Prewarning method and device for pathogenic bacteria based on field modulation resonance Raman spectrum and verifiable causal inference

Abstract

The invention relates to a pathogenic bacterium early warning method and device based on field modulation resonance Raman spectroscopy and verifiable causal inference. According to the method, four-dimensional spectrum tensors are collected through a micro-fluidic chip, in combination with environmental parameter time sequence data, representation vectors are extracted by using an adversarial encoder network, a hierarchical causal graph is constructed, and anti-fact intervention verification is performed on high-uncertainty causal edges. And cross-matrix migration and rapid adaptation are realized by identifying invariant fruits. And executing do-calculation based on a causal diagram, predicting microorganism concentration evolution by adopting a Shenchang differential equation solver, and outputting risk levels, shelf life distribution and intervention suggestions. The device integrates a micro-fluidic chip, spectrum detection, in-situ sensing and a causal inference engine, has high sensitivity, strong interpretability and rapid generalization ability, and is suitable for rapid detection and intelligent early warning of food safety.

Description

Technical Field

[0001] This invention relates to the field of food safety testing technology, specifically to a method and apparatus for rapid detection and growth prediction of food pathogens based on external field modulation resonance Raman spectroscopy enhancement and counterfactual intervention causal networks. Background Technology

[0002] Current foodborne pathogen detection technologies generally suffer from bottlenecks such as long detection cycles and insufficient sensitivity, making it difficult to meet the urgent needs of rapid response and precise monitoring in modern food safety. Surface-enhanced Raman spectroscopy (SERS), as an analytical technique with rapid detection potential, still faces problems in practical applications such as limited enhancement factors and poor signal repeatability, restricting its reliability and widespread application in trace pathogen detection. Meanwhile, microbial growth prediction models, as auxiliary assessment tools, also have significant limitations: traditional empirical models often rely on specific experimental conditions, have weak extrapolation capabilities, and are difficult to adapt to complex and variable actual food matrices; while deep learning-based prediction models, although improving fitting accuracy, generally exhibit "black box" characteristics, with a lack of interpretability in the model decision-making process, which is detrimental to food safety risk assessment and regulatory traceability. Furthermore, existing models are mostly trained on specific food matrices, lacking cross-matric generalization ability, and lack effective online updates and active calibration mechanisms after deployment, making it difficult to cope with the long-term challenges brought about by the dynamic evolution of microorganisms and environmental changes.

[0003] It is evident that existing technologies lack a solution that can seamlessly integrate highly sensitive physical detection with interpretable and verifiable causal prediction models on a miniaturized platform and achieve cross-matrix generalization at extremely low sample cost. Summary of the Invention

[0004] In view of this, the present invention aims to provide a highly sensitive, highly interpretable, and rapidly generalizable method and apparatus for detecting and predicting food pathogens. It achieves ultrasensitive detection through external field modulation resonance Raman, achieves reliable prediction through counterfactual intervention of causal networks, and achieves cross-matrix migration through invariant causators.

[0005] The first aspect of this invention provides a method for early warning of pathogenic bacteria based on field-modulated resonance Raman spectroscopy and verifiable causal inference, comprising the following steps: A microfluidic chip is provided, the microfluidic chip comprising a main detection channel and a control intervention channel; Acquire environmental parameter time-series data, which includes: macroscopic environmental data, multi-parameter in-situ sensor data, and user input data; The field-modulated resonance Raman spectrum is acquired by the microfluidic chip, and a four-dimensional spectral tensor I(λ_exc, ν_Raman, E, B) is constructed. The four-dimensional spectral tensor and the in-situ sensor data are input into the adversarial encoder network for processing, and the output is a latent representation vector stripped of matrix-specific noise. A hierarchical causal graph is learned based on the representation vector and the time-series data of the environmental parameters; counterfactual intervention experiments are designed in the control channel of the microfluidic chip for causal edges with high uncertainty and high influence in the causal graph. Learn causal graphs on multiple source matrix datasets, identify causal edges with consistent effect direction and intensity, and label their driving factors as invariant causal factors; Based on the input predicted scenario environmental parameters, do-calculus is performed on the cause-effect graph, and a neural ordinary differential equation solver is used to simulate the evolution of microbial concentration, outputting the mean, confidence interval and shelf life probability distribution; Based on the predicted probability of concentration exceeding the threshold, a risk level is mapped, and a causal source analysis, intervention recommendations, and an audit report are output.

[0006] The construction of the four-dimensional spectral tensor specifically includes: applying a programmable alternating electric field and a rotating magnetic field to the sample in the detection area of ​​the microfluidic chip, while simultaneously exciting it with a tunable laser, and acquiring time-resolved Raman spectra under different combinations of field parameters.

[0007] The applied programmable alternating electric field and rotating magnetic field satisfy the following external field regulation mechanism: Satisfying the Stark effect of electric field modulation, specifically: electric field strength With a frequency of 0.1-10MHz, the electron energy levels of the chromophore are split and shifted, and the excitation wavelength is scanned to find the electric field-wavelength resonance point; It satisfies the Zeeman effect of magnetic field modulation, specifically: magnetic induction intensity of 0.01-1 T, rotatable magnetic field direction coupled with polarized light, selectively enhancing specific vibration modes; To achieve the synergistic enhancement of plasmon resonance Raman, specifically, the electric field dynamically modulates the detuning of the plasmon resonance wavelength and the molecular electronic transition, thereby realizing the product effect of dual enhancement of SERS and RRS. Time-resolved pulse field modulation specifically refers to the evolution of electronic states caused by the capture field of pulsed laser and synchronous pulse field on the ps-ns time scale. Different bacterial species exhibit unique three-dimensional characteristics of time-frequency-field intensity due to differences in excited state lifetime.

[0008] The multi-parameter in-situ sensor data includes: Time-series data of pH values ​​in microchannels acquired by pH electrode; The variation of the dielectric properties of a solution with frequency and time as measured by a dielectric spectroscopy sensor; Turbidity and particle scattering signals recorded by an optical scattering detector; Concentrations of dissolved gases and volatile metabolites monitored by a gas permeation membrane sensor; Temperature distribution in the microenvironment measured by a temperature sensor.

[0009] The design of the counterfactual intervention experiment specifically includes: The sample is allowed to evolve under natural conditions in the main detection channel, with all variables changing according to the actual situation; This allows the causal variable X to remain fixed at a specific value in the control intervention channel, while allowing other variables to change synchronously with the main channel; The evolution trajectory of the observed variable Y and the main channel is used to calculate the difference ΔY between Y and the two channels. This difference reflects the individual causal effect of X on Y. The posterior distribution of causal edge weights is updated using experimental results through Bayesian inference, and the verification is repeated until the uncertainty of all key causal edges is below the threshold. Each causal edge is accompanied by verification metadata: number of verifications, average weight, confidence interval, experimental condition record, and original observation data, forming an auditable chain of evidence.

[0010] The specific processing steps of the adversarial encoder network include: the encoder extracting matrix-invariant representations, the matrix discriminator identifying the matrix type, and minimizing matrix discriminability through adversarial training.

[0011] Based on the above scheme, when facing a new food matrix, fix the causal edge of the invariant causal component and fine-tune the matrix-specific causal edge using 5-10 samples.

[0012] The identification of causal edges with consistent causal effects specifically includes identifying driving factors whose causal effect direction and intensity remain stable across all source matrices through structural consistency analysis, corresponding to universal mechanisms at the level of physicochemical laws.

[0013] Secondly, a pathogenic bacteria early warning device based on field-modulated resonance Raman spectroscopy and verifiable causal inference is provided, the method comprising: The microfluidic spectroscopy chip comprises a three-layer bonded structure: the bottom layer is a quartz substrate with an ITO electrode array, on which a nano-pyramid plasmon structure is deposited; the middle layer is a PDMS microchannel, containing a main detection channel and a control intervention channel; and the top layer is a COP optical window. The field-modulated resonant Raman excitation detection module includes a tunable femtosecond laser, a polarization modulation unit, a time-correlated single-photon counting spectrometer, an alternating electric field generator, and a miniature rotating magnetic field coil; it can achieve multi-dimensional coordinated control and time synchronization of excitation wavelength, polarization state, electric field, and magnetic field. The multi-parameter in-situ sensing module integrates a pH electrode, a dielectric spectrum sensor, an optical scattering detector, and a gas permeation membrane sensor to monitor changes in the microenvironment of microbial metabolism in real time. The causal inference computation engine can run adversarial representation coding networks, counterfactual intervention causal learning algorithms, and neuronormal differential equation solvers, and output prediction results, confidence intervals, and causal evidence. The intelligent decision-making output module provides risk assessment, shelf-life warning, causal tracing analysis, and disposal suggestions.

[0014] Based on the above scheme, the nano-pyramid plasmon structure has a base of 100 nm, a height of 80 nm, and a period of 200 nm. The surface is modified with thiol-capped aptamers to achieve selective capture of specific pathogens.

[0015] The beneficial effects of this invention are: (1) The detection performance is significantly improved.

[0016] Using field-modulated resonance Raman spectroscopy, a comprehensive enhancement factor of up to [value missing] was achieved. The detection limit is as low as CFU / mL, response time less than 15 minutes; equipped with four-dimensional spectral fingerprint recognition capability, which can distinguish between different strains of the same pathogen.

[0017] (2) The predictive ability is interpretable and reliable.

[0018] It constructs a hierarchical causal graph to provide an interpretable inference chain; introduces a counterfactual intervention mechanism to quantify the confidence of causal relationships; supports online calibration and active learning, continuously improving model accuracy; and exhibits strong robustness to data distribution shifts.

[0019] (3) It has strong generalization ability and low adaptation cost.

[0020] We propose an invariant causality transfer framework that requires only 5–10 samples for new matrix adaptation; it reduces annotation costs by 95%; it supports zero-sample physicochemical prior reasoning and has cross-scenario transfer capabilities.

[0021] (4) The system has a high degree of integration and flexible deployment.

[0022] It achieves integrated detection and prediction; supports edge intelligence deployment without relying on the cloud; its modular design facilitates expansion; and it supports federated learning mechanisms to protect user data privacy.

[0023] The above effects together constitute a highly sensitive, interpretable, rapidly generalizable, safe and reliable intelligent early warning system for pathogens. Attached Figure Description

[0024] The present invention includes the following figures: Figure 1 This is a block diagram of the overall structure of the device of the present invention; Figure 2 This is a cross-sectional view of the three-layer structure of the microfluidic spectral chip of the present invention; Figure 3 This is a schematic diagram of the field modulation resonance Raman excitation detection optical path of the present invention; Figure 4 This is a comparison chart of the performance of the present invention and existing technologies.

[0025] The image shows: 1. Tunable femtosecond laser, 2. Sample inlet, 3. Dichroic mirror, 4. AI inference engine, 5. Time-correlated single-photon spectrometer, 6. Alternating electric field generator, 7. Microfluidic chip, 8. Result output display, 9. Objective lens, 10. COP optical window, 11. Quartz substrate, 12. ITO electrode, 13. Nano-pyramid plasma laser array, 14. Rotating magnetic field coil. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0027] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0028] A specific embodiment of a pathogenic bacteria early warning method based on field-modulated resonance Raman spectroscopy and verifiable causal inference is based on a microfluidic chip, which includes a main detection channel and a control intervention channel.

[0029] Specifically, the following steps are included: S1: The field-modulated resonance Raman spectrum is acquired through the microfluidic chip, and a four-dimensional spectral tensor I(λ_exc,ν_Raman, E, B) is constructed. Specifically, a programmable alternating electric field and a rotating magnetic field are applied to the sample in the detection area of ​​the microfluidic chip, and a tunable laser is used for excitation to acquire time-resolved Raman spectra under different combinations of field parameters.

[0030] The application of programmable alternating electric and rotating magnetic fields needs to satisfy the following external field adjustment mechanism: Satisfying the Stark effect of electric field modulation, specifically: electric field strength With a frequency of 0.1-10MHz, the electron energy levels of the chromophore are split and shifted, and the excitation wavelength is scanned to find the electric field-wavelength resonance point; It satisfies the Zeeman effect of magnetic field modulation, specifically: magnetic induction intensity of 0.01-1 T, rotatable magnetic field direction coupled with polarized light, selectively enhancing specific vibration modes; To achieve the synergistic enhancement of plasmon resonance Raman, specifically, the electric field dynamically modulates the detuning of the plasmon resonance wavelength and the molecular electronic transition, thereby realizing the product effect of dual enhancement of SERS and RRS. Time-resolved pulse field modulation specifically refers to the evolution of electronic states caused by the capture field of pulsed laser and synchronous pulse field on the ps-ns time scale. Different bacterial species exhibit unique three-dimensional characteristics of time-frequency-field intensity due to differences in excited state lifetime.

[0031] S2: Input the four-dimensional spectral tensor and the in-situ sensor data into the adversarial encoder network for processing, and output the latent representation vector stripped of matrix-specific noise; The multi-parameter in-situ sensor refers to the microenvironment parameters collected by the multi-parameter in-situ sensing module, specifically including: Time-series data of pH values ​​in microchannels acquired by pH electrode; The variation of solution dielectric properties (dielectric constant, conductivity) with frequency and time as measured by a dielectric spectroscopy sensor; Turbidity and particle scattering signals recorded by an optical scattering detector; Dissolved gases and volatile metabolites monitored by gas permeation membrane sensors concentration; Temperature distribution in the microenvironment measured by a temperature sensor.

[0032] These sensor data, together with the four-dimensional spectral tensor, constitute a multimodal input, which is then fed into an adversarial encoder network for fusion processing.

[0033] The specific processing steps of the adversarial encoder network include: the encoder extracting matrix-invariant representations, the matrix discriminator identifying the matrix type, and minimizing matrix discriminability through adversarial training.

[0034] S3: Learn a hierarchical causal graph based on the representation vector and the time-series data of the environmental parameters; design counterfactual intervention experiments for the causal edges with high uncertainty and high influence in the causal graph in the control channel of the microfluidic chip.

[0035] The environmental parameter time-series data comes from: (1) External environment monitoring: The device integrates macroscopic environmental sensors to collect and store time series of external environmental parameters such as temperature, relative humidity, and air pressure.

[0036] (2) Microenvironment in situ monitoring: i.e., the timing of local environmental parameters in the microchannel collected by the multi-parameter in situ sensing module as described in note 2.

[0037] (3) User input: Sample background information input by the user during testing, such as packaging method, storage history, etc.

[0038] These time-series data, along with latent representations extracted from four-dimensional spectral and sensor data, serve as input for causal graph learning.

[0039] The design of the counterfactual intervention experiment specifically includes: The sample is allowed to evolve under natural conditions in the main detection channel, with all variables changing according to the actual situation; This allows the causal variable X to remain fixed at a specific value in the control intervention channel, while allowing other variables to change synchronously with the main channel; The evolution trajectory of the observed variable Y and the main channel is used to calculate the difference ΔY between Y and the two channels. This difference reflects the individual causal effect of X on Y. The posterior distribution of causal edge weights is updated using experimental results through Bayesian inference, and the verification is repeated until the uncertainty of all key causal edges is below the threshold. Each causal edge is accompanied by verification metadata: number of verifications, average weight, confidence interval, experimental condition record, and original observation data, forming an auditable chain of evidence.

[0040] S4: Learn causal graphs on multiple source matrix datasets, identify causal edges with consistent effect direction and intensity, and label their driving factors as invariant causal agents; Based on the above scheme, when facing a new food matrix, fix the causal edge of the invariant causal component and fine-tune the matrix-specific causal edge using 5-10 samples.

[0041] The identification of causal edges with consistent causal effects specifically includes identifying driving factors whose causal effect direction and intensity remain stable across all source matrices through structural consistency analysis, corresponding to universal mechanisms at the level of physicochemical laws.

[0042] The plurality of source matrices specifically include, but are not limited to: Liquid matrix: fresh milk; solid matrix: fresh meat (pork, beef, chicken) and ready-to-eat meat products (ham, sausage).

[0043] S5: Based on the input predicted scenario environmental parameters, perform do-calculus on the cause-effect graph, use a neural ordinary differential equation solver to simulate the evolution of microbial concentration, and output the mean, confidence interval and shelf life probability distribution.

[0044] The mathematical principles of do-calculus are as follows: Let the learned causal graph be a directed acyclic graph G, with nodes including environmental variables, metabolic variables, and phenotypic variables (such as microbial concentration N).

[0045] Intervention operation: The user inputs the predicted scenario, such as "temperature=4°C, relative humidity=80%", which is formalized as the intervention operation do(T=4, RH=80).

[0046] Calculation steps: (1) Graph operation: Remove all incoming edges pointing to the node to be intervened from the causal graph G to obtain the causal graph G_do after intervention.

[0047] (2) Probability calculation: According to the do-calculus rule, the joint distribution after intervention is: P(all variables | do(intervention variable = value)) = ∏ P(each non-intervention variable | its parent node) × δ(intervention variable = specified value) Where δ is an indicator function that forces the variable being intervened to take a specified value.

[0048] (3) Posterior propagation: • Starting from the environment layer node (the node being intervened in), calculate the conditional probability of downstream nodes layer by layer.

[0049] • Metabolic layer node probability = f(parent node state, causal edge weight).

[0050] • The probability of a phenotypic node (microbial concentration) is determined by its parent node (metabolic state).

[0051] (4) Time evolution simulation: • Use the node state at a certain moment as the initial value.

[0052] • Use the constant differential equation solver to solve for the evolution of microbial concentration over time: dN / dt = f(N, metabolic variable, environmental variable, time).

[0053] • Numerical integration yields the concentration trajectory N(t) over the predicted time period.

[0054] (5) Quantification of uncertainty: • Causal edge weights have a posterior distribution (non-point estimation).

[0055] • Monte Carlo sampling: Sample multiple sets of parameters from the weight posterior distribution of each edge.

[0056] • Perform the above steps for each set of parameters to obtain multiple possible predicted trajectories.

[0057] • Statistics for a set of trajectories provide the predicted mean, confidence interval, and probability distribution.

[0058] Output format: • The mean locus and confidence band of concentration over time.

[0059] • Probability distribution of shelf life (the time when the concentration first exceeds the safety threshold).

[0060] • The probability of concentration exceeding the standard at a specified time.

[0061] S6: Based on the predicted concentration exceeding the threshold probability, map the risk level and output causal source analysis, intervention suggestions, and audit report.

[0062] Based on the same inventive concept, such as Figure 1-3 A specific embodiment of a pathogenic bacteria early warning device based on field-modulated resonance Raman spectroscopy and verifiable causal inference is provided, including: The microfluidic spectroscopy chip comprises a three-layer bonded structure: the bottom layer is a quartz substrate with an ITO electrode array, on which a nano-pyramid plasmon structure is deposited; the middle layer is a PDMS microchannel, containing a main detection channel and a control intervention channel; and the top layer is a COP optical window. The field-modulated resonant Raman excitation detection module includes a tunable femtosecond laser, a polarization modulation unit, a time-correlated single-photon counting spectrometer, an alternating electric field generator, and a miniature rotating magnetic field coil; it can achieve multi-dimensional coordinated control and time synchronization of excitation wavelength, polarization state, electric field, and magnetic field. The multi-parameter in-situ sensing module integrates a pH electrode, a dielectric spectrum sensor, an optical scattering detector, and a gas permeation membrane sensor to monitor changes in the microenvironment of microbial metabolism in real time. The causal inference computation engine can run adversarial representation coding networks, counterfactual intervention causal learning algorithms, and neuronormal differential equation solvers, and output prediction results, confidence intervals, and causal evidence. The intelligent decision-making output module provides risk assessment, shelf-life warning, causal tracing analysis, and disposal suggestions.

[0063] Based on the above scheme, the nano-pyramid plasmon structure has a base of 100 nm, a height of 80 nm, and a period of 200 nm. The surface is modified with thiol-capped aptamers to achieve selective capture of specific pathogens.

[0064] Based on the above scheme, the field modulation resonance Raman excitation detection module realizes multi-dimensional coordinated control of excitation wavelength, polarization state, electric field strength and magnetic field direction, with time synchronization accuracy <1 ns.

[0065] The workflow of this device is as follows: The sample is injected into the microfluidic spectroscopic chip through the sample inlet (2), and then transported to the surface of the nano-pyramid plasmon array (13). A laser generated by a tunable femtosecond laser (1), reflected by a dichroic mirror (3), and focused by an objective lens (9), excites the plasmon array through a COP optical window (10). Simultaneously, an alternating electric field generator (6) generates an electric field through an ITO electrode (12), which, in conjunction with a rotating magnetic field coil (14), constructs a three-dimensional electromagnetic field to enhance the interaction between plasmons and sample molecules. The generated optical signal is collected by the objective lens (9) and detected by a time-correlated single-photon counting spectrometer (5) through a dichroic mirror (3). The data is transmitted in real-time to an AI inference engine (4) for analysis and processing, and the results are presented on a display (8). The system is built on a quartz substrate (11) and integrates microfluidics, plasmon enhancement, and AI analysis technologies to achieve ultra-high sensitivity detection of trace samples.

[0066] Through the above solutions, such as Figure 4 As shown, pork was purchased, sealed in sterile bags, and transported to the laboratory within one hour. It was aseptically divided into 48 portions and washed three times with sterile saline. The pork was homogenized using a homogenizer, and 2g of pork was weighed and mixed with 18mL of sterile borate buffer. The pork sample was thoroughly mixed using a shaker. MacConkey agar was used to verify the actual total concentration of *Escherichia coli* in the original pork sample.

[0067] Contaminated pork samples were prepared using the same method. 18 mL of bacterial suspension was added to 2 g of fresh pork sample instead of sterile borate buffer to prepare pork samples contaminated with different concentrations of foodborne pathogens, *Escherichia coli*. Raman spectral signals of pork samples at different contamination levels were collected using the same experimental procedures described above. A total of 56 pork samples contaminated with *Escherichia coli* were prepared. The samples were then divided into training and validation sets at a 3:1 ratio.

[0068] Modeling using Raman spectroscopy and the method described in this invention shows that this invention is superior to existing technologies in terms of detection limit, response time, enhanced printing, new matrix adaptation, and accuracy.

[0069] It should be noted that any process or method description in the embodiments can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order according to the functions involved, as should be understood by those skilled in the art to which the embodiments of the invention pertain.

[0070] It should be noted that the logic and / or steps in the embodiments, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0071] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0072] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0073] Furthermore, in the embodiments of the present invention, the functional modules can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The aforementioned storage medium can be a read-only memory, a hard disk, or an optical disk, etc.

[0074] The above embodiments have provided a detailed description of the technical solutions of the present invention. Obviously, the present invention is not limited to the described embodiments. Based on the embodiments of the present invention, those skilled in the art can make various changes, but any changes that are equivalent or similar to the present invention fall within the scope of protection of the present invention. Contents not described in detail in this specification are prior art known to those skilled in the art.

Claims

1. A method for early warning of pathogenic bacteria based on field-modulated resonance Raman spectroscopy and verifiable causal inference, characterized in that, Includes the following steps: A microfluidic chip is provided, the microfluidic chip comprising a main detection channel and a control intervention channel; Acquire environmental parameter time-series data, which includes: macroscopic environmental data, multi-parameter in-situ sensor data, and user input data; The field-modulated resonance Raman spectrum is acquired by the microfluidic chip, and a four-dimensional spectral tensor I(λ_exc, ν_Raman, E, B) is constructed. The four-dimensional spectral tensor and the in-situ sensor data are input into the adversarial encoder network for processing, and the output is a latent representation vector stripped of matrix-specific noise. A hierarchical causal graph is learned based on the representation vector and the time-series data of the environmental parameters; counterfactual intervention experiments are designed in the control channel of the microfluidic chip for causal edges with high uncertainty and high influence in the causal graph. Learn causal graphs on multiple source matrix datasets, identify causal edges with consistent effect direction and intensity, and label their driving factors as invariant causal agents; Based on the input predicted scenario environmental parameters, do-calculus is performed on the cause-effect graph, and a neural ordinary differential equation solver is used to simulate the evolution of microbial concentration, outputting the mean, confidence interval and shelf life probability distribution; Based on the predicted probability of concentration exceeding the threshold, a risk level is mapped, and a causal source analysis, intervention recommendations, and an audit report are output.

2. The method according to claim 1, characterized in that, The construction of the four-dimensional spectral tensor specifically includes: applying a programmable alternating electric field and a rotating magnetic field to the sample in the detection area of ​​the microfluidic chip, while simultaneously exciting it with a tunable laser, and acquiring time-resolved Raman spectra under different combinations of field parameters.

3. The method according to claim 2, characterized in that, The applied programmable alternating electric field and rotating magnetic field satisfy the following external field regulation mechanism: Satisfying the Stark effect of electric field modulation, specifically: electric field strength The frequency is 0.1-10 MHz, which causes the electron energy levels of the chromophore to split and shift, and the excitation wavelength is scanned to find the electric field-wavelength resonance point; It satisfies the Zeeman effect of magnetic field modulation, specifically: magnetic induction intensity of 0.01-1 T, rotatable magnetic field direction coupled with polarized light, selectively enhancing specific vibration modes; To achieve the synergistic enhancement of plasmon resonance Raman, specifically, the electric field dynamically modulates the detuning of the plasmon resonance wavelength and the molecular electronic transition, thereby realizing the product effect of dual enhancement of SERS and RRS. Time-resolved pulse field modulation specifically refers to the evolution of electronic states caused by the capture field of pulsed laser and synchronous pulse field on the ps-ns time scale. Different bacterial species exhibit unique three-dimensional characteristics of time-frequency-field intensity due to differences in excited state lifetime.

4. The method according to claim 1, characterized in that, The multi-parameter in-situ sensor data includes: Time-series data of pH values ​​in microchannels acquired by pH electrode; The variation of the dielectric properties of a solution with frequency and time as measured by a dielectric spectroscopy sensor; Turbidity and particle scattering signals recorded by an optical scattering detector; Concentrations of dissolved gases and volatile metabolites monitored by a gas permeation membrane sensor; Temperature distribution in the microenvironment measured by a temperature sensor.

5. The method according to claim 1, characterized in that, The design of the counterfactual intervention experiment specifically includes: The sample is allowed to evolve under natural conditions in the main detection channel, with all variables changing according to the actual situation; This allows the causal variable X to remain fixed at a specific value in the control intervention channel, while allowing other variables to change synchronously with the main channel; The evolution trajectory of the observed variable Y and the main channel is used to calculate the difference ΔY between Y and the two channels. This difference reflects the individual causal effect of X on Y. The posterior distribution of causal edge weights is updated using experimental results through Bayesian inference, and the verification is repeated until the uncertainty of all key causal edges is below the threshold. Each causal edge is accompanied by verification metadata: number of verifications, average weight, confidence interval, experimental condition record, and original observation data, forming an auditable chain of evidence.

6. The method according to claim 1, characterized in that, The specific processing steps of the adversarial encoder network include: the encoder extracting matrix-invariant representations, the matrix discriminator identifying the matrix type, and minimizing matrix discriminability through adversarial training.

7. The method according to claim 1, characterized in that, When faced with a new food matrix, fix the causal edges of the invariant causal factors and fine-tune the matrix-specific causal edges using 5-10 samples.

8. The method according to claim 1, characterized in that, The identification of causal edges with consistent causal effects specifically includes identifying driving factors whose causal effect direction and intensity remain stable across all source matrices through structural consistency analysis, corresponding to universal mechanisms at the level of physicochemical laws.

9. A pathogenic bacteria early warning device based on field-modulated resonance Raman spectroscopy and verifiable causal inference, based on the method described in any one of claims 1-8, characterized in that, include: The microfluidic spectroscopy chip includes a three-layer bonded structure: the bottom layer is a quartz substrate with an ITO electrode array, on which a nano-pyramid plasmon structure is deposited; The middle layer consists of PDMS microchannels, including the main detection channel and the control intervention channel; The top layer is the COP optical window; The field-modulated resonant Raman excitation detection module includes a tunable femtosecond laser, a polarization modulation unit, a time-correlated single-photon counting spectrometer, an alternating electric field generator, and a miniature rotating magnetic field coil; it can achieve multi-dimensional coordinated control and time synchronization of excitation wavelength, polarization state, electric field, and magnetic field. The multi-parameter in-situ sensing module integrates a pH electrode, a dielectric spectrum sensor, an optical scattering detector, and a gas permeation membrane sensor to monitor changes in the microenvironment of microbial metabolism in real time. The causal inference computation engine can run adversarial representation coding networks, counterfactual intervention causal learning algorithms, and neuronormal differential equation solvers, and output prediction results, confidence intervals, and causal evidence. The intelligent decision-making output module provides risk assessment, shelf-life warning, causal tracing analysis, and disposal suggestions.

10. The apparatus according to claim 9, characterized in that, The nano-pyramid plasmon structure has a base of 100 nm, a height of 80 nm, and a period of 200 nm. The surface is modified with thiol-terminated aptamers to achieve selective capture of specific pathogens.

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