Method for monitoring collagen extraction activity based on fine enzymatic extraction technology

Abstract

The present application relates to the field of biotechnology, and more particularly to a collagen extraction activity monitoring method based on fine enzymatic extraction technology, comprising: preparing a bimodal probe by labeling and modifying collagen with quantum dots and Raman tags; constructing a microfluidic monitoring system based on an integrated reaction chamber, a temperature control module, a pH monitoring module, and a signal acquisition module; mixing the sample to be detected with the bimodal probe, adding a structure protective agent, and then injecting the reaction chamber of the microfluidic detection system; synchronously collecting fluorescence signals and Raman signals through the signal acquisition module, determining whether to recalibrate the signal acquisition module based on the signal-to-noise ratio, and returning to step S2; and performing fusion analysis on the fluorescence signals and Raman signals to obtain collagenase activity, triple helix structure integrity, and enzymatic kinetics parameters. The present application solves the problem of being unable to accurately obtain collagenase activity and collagen triple helix structure integrity data in the fine enzymatic extraction scenario.

Description

Collagen Extraction Activity Monitoring Method Based on Refined Enzymatic Extraction Technology Technical Field

[0001] This invention relates to the field of biotechnology, and in particular to a method for monitoring the activity of collagen extraction based on refined enzymatic extraction technology. Background Technology

[0002] Collagen, as a biocompatible and bioactive natural polymer, has irreplaceable application value in the fields of medicine, cosmetics, and food. Refined enzymatic extraction is one of the core technologies for the industrial production of collagen. The activity level of collagenase and the integrity of the collagen triple helix structure directly determine the function and quality of the product. Therefore, establishing precise and efficient methods for monitoring collagen extraction activity is of great significance for optimizing the extraction process and controlling product quality.

[0003] However, existing collagen extraction activity monitoring technologies have the following significant limitations:

[0004] The signal detection has weak anti-interference ability: traditional detection methods mostly rely on a single signal mode (such as fluorescence labeling or Raman spectroscopy). Among them, fluorescence labeling is easily affected by pH fluctuations and changes in ionic strength in the extraction system (a deviation of ±0.3 pH can cause more than 5% signal drift), while Raman spectroscopy, when used alone, generally has a signal-to-noise ratio of less than 10:1, making it difficult to achieve accurate quantification of trace active substances in complex matrices.

[0005] The contradiction between sample consumption and throughput is prominent: conventional detection methods require 50μL to 100μL of sample, and miniaturized detection systems (such as simple microfluidic chips) have a throughput of only 2μL / min to 5μL / min due to flow channel design defects, which cannot meet the high-throughput screening requirements in industrial production.

[0006] Insufficient long-term monitoring capability: The triple helix structure of collagen is prone to unwinding in the enzymatic hydrolysis and extraction environment (the unwinding rate can reach 45% in 2 hours at 37°C). Existing protective agents (such as glycerol and sucrose) can only extend the structural stability time to 3 hours, which makes it impossible to capture the dynamic changes in the entire enzymatic hydrolysis process and makes it difficult to support process optimization.

[0007] Poor technological synergy: Existing technologies are mostly isolated applications of single technologies (such as quantum dot labeling or microfluidic chips used alone), failing to achieve deep integration of probe design, microenvironment regulation and signal analysis, resulting in detection sensitivity (usually >0.1U / mL) and stability that cannot meet the needs of fine production. Summary of the Invention

[0008] To address this, the present invention provides a method for monitoring collagen extraction activity based on refined enzymatic extraction technology, thereby overcoming the problem in the prior art that it is difficult to accurately obtain data on collagenase activity and the integrity of the collagen triple helix structure in refined enzymatic extraction scenarios.

[0009] To achieve the above objectives, this invention provides a method for monitoring collagen extraction activity based on refined enzymatic extraction technology, comprising the following steps:

[0010] Step S1: Prepare a dual-modal probe from collagen modified with quantum dot labeling and Raman tagging;

[0011] Step S2: Construct a microfluidic monitoring system based on the integrated reaction chamber, temperature control module, pH monitoring module, and signal acquisition module;

[0012] Step S3: Mix the sample to be tested with the dual-modal probe, add a structure protectant, and then inject the mixture into the reaction chamber of the microfluidic detection system.

[0013] Step S4: Simultaneously acquire fluorescence signal and Raman signal through the signal acquisition module, and based on the signal-to-noise ratio, determine to return to step S2 to recalibrate the signal acquisition module;

[0014] Step S5: The fluorescence signal and Raman signal are fused and analyzed to obtain collagenase activity, triple helix structure integrity and enzymatic hydrolysis kinetic parameters.

[0015] Further, in step S1, the step of preparing the dual-modal probe includes:

[0016] Step S11: Take a CdSeTe quantum dot solution of a preset concentration, add EDC and NHS to the CdSeTe quantum dot solution, and react in MES buffer at a preset pH value for 25 to 35 minutes to obtain quantum dots;

[0017] Step S12: Add the quantum dots and collagen in a molar ratio of 1:45 to 1:55 to the collagen solution, stir, and then purify by dialysis and centrifugation to obtain quantum dot-labeled collagen.

[0018] Step S13: Add 4-mercaptobenzoic acid (MBT) to the quantum dot-labeled collagen solution and purify by ultrafiltration to obtain the dual-modal probe.

[0019] Furthermore, the quantum dot has a diameter of 4mm to 6mm and an emission wavelength of 770mm to 790mm.

[0020] Furthermore, the 4-mercaptobenzoic acid (MBT) was obtained through a 1064 cm⁻¹ -1 Characteristic peaks represent the integrity of the triple helix structure.

[0021] Further, in step S11, the concentration of the CdSeTe quantum dot solution is 8 mg / mL to 12 mg / mL, the concentration of EDC is 4 mM to 6 mM, and the concentration of NHS is 8 mM to 12 mM.

[0022] The dialysis is performed using a dialysis bag with a molecular weight cutoff of 10 kDa, and the dialysis time is 47 to 49 hours; the centrifugation conditions are 9500×g to 10500×g, and centrifugation time is 8 to 12 minutes.

[0023] Furthermore, in step S2, the step of constructing the microfluidic monitoring system includes:

[0024] Step S21: Integrate the PDMS / glass bonded chip into a helical rotary reaction chamber. The surface of the reaction chamber is coated with a gold nanoparticle coating, and the flow channel is rinsed with PBS buffer at a preset pH.

[0025] Step S22: Adjust the temperature inside the reaction chamber based on the pH of the reaction system inside the reaction chamber.

[0026] Furthermore, the microfluidic monitoring system also includes a branch flow path, which determines and adjusts the flow path width based on the flow path pressure loss, and determines and optimizes the flow path corner curvature based on the mass transfer efficiency of the branch flow path.

[0027] Further, in step S3, the sample to be tested is a collagenase solution, the concentration of the dual-modal probe is 0.08-0.12 mg / mL, the concentration of collagenase is 0.8 U / mL-1.2 U / mL, the mixing volume ratio of the collagenase solution to the dual-modal probe is 1:1, and ultrasonic degassing is determined based on the bubble rate after mixing.

[0028] Further, in step S4, based on the fluorescence intensity drift of the fluorescence signal, it is determined to enable real-time Raman signal correction.

[0029] Further, in step S5, the fusion analysis employs a CNN algorithm, correcting fluorescence signal drift based on Raman signal to obtain joint detection accuracy. Based on this joint detection accuracy, it is determined to increase the training sample size and retrain the algorithm. And because the enzymatic hydrolysis kinetic parameters are greater than the standard enzymatic hydrolysis kinetic parameters, it is determined to return to step S3 to adjust the reaction temperature.

[0030] The CNN algorithm consists of 3 convolutional layers (kernel size 3×3, stride 1), 2 pooling layers (max pooling, size 2×2) and 1 fully connected layer. The activation function is ReLU and the loss function is cross-entropy.

[0031] The enzymatic hydrolysis kinetic parameters include the Km value and the Vmax value.

[0032] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0033] By constructing a monitoring system that combines a dual-modal probe with a microfluidic monitoring system, and simultaneously collecting and fusing fluorescence and Raman signals, dual monitoring of collagenase activity and collagen triple helix structure integrity was achieved. This method breaks through the predicament of difficulty in accurately obtaining key data in the context of refined enzymatic extraction, significantly improves the comprehensiveness and accuracy of activity monitoring during collagen extraction, and provides reliable data support for subsequent optimization of the extraction process.

[0034] Furthermore, by preparing a bimodal probe from collagen modified with quantum dot labeling and Raman tagging, the quantum dots can provide a stable fluorescence signal as a basic indicator for monitoring collagenase activity, while the Raman tag (4-mercaptobenzoic acid) can detect specific characteristic peaks (1064 cm⁻¹). -1 It accurately characterizes the integrity of the triple helix structure of collagen; it effectively integrates the advantages of high sensitivity of fluorescence signals and strong specificity of Raman signals, avoiding the problem of easy interference in complex reaction systems by single labeling methods, and greatly improving the accuracy and reliability of probe monitoring of key collagen properties.

[0035] Furthermore, by integrating the reaction chamber, temperature control module, pH monitoring module, and signal acquisition module to construct a microfluidic monitoring system, each functional module is highly integrated onto a microchip, reducing the volume of the reaction system and reagent consumption, while achieving precise control and real-time monitoring of the reaction environment (temperature, pH); thereby shortening the time for the reaction to reach a steady state, reducing the impact of environmental fluctuations on the monitoring results, and significantly improving the timeliness and stability of collagen enzymatic hydrolysis reaction monitoring.

[0036] Furthermore, by mixing the sample to be tested with the dual-modal probe at a specific volume ratio, adding a structure protectant, and then injecting the mixture into the reaction chamber of the microfluidic detection system, ultrasonic degassing is performed based on the bubble rate after mixing. The structure protectant reduces the non-specific damage to the triple helix structure of collagen during mixing and reaction, while ultrasonic degassing avoids interference from bubbles on signal acquisition. This ensures the homogeneity of the reaction system and the initial stability of the collagen structure, improving the accuracy of subsequent signal acquisition and the repeatability of the reaction results.

[0037] The system simultaneously acquires fluorescence and Raman signals via a signal acquisition module, and determines whether to recalibrate the module based on the signal-to-noise ratio (SNR). Simultaneously, it enables real-time Raman signal correction based on fluorescence signal intensity drift. SNR calibration eliminates signal distortion caused by equipment errors or external interference, while real-time Raman signal correction compensates for the susceptibility of fluorescence signals to environmental factors (such as temperature and pH). This significantly improves the stability and accuracy of signal acquisition, providing a high-quality data foundation for subsequent signal fusion analysis.

[0038] Furthermore, a CNN algorithm was employed to fuse and analyze fluorescence and Raman signals. Raman signal correction was used to correct fluorescence signal drift. Simultaneously, the training sample size was adjusted based on the joint detection accuracy, and the reaction temperature was adjusted according to enzymatic hydrolysis kinetic parameters. The multi-layer convolution and pooling structure of the CNN algorithm effectively extracts key features from the signal, while the cross-entropy loss function ensures the effectiveness of model training. Raman signal correction further enhances the reliability of the fluorescence signal. Dynamically adjusting the sample size and reaction temperature based on the detection results forms a closed loop of "monitoring-feedback-optimization." This process not only improves the accuracy of collagenase activity, triple helix structure integrity, and enzymatic hydrolysis kinetic parameter detection but also optimizes reaction conditions in real time, ensuring efficient and stable enzymatic hydrolysis. Attached Figure Description

[0039] Figure 1 is a flowchart of the collagen extraction activity monitoring method based on refined enzymatic extraction technology according to an embodiment of the present invention;

[0040] Figure 2 is a flowchart of the steps for preparing a dual-modal probe according to an embodiment of the present invention;

[0041] Figure 3 is a flowchart of the steps for constructing a microfluidic monitoring system according to an embodiment of the present invention. Detailed Implementation

[0042] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0043] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0044] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0045] Please refer to Figure 1, which is a flowchart of the collagen extraction activity monitoring method based on refined enzymatic extraction technology according to an embodiment of the present invention.

[0046] This invention provides a method for monitoring collagen extraction activity based on refined enzymatic extraction technology, comprising the following steps:

[0047] Step S1: Prepare a dual-modal probe from collagen modified with quantum dot labeling and Raman tagging;

[0048] Step S2: Construct a microfluidic monitoring system based on the integrated reaction chamber, temperature control module, pH monitoring module, and signal acquisition module;

[0049] Step S3: Mix the sample to be tested with the dual-modal probe, add a structure protectant, and then inject the mixture into the reaction chamber of the microfluidic detection system.

[0050] Step S4: Simultaneously acquire fluorescence and Raman signals through the signal acquisition module. Based on the signal-to-noise ratio, determine to return to step S2 to recalibrate the signal acquisition module.

[0051] Step S5 involves fusing and analyzing the fluorescence and Raman signals to obtain collagenase activity, triple helix structure integrity, and enzymatic hydrolysis kinetic parameters.

[0052] Please refer to Figure 2, which is a flowchart of the steps for preparing a dual-modal probe according to an embodiment of the present invention.

[0053] Specifically, in step S1, the steps for preparing the dual-modal probe include:

[0054] Step S11: Take a CdSeTe quantum dot solution of a preset concentration, add EDC and NHS to the CdSeTe quantum dot solution, and react in MES buffer at a preset pH value for 25 to 35 minutes to obtain quantum dots;

[0055] Step S12: Add quantum dots and collagen in a molar ratio of 1:45 to 1:55 to the collagen solution, stir, and then purify by dialysis and centrifugation to obtain quantum dot-labeled collagen.

[0056] Step S13: 4-Mercaptobenzoic acid (MBT) is added to the quantum dot-labeled collagen solution and purified by ultrafiltration to obtain a dual-modal probe.

[0057] In this embodiment of the invention, in step S13, the amount of 4-mercaptobenzoic acid (MBT) added is 1:10 to 1:15 of the molar amount of collagen, dialysis is performed using PBS buffer at pH 7.4, and ultrafiltration is performed by centrifugation at 4000×g for 15 minutes.

[0058] Specifically, the quantum dot has a diameter of 4mm to 6mm and an emission wavelength of 770mm to 790mm.

[0059] Specifically, the 4-mercaptobenzoic acid (MBT) was processed at 1064 cm⁻¹. -1 Characteristic peaks represent the integrity of the triple helix structure.

[0060] Specifically, in step S11, the concentration of the CdSeTe quantum dot solution is 8 mg / mL to 12 mg / mL, the concentration of EDC is 4 mM to 6 mM, and the concentration of NHS is 8 mM to 12 mM.

[0061] The dialysis was performed using a dialysis bag with a molecular weight cutoff of 10 kDa, with a dialysis time of 47 to 49 hours; the centrifugation conditions were 9500×g to 10500×g, with a centrifugation time of 8 to 12 minutes.

[0062] In this embodiment of the invention, 1 mL of dSeTe quantum dot solution (10 mg / mL) was taken, 5 mMMEDC and 10 mMMNHS were added, and the mixture was reacted at room temperature for 30 minutes in MES buffer at pH 6.0. The activation efficiency was verified to be >80% by UV absorption method.

[0063] Dialysis was performed using a dialysis bag for 48 hours (conductivity <20μS / cm), followed by centrifugation at 10000×g for 10 minutes. The supernatant (turbidity <0.1NTU) was collected, and the biological activity retention rate was 92%.

[0064] Add 2 mL of MBT solution and react at 37 °C for 2 hours; purify using a 10 kDa ultrafiltration tube, concentrate to 0.1 mg / mL, and store at 4 °C; verify the 1064 cm⁻¹ value by measuring Raman spectra under 785 nm laser excitation, 10 mW power, and 1 s integration time. -1 Characteristic peak intensity > 0.1au;

[0065] The pH range of the MES buffer is 5.5–6.5 (preferably pH 6.0). This range ensures that the amino activation efficiency of EDC / NHS is ≥80% (verified by UV absorption method, absorbance change rate at 380nm ≤20%). The concentration range of the CdSeTe quantum dot solution is 8 mg / mL–12 mg / mL. A concentration below 8 mg / mL will result in collagen labeling efficiency <70%, and a concentration above 12 mg / mL will cause quantum dot aggregation (turbidity >0.1 NTU).

[0066] Please refer to Figure 3, which is a flowchart of the steps for constructing a microfluidic monitoring system according to an embodiment of the present invention.

[0067] Specifically, in step S2, the steps for constructing the microfluidic monitoring system include:

[0068] Step S21: Integrate the PDMS / glass bonded chip into a spiral rotary reaction chamber, modify the surface of the reaction chamber with a gold nanoparticle coating, and rinse the flow channel with PBS buffer at a preset pH.

[0069] Step S22: Adjust the temperature inside the reaction chamber based on the pH of the reaction system inside the reaction chamber.

[0070] In this embodiment of the invention, the temperature adjustment algorithm is based on the following empirical formula:

[0071] Tadj = 37 + (pH - 7.0) × K

[0072] Where K is the adjustment coefficient, K = -2.0 when pH < 7.0, and K = 1.5 when pH > 7.0;

[0073] The thickness of the gold nanocoating is 20nm to 50nm. When the flow path pressure loss is >5kPa, the flow path width is adjusted to 1.2 to 1.5 times the original width, and the corner curvature is optimized within the range of 30° to 60°.

[0074] Understandably, the value of the adjustment coefficient K is determined based on:

[0075] K = -2.0 (acidic conditions): inhibits the decline of collagenase activity (Q10 = 2.3);

[0076] K = 1.5 (alkaline conditions): accelerates the enzymatic hydrolysis rate (ΔEa = 15 kJ / mol).

[0077] Specifically, the microfluidic monitoring system also includes branch flow paths, which determine the adjustment of flow path width based on flow path pressure loss, and determine the optimization of flow path corner curvature based on the mass transfer efficiency of the branch flow paths.

[0078] In this embodiment of the invention, the PDMS / glass bonded chip was rinsed three times with pH 7.4 PBS, and the leakage rate was checked to be <1%; the pressure loss of the branch flow path was <5 kPa, and the mass transfer efficiency was 20 times higher than that of the direct flow path.

[0079] The initial temperature is set to 37℃, and the pH is monitored in real time by the Pt microelectrode; when pH < 6.5, the temperature is automatically reduced to 35℃, and when pH > 7.5, the temperature is increased to 39℃, with an adjustment response time of < 5 seconds;

[0080] The logic for adjusting the width of the branch flow path is as follows: When the pressure loss of the flow path is 3 kPa to 5 kPa, the flow path width is set to 0.2 mm to 0.25 mm; when the pressure loss is 5 kPa to 10 kPa, the flow path width is adjusted to 0.15 mm to 0.2 mm (pressure loss is measured by a differential pressure sensor, in kPa); the corner radius optimization standard is: with a mass transfer efficiency ≥90% as the target, the corner radius R = 0.5 to 1 mm (mass transfer efficiency is measured by fluorescence tracer method, using Rhodamine B as a tracer, and measuring the concentration ratio of the flow path outlet to the inlet); when R < 0.5 mm, the mass transfer efficiency is < 85%; when R > 1 mm, the flow path area increases by > 30%.

[0081] Specifically, in step S3, the sample to be tested is a collagenase solution, the concentration of the dual-modal probe is 0.08 mg / mL to 0.12 mg / mL, the concentration of collagenase is 0.8 U / mL to 1.2 U / mL, the mixing volume ratio of collagenase solution to dual-modal probe is 1:1, and ultrasonic degassing is determined based on the bubble rate after mixing.

[0082] In this embodiment of the invention, 0.1 mg / mL of dual-modal probe and 1 U / mL of collagenase were mixed at a ratio of 1:1, and 5 mM of proline derivative (initial triple helix retention rate >70%) was added. After mixing, the bubble rate was >5%, and the mixture was degassed by sonication for 1 minute. 5 μL of sample was then injected into the reaction chamber (injection deviation <1 μL).

[0083] The structure protectant is selected from proline derivatives, hydroxyproline derivatives, or glycine derivatives (preferably proline derivatives), with a concentration range of 3mM to 7mM: when the concentration is <3mM, the triple helix structure retention rate is <60% (2 hours at 37℃), and when the concentration is >7mM, it will inhibit collagenase activity (enzyme activity loss >15%). Its mechanism of action is: the protectant molecule forms hydrogen bonds with the amino groups of the collagen triple helix structure through the carboxyl group, and at the same time generates electrostatic interactions with the hydrophobic regions of collagen through the methyl group, which together inhibits the triple helix unwinding.

[0084] Understandably, when the collagenase concentration is below 0.5 U / mL, the signal acquisition time needs to be extended to 10 minutes / time, and when it is above 2 U / mL, the probe concentration needs to be increased to 0.15 mg / mL.

[0085] Specifically, in step S4, based on the fluorescence intensity drift of the fluorescence signal, it is determined to enable real-time Raman signal correction.

[0086] In this embodiment of the invention, the fluorescence signal is: excitation at 488nm, monitoring at 780nm, with a frequency of 1 Hz; Raman correction is enabled when fluorescence drift is >10% / h.

[0087] Raman signal: 785nm excitation, 1064cm -1 Monitoring frequency: once every 5 seconds; when peak intensity fluctuation is >8%, extend the integration time to 1 second.

[0088] If the signal-to-noise ratio is <30:1, return to step S2 to calibrate the signal acquisition module;

[0089] Signal-to-noise ratio (SNR) = 10 × log10(Isignal / Inoise), where Isignal is 1064 cm⁻¹ -1 Peak area integral value, Inoise is 1000cm -1 ~1100cm -1 Baseline fluctuation standard deviation;

[0090] Raman signal correction is initiated when the fluorescence signal intensity changes by more than ±10% of the baseline value.

[0091] Raman signal correction uses baseline normalization, with a 1000 cm⁻¹ base. -1 Signal at location.

[0092] Specifically, in step S5, the fusion analysis employs a CNN algorithm, correcting fluorescence signal drift based on Raman signal to obtain the joint detection accuracy. Based on this accuracy, it is determined to increase the training sample size and retrain the algorithm. Furthermore, since the enzymatic hydrolysis kinetic parameters are greater than the standard enzymatic hydrolysis kinetic parameters, it is determined to return to step S3 to adjust the reaction temperature.

[0093] The CNN algorithm consists of 3 convolutional layers (kernel size 3×3, stride 1), 2 pooling layers (max pooling, size 2×2) and 1 fully connected layer. The activation function is ReLU and the loss function is cross-entropy.

[0094] Enzymatic hydrolysis kinetic parameters include Km value and Vmax value.

[0095] Optionally, the CNN algorithm includes 2 to 4 convolutional layers and 1 to 3 pooling layers.

[0096] In this embodiment of the invention, the CNN algorithm is used to fuse the signals, and the joint detection accuracy after Raman correction is 95%; if the accuracy is <90%, the training sample size is increased and the system is retrained.

[0097] Output result:

[0098] Enzymatic hydrolysis kinetic parameters: Km = 0.8 μM, Vmax = 12 nM / s (if outside the range, return to step S3 to adjust the temperature);

[0099] Performance indicators: Detection limit 0.01 U / mL, linear range 0.01 U / mL~10 U / mL (R 2 =0.998), triple helix retention rate at 37℃ was 82%, and the monitoring window period was 8 hours;

[0100] Training conditions for CNN algorithm:

[0101] Training sample composition: Contains 500 sets of dual-modal signal data, covering collagenase activity from 0.01 U / mL to 10 U / mL, pH from 2 to 8, and temperature from 35℃ to 39℃. Each set of data includes 100 fluorescence signal points (intensity at 780 nm) and 20 Raman signal points (intensity at 1064 cm⁻¹). -1 Peak intensity), the sample label is the measured collagenase activity (calibrated by high performance liquid chromatography);

[0102] Data preprocessing: Fluorescence signals were denoised using wavelet (db4 wavelet basis, decomposition level 3), and Raman signals were corrected using baseline (polynomial fitting, order 5).

[0103] Training termination criteria: Joint detection accuracy ≥ 90% and cross-entropy loss < 0.05; When the training sample size is insufficient, add 100 samples each time to retrain until the termination criteria are met.

[0104] The standard enzymatic hydrolysis kinetic parameter range is: Km = 0.7 μM to 0.9 μM, Vmax = 1113 nM / s to 13 nM / s (the standard values ​​are determined by three parallel experiments, with a relative standard deviation of <5%). When the measured Km < 0.7 μM or Vmax > 13 nM / s, return to step S3 and lower the reaction temperature by 0.5℃ to 1℃. When the measured Km > 0.9 μM or Vmax < 11 nM / s, return to step S3 and raise the reaction temperature by 0.5℃ to 1℃. After temperature adjustment, signal acquisition and analysis are repeated until the parameters fall within the standard range.

[0105] Example: Monitoring of collagen extraction activity based on dual-modal probe and microfluidic system

[0106] I. Experimental Materials and Instruments

[0107] 1. Materials:

[0108] CdSeTe quantum dot solution (particle size 5nm, emission wavelength 780nm, concentration 10mg / mL);

[0109] Bovine dermal collagen solution (50 mg / mL);

[0110] Reagents: EDC (5mM), NHS (10mM), 4-mercaptobenzoic acid (MBT, 2mM), proline derivative (5mM), MES buffer (pH 6.0), PBS buffer (pH 7.4), collagenase (1U / mL);

[0111] Microfluidic chip: PDMS / glass bonded chip (integrated 5μL spiral rotary reaction chamber, 20μm deep, 50μm wide, surface coated with 30nm gold nanoparticles; branch flow path 0.2mm wide, 60μm deep).

[0112] 2. Instruments:

[0113] Fluorescence spectrometer (excitation wavelength 488 nm), Raman spectrometer (excitation wavelength 785 nm);

[0114] Temperature control module (Peltier, accuracy ±0.1℃), Pt microelectrode (50μm diameter, pH resolution ±0.01);

[0115] Dialysis bag (10kDa), ultrafiltration tube (10kDa), centrifuge (10000×g), stirrer.

[0116] II. Specific Operating Steps

[0117] S1: Preparation of dual-modal probe

[0118] S11 quantum dot activation

[0119] Take 1 mL of dSeTe quantum dot solution (10 mg / mL), add 5 mMMEDC and 10 mMMNHS, place in MES buffer at pH 6.0, and react at room temperature (25℃) for 30 minutes. Measure the change in absorbance of the quantum dot solution before and after activation at 380 nm using a UV spectrophotometer. Activation efficiency = (A0-A1) / A0×100%, where A0 is the initial absorbance and A1 is the absorbance of the supernatant without bound quantum dots.

[0120] S12 Collagen Coupling

[0121] At a quantum dot to collagen molar ratio of 1:50, 50 mg / mL collagen solution was added to the activated quantum dot solution, and the mixture was stirred at 4°C for 12 hours. After the reaction, the solution was dialyzed through a 10 kDa dialysis bag for 48 hours (conductivity < 20 μS / cm), centrifuged at 10000 × g for 10 minutes, and the supernatant (turbidity < 0.1 NTU) was collected to obtain quantum dot-labeled collagen (92% bioactivity retention).

[0122] S13 Raman label embellishment

[0123] 2 mL of MBT solution was added to the quantum dot-labeled collagen solution, and the reaction was carried out at 37 °C for 2 hours. The solution was then purified using a 10 kDa ultrafiltration tube (probe concentration concentrated to 0.1 mg / mL) and stored at 4 °C. The 1064 cm⁻¹ value was verified by Raman spectroscopy. -1 The characteristic peak intensity (>0.1au) confirms the effectiveness of the triple helix structure characterization.

[0124] S2: Construction and Debugging of Microfluidic Monitoring System

[0125] S21 chip preprocessing

[0126] The PDMS / glass-bonded chip was rinsed three times with PBS buffer at pH 7.4 to check chip seal (leakage rate <1%). The pressure loss of the branch flow path was monitored by a pressure sensor (<5 kPa) to confirm the mass transfer efficiency of the flow path (20 times higher than the direct flow path).

[0127] S22 parameter settings

[0128] The initial temperature of the temperature control module is set to 37℃, and the pH of the reaction chamber is monitored in real time via Pt microelectrodes.

[0129] Set dynamic control logic: when pH < 6.5, the temperature will automatically drop to 35℃, and when pH > 7.5, it will rise to 39℃ (control response time < 5 seconds).

[0130] S3: Sample Processing and Injection

[0131] Mix 0.1 mg / mL dual-modal probe solution and 1 U / mL collagenase solution at a 1:1 volume ratio, and add 5 mM proline derivative (initial triple helix retention >70%). If the bubble rate is >5% after mixing, degas by sonication for 1 minute, and then inject 5 μL of the mixed sample into the microfluidic chip reaction chamber (injection volume deviation <1 μL).

[0132] S4: Dual-mode signal acquisition

[0133] Fluorescence signal: Excited at 488nm, monitor fluorescence intensity at 780nm, acquisition frequency 1 time / second; if fluorescence intensity drift >10% / h, enable real-time Raman signal correction.

[0134] Raman signal: excited at 785 nm, monitored at 1064 cm⁻¹ －1 The characteristic peak intensity is sampled once every 5 seconds; if the peak intensity fluctuation is greater than 8%, the integration time is extended to 1 second.

[0135] Signal quality assessment: If the signal-to-noise ratio is less than 30:1, return to step S2 to recalibrate the signal acquisition module.

[0136] S5: Data Analysis and Results Output

[0137] A CNN algorithm was used to fuse the dual-modal signals, and fluorescence drift was corrected using Raman spectroscopy to achieve a joint detection accuracy of 95%. If the accuracy was less than 90%, the algorithm was retrained by increasing the number of training samples.

[0138] Analysis results:

[0139] Enzymatic hydrolysis kinetic curve: Km=0.8μM, Vmax=12nM / s (If the parameters exceed the range of 0.7μM~0.9μM or 11nM / s~13nM / s, return to step S3 to adjust the reaction temperature to 37℃±0.5℃);

[0140] Performance indicators: Detection limit 0.01 U / mL (S / N = 3), linear range 0.01 U / mL to 10 U / mL (R0). 2 =0.998), the retention rate of the triple helix structure at 37℃ was 82%, and the monitoring window period was 8 hours.

[0141] III. Verification Conclusion

[0142] This embodiment achieves precise monitoring of collagen extraction activity through the synergistic effect of a dual-modal probe and a microfluidic system, with an enzymatic hydrolysis endpoint error of ≤2% and an activity recovery rate of 92%. It is suitable for real-time evaluation of collagenase activity and structural integrity in industrial production.

[0143] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0144] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for monitoring collagen extraction activity based on refined enzymatic extraction technology, characterized in that, Includes the following steps: Step S1: Prepare a dual-modal probe from collagen modified with quantum dot labeling and Raman tagging; Step S2: Construct a microfluidic monitoring system based on the integrated reaction chamber, temperature control module, pH monitoring module, and signal acquisition module; Step S3: Mix the sample to be tested with the dual-modal probe, add a structure protectant, and inject it into the reaction chamber of the microfluidic detection system; Step S4: Simultaneously acquire fluorescence and Raman signals through the signal acquisition module, and based on the signal-to-noise ratio, determine to return to step S2 to recalibrate the signal acquisition module; Step S5: Perform fusion analysis on the fluorescence signal and Raman signal to obtain collagenase activity, triple helix structure integrity and enzymatic hydrolysis kinetic parameters; In step S1, the preparation of the bimodal probe includes: Step S11, taking a CdSeTe quantum dot solution of a preset concentration, adding EDC and NHS to the CdSeTe quantum dot solution, and reacting in MES buffer at a preset pH value for 25-35 minutes to obtain quantum dots; Step S12, adding the quantum dots and collagen to a collagen solution at a molar ratio of 1:45-1:55, stirring, and then purifying by dialysis and centrifugation to obtain quantum dot-labeled collagen; Step S13, adding 4-mercaptobenzoic acid to the quantum dot-labeled collagen solution, and purifying by ultrafiltration to obtain the bimodal probe; the quantum dots have a diameter of 4 mm-6 mm and an emission wavelength of 770 mm-790 mm; the 4-mercaptobenzoic acid is purified by ultrafiltration at 1064 cm⁻¹. -1 Characteristic peaks represent the integrity of the triple helix structure.

2. The method for monitoring collagen extraction activity using refined enzymatic extraction technology according to claim 1, characterized in that, In step S11, the concentration of the CdSeTe quantum dot solution is 8 mg / mL to 12 mg / mL, the concentration of EDC is 4 mm to 6 mm, and the concentration of NHS is 8 mM to 12 mM; wherein, the dialysis uses a dialysis bag with a molecular weight cutoff of 10 kDa, and the dialysis time is 47 hours to 49 hours; the centrifugation conditions are 9500×g to 10500×g, and centrifugation time is 8 minutes to 12 minutes.

3. The method for monitoring collagen extraction activity using refined enzymatic extraction technology according to claim 2, characterized in that, In step S2, the steps of constructing the microfluidic detection system include: step S21, integrating the PDMS / glass bonded chip into a helical rotary reaction chamber, the surface of the reaction chamber being coated with a gold nanoparticle coating, and rinsing the flow channel with a PBS buffer of a preset pH; step S22, adjusting the temperature inside the reaction chamber based on the pH of the reaction system inside the reaction chamber.

4. The method for monitoring collagen extraction activity using the refined enzymatic extraction technology according to claim 3, characterized in that, The microfluidic monitoring system also includes branch flow paths, which determine and adjust the flow path width based on the flow path pressure loss, and determine and optimize the flow path corner curvature based on the mass transfer efficiency of the branch flow paths.

5. The method for monitoring collagen extraction activity using the refined enzymatic extraction technology according to claim 4, characterized in that, In step S3, the sample to be tested is a collagenase solution, the concentration of the dual-modal probe is 0.08 mg / mL to 0.12 mg / mL, the concentration of collagenase is 0.8 U / mL to 1.2 U / mL, the mixing volume ratio of the collagenase solution to the dual-modal probe is 1:1, and ultrasonic degassing is determined based on the bubble rate after mixing.

6. The method for monitoring collagen extraction activity using refined enzymatic extraction technology according to claim 5, characterized in that, In step S4, based on the fluorescence intensity drift of the fluorescence signal, it is determined to enable real-time Raman signal correction.

7. The method for monitoring collagen extraction activity using the refined enzymatic extraction technology according to claim 6, characterized in that, In step S5, the fusion analysis uses a CNN algorithm to correct fluorescence signal drift based on Raman signal to obtain joint detection accuracy. Based on the joint detection accuracy, it is determined to increase the training sample size and retrain the algorithm. Since the enzymatic hydrolysis kinetic parameters are greater than the standard enzymatic hydrolysis kinetic parameters, it is determined to return to step S3 to adjust the reaction temperature. The CNN algorithm includes 3 convolutional layers, 2 pooling layers and 1 fully connected layer. The activation function is ReLU and the loss function is cross-entropy. The enzymatic hydrolysis kinetic parameters include Km value and Vmax value.

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