelled turtle by-product; after enzymolysis, the residue is removed based on centrifugal separation, and the supernatant is collected as a crude extract; the crude extract is sequentially filtered by ultrafiltration membrane and desalted to obtain an active peptide component with uniform molecular weight distribution, and the active peptide component is freeze-dried to obtain a powdered sample.

[0022] ​It can be understood that through the mechanical crushing process of the soft-shelled turtle by-products, the large pieces of tissue are broken into smaller particles, significantly increasing the specific surface area of the material, so that the subsequent addition of complex enzyme solution can fully penetrate and contact with the protein substrate. Through this physical treatment means, the enzymatic efficiency can be effectively improved, the reaction time can be shortened, and the incomplete reaction caused by limited mass transfer can be reduced. Secondly, the complex enzyme solution is mixed by proteinase and buffer solution according to the preset ratio, in which the proteinase as a catalyst can break the peptide bond in the protein molecule chain, and gradually hydrolyze the macromolecular protein into polypeptide fragments with small molecular weight and biological activity. The buffer solution provides a suitable acid-base environment to maintain the pH stability of the system, so that the enzyme activity is in the optimal state, avoiding the decrease or inactivation of enzyme activity caused by pH fluctuation, thereby ensuring the stability and controllability of the reaction. In addition, the stirring reaction under constant temperature conditions can ensure the uniformity of the temperature in the reaction system, and accelerate the mixing and contact of the substrate and enzyme molecules through continuous stirring, thereby improving the hydrolysis rate. The dynamic adjustment of the reaction time is set according to the protein structure characteristics and content differences of different types of soft-shelled turtle by-products, so that different raw materials can be moderately hydrolyzed, avoiding excessive hydrolysis leading to too many small peptides and losing activity, or insufficient hydrolysis causing the product function not to be significant. After the enzymatic hydrolysis is completed, the insoluble residues in the system are removed by centrifugal separation means using the sedimentation principle, and only the supernatant containing polypeptide components is retained as the crude extract, thereby reducing the interference of impurities on the subsequent purification steps. Subsequently, ultrafiltration membrane filtration can realize the classification and separation of peptide molecules according to the molecular weight cutoff principle, so that the obtained active peptide components are more uniform in molecular weight distribution, which is convenient for subsequent detection and application. Desalting further removes small molecular salts and other low molecular impurities remaining in the reaction solution, thereby reducing their potential impact on the structural stability and biological activity of the active peptides. Finally, the freeze-drying process can remove water under low temperature and vacuum conditions, which can avoid the destruction of the active peptide structure by high temperature, maintain its natural conformation and biological activity, and significantly prolong the storage time. The powdered sample is not only conducive to long-term storage and transportation, but also facilitates subsequent application in quality detection, function evaluation and product development.

[0023] In the specific embodiments in the present application, the above steps realize the scene in the following manner: for example, the collected soft-shelled turtle offal by-products are washed clean and mechanically broken to obtain fragments with a particle size of about 2-3 mm. Then a composite enzymatic hydrolysis solution prepared by mixing neutral protease and buffer solution at a mass ratio of 1:10 is configured, and the broken by-products are added into the solution. The stirring reaction is carried out under constant temperature at 37°C for 4 hours, and after the reaction is completed, the insoluble residues are removed by centrifugal separation to obtain the supernatant as a crude extract. For example, the soft-shelled turtle bone residues are used as raw materials, the bone residues are broken to particles below 1 mm, and then a composite enzymatic hydrolysis solution prepared by mixing alkaline protease and phosphate buffer solution is added, the reaction temperature is maintained at 45°C, and the reaction time is extended to 6 hours to ensure that the proteins in the bone are fully hydrolyzed. After the enzymatic hydrolysis is completed, the clear supernatant is obtained by a high-speed centrifugal machine as a crude extract. In further embodiments, the above-mentioned crude extract is sequentially filtered by an ultrafiltration membrane with a molecular weight cut-off of 3 kDa to remove macromolecular impurities and retain small molecular peptide segments, and then a desalting column is used to remove inorganic salts and small molecular impurities to obtain an active peptide component with relatively uniform molecular weight distribution. Finally, the obtained solution is placed in a freeze-drying machine and freeze-dried under vacuum conditions at -50°C for 24 hours to obtain a powder-like active peptide sample, which is convenient for storage and subsequent quality detection.

[0024] It can be seen that by mechanical breaking combined with composite enzymatic hydrolysis solution treatment, the proteins are fully hydrolyzed, and the release rate of active peptides is significantly improved; after ultrafiltration and desalting treatment, an active peptide component with uniform molecular weight distribution and low impurity content is obtained, effectively ensuring the stability and functional activity of the product; the powder sample prepared by freeze-drying is convenient for storage and transportation, and still maintains high biological activity after being stored for three months, proving that the method can improve the consistency and application value of the sample quality while ensuring the yield.

[0025] The above scene is only a preferred embodiment of the present application and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0026] Step S200, specific quality markers in the active peptide are identified and quantitatively analyzed based on biochemical or molecular detection means.

[0027] Specifically, the identification and quantitative analysis of specific quality markers in active peptides using biochemical or molecular detection methods include: separation of active peptide components based on high-performance liquid chromatography system, the mobile phase is composed of aqueous phase and organic phase, and the gradient elution program is optimized according to the polarity characteristics of the target marker; qualitative analysis of the separated components based on mass spectrometry, the mass spectrometer uses electrospray ionization mode, and the scanning range covers the molecular weight interval of the target marker; In the quantitative analysis stage, the internal standard substance is selected as the reference, the standard curve is established and the concentration of the target marker is calculated, and for some markers that are difficult to detect directly, specific antibodies are designed based on immunological detection methods for indirect determination.

[0028] It can be understood that by separating the active peptide components using a high-performance liquid chromatography (HPLC) system, different polarities and molecular weights of peptide segments are eluted at different rates through the interaction between the stationary phase and the mobile phase. By reasonably designing the mobile phase system composed of aqueous phase and organic phase, and combining the gradient elution of the polarity characteristics of the target quality marker, effective separation of complex peptide mixtures can be achieved, thereby creating conditions for subsequent accurate detection. Secondly, in the detection link after separation, mass spectrometry is used for qualitative analysis of active peptide components. The technical principle is to convert peptide molecules into charged ions by electrospray ionization (ESI) method, and detect them according to the mass-to-charge ratio (m / z) in the mass spectrometer. By setting the scanning range covering the molecular weight interval of the target marker, specific peptide segment species can be quickly and sensitively identified, and the molecular level of the quality marker is confirmed. Finally, in the quantitative analysis aspect, the internal standard substance is used as a reference, and the standard curve is established to eliminate the deviation caused by instrument fluctuations or sample processing on the detection results. Further, it can significantly improve the accuracy and repeatability of quantification. In addition, for some quality markers that are difficult to detect directly by mass spectrometry, specific antibodies are used to specifically bind to target molecules in combination with immunological detection methods, and then the concentration of the target marker is indirectly inferred through the amplification and detection of immunological signals, thereby realizing the expansion of the detection range and the improvement of the detection sensitivity.

[0029] In the specific embodiments in the present application, the above steps realize the scene in the following manner: from the soft-shelled turtle by-product active peptide powder sample obtained by the foregoing extraction, an appropriate amount is dissolved in a buffer solution as a sample to be tested. First, the sample is separated by using a high-performance liquid chromatography (HPLC) system, a C18 reversed-phase column is selected as a stationary phase, a mobile phase is composed of an aqueous phase (containing 0.1% formic acid) and an organic phase (acetonitrile containing 0.1% formic acid), and a gradient elution program is adopted: 0-5 min 5% acetonitrile, 5-20 min 5-35% acetonitrile, and 20-25 min 35-95% acetonitrile, so as to optimize the separation effect and make the target quality marker be fully separated. The separated peptide components are subjected to qualitative analysis by mass spectrometry (LC-MS), the mass spectrometer adopts an electrospray ionization (ESI) mode, the scanning range covers the molecular weight of the target marker 500-2000 Da, the molecular peak and fragment ion information of the specific marker in the active peptide can be identified, and thus the structure and existence of the specific marker are confirmed. In the quantitative analysis link, a known concentration of an internal standard (such as a synthetic peptide standard) is selected to establish a standard curve, and the concentration of the target quality marker in the sample is calculated by the peak area ratio. For some markers that are difficult to be directly detected by mass spectrometry, an enzyme-linked immunosorbent assay (ELISA) method is adopted, a specific antibody is designed to specifically bind to the target peptide, and the content of the target peptide is indirectly determined by the color change intensity, so as to realize the quantitative analysis of the difficult-to-detect markers.

[0030] It can be seen that the physical separation of sample components is realized by chromatographic separation, mass spectrometry provides high-sensitivity molecular-level identification, the internal standard and the standard curve ensure the accuracy of the quantitative results, and the immunological detection means further makes up for the limitations of direct mass spectrometry, and together constructs a multi-dimensional and accurate quality marker identification and quantitative analysis system.

[0031] The above scene is only a preferred embodiment of the present application and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0032] Step S300, evaluating the quality, activity and stability of the active peptide based on the detected quality marker data.

[0033] Specifically, the quality, activity and stability of the active peptide are evaluated based on the detected quality marker data, including: inputting the detected quality marker data into a preset multidimensional data analysis model, the model uses principal component analysis method to reduce dimension and extract key features; classifying the active peptide samples based on a clustering algorithm, and evaluating the quality grade according to the classification result; introducing a cell culture model and an in vitro enzymatic reaction model to verify the functional characteristics of the marker in the activity evaluation stage; performing an accelerated aging test on the active peptide samples in the stability evaluation stage, recording the changes of the marker content at different time points, drawing a stability curve and predicting the shelf life.

[0034] It can be understood that the active peptide samples are systematically processed by the preset multidimensional data analysis model. The multidimensional data model uses principal component analysis (PCA) method to reduce the dimension of complex data and extract the main variables reflecting the differences and key features of the samples. In this way, the interference of redundant information can be reduced, the discrimination ability of the quality of the active peptide components can be improved, and a reliable quantitative basis can be provided for subsequent classification and evaluation. In terms of sample classification and quality grade evaluation, a clustering algorithm is introduced to group the active peptide samples, and the quality is graded according to the characteristic differences between groups. The technical principle is to identify the similarities and differences between samples through statistical and mathematical models, so as to realize the objective comparison of active peptides from different batches or different sources, and avoid the subjectivity and inconsistency caused by traditional experience judgment. Secondly, in the activity evaluation link, the functional characteristics of the target marker are verified through a cell culture model and an in vitro enzymatic reaction model. The principle is to use the performance of the marker in the biological model to reflect the biological function of the active peptide, such as antioxidant capacity, enzyme inhibition activity, etc., so as to realize the correlation analysis between functional activity and marker content. Finally, in the stability evaluation link, the active peptide samples are subjected to an accelerated aging test, and the changes of the marker content are measured at different time points, a stability curve is drawn to predict the shelf life. The technical principle is to reflect the degradation trend of peptide molecules under storage conditions through time series data, and to use mathematical models for stability prediction, so as to scientifically evaluate the long-term storage performance of the samples.

[0035] In the specific embodiments in the present application, the above steps realize the scene in the following manner: from the soft-shelled turtle by-product active peptide powder sample, an appropriate amount of sample is dissolved in buffer, and the content data of the target quality marker are obtained through HPLC-MS or immunological detection. These data are input into a pre-set multidimensional data analysis model, the model uses the principal component analysis (PCA) method to reduce the dimension of the data, and extracts the key features that can reflect the quality difference of the sample. Subsequently, the clustering algorithm based on K-means is used to group the active peptide samples, and the samples are divided into high, medium and low quality levels according to the clustering results, so as to objectively evaluate the quality level of different batches of samples. Secondly, in the activity evaluation stage, typical grouped samples are selected for in vitro cell model experiments, such as detecting the antioxidant capacity by using fibroblasts, or detecting the ACE inhibitory activity by using in vitro enzymatic reaction model. The experimental results show that the active peptide samples of high quality level show obvious functional activity better than the low level samples in the biological function experiment, verifying the correlation between the marker data and the actual functional activity. In addition, in the stability evaluation stage, the samples are stored under accelerated aging conditions (such as 40°C, relative humidity 75%), and the content change of the target marker is detected regularly. By drawing the stability curve and analyzing the decline rate, the shelf life of the sample can be predicted, for example, the high-quality level active peptide is expected to maintain more than 6 months at 90% of the marker content under this condition.

[0036] It can be seen that by extracting key features through multidimensional data analysis, realizing quality level evaluation through clustering algorithm, verifying functional activity by combining biological model, and predicting stability by using time series data, a systematic and quantifiable active peptide quality, activity and stability evaluation system is constructed.

[0037] The above scene is only a preferred embodiment of the present application and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0038] Step S400, a standardized quality model is generated based on multidimensional data fusion, and the source active peptide quality in the soft-shelled turtle by-product extraction is determined according to the standardized quality model and the quality, activity and stability of the active peptide.

[0039] Specifically, when generating a standardized quality model through multidimensional data fusion, it includes: correlating and analyzing the quality marker data with other auxiliary information to construct a multidimensional data matrix, the auxiliary information includes extraction process parameters and environmental conditions; training the data matrix based on a machine learning algorithm, the model selects a support vector machine or a random forest algorithm, and cross-validation is used to optimize the model parameters in the training process; a standardized quality score function is generated in the model output stage, wherein the score function includes the marker concentration, the activity index and the stability index.

[0040] Specifically, according to the standardized quality model and the quality, activity and stability of the active peptide, when determining the quality of the source active peptide in the extraction of the soft-shelled turtle byproduct, the concentration value, activity value and stability value of the extracted active peptide are obtained, and the quality score of the source active peptide is determined according to the relationship between the concentration value, activity value and stability value of the extracted active peptide and the marker concentration, activity index and stability index. According to the quality score, it is determined whether the quality of the source active peptide is qualified.

[0041] Specifically, when determining the quality score of the source active peptide according to the relationship between the concentration value, activity value and stability value of the extracted active peptide and the marker concentration, activity index and stability index, the concentration difference value between the concentration value of the extracted active peptide and the marker concentration is obtained; the activity difference value between the activity value of the extracted active peptide and the activity index is obtained; the stability value difference between the stability value of the extracted active peptide and the stability index is obtained; the concentration difference value, the activity difference value and the stability value difference are normalized respectively, and the quality score of the source active peptide is determined by weighted fusion according to the normalized concentration difference value, activity difference value and stability value difference.

[0042] Specifically, when determining whether the quality of the source active peptide is qualified according to the quality score, the relationship between the quality score of the source active peptide and the configured preset quality score is determined to determine whether the quality of the source active peptide is qualified. When the quality score is lower than the preset quality score, it is determined that the quality of the source active peptide is unqualified. When the quality score is higher than or equal to the preset quality score, it is determined that the quality of the source active peptide is qualified.

[0043] It can be understood that the standardized quality model is generated by multi-dimensional data fusion, and the core technical principle thereof lies in that the quality marker data of the active peptide is analyzed in association with auxiliary information (such as extraction process parameters, environmental conditions, etc.), and a multi-dimensional data matrix is constructed. The matrix can reflect the composition characteristics, activity indexes, stability information and production process variables of the active peptide at the same time, and provides a data basis for establishing a unified and quantitative quality evaluation system. Subsequently, a machine learning algorithm is used to train the multi-dimensional data matrix, including a support vector machine (SVM) or a random forest (RF) algorithm, and cross-validation is used to optimize the model parameters during the training process. By learning the relationship between the marker characteristics, activity and stability indexes of different samples and the process conditions, the model can extract the potential rules in the complex data, generate a standardized quality scoring function, and thus realize scientific quantification and prediction of the quality of the active peptide. In the quality score calculation stage, the method compares the differences between the actual concentration value, activity value and stability value of the extracted active peptide and the marker concentration, activity index and stability index in the model, respectively calculates the concentration difference value, activity difference value and stability difference value, normalizes these difference values, and then combines a weighted fusion strategy to integrate the data in multiple dimensions into a single quality score, so as to realize quantitative evaluation of the comprehensive quality of the source active peptide. Finally, according to the comparison between the quality score and the preset quality threshold value, it can be judged whether the quality of the source active peptide is qualified. When the quality score is higher than or equal to the preset threshold value, it is determined to be qualified; and when the threshold value is lower, it is determined to be unqualified. The technical principle converts the complex quality, activity and stability information into an operable judgment index through a quantitative and standardized evaluation method, and realizes fine control and batch consistency management of the active peptide production process.

[0044] Specifically, the concentration value of the extracted active peptide is obtained by biochemical analysis methods, for example, using colorimetric methods (such as BCA protein quantification method or Lowry method) to determine the total peptide content in the active peptide solution, or by high performance liquid chromatography (HPLC) to quantify the peak area of specific marker peptide segments and convert it into concentration. After obtaining the concentration value, it is compared with the corresponding marker concentration in the standardized quality model to determine the concentration difference. Secondly, the activity value and activity difference of the extracted active peptide are obtained by in vitro functional experiments or biological models. Specifically, first, select an appropriate enzymatic reaction model, for example, use angiotensin converting enzyme (ACE), lipase or other specific target enzymes to measure the inhibition or activation of the enzyme by the active peptide, and calculate the activity value of the sample according to the reaction rate or product generation amount; at the same time, cell culture models can also be used, such as applying active peptides to fibroblasts, hepatocytes or immune cells to detect their effects on antioxidant capacity, anti-inflammatory capacity or proliferation capacity, and convert the experimental results into numerical functional activity indicators. After obtaining the activity value, the activity difference can be calculated by comparing the measured sample activity value with the corresponding activity index in the standardized quality model, and the expression is ΔA = A_sample A_standard, where A_sample is the functional activity value of the extracted active peptide, and A_standard is the preset activity index in the model. This difference reflects the deviation of the actual sample from the standard in terms of biological functional activity, providing a quantitative basis for subsequent normalization processing and comprehensive quality scoring. Specifically, the stability value and stability difference value of the extracted active peptide are obtained by accelerated aging test or long-term storage experiment to reflect the retention ability of the sample under storage or processing conditions. Specifically, the active peptide sample can be placed in accelerated aging conditions such as high temperature, high humidity or light, and sampled at predetermined time intervals, and the concentration or functional activity of key quality markers in the sample is detected, such as using HPLC-MS to determine the change in marker content, or by in vitro enzymatic reaction model and cell experiment to determine the functional activity retention rate. According to the detection results at different time points, a stability curve is drawn, and the retention rate of the sample during the entire experimental period or the retention value at the key time point is calculated as the stability value of the active peptide. Subsequently, by comparing the measured stability value with the corresponding stability index in the standardized quality model, the stability difference value can be calculated, and the expression is ΔS = S_sample S_standard, where S_sample is the actual measured stability value, and S_standard is the preset stability index in the model. This difference quantifies the stability difference between the sample and the standard during storage or processing, providing a scientific basis for subsequent normalization processing and comprehensive quality scoring.

[0045] In the specific embodiments in the present application, the above steps realize the scene in the following manner: the concentration data of the target quality markers are obtained through HPLC-MS and immunological detection, and the extraction process parameters (such as enzymolysis temperature, time, enzyme dosage) and environmental conditions (such as pH value, humidity) are recorded at the same time. These data are constructed into a multidimensional data matrix, and input into a preset multidimensional data analysis model. The model is trained by using a support vector machine (SVM) algorithm, and the kernel function parameters are optimized through cross-validation during the training process to ensure that the model can accurately extract the key features reflecting the quality of the sample, and output a standardized quality score function containing the marker concentration, activity index and stability index. In the quality score calculation stage, the actual extracted active peptide concentration value, activity value and stability value are compared with the corresponding marker concentration, activity index and stability index in the model, and the concentration difference, activity difference and stability difference are calculated respectively. After the difference value is normalized, the final source active peptide quality score is obtained by weighted fusion according to the pre-set weight coefficient. According to the comparison result of the quality score and the preset threshold value, it is judged whether the quality of the source active peptide is qualified. For example, when the sample quality score is 85 points, and the pre-set qualified score is 80 points, it is determined that the quality of the batch of active peptides is qualified; if the score is lower than 80 points, it is determined to be unqualified. Through this method, the quality of different batches of active peptides can be objectively and quantitatively evaluated, and combined with the activity and stability indexes, reliable basis is provided for production process optimization and batch consistency control.

[0046] It can be seen that through multidimensional data fusion and machine learning algorithm, the complex marker data, activity index and stability information are converted into quantifiable quality score, realizing the standardized and objective evaluation of the source active peptide; at the same time, different batches of samples can be quickly judged, which is helpful to guarantee the product quality consistency and the reliability of functional activity.

[0047] The above scene is only a preferred embodiment of the present application and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0048] In the above embodiments, by extracting and processing the soft-shelled turtle by-products, the active peptide components can be efficiently obtained, avoiding waste of raw materials and improving the comprehensive utilization value of the by-products. Secondly, by introducing biochemical or molecular detection methods to identify and quantitatively analyze specific quality markers, the authenticity and functionality of the active peptides can be accurately determined, overcoming the shortcomings of traditional physical and chemical indicators and single activity experimental methods in terms of detection sensitivity and accuracy. In the quality evaluation link, based on the quality marker data, the quality, activity and stability of the active peptides are comprehensively evaluated to ensure that the detection results are more objective and scientific, and to provide a reliable basis for subsequent functional verification and product development. Finally, by multi-dimensional data fusion to generate a standardized quality model, a unified quality evaluation standard can be realized in different batches and different sources of soft-shelled turtle by-products, effectively reducing the influence of batch differences on application results, thereby improving the consistency and industrialization feasibility of the source active peptide products.

[0049] In another preferred mode based on the above embodiments, as shown in Figure 3 The present embodiment provides a quality marker detection system for soft-shelled turtle by-product source active peptides, which includes an extraction unit, a detection unit, an evaluation unit, a modeling unit and a monitoring unit.

[0050] Specifically, the extraction unit is configured to extract and process the soft-shelled turtle by-products to obtain active peptide components; the detection unit is configured to identify and quantitatively analyze specific quality markers in the active peptides using biochemical or molecular detection methods; the evaluation unit is configured to evaluate the quality, activity and stability of the active peptides based on the detected quality marker data; the modeling unit is configured to generate a standardized quality model through multi-dimensional data fusion; and the monitoring unit is configured to realize real-time monitoring of active peptide products in combination with a rapid detection module.

[0051] Specifically, the monitoring unit is also connected to a cloud database based on a wireless network, and the monitoring unit is further configured to transmit the quality data of the active peptide products to the cloud database.

[0052] It can be understood that the quality marker detection system and method for soft-shelled turtle by-product source active peptides in the above embodiments of the present application have the same beneficial effects, which will not be repeated.

[0053] Those skilled in the art will appreciate that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0054] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0055] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0056] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0057] Finally, it should be noted that the above-mentioned embodiments are merely intended to illustrate the technical solutions of the present application, rather than limit the technical solutions of the present application. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and any modification or equivalent replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A method for detecting quality markers of active peptides from soft-shelled turtle by-products, characterized in that, The method comprises the following steps: extracting and processing the fishery by-product to obtain active peptide components; identifying and quantitatively analyzing specific quality markers in the active peptides based on biochemical or molecular detection means; evaluating the quality, activity and stability of the active peptides based on the detected quality marker data; generating a standardized quality model based on multi-dimensional data fusion, and determining the quality of the source active peptides in the fishery by-product extraction according to the standardized quality model and the quality, activity and stability of the active peptides.

2. The method for detecting quality markers of active peptides from soft-shelled turtle by-products according to claim 1, characterized in that, When extracting and processing the fishery by-product to obtain active peptide components, the method comprises the following steps: mechanically crushing the fishery by-product and then adding a complex enzymatic solution for enzymolysis, wherein the complex enzymatic solution is obtained by mixing a protease and a buffer solution according to a preset ratio; stirring and reacting under constant temperature conditions, and dynamically adjusting the reaction time according to the type of the fishery by-product; removing residues based on centrifugal separation after the enzymolysis is completed, and collecting the supernatant as a crude extract; filtering the crude extract through an ultrafiltration membrane and desalting the filtered product to obtain active peptide components with uniform molecular weight distribution, and freeze-drying the active peptide components to obtain a powdered sample.

3. The method of claim 2, wherein the active peptides are selected from the group consisting of SEQ ID NOs: 1- 10. When identifying and quantitatively analyzing specific quality markers in the active peptides based on biochemical or molecular detection means, the method comprises the following steps: separating the active peptide components based on a high-performance liquid chromatography system, wherein the mobile phase is composed of an aqueous phase and an organic phase, and the gradient elution program is optimized and set according to the polarity characteristics of the target markers; qualitatively analyzing the separated components based on a mass spectrometry technique, wherein the mass spectrometer adopts an electrospray ionization mode, and the scanning range covers the molecular weight interval of the target markers; selecting an internal standard substance as a reference in the quantitative analysis stage, establishing a standard curve and calculating the concentration of the target markers, and for some markers that are difficult to directly detect, designing specific antibodies based on immunological detection means for indirect determination.

4. The method of claim 3, wherein the active peptide is selected from the group consisting of SEQ ID NOs: 1 to 6. When evaluating the quality, activity and stability of the active peptides based on the detected quality marker data, the method comprises the following steps: inputting the detected quality marker data into a preset multi-dimensional data analysis model, and reducing the dimension and extracting key features of the model by using a principal component analysis method; classifying the active peptide samples based on a clustering algorithm, and evaluating the quality grade thereof according to the classification result; introducing a cell culture model and an in-vitro enzymatic reaction model in the activity evaluation stage to verify the functional characteristics of the markers; performing an accelerated aging test on the active peptide samples in the stability evaluation stage, recording the content changes of the markers at different time points, drawing a stability curve and predicting the shelf life.

5. The method for detecting quality markers of bioactive peptides derived from soft-shelled turtle by-products as described in claim 4, characterized in that, When generating a standardized quality model through multi-dimensional data fusion, the method comprises the following steps: correlatively analyzing the quality marker data and other auxiliary information to construct a multi-dimensional data matrix, wherein the auxiliary information includes extraction process parameters and environmental conditions; training the data matrix based on a machine learning algorithm, wherein the model selects a support vector machine or a random forest algorithm, and cross-validation is used to optimize the model parameters during the training process; generating a standardized quality score function in the model output stage, wherein the score function includes the marker concentration, the activity index and the stability index.

6. The method of claim 5, wherein the active peptides are selected from the group consisting of SEQ ID NOs: 1 to 6. When determining the quality of the source active peptides in the fishery by-product extraction according to the standardized quality model and the quality, activity and stability of the active peptides, the method comprises the following steps: The concentration value, activity value and stability value of the extracted active peptide are obtained, and the quality score of the source active peptide is determined according to the relationship between the concentration value, activity value and stability value of the extracted active peptide and the marker concentration, activity index and stability index. According to the quality score, it is determined whether the quality of the source active peptide is qualified.

7. The method for detecting quality markers of bioactive peptides derived from soft-shelled turtle by-products as described in claim 6, characterized in that, According to the relationship between the concentration value, activity value and stability value of the extracted active peptide and the marker concentration, activity index and stability index, the quality score of the source active peptide is determined, comprising: Obtain the concentration difference value between the concentration value of the extracted active peptide and the marker concentration; Obtain the activity difference value between the activity value of the extracted active peptide and the activity index; Obtain the stability value difference between the stability value of the extracted active peptide and the stability index; The concentration difference value, activity difference value and stability value difference are normalized respectively, and the quality score of the source active peptide is determined by weighted fusion of the normalized concentration difference value, activity difference value and stability value difference.

8. The method for detecting quality markers of bioactive peptides derived from soft-shelled turtle by-products as described in claim 7, characterized in that, According to the quality score, it is determined whether the quality of the source active peptide is qualified, comprising: According to the relationship between the quality score of the source active peptide and the configured preset quality score, it is determined whether the quality of the source active peptide is qualified: When the quality score is lower than the preset quality score, it is determined that the quality of the source active peptide is unqualified; When the quality score is higher than or equal to the preset quality score, it is determined that the quality of the source active peptide is qualified.

9. A detection system for quality markers of active peptides from soft-shelled turtle by-products, for applying the detection method for quality markers of active peptides from soft-shelled turtle by-products according to any one of claims 1-8, characterized in that, Comprising: The extraction unit is configured to extract and process the soft-shelled turtle by-products to obtain active peptide components; The detection unit is configured to identify and quantitatively analyze specific quality markers in the active peptide by biochemical or molecular detection means; The evaluation unit is configured to evaluate the quality, activity and stability of the active peptide based on the detected quality marker data; The modeling unit is configured to generate a standardized quality model by multi-dimensional data fusion; The monitoring unit is configured to realize real-time monitoring of the active peptide product in combination with the rapid detection module.

10. The quality marker detection system of active peptides from the by-products of Chinese soft-shelled turtles according to claim 9, wherein, The monitoring unit is also connected to the cloud database based on the wireless network, and the monitoring unit is further configured to transmit the quality data of the active peptide product to the cloud database.

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