A product quality testing method

Abstract

The present application provides a product quality testing method, which relates to the field of product quality testing technology, including: the product life chain of interactive collagen products; combined process and result testing, and the construction of a dual-drive test module; monitoring and returning product data streams, triggering dynamic testing, and performing phased data stream off-axis analysis and evaluation to determine the first test matrix, triggering static testing, performing component content analysis and molecular texture analysis, and predicting denaturation thresholds to determine the second test matrix; performing homology mapping verification on the first test matrix and the second test matrix to generate a product quality test sheet. This application can solve the technical problem in the prior art that the existing collagen product quality testing method cannot comprehensively evaluate the dynamic and static characteristics of the product, resulting in inaccurate product quality test results. By constructing a dual-drive test module, the quality of the collagen product can be comprehensively evaluated, and the accuracy of the product quality test results can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of product quality testing, and in particular to a product quality testing method. Background Art

[0002] Collagen products, such as collagen liquid dressings, collagen gels, and collagen patches, are widely used in healthcare, beauty, and other fields. The quality of these products is directly related to their efficacy and safety, making strict quality control throughout the production process crucial. Existing quality testing methods for collagen products typically rely on single methods, such as high-performance liquid chromatography (HPLC) or molecular texture analysis, which fail to comprehensively assess the product's dynamic and static properties. This results in inaccurate product quality assessments and increases production risks.

[0003] In summary, there is a technical problem in the prior art that the existing collagen product quality testing method cannot comprehensively evaluate the dynamic and static characteristics of the product, resulting in inaccurate product quality test results. Summary of the Invention

[0004] The purpose of this application is to provide a product quality testing method to solve the technical problem in the prior art that the product quality testing results are not accurate because the existing collagen product quality testing method cannot comprehensively evaluate the dynamic and static characteristics of the product.

[0005] In view of the above problems, the present application provides a product quality testing method, wherein the product quality testing method includes: a product life chain of an interactive collagen product, wherein the product life chain includes key nodes in the raw material-production-storage stage; combining process testing and result testing, and constructing a dual-drive test module by combining dynamic and static methods, wherein the dual-drive test module includes a dynamic test branch and a static test branch; monitoring and returning the product data flow, triggering the dynamic test branch, performing phased data flow off-axis analysis and evaluation, determining a first test matrix, triggering the static test branch, performing component content analysis based on high-performance liquid chromatography detection, and molecular texture analysis, and predicting the denaturation threshold, and determining a second test matrix; performing homology mapping verification on the first test matrix and the second test matrix to generate a product quality test sheet.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] By integrating the product lifecycle of interactive collagen products, which includes key nodes in the raw materials, production, and storage stages, and combining process testing with result testing, a dual-driven test module is constructed, which includes a dynamic test branch and a static test branch. The product data stream is monitored and transmitted back, triggering the dynamic test branch to conduct phased data flow off-axis analysis and evaluation to determine the first test matrix. The static test branch is triggered to perform component content analysis and molecular texture analysis based on high-performance liquid chromatography, predict the denaturation threshold, and determine the second test matrix. A homology mapping verification is performed on the first and second test matrices to generate a product quality test sheet. In other words, by combining process testing with result testing, combining dynamic and static testing, and constructing a dual-driven test module, the results of the dynamic and static test branches are subjected to homology mapping verification to generate a product quality test sheet, achieving comprehensive and precise control of product quality and improving the accuracy of product quality test results.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the description, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically listed below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in this application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without creative work.

[0010] Figure 1 A flowchart of a product quality testing method for this application;

[0011] Figure 2 This is a flow chart of determining texture test results in a product quality testing method of this application. DETAILED DESCRIPTION

[0012] This application solves the technical problem in the prior art that the existing collagen product quality testing method cannot fully evaluate the dynamic and static characteristics of the product, resulting in inaccurate product quality test results, by providing a product quality testing method. By combining process testing with result testing, combining dynamic and static, and constructing a dual-drive test module, the results of the dynamic test branch and the static test branch are homologously mapped and verified to generate a product quality test sheet, thereby achieving comprehensive and precise control of product quality and improving the accuracy of product quality test results.

[0013] Below, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited to the example embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the convenience of description, only the parts related to this application, rather than all of them, are shown in the accompanying drawings.

[0014] For examples, please see the attached Figure 1 The present application provides a product quality testing method, wherein the product quality testing method specifically includes the following steps:

[0015] Step 1: Interactive collagen product life cycle, which includes key nodes in the raw materials-production-storage stages.

[0016] Specifically, the interactive product lifecycle refers to the entire lifecycle of a product, from the raw material stage to the production stage to the storage stage, which involves the interactions and influences between different stages. Each stage of the product lifecycle does not exist in isolation, but is interconnected and influences each other. In the interactive product lifecycle, the key nodes of each stage have a significant impact on the final quality and performance of the product. Collagen products refer to a series of products containing recombinant collagen, such as liquid dressings, gels, and dressing patches, which interact with the skin or other tissues to provide specific biological functions. The product lifecycle refers to the entire lifecycle of a product, from raw material acquisition to finished product storage, including stages such as raw materials, production, and storage. The raw material stage may include nodes such as raw material selection, raw material testing, and storage management. The production stage may include nodes such as raw material preparation, extraction process, mixing, formulation, molding, and quality inspection. The storage stage may include nodes such as finished product inspection, packaging and labeling, and storage monitoring.

[0017] Identify and monitor the key nodes of each stage, that is, the steps or conditions in the raw materials, production and storage processes that have a significant impact on product quality. In the raw materials stage, key nodes may include the source of raw materials, quality inspection and storage conditions; in the production stage, key nodes may include fermentation conditions, purification steps and sterilization processes; in the storage stage, key nodes may include temperature, humidity and light conditions. For example, during the production process, the content of recombinant type III collagen in collagen liquid dressings, gels, and dressings is precisely controlled to ensure product efficacy. By identifying and controlling key nodes in the product life chain, comprehensive monitoring and management of the entire life cycle of collagen products is ensured, especially for key nodes and core technical indicators.

[0018] Step 2: Combine process testing and result testing, and construct a dual-drive test module by combining dynamic and static testing. The dual-drive test module includes a dynamic test branch and a static test branch.

[0019] Specifically, product quality is assessed through a combination of process-based and outcome-based testing. Process-based testing refers to testing of intermediate products during the production process to monitor key parameters such as temperature and pressure. Outcome-based testing involves testing the final product to assess whether it meets predetermined quality standards. Combining process-based and outcome-based testing, a dual-driven testing module is constructed, combining dynamic and static testing. The dynamic testing branch focuses on changes during the production process, monitoring key parameters such as temperature, pressure, and humidity in real time. The static testing branch, on the other hand, focuses on the final product quality, including testing of its physical, chemical, and biological properties, such as collagen content and molecular structure. For example, temperature and pH are monitored during fermentation to ensure optimal fermentation conditions. High-performance liquid chromatography (HPLC) and other analytical methods are used to test collagen purity and content. Instrumental analysis of collagen's molecular structure and size ensures product quality and potency. By combining process testing with result testing, and building a dual-drive test module with both dynamic and static methods, we can comprehensively evaluate product quality and performance, which helps improve the controllability and traceability of product quality and reduce quality risks.

[0020] Step 3: Monitor and transmit back the product data flow, trigger the dynamic test branch, perform phased data flow off-axis analysis and evaluation, determine the first test matrix, trigger the static test branch, perform component content analysis based on high-performance liquid chromatography detection and molecular texture analysis, predict the denaturation threshold, and determine the second test matrix.

[0021] Specifically, sensors and monitoring equipment are installed during the production, storage and use of the product to collect real-time data such as temperature, pressure, pH value, viscosity, collagen concentration, etc., and these data are transmitted back to the central control system or data storage platform in real time via wired or wireless networks. According to the product quality requirements, the conditions for triggering dynamic testing are pre-set. For example, when the temperature in the production process exceeds the set range, or when the product does not meet the standards, a dynamic test will be triggered. When changes in the data stream are monitored or specific thresholds are reached, the dynamic test branch is triggered to conduct more in-depth testing and analysis of the product. The entire production process is divided into several stages, such as raw material preparation, mixing, fermentation, purification, filling, packaging, etc. The data at each stage is analyzed to identify outliers or trends in the data stream. These anomalies may be early signs of product quality problems.

[0022] Staged data flow involves analyzing and evaluating the data flow of the production process in stages. Off-axis analysis is a statistical process control method used to identify anomalies in the production process and determine which parameters deviate from the normal range, potentially affecting product quality. This includes not only deviation and bias analysis but also other types of analysis, such as trend analysis and pattern recognition. Off-axis analysis of data flows assesses deviations and biases below the standard quality line. The standard quality line is a reference line established based on product quality standards and performance requirements, representing the quality and performance level that products should achieve during production. Deviation and bias analysis analyzes collected data to determine whether product performance during production meets the standard quality line. Deviation analysis focuses on the absolute deviation between product performance and the standard quality line—that is, whether product performance is within the acceptable range. The deviation value for each parameter is calculated. Bias analysis examines whether there are systematic deviations in product performance—that is, whether product performance is above or below the standard quality line—to determine whether the deviation is excessive or relative.

[0023] Based on the off-axis analysis results, a first test matrix was defined, encompassing the normal ranges and thresholds for all key parameters to guide quality control during production. Specifically, key test indicators, such as collagen purity, concentration, molecular weight, and bioactivity, were determined based on the off-axis analysis results. A test matrix was developed, consisting of a series of test cases, each targeting a different production stage and key indicator, to ensure that all important quality aspects were covered. Specific test cases were designed, including test conditions, test methods, expected results, and acceptance criteria, to create the first test matrix.

[0024] After product production is complete, static testing is performed on the product. This triggers the static testing branch, where a representative sample is taken from the production batch and prepared according to the standard operating procedures for high-performance liquid chromatography (HPLC) testing. HPLC is a technique used to separate, identify, and quantify compounds in a mixture. In the static testing of collagen products, HPLC can be used to determine key indicators such as collagen content, purity, and molecular weight. HPLC is used to qualitatively and quantitatively analyze the collagen content in the product sample. The components in the sample are separated and analyzed, and then compared with a standard sample to determine whether the collagen content in the product meets the predetermined quality standards and confirm the composition test results. Microscopic analysis techniques are used to detect changes in the molecular structure of the collagen product. By analyzing these changes in molecular structure, the product's rheological properties, such as viscosity and elastic modulus, are evaluated. The product's rheological properties are then evaluated for biostability and biocompatibility. Testing for biodegradability, cytotoxicity, and allergic reactions is performed to generate a secondary test characteristic. The primary test characteristic (rheological properties) is combined with the secondary test characteristic (biostability and biocompatibility) to determine the texture test results. Combining HPLC analysis results with molecular texture analysis, we predict the threshold for collagen denaturation under specific conditions. We establish a second test matrix encompassing ingredient content, textural properties, and predicted denaturation thresholds, forming a comprehensive quality assessment framework. Through dynamic and static testing, we comprehensively assess product performance and stability, identifying issues promptly and implementing appropriate adjustments and optimization measures.

[0025] Step 4: Perform homology mapping verification on the first test matrix and the second test matrix to generate a product quality test sheet.

[0026] Specifically, the overall quality indicators of the collagen product are determined by mapping identical or similar indicators in the first and second test matrices. Indicators with high homology are aggregated. These aggregated indicators are then verified and calculated to ensure data consistency and accuracy. Homology mapping verification involves comparing and verifying indicators with identical or similar properties and functions in the first and second test matrices. Corresponding indicators in the two matrices are matched and then compared for differences to determine whether the product quality meets requirements. By performing homology mapping verification on the first and second test matrices, homology indicators are compared, their differences are calculated, and verification is performed using a preset difference threshold to determine product quality indicator data. This data is then compiled and summarized into a product quality test sheet, a document that details product quality test results, including information such as product name, batch number, test date, tester, test method, and test results. This sheet summarizes the overall quality status of the product, with statements such as "All indicators are within acceptable range" or "Some non-conforming items require reassessment." For any non-conforming items, improvement suggestions are provided, such as adjusting the production process or optimizing the raw material formula. By performing homology mapping verification on the first test matrix and the second test matrix and generating a product quality test sheet, the quality status of the product is comprehensively recorded and proven, which helps to improve the quality and performance of the product.

[0027] Furthermore, step one of this application includes:

[0028] Identify the product lifecycle stages and determine the sub-step sequence of the first stage, where the first stage is any stage in the raw materials-production-storage stage; traverse the sub-step sequence, perform step screening and positive serialization integration based on the fusion of key steps and strongly related steps, and determine the first stage link.

[0029] Specifically, identify the entire life cycle of collagen products, including the raw material stage, production stage, and storage stage. Conduct in-depth analysis of any of these stages and subdivide them into smaller sub-stages or steps. For example, if the first stage is the production stage, then the sub-stage steps may include raw material preparation, mixing, fermentation, purification, filling, sterilization, etc. Traverse the sequence of sub-stage steps and review each sub-step in detail to ensure that no key links are missed, and fully understand and analyze the impact of each step on product quality. Identify the steps that have a direct and significant impact on product quality, as well as the steps that are strongly correlated with these key steps. Although strongly related steps are not directly critical, their execution quality will indirectly affect the effectiveness of the key steps.

[0030] Integrate key steps with strongly related steps, conduct step screening, and conduct a detailed analysis of each step in the product production process to determine which steps are most critical to product quality and final performance. The key steps are screened out and integrated with those steps that are closely related to them and have a significant impact on product quality. Identify and focus on those links that have the greatest impact on product quality. For example, in the production of collagen products, steps such as raw material processing, mixing, coagulation, drying and cutting may be key steps that directly affect the texture, performance and safety of the product. Subsequently, the screened steps are positively serialized and integrated, that is, the screened steps are arranged in a logical order to form a coherent production process that includes not only key steps, but also strongly related steps that are closely linked to the key steps. For example, in the production of collagen dressings, the preparation and mixing of raw materials may be key steps. The specific steps are to design a suitable formula, calculate the formula ratio of each raw material, and use a blender or mixer to mix them. Coagulation, drying and cutting are auxiliary steps that are closely linked to them. By integrating traversal and positive serialization, we ensure that each stage of the product life cycle is reviewed in detail, and that key steps and strongly related steps are effectively identified and integrated, which helps to establish a comprehensive quality control system.

[0031] Furthermore, the present application further comprises the following steps:

[0032] Identify the quality indicators of each sub-step in the sub-step sequence; divide the key steps and non-key steps based on the relevance of the stage assembly; traverse the non-key steps, perform step fusion and quality indicator fusion based on the step correlation, and determine the fusion step; integrate the key steps and the fusion steps, and perform step quality indicator mapping to integrate and determine the first stage link.

[0033] Specifically, based on the specific impact of each step on product quality, specific quality indicators are determined for each step in the sub-step sequence. Quality indicators for raw material preparation include purity, molecular weight, etc.; quality indicators for the mixing step include mixing uniformity, temperature, and time, etc.; quality indicators for the coagulation step include temperature, rate, etc.; quality indicators for the drying step include rate, temperature, moisture content, etc.; quality indicators for the cutting step include accuracy, flatness, etc.; quality indicators for the fermentation step include pH value, temperature, fermentation time, and fermentation efficiency, etc. For example, for the sub-step sequence of the recombinant collagen raw material stage, the following quality indicators are identified: raw material purity ≥98% in the procurement stage, with no contamination record; protein content ≥98.5% in the inspection stage, with a molecular weight in the range of 30-40kDa, and biological activity meeting the standards; microbial load ≤100CFU / g in the processing stage, with the appearance of the processed raw materials uniform; the temperature in the storage stage is maintained at 2-8°C, and the raw material stability during the storage period is ≥95%.

[0034] Based on the degree of influence of each step on product quality, they are divided into critical steps and non-critical steps. Critical steps are those steps that have a significant impact on product quality, or whose failure may cause the product to fail to meet performance requirements, while non-critical steps are steps that have less impact on product quality. Stage-to-stage relevance refers to the degree to which a step affects the quality of the final product throughout the entire stage. For example, in the production of collagen dressings, the preparation and mixing of raw materials may be critical steps because they directly affect the efficacy and safety of the product. Conversely, the cutting of the dressing may be a non-critical step because it mainly affects the appearance and packaging of the product rather than its efficacy and safety.

[0035] Traverse non-critical steps, analyze the relationships and dependencies between non-related steps, and identify steps that can be merged, that is, those with similar operations or that can be performed simultaneously. Merge steps with similar operations or that can be performed simultaneously to form a new fused step. If certain steps are similar or overlapping in function and impact, and using the same standards for analysis is redundant and inefficient, and has a minimal impact on the final result, then they will be merged into a single step. At the same time, the quality indicators of these steps will also be merged to form a comprehensive quality indicator. Non-critical steps, such as equipment cleaning, can be merged with the equipment maintenance step to form a new "Equipment Maintenance and Cleaning" step, whose quality indicators may include equipment cleanliness and maintenance frequency. Steps with minimal impact on product quality and weak correlation with other steps can be considered for elimination. For example, if an inspection step has a minimal impact on product quality and can be indirectly controlled by other steps, then it can be eliminated. By merging and eliminating steps, the fused step is determined.

[0036] Integrate the key steps and the integrated non-key steps to ensure that the logical order and dependencies of each step are properly handled. For each step, identify and define its corresponding quality indicators to ensure that the quality indicators can fully reflect the performance of the step and the product quality requirements. Arrange all steps in a logical order to form a complete production chain, and ensure that each step in the chain has clear quality indicators and control measures. For example, in the production of collagen dressings, key steps such as raw material preparation and mixing, as well as integration steps such as inspection and packaging, need to be integrated into a complete production chain. For the raw material preparation step, the quality indicators include the purity and molecular weight of the collagen; for the mixing step, the quality indicators include mixing uniformity and mixing time; for the inspection and packaging integration steps, the quality indicators include product appearance quality and packaging integrity. By dividing the key steps from the non-key steps, resources and energy are concentrated on the most important links, non-key steps are integrated, analysis efficiency is improved, and redundant work is reduced.

[0037] Furthermore, step three of this application includes:

[0038] Control the chromatographic column and detector, perform chromatographic detection on the mobile phase product, and determine the chromatogram; combine with the static test branch to identify the chromatographic characteristics and perform qualitative and quantitative analysis to determine the component test results, wherein the chromatographic characteristics at least include the peak area and relative concentration of different components.

[0039] Specifically, an appropriate chromatographic column, such as a reversed-phase column, ion-exchange column, or affinity column, is selected based on the properties of the compound to be analyzed. The chromatographic column is a core component in an HPLC system, used to separate compounds in a mixture. In HPLC analysis, a mobile phase (usually a liquid) passes through the chromatographic column, where the stationary phase interacts with the different components in the mobile phase, resulting in the separation of the components. Detector parameters, such as a UV detector or a fluorescence detector, are set to detect light absorption or emission at specific wavelengths and generate a chromatogram. The mobile phase is the solvent used to carry the sample through the chromatographic column. The composition and pH of the mobile phase are typically optimized based on the properties of the analyte and the choice of chromatographic column. The product sample is injected into the HPLC system, and the mobile phase carries the sample into the chromatographic column for separation. Parameters such as flow rate and temperature are controlled to ensure the separation effect. The separated compounds are detected by the detector, and a chromatogram is generated.

[0040] Combined with the static test branch, the chromatographic characteristics are identified, that is, the peaks formed by different components on the chromatogram. Each peak represents a specific component, and the area and height of the peak can reflect the concentration of the component. Qualitative analysis refers to comparing the chromatographic peaks of the product sample with the chromatographic peaks of the standard sample to identify the components in the sample. Quantitative analysis is to determine the concentration of each component by measuring the peak area or peak height. Quantitative analysis can be performed using the external standard method or the internal standard method. The external standard method uses a standard sample of known concentration to establish a calibration curve, and the internal standard method uses an internal standard to correct the analysis results. Based on the results of qualitative and quantitative analysis, the content of each component in the product is determined, and a test report on the component content is issued. Chromatographic characteristics refer to the peaks formed by different components on the chromatogram, including peak area and relative concentration. The peak area is the area of the peak on the chromatogram, which is used to quantitatively analyze the component content, and the relative concentration is the concentration ratio of the component in the mixture. By controlling the chromatographic column and detector, performing chromatographic detection on the mobile phase product, determining the chromatogram, and combining it with the static test branch to identify the chromatographic characteristics and perform qualitative and quantitative analysis, the content of each component in the product can be accurately determined, which helps to ensure that the quality and performance of the product meet the predetermined standards and improve the controllability and traceability of product quality.

[0041] Further, as attached Figure 2 As shown, this application also includes the following steps:

[0042] In the microscopic dimension, by detecting changes in molecular structure, the product rheological properties are analyzed to determine the first detection feature; after identifying the first detection feature, the biostability and biocompatibility are evaluated to determine the second detection feature; based on the first and second detection features, the texture test results are determined.

[0043] Specifically, microscopic analysis techniques, such as transmission electron microscopy (TEM) or atomic force microscopy (AFM), are used to examine changes in the molecular structure of collagen products and evaluate their rheological properties, such as viscosity and elastic modulus. For example, Fourier transform infrared spectroscopy (FTIR) is used to analyze the secondary structure of collagen, and circular dichroism (CD) spectroscopy is used to determine the tertiary structure. Product rheological properties refer to the deformation and flow characteristics of a product under external forces. A rotational rheometer is used to measure the viscosity and shear stress of the sample, and the flow behavior of the sample is recorded at different shear rates. Rheological parameters such as viscosity, elastic modulus, viscoelasticity, and flow behavior are measured using a rheometer. Testing is performed under different temperatures, shear rates, and stresses to simulate actual use environments, and the rheological characteristics under different conditions are recorded. The primary test characteristic is determined, including key rheological indicators such as yield stress and viscoelastic modulus. After identifying the primary test characteristic, biostability (such as degradation rate) and biocompatibility (such as cellular interaction) tests are conducted to determine the secondary test characteristic.

[0044] Biostability assessment evaluates the stability and durability of a product in a biological environment. Accelerated aging tests and thermal stability tests are used to assess product stability under simulated storage and use conditions, identifying key factors influencing biostability, such as temperature, humidity, and light. Biocompatibility assessment evaluates the compatibility and safety of a product's interactions with biological tissues or organisms. Cytotoxicity, hemolysis, and irritation tests are performed to assess compatibility with biological tissues. Secondary test characteristics are determined, including indicators such as degradation rate, cell proliferation rate, and cell morphological changes. A comprehensive analysis of the primary test characteristic (rheological properties) and the secondary test characteristic (biostability and biocompatibility) yields the product's texture test results. Texture test results reflect the product's molecular structure at the microscopic level and its biological performance, including strength and elasticity, structural stability, and change characteristics (texture change and rate of texture change). Based on these test results, the product is evaluated to determine whether it meets the predetermined quality standards and performance requirements. If not, the production process or formulation is adjusted to achieve the desired texture properties. The first test characteristic is determined by analyzing structural changes and rheological properties, and then stability and compatibility are evaluated to determine the second test characteristic. Texture test results based on the first and second test characteristics provide a comprehensive understanding of the product's physical and functional properties, helping to improve product quality and performance, reduce quality risks, and increase production efficiency.

[0045] Furthermore, the present application further comprises the following steps:

[0046] Identify the ingredient test results and the texture test results, combine them with a biological database, and determine an indicator threshold for product denaturation; combine the indicator threshold to explore product denaturation conditions; based on the indicator threshold and the product denaturation conditions, determine the denaturation test results, and based on the product denaturation conditions, perform quality management of the product life cycle.

[0047] Specifically, results from composition testing (such as collagen content from HPLC analysis) and texture testing (such as rheological properties and biostability assessments) are combined and compared with known biological databases to identify product composition and texture characteristics. Based on standards in the biological database, critical values for product denaturation are set, such as the degradation rate of recombinant collagen and changes in secondary structure content. The denaturation index threshold represents the acceptable limit for product denaturation (such as changes in protein structure). Conditions leading to product denaturation are analyzed, including storage environment management (such as temperature and humidity control) and strong control constraints during the raw materials and production stages (such as raw material quality control and key parameters in the production process). Based on the index threshold and product denaturation conditions, denaturation testing is conducted to assess the degree of product denaturation under specific conditions. Denaturation test data, such as denaturation rate and degree of denaturation, are recorded. A determination is made as to whether the denaturation threshold has been reached, and possible causes are analyzed to generate the denaturation test results. Based on product denaturation conditions, such as thresholds for certain environmental characteristics, we can avoid anomalies during production and storage, such as by imposing constraints on raw material handling, production conditions, and storage conditions. We manage quality at every stage of the product lifecycle, including raw material procurement, production processes, storage, and transportation. We implement targeted quality management measures to ensure product quality throughout the entire lifecycle. The product lifecycle encompasses the entire process from raw material procurement to product use. We establish a continuous monitoring mechanism, regularly evaluate storage and production conditions, and promptly adjust management strategies to reduce denaturation risks and ensure product safety and effectiveness.

[0048] In a specific example, ingredient testing results indicate that the recombinant collagen content in the product is 95%, meeting the standard. Texture testing results indicate an elastic modulus of 200 Pa and a viscosity of 40 mPa·s. The thresholds for determining the degradation rate of the recombinant collagen are set at 5% and a threshold for the change in elastic modulus of ±50 Pa. Accelerated aging testing revealed that after four weeks of storage at 60°C, the degradation rate of the recombinant collagen reached 8%, exceeding the established thresholds. Denaturation testing revealed an 8% degradation rate and a decrease in elastic modulus to 150 Pa. Based on the product's denaturation conditions, storage conditions were adjusted, lowering the storage temperature from 60°C to 40°C to minimize denaturation, and strengthening raw material inspection to ensure consistent raw material quality. By determining denaturation test results based on the thresholds and product denaturation conditions, and conducting quality management throughout the product lifecycle based on these results, a comprehensive understanding of product stability and safety is achieved, helping to improve product quality and performance.

[0049] Furthermore, step 4 of this application includes:

[0050] Determine the overall quality index of the collagen product; traverse the overall quality index, and based on the index homology, perform matrix item mapping aggregation and mutual verification calculation on the first test matrix and the second test matrix to determine the overall index data; integrate the overall index data to generate the product quality test sheet.

[0051] Specifically, a series of overall quality indicators are determined for collagen products, covering all key aspects of the product, including ingredient content, molecular structure, rheological properties, biostability, biocompatibility, etc. Overall quality indicators refer to a comprehensive indicator system for measuring product quality, including ingredient content, purity, bioactivity, stability and other aspects. Each overall quality indicator is reviewed one by one to ensure that all indicators are taken into account. The relevant items in the first test matrix and the second test matrix are mapped, and indicators with high homology are aggregated. The same or similar indicators in the first test matrix and the second test matrix are matched. For example, if both matrices have the indicator "protein content", and the "protein content" indicator in the first test matrix is 92%, and the same indicator in the second test matrix is also 92%, they are mapped together. The mapped indicators are cross-validated to ensure the accuracy and consistency of the test results. For example, the test results of protein content in two matrices were compared to see if there was a significant difference. The "molecular weight" index in the first test matrix was 40-50 kDa, and the same index in the second test matrix was 42-48 kDa. After verification, it was confirmed that the data was within the acceptable range.

[0052] Indicator homology refers to whether the indicators in different test matrices have the same or similar properties and functions; mapping aggregation refers to the mapping and combination of indicators in the first test matrix and the second test matrix to form a new assembly indicator data; mutual verification calculation refers to the verification and calculation of the assembly indicator data after mapping and aggregation to ensure the accuracy and reliability of the data. Through mapping aggregation and mutual verification calculation, the specific data of each assembly quality indicator is obtained to form the final assembly indicator data set. All assembly indicator data is integrated into a single document to generate a product quality test sheet, which contains the data and evaluation results of all key indicators and serves as the official record of product quality. Through assembly quality indicators, the comprehensive quality attributes of the product are evaluated, ensuring the comprehensiveness and accuracy of product quality testing and reducing product quality risks.

[0053] Furthermore, the present application further comprises the following steps:

[0054] A preset difference degree is set, and the matrix items of the homologous mapping aggregation are verified to determine the verification data; if the verification is qualified and the mapped first test matrix item and the second test matrix item are of the same dimension, mean processing is performed and the result is used as the aggregate indicator data; if the mapped first test matrix item and the second test matrix item are of different dimensions, dimension fusion processing is performed and the result is used as the aggregate indicator data.

[0055] Specifically, based on the product quality standards and the accuracy of the test method, a reasonable range of differences is set in advance to determine the acceptable range of differences between the matrix items of the two homology mapping aggregations. The preset difference degree is a pre-set numerical value used to evaluate whether the difference between the matrix items of the homology mapping aggregation is within an acceptable range. The matrix items of the homology mapping aggregation refer to the indicator items with the same or similar properties and functions in the first test matrix and the second test matrix, including component content, molecular structure, physical properties, biological performance, etc. Compare the homology indicator items mapped in the first test matrix and the second test matrix to check whether the difference between them is within the preset difference degree range.

[0056] If the difference between the two matrix items is within the preset difference range, the verification is considered qualified, and if they belong to the same dimension, mean processing is performed. The same dimension means that the mapped first test matrix item and the second test matrix item are consistent in nature, function and measurement unit. Mean processing is to take the average of the data of the two matrix items as part of the assembly index data. By calculating the average value of the first test matrix item and the second test matrix item, a more stable and reliable assembly index data is obtained. For example, assuming that the concentration of the mapped first test matrix item is 80 mg / mL and the concentration of the second test matrix item is 82 mg / mL, the difference is 2.5%, which is less than the set threshold, and mean processing is performed: assembly index data = (80+82) / 2=81 mg / mL.

[0057] If the mapped first test matrix item and the second test matrix item are of different dimensions, dimension fusion processing is required. Different dimensions mean that the mapped first test matrix item and the second test matrix item are inconsistent in nature, function and measurement unit. For example, one is a temperature indicator and the other is a viscosity indicator, which belong to different dimensions. Dimension fusion processing is to convert data of different dimensions into the same dimension, or find a way to merge them into a comprehensive indicator, such as weighted average, standardization or other data fusion technologies. Whether it is processed by mean or dimension fusion, the final aggregate index data will comprehensively reflect the quality characteristics of collagen products. The accuracy and reliability of the data are ensured by pre-setting the difference and verification process. The mean processing and dimension fusion processing ensure the consistency of different test results, making the aggregate index data more scientific and reasonable.

[0058] In summary, the product quality testing method provided by this application has the following technical effects:

[0059] By integrating the product lifecycle of interactive collagen products, which includes key nodes in the raw materials, production, and storage stages, and combining process testing with result testing, a dual-driven test module is constructed, which includes a dynamic test branch and a static test branch. The product data stream is monitored and transmitted back, triggering the dynamic test branch to conduct phased data flow off-axis analysis and evaluation to determine the first test matrix. The static test branch is triggered to perform component content analysis and molecular texture analysis based on high-performance liquid chromatography, predict the denaturation threshold, and determine the second test matrix. A homology mapping verification is performed on the first and second test matrices to generate a product quality test sheet. In other words, by combining process testing with result testing, combining dynamic and static testing, and constructing a dual-driven test module, the results of the dynamic and static test branches are subjected to homology mapping verification to generate a product quality test sheet, achieving comprehensive and precise control of product quality and improving the accuracy of product quality test results.

[0060] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein. Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A product quality testing method, characterized in that: include: The product life cycle of interactive collagen products, including key nodes in the raw materials-production-storage stages; Combine process testing and result testing, and build a dual-drive test module with dynamic and static testing. The dual-drive test module includes a dynamic test branch and a static test branch. Monitor and return product data flows, trigger the dynamic test branch, perform phased data flow off-axis analysis and evaluation, and determine a first test matrix, where the first test matrix is a specific test case, including test conditions, test methods, expected results, and acceptance criteria; triggering the static test branch, performing component content analysis based on high performance liquid chromatography detection and molecular texture analysis, and predicting a denaturation threshold, and determining a second test matrix, wherein the second test matrix includes component content, texture characteristics, and predicted denaturation threshold; Performing homology mapping verification on the first test matrix and the second test matrix to generate a product quality test sheet; Generating a product quality test sheet includes: Determining the overall quality index of the collagen product; Traversing the assembly quality indicators, and based on indicator homology, performing matrix item mapping aggregation and mutual verification calculation on the first test matrix and the second test matrix to determine assembly indicator data; Integrate the assembly index data to generate the product quality test sheet; Performing mapping aggregation and mutual verification calculation of matrix items on the first test matrix and the second test matrix, including: Set a preset difference, verify the matrix items of homology mapping aggregation, and determine the verification data; If the verification is qualified and the mapped first test matrix item and the second test matrix item are of the same dimension, average processing is performed and the average value is used as the assembly index data; If the mapped first test matrix item and the second test matrix item are of different dimensions, dimension fusion processing is performed to serve as the aggregate indicator data.

2. A product quality testing method according to claim 1, characterized in that: The product life cycle of the interactive collagen product includes: Identify the product life cycle and determine the sequence of sub-stage steps in the first stage, where the first stage is any stage in the raw materials-production-storage stage; The sub-stage step sequence is traversed, and based on the fusion of key steps and strongly related steps, step screening and positive serialization integration are performed to determine the first-stage link.

3. A product quality testing method according to claim 2, characterized in that: The determining of the first-stage link includes: Identifying a quality indicator for each sub-step in the sequence of sub-steps; Based on the relevance of the stage assembly, key steps and non-key steps are divided; Traversing the non-critical steps, performing step fusion and quality indicator fusion based on step relevance, and determining the fusion step; Integrate the key steps and the fusion steps, perform step quality indicator mapping, and integrate to determine the first stage link.

4. A product quality testing method according to claim 1, characterized in that: The component content analysis based on high performance liquid chromatography detection includes: Control the chromatographic column and detector, perform chromatographic detection on the mobile phase product, and determine the chromatogram; Combined with the static test branch, the chromatographic characteristics are identified and qualitative and quantitative analysis is performed to determine the component test results, wherein the chromatographic characteristics at least include the peak areas and relative concentrations of different components.

5. A product quality testing method according to claim 4, characterized in that: Perform molecular texture analysis, including: Focusing on the microscopic dimension, by detecting changes in molecular structure, the product rheological properties are analyzed to determine the first detection feature; identifying the first detection feature, performing a biostability and biocompatibility assessment, and determining a second detection feature; A texture test result is determined based on the first detection feature and the second detection feature.

6. A product quality testing method according to claim 5, characterized in that: The predicted variability threshold comprises: Identify the ingredient test results and the texture test results, and determine the index threshold of product denaturation by combining them with a biological database; Combining the indicator thresholds, mining product denaturation conditions; Based on the indicator threshold and the product denaturation condition, a denaturation test result is determined, and based on the product denaturation condition, quality management of the product life chain is performed.

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