A system for traceability analysis of a natural flavor of sea bass and a synthetic flavor

Abstract

The present application relates to the technical field of food flavor detection and analysis, and discloses a comparison and traceability analysis system for natural flavor and synthetic flavor of sea bass, comprising a variable-temperature sampling device, a detection device and a data processing terminal. The data processing terminal control device performs non-continuous step heating sampling on homologous samples, calculates a matrix perturbation factor based on an internal standard response to eliminate physical errors and outputs corrected data; selects low-temperature section data to invert an equivalent binding energy parameter representing the binding strength of the matrix; uses the parameter to respectively construct a synthetic solvent and a biological matrix prediction model, calculates the residual error vector of the measured data and the theoretical value at a high-temperature point, and determines the traceability result according to the minimum residual error principle. The present application effectively overcomes the interference of matrix effect by analyzing the thermodynamic occurrence state of flavor substances, realizes accurate identification of natural biological tissues and synthetic solvent systems, and improves the accuracy of traceability analysis.

Description

Technical Field

[0001] This invention relates to the field of food flavor detection and analysis technology, specifically a comparative traceability analysis system for the natural and synthetic flavors of sea bass. Background Technology

[0002] Sea bass has a delicious flavor, and its unique natural taste is a key factor determining its market value. With the development of the food industry, various synthetic flavorings that can highly replicate the flavor of sea bass have appeared on the market. These synthetic flavorings are chemically similar to the flavor compounds in natural sea bass, with aldehydes, alcohols, and ketones as their main components. During food processing and distribution, in order to reduce costs or cover up defects in raw materials, there is adulteration by using synthetic flavorings to imitate the flavor of natural sea bass, disrupting market order and harming consumer rights. Therefore, establishing a traceability and identification technology that can accurately distinguish between natural and synthetic flavors is of great significance.

[0003] Current flavor identification techniques primarily rely on instrumental analysis methods such as gas chromatography-mass spectrometry (GC-MS). These existing techniques typically focus on qualitative screening and quantitative analysis of flavor compounds, distinguishing them by comparing the fingerprint spectra of characteristic flavor compounds in the sample. However, because modern synthetic flavoring technology can accurately simulate the chemical structure of natural flavor compounds, the static volatile spectra of natural and synthetic samples at room temperature often highly overlap. Existing techniques mainly focus on the presence and quantity of chemical components, neglecting the differences in the occurrence state of flavor compounds in the matrix. Natural flavor compounds are usually bound within tissues by biomacromolecules such as muscle proteins and fats through physical adsorption, hydrogen bonding, or hydrophobic interactions, while synthetic flavor compounds mainly exist in a free state in solvents. Existing methods lack characterization tools for this difference in microscopic binding states, resulting in low identification accuracy when faced with highly realistic synthetic samples.

[0004] Existing headspace sampling techniques typically employ extraction at a single constant temperature. This static sampling model fails to reveal the dynamic changes in biological tissues during thermal processing. Natural biological tissues undergo complex physicochemical changes during heating, such as protein denaturation and cell structure breakdown. These changes trigger the sudden release of bound flavor compounds, exhibiting a nonlinear thermal release pattern. Current single-point sampling methods can only acquire volatilization data at a specific state, failing to capture the nonlinear release characteristics during the pyrolysis of biological matrices. Furthermore, they struggle to thermodynamically distinguish between synthetic solvent systems following simple linear evaporation patterns and biological tissue systems with complex release mechanisms.

[0005] Meat samples such as sea bass belong to complex biological matrices, and their physical properties, such as viscosity, ionic strength, and water content, vary among different individuals. This matrix effect interferes with the gas-liquid partitioning process of volatile components, leading to physical shifts in the detection signal. Although existing analytical methods sometimes introduce internal standards for quantitative correction, they typically only use internal standards to correct for instrument response fluctuations, lacking mathematical correction models for matrix physical interference effects. In the absence of effective matrix effect correction, the raw detection data is contaminated with systematic errors introduced by matrix physical properties, further reducing the robustness and reliability of the traceability and identification model. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a comparative analysis system for the natural and synthetic flavors of sea bass. This system solves the problems of existing technologies that rely solely on chemical component analysis and cannot effectively distinguish the state of flavor substances, lack characterization methods for the thermodynamic release of biological matrices, and suffer from low identification accuracy due to interference from complex matrix effects.

[0007] To achieve the above objectives, this invention provides a comparative analysis system for the natural and synthetic flavors of sea bass, comprising a variable-temperature sampling device, a detection device, and a data processing terminal. The data processing terminal is communicatively connected to both the variable-temperature sampling device and the detection device, and the variable-temperature sampling device is connected to the detection device via a gas path. The data processing terminal integrates a thermal stress control module, a signal correction module, a parameter inversion module, and a source tracing decision module. These modules work collaboratively to analyze the thermodynamic behavior of samples from the same source and output source tracing results.

[0008] The thermal stress control module is configured to generate a discontinuous temperature sequence vector, controlling the variable-temperature sampling device to heat homologous samples to three discrete temperature points with an increasing relationship. These three temperature points are sequentially set as a simulated room temperature baseline, a critical range for thermal denaturation of biomacromolecules, and a high-temperature point for matrix disintegration. The simulated room temperature baseline is used to obtain baseline volatilization data of the sample under conditions of no thermal denaturation, controlled by the gas-liquid partition coefficient; the critical range for thermal denaturation of biomacromolecules is used to induce the release of adsorbed flavor substances due to protein structure unfolding; and the high-temperature point for matrix disintegration is used to obtain total volatilization data of the sample under conditions of tissue structural damage and lipid oxidative cleavage. The thermal stress control module establishes parallel sampling mapping logic to associate homologous sample vials from the same batch to the aforementioned different temperature points.

[0009] The signal correction module is used to eliminate physical errors caused by matrix effects. This module receives the raw detection data transmitted by the detection device and calculates the theoretical response prediction of the internal standard as a function of temperature in an ideal solvent environment based on thermodynamic equations. By comparing the measured peak area of ​​the internal standard with the theoretical response prediction, the matrix perturbation factor is calculated. Subsequently, this matrix perturbation factor is used to perform inverse compensation on the original peak area of ​​the target flavor compound. The specific calculation logic is as follows: The corrected response data is obtained by multiplying the original peak area of ​​the target flavor compound by a power of the matrix perturbation factor, where the exponent of the power is the negative of the diffusion correlation coefficient of the target flavor compound relative to the internal standard.

[0010] The parameter inversion module selects corrected response data corresponding to the simulated room temperature baseline and the critical range of thermal denaturation of biomacromolecules to construct an apparent activation energy equation. This equation decomposes the apparent activation energy released by flavor substances from the matrix into two parts: The first part is the standard enthalpy of vaporization required for the flavor substance to transform from the liquid or solid phase to the gas phase. The second part is the equivalent binding energy parameter required for the flavor substance to overcome the physical constraints of the matrix. The parameter inversion module retrieves the standard enthalpy of vaporization corresponding to the target flavor substance and uses the two-point logarithmic difference method to calculate the specific equivalent binding energy parameter of the current sample. This parameter is used to quantify the binding strength between the flavor substance and the sample matrix.

[0011] The source tracing and judgment module uses the equivalent binding energy parameter to construct two prediction models to theoretically predict the release behavior at high temperatures. First, a synthesis solvent prediction model is constructed. This model sets the matrix binding energy term to zero and extrapolates the data in the low-temperature range based solely on the standard enthalpy of vaporization to calculate the first theoretical prediction value at the high temperature point of matrix disintegration. Secondly, a biological matrix prediction model is constructed. This model introduces a matrix disintegration coefficient, extrapolates the data in the low-temperature range based on the sum of the standard enthalpy of vaporization and the equivalent binding energy parameter, and calculates the second theoretical prediction value at the high temperature point of matrix disintegration by combining the matrix disintegration coefficient.

[0012] The source tracing and decision-making module acquires the measured data vector of the matrix disintegration high-temperature point, and calculates the synthetic residual distance between this measured data vector and the synthetic solvent prediction vector composed of the first theoretical prediction value, as well as the natural residual distance between the measured data vector and the biological matrix prediction vector composed of the second theoretical prediction value. All residual distances are standardized Euclidean distances. The decision is made based on the minimum residual principle. If the natural residual distance is less than the synthetic residual distance and less than a preset confidence threshold, a first-level signal characterizing the natural flavor is generated; if the synthetic residual distance is less than the natural residual distance and less than a preset confidence threshold, a second-level signal characterizing the synthetic flavor is generated. Furthermore, for samples determined to have a synthetic flavor, the system further calculates the cosine similarity between the relative abundance distribution of components and the standard fingerprint spectrum, and outputs a solvent type indication.

[0013] This invention provides a comparative analysis system for the natural and synthetic flavors of sea bass. It offers the following advantages: 1. This invention directly quantifies the binding strength between flavor substances and the sample matrix by calculating the equivalent binding energy parameter through a parameter inversion module. Unlike traditional methods that only compare chemical components, this invention utilizes synthetic solvent prediction models and biological matrix prediction models to calculate the residual distance between measured data and theoretical models, respectively. By leveraging the nonlinear characteristics of natural sea bass tissue releasing bound flavor substances during high-temperature disintegration, and the simple linear evaporation characteristic of synthetic flavorings, it is possible to accurately distinguish between natural and synthetic samples with similar chemical compositions but different matrix physical structures.

[0014] 2. This invention utilizes a thermal stress control module to construct a discontinuous temperature sequence encompassing simulated ambient temperature, the critical range for protein denaturation, and the high-temperature point of matrix disintegration. Based on the biophysical characteristics of sea bass muscle tissue, volatilization data were acquired under three key states: gas-liquid equilibrium, protein tertiary structure unfolding, and tissue structure destruction. Compared to single-temperature detection, this differentiated thermal field excitation can fully expose the release patterns of the biological matrix under different thermal states, providing a highly identifiable data foundation for subsequent kinetic inversion.

[0015] 3. This invention integrates a signal correction module and employs a matrix perturbation algorithm based on internal standards to perform nonlinear correction on the original detection data. By calculating the matrix perturbation factor, the system effectively eliminates physical errors caused by differences in sample matrix viscosity and ionic strength in the gas-liquid partitioning process. This ensures that the data input to the inversion model accurately reflects the thermodynamic release behavior of flavor substances, avoids the influence of matrix physical property fluctuations on the identification results, and improves the stability and data reliability of the system in the detection of different batches of samples. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the overall system architecture of the present invention; Figure 2 This is a flowchart illustrating the operational logic of the thermal stress control module of the present invention. Figure 3 This is a data processing flowchart of the signal correction module of the present invention; Figure 4This is a flowchart illustrating the operational logic of the dynamic parameter inversion module of the present invention. Figure 5 This is a flowchart of the virtual image reconstruction and source tracing decision module of the present invention.

[0017] Among them, 10 is the data processing terminal; 20 is the variable temperature sampling device; 30 is the detection device; 101 is the thermal stress control module; 102 is the signal correction module; 103 is the parameter inversion module; and 104 is the source tracing decision module. Detailed Implementation

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] See attached document Figure 1 The present invention provides a comparative analysis system for the natural and synthetic flavors of sea bass, which mainly includes a data processing terminal 10, a variable temperature sampling device 20, and a detection device 30.

[0020] The data processing terminal 10, serving as the system's computing core, establishes communication connections with both the variable-temperature sampling device 20 and the detection device 30. It issues discontinuous temperature sequence control commands and receives raw detection data transmitted from the detection device 30. The variable-temperature sampling device 20 is equipped with an independent temperature control unit, which, in response to commands, heats homologous samples to preset discontinuous discrete temperature points and maintains gas-liquid equilibrium at different temperatures to extract headspace volatiles. The detection device 30 is connected to the variable-temperature sampling device 20 via a gas path and performs material separation and signal conversion on the extracted headspace volatiles, outputting raw data containing retention time and ion abundance information.

[0021] The data processing terminal 10 integrates a thermal stress control module 101, a signal correction module 102, a parameter inversion module 103, and a source tracing decision module 104.

[0022] The thermal stress control module 101 generates a discontinuous temperature sequence vector and sends control commands to the variable temperature sampling device 20 based on a preset room temperature reference point, denaturation critical range, and matrix disintegration high temperature point. The signal correction module 102 eliminates matrix effect physical errors, receives raw data and identifies the internal standard response signal, calls the internal standard physical volatility model to calculate the matrix perturbation factor, and uses this factor to perform nonlinear correction on the original peak area of ​​the target flavor substance to output corrected response data.

[0023] The parameter inversion module 103 is used to acquire the corrected response data corresponding to the low-temperature section after processing by the signal correction module 102. Based on the Arrhenius kinetic equation, it solves the equivalent binding energy parameter specific to the current sample, which characterizes the binding strength between flavor substances and the sample matrix. The source determination module 104 is used to receive the equivalent binding energy parameter, construct a synthetic solvent prediction model and a biological matrix prediction model, use the two models to predict the theoretical response value at the high-temperature point, calculate the residual vector between the measured data and the theoretical prediction value, and output the source determination result of natural flavor, synthetic flavor or specific solvent type based on the topological distribution characteristics of the residual vector and the principle of minimum residual.

[0024] The system data interaction process is as follows: the thermal stress control module 101 triggers physical sampling, the generated data flows into the signal correction module 102, the corrected data is transmitted to the parameter inversion module 103, and the physicochemical parameters obtained by inversion are input to the source tracing decision module 104 to complete the identification.

[0025] The system provided in this embodiment mainly includes a data processing terminal 10, a variable temperature sampling device 20, and a detection device 30.

[0026] The data processing terminal 10 serves as the system's control core and computing platform, executing data processing logic, issuing hardware control commands, and storing analysis results. In specific implementations, the data processing terminal 10 can be a general-purpose computer, an industrial control computer, a server cluster, or a cloud computing platform terminal. The data processing terminal 10 internally includes a processor, memory, a communication interface, and a system bus. The processor can be a central processing unit, a microcontroller unit, a digital signal processor, a field-programmable gate array, or an application-specific integrated circuit. The memory includes a computer-readable storage medium storing computer program instructions. When these instructions are executed by the processor, they implement the functions of the thermal stress control module 101, the signal correction module 102, the parameter inversion module 103, and the source tracing decision module 104, which are described later in this specification.

[0027] The communication interface is used to realize data interaction between the data processing terminal 10, the variable temperature sampling device 20, and the detection device 30. The physical interface forms include RS-232 serial port, universal serial bus, Ethernet interface or IEEE 488 interface, and the communication protocol adopts TCP / IP, Modbus or industrial standard instrument control protocol.

[0028] The variable-temperature sampling device 20 is used to apply discontinuous stepped thermal stress to the sample and extract headspace volatiles under gas-liquid equilibrium conditions. This device is connected to the data processing terminal 10 via a data cable and receives control commands containing temperature sequences, equilibrium times, and injection parameters. In terms of hardware implementation, the variable-temperature sampling device 20 is an automatic headspace sampler, internally equipped with an independent temperature-controlled heating furnace or constant-temperature bath. The heating component employs proportional-integral-derivative (PID) control logic, achieving a temperature control accuracy better than ±0.5℃.

[0029] To implement a discontinuous tiered sampling strategy and avoid quality loss caused by repeated heating of a single sample, the variable-temperature sampling device 20 is equipped with a multi-station sample tray, which is configured to simultaneously accommodate multiple homologous sample vials. The robotic arm or transfer mechanism inside the variable-temperature sampling device 20 is configured to sequentially feed different homologous sample vials into the heating positions for a specific discontinuous temperature sequence (e.g., sequentially heating to corresponding temperature points) according to control commands. The temperature-controlled sampling device 20 also includes a sampling needle, a quantitative loop, and a pressurized gas pipeline for quantitatively introducing the extracted gas sample into the detection device 30.

[0030] The detection device 30 and the variable-temperature sampling device 20 are connected via a heated transmission pipeline, which maintains a constant high temperature to prevent condensation of volatile components. A synchronous trigger signal line is also connected between the variable-temperature sampling device 20 and the detection device 30 to trigger the detection device 30 to start the acquisition process upon completion of the injection. The detection device 30 is used to physically separate the mixed volatile components and perform qualitative and quantitative detection. In this embodiment, the detection device 30 is a gas chromatography-mass spectrometry (GC-MS) system. The gas chromatography section includes an injection port, a column oven, and a capillary column, used to separate the mixture into individual components based on their boiling points and polarity differences. The mass spectrometry section includes an ion source (such as an electron impact source, EI), a mass analyzer (such as a quadrupole or time-of-flight analyzer, TOF), and a detector, used to acquire the mass-to-charge ratio (m / z) and ion abundance information of the eluted components. The detection device 30 digitizes the generated raw chromatograms and mass spectrometry data and transmits them to the data processing terminal 10 via a data interface. For the specific operating parameter settings, carrier gas flow control, and vacuum system maintenance of the detection device 30, those skilled in the art can perform routine configurations based on the characteristics of the substance to be tested and the instrument manual, which will not be elaborated here.

[0031] The physical connection architecture of the system also involves the selection and pretreatment of sample containers. The sea bass samples to be tested are dispensed into headspace vials of standard volume and sealed with PTFE / silicone rubber composite septa and metal caps to create a closed thermodynamic equilibrium system. To eliminate matrix effects, the system operation procedure includes adding a quantitative amount of inert internal standard to each headspace vial before physical sampling. This operation can be automatically completed by the liquid injection module integrated into the variable temperature sampling device 20, or by manual pretreatment.

[0032] Through the aforementioned hardware connection, the data processing terminal 10 controls the variable temperature sampling device 20 to construct a differentiated temperature field, and the detection device 30 converts the material response excited by the temperature field into a digital signal and sends it back to the data processing terminal 10, thereby providing a physical data basis for the subsequent thermodynamic inversion algorithm.

[0033] See attached document Figure 2 The thermal stress control module 101 is configured to generate a discontinuous temperature sequence vector and construct a hardware control instruction set for the variable temperature sampling device 20 accordingly to drive the hardware to perform differentiated heating operations on the sample.

[0034] The thermal stress control module 101 executes step S201 to determine the discontinuous temperature sequence vector. This vector consists of at least three discrete temperature control points, defined as: ; in, These represent three temperature points that have an increasing relationship, satisfying... .

[0035] In step S201, the temperature point The temperature was set as a simulated ambient temperature baseline, ranging from 30℃ to 45℃. This temperature point was determined based on the physical state of the sample under normal storage conditions. At this temperature, the release of flavor compounds in the sample is primarily controlled by the gas-liquid partition coefficient, and it is used to obtain baseline volatility data under conditions where thermal denaturation has not occurred. Temperature Point The temperature range is defined as the critical temperature range for thermal denaturation of biomacromolecules, from 55℃ to 65℃. This temperature corresponds to the initial temperature range of thermal denaturation of myofibril proteins in sea bass muscle tissue, and is used to stimulate the release of adsorbed flavor compounds caused by the unfolding of the protein's tertiary structure. Temperature point The temperature was set as the matrix disintegration high temperature point, ranging from 80℃ to 95℃. At this temperature, biological tissues undergo structural damage and partial lipid oxidative cleavage, which is used to obtain the total volatile matter data of the sample under extreme high temperature conditions.

[0036] After determining the temperature sequence, the thermal stress control module 101 executes step S202 to construct the mapping logic for parallel sampling. To avoid mass loss caused by multiple evacuations during continuous heating of a single sample, the thermal stress control module 101 maps three homogeneous sample bottles from the same batch to different temperature points. A one-to-one mapping relationship is established. Specifically, the thermal stress control module 101 generates a mapping table containing sample reference numbers, and associates the first sample reference number with a temperature point. The second sample tag number is associated with the temperature point. The third sample tag number is associated with the temperature point. This parallel processing logic ensures that the detection data at each temperature point is based on independent samples with the same initial quality.

[0037] In step S203, the thermal stress control module 101 generates hardware drive instructions containing gas-liquid equilibrium time parameters. For each temperature point... The thermal stress control module 101 sets the corresponding gas-liquid equilibrium time. The balancing time The optimal time range is 20 to 40 minutes to ensure that the internal heat conduction of the sample reaches a steady state and the headspace phase volatile concentration reaches equilibrium. The generated control command data package includes: the target heating position number and the set temperature point. Heating rate (e.g., from 10°C / min to 50°C / min) and gas-liquid equilibrium time .

[0038] In step S204, the thermal stress control module 101 sends control commands to the variable temperature sampling device 20 via the communication interface. The microcontroller of the variable temperature sampling device 20 parses the commands and adjusts the heating furnace power to match the set temperature point. This drives the mechanical transfer mechanism to deliver the corresponding sample bottle to the heating position. The time required to reach gas-liquid equilibrium is... Subsequently, the thermal stress control module 101 generates a trigger signal, instructing the variable temperature sampling device 20 to perform pressurization and sample injection actions, and simultaneously triggering the detection device 30 to start data acquisition. Through the above steps, the thermal stress control module 101 completes the construction of the physical sampling environment, providing the corresponding raw data for subsequent signal correction and parameter inversion.

[0039] See attached document Figure 3 The signal correction module 102 is configured in the data processing terminal 10 to receive the raw data transmitted by the detection device 30 and execute a nonlinear correction algorithm based on the internal standard perturbation principle.

[0040] The signal correction module 102 executes step S301, performing feature peak extraction and internal standard identification. The signal correction module 102 receives the vector data from the discontinuous temperature sequence. The gas chromatography-mass spectrometry data were collected. The signal correction module 102 called up the feature library preset in the memory, and used retention time windows and quantitative feature ions for matching to identify the chromatographic peaks of the target flavor substances and preset internal standards from the chromatogram. For each temperature point The signal correction module 102 extracts the first signal through integral operation. Measured peak area of ​​the target flavor compounds and the measured peak area of ​​the internal standard In this embodiment, the internal standard is a chemically stable substance with zero background in the sea bass sample to be tested, such as deuterated hexanal (d3-Hexanal) or 2-octanone (2-Octanone).

[0041] In step S302, the signal correction module 102 constructs a physical volatilization benchmark model for the internal standard. The signal correction module 102 accesses the internal database of the data processing terminal 10 and reads the standard thermodynamic parameters of the internal standard. Based on the discrete form of the Clausius-Clapeyron equation, the signal correction module 102 calculates the theoretical response value of the internal standard to temperature changes in an ideal solvent environment. The calculation formula is as follows: ; In the formula, For internal standard at temperature point The theoretical response prediction value is as follows; For internal standard at reference temperature The standard response constant at (e.g., 298.15 K) is pre-calibrated using ideal solvent systems such as pure water or pure ethanol and stored in a database; The standard enthalpy of vaporization of the internal standard; It is the ideal gas constant; This is the absolute temperature corresponding to the current temperature point; It is a natural exponential function.

[0042] In step S303, the signal correction module 102 calculates the matrix perturbation factor. The signal correction module 102 quantifies the degree of physical interference of the current sample matrix on the gas-liquid partitioning process of volatile components by comparing the measured value with the theoretical value of the internal standard. Matrix perturbation factor The calculation formula is: ; In the formula, The coefficient is dimensionless. For internal standard at temperature point The measured peak area below; For internal standard at temperature point The theoretical response prediction value is given. When When, it indicates the presence of physical binding or competitive adsorption of the matrix, leading to an inhibitory effect; when At this time, it characterizes the enhancement effect caused by azeotropy or salting out of the matrix.

[0043] In step S304, the signal correction module 102 performs nonlinear correction of the target signal. The signal correction module 102 utilizes the calculated matrix perturbation factor. The original peak area of ​​the target flavor compounds Perform reverse compensation. The corrected formula is as follows: ; In the formula, Target flavor compounds At temperature point The corrected response value below; Target flavor compounds The diffusion correlation coefficient relative to the internal standard. In a preferred embodiment, Based on Graham's law of gas diffusion, the calculation formula is as follows: ; in, Target flavor compounds molar mass, This represents the molar mass of the internal standard. To simplify calculations, it can be set to... .

[0044] After the above steps, the signal correction module 102 generates a corrected response value. The data matrix is ​​then passed to the dynamic parameter inversion module 103.

[0045] See attached document Figure 4 The kinetic parameter inversion module 103 is used to dynamically analyze the thermodynamic binding characteristics between flavor substances and the matrix in the current sample based on the detection data in the low-temperature range, so as to achieve the calibration of binding energy parameters for different individual samples. The kinetic parameter inversion module 103 decouples the purely physical evaporation contribution and the matrix binding contribution in the flavor release process by solving a variant of the Arrhenius equation.

[0046] The kinetic parameter inversion module 103 executes step S401 to acquire the corrected response data for the low-temperature region. The kinetic parameter inversion module 103 extracts temperature points from the data matrix output by the signal correction module 102. and The corresponding corrected response data. Select. and The input for the calculation is based on the fact that these two temperature points are below the denaturation threshold of biomolecules, and the release behavior of flavor substances is mainly controlled by physical adsorption forces and intermolecular forces, which conforms to the boundary conditions of the thermodynamic equilibrium model. The extracted data is represented as vector pairs. and ,in Representing the Target flavor compounds.

[0047] In step S402, the kinetic parameter inversion module 103 constructs the apparent activation energy equation. Based on the Arrhenius kinetic equation, the kinetic parameter inversion module 103 defines the target flavor compound. Apparent activation energy released from the matrix In the physical model of this embodiment, the apparent activation energy is decomposed into two parts: the first part is the standard enthalpy of vaporization required for the target flavor substance to transform from the liquid or solid phase to the gas phase. The second part is the additional binding energy required for the target flavor compound to overcome the physical constraints of the matrix. .

[0048] The dynamic parameter inversion module 103 establishes the following two-point logarithmic difference equation: ; Substitution The expanded form is obtained as follows: ; In the formula, Target flavor substances At temperature point and The peak area response value after correction; It is the ideal gas constant; Target flavor compounds Standard enthalpy of vaporization of pure components; The target flavor compound to be solved The equivalent binding energy parameter with the current sample matrix.

[0049] In step S403, the kinetic parameter inversion module 103 performs the inversion calculation of the equivalent binding energy parameter. The kinetic parameter inversion module 103 accesses a physical property parameter database stored in the memory of the data processing terminal 10. This database pre-stores the chemical identifier (such as CAS number) and standard enthalpy of vaporization of the target flavor substance. The corresponding relationship table. The kinetic parameter inversion module 103 is based on the target flavor compounds. The identifier is used to retrieve the corresponding standard enthalpy of vaporization, and the above equation is transformed to calculate the binding energy parameter specific to the current sample. The calculation formula is as follows: ; Through this step, the system can quantify the binding strength of the current sample to a specific flavor compound. If A positive value indicates that the matrix has a strong adsorption effect on flavor substances; if... A value close to zero or negative indicates that the sample matrix has a very weak binding force on flavor substances.

[0050] In step S404, the dynamic parameter inversion module 103 performs boundary constraints and formatting on the inversion results. This is to account for experimental errors that could lead to variations in the calculated results. If a negative value appears that is not physically meaningful, the dynamic parameter inversion module 103 is equipped with non-negative truncation logic: a preset lower limit for the allowable system calculation error is defined. (For example, set to -2kJ / mol), kinetic parameter inversion module 103 comparison and The size; if the calculation result Then force setting .

[0051] After completing the above calculations, the kinetic parameter inversion module 103 generates a feature vector containing the binding energy parameters of all target flavor substances. The feature vector, along with the standard enthalpy of vaporization data, is then passed to the subsequent virtual image reconstruction and source tracing decision module 104 as input parameters for constructing the high-temperature segment prediction model.

[0052] See attached document Figure 5 The source tracing and decision module 104 is configured in the data processing terminal 10 to receive the binding energy characteristic vector and standard enthalpy of vaporization data output by the kinetic parameter inversion module 103, and to combine it with the high temperature point. The measured data are used to execute a logic for authenticity identification based on bidirectional theoretical reconstruction. The source tracing decision module 104 constructs a synthetic solvent prediction model (synthetic virtual image) and a biological matrix prediction model (biological virtual image) to calculate the deviation between the actual thermodynamic behavior of the sample and the two theoretical models, thereby determining the flavor source attribute of the sample.

[0053] The source tracing and decision module 104 executes step S501, constructing a synthesis solvent prediction model and calculating the first prediction vector. This step is based on the following physical assumptions: If the sample to be tested is a synthetic flavor (i.e. a flavoring solution), its volatilization behavior still follows the continuous Arrhenius equation at high temperatures, and there is no sudden release caused by the destruction of biological tissue structure.

[0054] The source tracing and decision module 104 calls the floating-point arithmetic unit in the processor to determine the source of the first... For each target flavor compound, calculate its first theoretical prediction under the synthetic assumption. The calculation formula is as follows: ; In the formula, Under the assumption of the synthesis solvent, the temperature point The predicted value of the first theoretical peak area at that time; Temperature point The corrected measured value is below; Target flavor compounds Standard enthalpy of evaporation; It is the ideal gas constant; and This corresponds to the absolute temperature value. In this calculation, the system forcibly sets the matrix binding energy term to zero to simulate the physical process in a free solvent system where the solute is controlled only by gas-liquid equilibrium. The source determination module 104 traverses all target flavor substances and generates a synthetic solvent prediction vector. .

[0055] In step S502, the source tracing decision module 104 constructs a biological matrix prediction model and calculates the second prediction vector. This step is based on the following physical assumption: if the sample to be tested has a natural flavor (i.e., real fish tissue), at the temperature point... Under these conditions, the physical disintegration of cell structure releases flavor compounds that are deeply bound, resulting in measured values ​​that are higher than the linear extrapolated values ​​based on low-temperature data.

[0056] The source tracing decision module 104 introduces the matrix disintegration coefficient pre-installed in the memory of the data processing terminal 10. Calculate the second theoretical prediction value under the biological matrix assumption. The calculation formula is as follows: ; In the formula, Under the biological matrix hypothesis, the temperature point The predicted area of ​​the second theoretical peak at that time; The equivalent binding energy parameter output by the dynamic parameter inversion module 103; Temperature point Targeting the flavor compounds The matrix disintegration gain coefficient, a dimensionless empirical scalar, is preferably ranging from 0.2 to 5.0. This coefficient was obtained statistically through destructive distillation experiments on similar natural sea bass samples, characterizing the excess release rate of specific flavor compounds at tissue disintegration temperatures. The source tracing decision module 104 generates a biological matrix prediction vector. .

[0057] In step S503, the source tracing decision module 104 calculates the residual vector distance between the measured data and the prediction model. The source tracing decision module 104 acquires the measured data vector of the high-temperature point output by the signal correction module 102. Subsequently, the source tracing decision module 104 calculates the standardized Euclidean distance between the measured vector and the two predicted vectors.

[0058] Define the composite residual distance Residual distance from nature : ; ; In the formula, Target flavor compounds At temperature point The peak area response value after correction; This represents the total number of target flavor compounds. This normalization process eliminates the interference of differences in the absolute response intensity of different flavor compounds on the distance calculation weights.

[0059] In step S504, the source tracing decision module 104 executes source tracing decision logic based on the minimum residual principle. The source tracing decision module 104 compares... and The numerical value, combined with a preset confidence threshold. Output the identification result. In this embodiment, the confidence threshold is... The value ranges from 0.05 to 0.25 and is used to define the tolerance boundary for model fitting.

[0060] The specific logic behind the judgment is as follows: like and The source tracing decision module 104 generates a first-level signal characterizing the natural flavor. This indicates that the release behavior of the sample at high temperatures has a higher fit with the matrix disintegration model and exhibits typical thermodynamic characteristics of biological tissues.

[0061] like and The source tracing and decision module 104 generates a second-level signal characterizing the synthetic flavor. This indicates that the sample conforms to a simple evaporation model and lacks a complex mechanism for the binding and release of flavor substances by the biological matrix.

[0062] like and All are greater than the threshold The source tracing and judgment module 104 generates an abnormal sample warning signal.

[0063] Furthermore, for samples determined to have a synthetic flavor, the source tracing decision module 104 further performs a solvent fingerprint matching sub-step. The source tracing decision module 104 calculates the measured vector. The system calculates the relative abundance distribution of each component and performs cosine similarity calculations between this distribution and standard fingerprint spectra of commonly used solvents (such as propylene glycol and glycerol triacetate) stored in the internal database. If the similarity is higher than a preset matching threshold (e.g., 0.9), the corresponding solvent type is indicated. The final traceability results are presented on the display interface of the data processing terminal 10 or uploaded to a remote server via a communication interface.

[0064] The sequence of coordinated actions of the variable temperature sampling device 20, the detection device 30, and the various algorithm modules within the data processing terminal 10 throughout the entire process from sample preprocessing to the final traceability result output.

[0065] The workflow begins with the system initialization phase. The user inputs the batch information of the sample to be tested through the data processing terminal 10 and calls the preset discontinuous temperature sequence vector. and gas-liquid equilibrium time (For example, 30 minutes). The data processing terminal 10 sends an initialization command to the variable temperature sampling device 20, drives the heating furnace to preheat to the starting temperature, and controls the gas path system to perform a purging operation to remove residual background interference.

[0066] The physical field construction and data acquisition phase then commences. To improve detection efficiency and ensure that each sample experiences the same thermal history, the data processing terminal 10 employs an interleaved timing control strategy to drive the variable-temperature sampling device 20 and the detection device 30.

[0067] The specific timing logic is as follows: The data processing terminal 10 issues a command to send the first sample bottle into the heating position and heat it to the specified temperature. The time to reach gas-liquid equilibrium When the first injection action is triggered, the detection device 30 starts the gas chromatography-mass spectrometry analysis cycle for the first sample vial (the analysis time is denoted as...). ).

[0068] During the analysis of the first sample bottle by the detection device 30, the data processing terminal 10 calculates the time difference, specifically the gas-liquid equilibrium time before the end of the analysis of the first sample bottle. At that moment, the heating operation on the second sample bottle was initiated (temperature point). To ensure that after the detection device 30 completes data acquisition from the first sample vial and resets, the second sample vial reaches gas-liquid equilibrium and immediately initiates the second injection. Similarly, the second injection is initiated for the third sample vial (temperature point). Heating and injection operations.

[0069] The detection device 30 outputs values ​​corresponding to the temperature points in sequence. The original data files, the data processing terminal 10 marks these three independent data files as follows: And it is stored in the buffer memory. When the third data file Once generated, the system automatically triggers subsequent data processing procedures.

[0070] The system enters the signal correction and preprocessing stage. The signal correction module 102 reads the data file from the buffer memory, identifies the internal standard chromatographic peak, and extracts the peak area. The signal correction module 102 calls the internal standard thermodynamic parameters from the database to calculate the matrix perturbation factor. And use this factor to determine the original peak area of ​​the target flavor compounds. Perform nonlinear correction to generate the corrected data matrix. .

[0071] Next, the dynamic parameter inversion stage begins. The dynamic parameter inversion module 103 reads the matrix. The corresponding low temperature section ( The data subset. The kinetic parameter inversion module 103 is for each target flavor compound. Combined with its standard enthalpy of vaporization The equivalent binding energy parameter was calculated by solving the two-point difference equation. If the calculation result exceeds the preset physical boundary, the dynamic parameter inversion module 103 performs non-negative truncation processing, ultimately generating a binding energy eigenvector. .

[0072] Finally, the process proceeds to the source tracing and decision-making stage. The source tracing and decision-making module 104 is based on feature vectors. And standard enthalpy of vaporization, calculate the high temperature point respectively. Synthesis solvent prediction vector and biological matrix prediction vector The source tracing and judgment module 104 calculates the measured high-temperature data. The standardized Euclidean distance between the two prediction vectors mentioned above and Based on the principle of minimum residual, the source tracing decision module 104 outputs the final identification result signal.

[0073] If the judgment result is natural flavor, the data processing terminal 10 generates a first status signal, which drives the display interface to show the pass mark and confidence value; if the judgment result is synthetic flavor, the data processing terminal 10 generates a second status signal, which drives the display interface to show a warning mark, and calls the solvent fingerprint matching subroutine to display the name of the suspected exogenous solvent with the highest matching degree.

Claims

1. A comparative analysis system for the natural and synthetic flavors of sea bass, characterized in that, It includes a variable temperature sampling device (20), a detection device (30), and a data processing terminal (10) that is communicatively connected to the variable temperature sampling device (20) and the detection device (30). The data processing terminal (10) integrates a thermal stress control module (101), a signal correction module (102), a parameter inversion module (103), and a source tracing decision module (104), and is configured as follows: The variable temperature sampling device (20) is controlled to heat the homologous sample to a discontinuous temperature point. When the concentration of volatiles in the headspace phase reaches equilibrium, the volatiles are detected by gas chromatography-mass spectrometry to obtain the original detection data. The matrix perturbation factor is calculated based on the internal standard response. The target flavor substances in the original detection data are corrected using the matrix perturbation factor to obtain the corrected response data. The corrected response data corresponding to the low temperature section is selected. The equivalent binding energy parameter characterizing the matrix binding strength is inverted. A synthetic solvent prediction model and a biological matrix prediction model are constructed to calculate the residual vector between the measured data and the theoretical value at the high temperature point. The source tracing result is output according to the principle of minimum residual. The thermal stress control module (101) generates a discontinuous temperature sequence vector, which includes three temperature points with an increasing relationship, as follows: A simulated room temperature reference point with a value range of 30℃-45℃ is used to obtain reference volatilization data under conditions where no thermal denaturation has occurred. The critical range of thermal denaturation of biomacromolecules is 55℃-65℃, which is used to stimulate the release of adsorbed flavor substances caused by the unfolding of protein structures. And the matrix disintegration high temperature point with a value range of 80℃-95℃, used to obtain the total volatilization data of the sample under extreme high temperature conditions; The simulated room temperature reference point and the critical range of thermal denaturation of the biomacromolecule constitute the low temperature section, and the high temperature point of matrix disintegration constitutes the high temperature point. The signal correction module (102) calculates the theoretical response prediction of the internal standard in an ideal solvent environment with temperature change based on the thermodynamic equation, and calculates the matrix perturbation factor by comparing the measured peak area of ​​the internal standard with the theoretical response prediction. The signal correction module (102) uses the calculated matrix perturbation factor to perform reverse compensation on the original peak area of ​​the target flavor substance; The compensation algorithm is as follows: multiply the original peak area of ​​the target flavor compound by a power of the matrix perturbation factor, where the exponent of the power is the negative value of the diffusion correlation coefficient of the target flavor compound relative to the internal standard. The parameter inversion module (103) retrieves the corresponding standard enthalpy of vaporization based on the chemical identifier of the target flavor substance, and uses the two-point logarithmic difference method to solve the equivalent binding energy parameter specific to the current sample. The solution logic is as follows: calculate the logarithm of the ratio of the critical range of thermal denaturation of the biomacromolecule to the corrected response data at the simulated room temperature reference point, multiply the logarithm by the ideal gas constant, divide by the difference between the reciprocal of the simulated room temperature reference point and the reciprocal of the critical range of thermal denaturation of the biomacromolecule, and then subtract the standard enthalpy of vaporization. When constructing the synthetic solvent prediction model, the source tracing decision module (104) forcibly sets the matrix binding energy term to zero and extrapolates the data of the low temperature range based only on the standard enthalpy of vaporization to calculate the first theoretical prediction value at the high temperature point. When constructing the biological matrix prediction model, the traceability judgment module (104) introduces the matrix disintegration coefficient, which is the excess release rate of the target flavor substance at the tissue disintegration temperature. Based on the sum of the standard enthalpy of vaporization and the equivalent binding energy parameter, it extrapolates the data of the low temperature range and calculates the second theoretical prediction value at the high temperature point in combination with the matrix disintegration coefficient.

2. The system for comparing and tracing the natural and synthetic flavors of sea bass according to claim 1, characterized in that, The parameter inversion module (103) extracts the corrected response data corresponding to the simulated room temperature reference point and the critical range of thermal denaturation of the biomacromolecule, and constructs the apparent activation energy equation; The apparent activation energy equation decomposes the apparent activation energy released by flavor substances from the matrix into two parts: the first part is the standard enthalpy of vaporization required for the substance to transform from the liquid or solid phase to the gas phase, and the second part is the equivalent binding energy parameter required for the substance to overcome the physical constraints of the matrix.

3. The system for comparing and tracing the natural and synthetic flavors of sea bass according to claim 1, characterized in that, The source tracing decision module (104) obtains the measured data vector of the high temperature point, and calculates the synthetic residual distance between the measured data vector and the synthetic solvent prediction vector composed of the first theoretical prediction value, and the natural residual distance between the measured data vector and the biological matrix prediction vector composed of the second theoretical prediction value. Both the synthetic residual distance and the natural residual distance are standardized Euclidean distances.

4. The system for comparing and tracing the natural and synthetic flavors of sea bass according to claim 3, characterized in that, The source tracing decision module (104) executes the following decision logic: If the natural residual distance is less than the synthetic residual distance and the natural residual distance is less than a preset confidence threshold, a first level signal characterizing the natural flavor is generated. If the synthetic residual distance is less than the natural residual distance and the synthetic residual distance is less than the preset confidence threshold, a second level signal characterizing the synthetic flavor is generated.

5. The system for comparing and tracing the natural and synthetic flavors of sea bass according to claim 4, characterized in that, For samples determined to have a synthetic flavor, the source tracing decision module (104) further performs a solvent fingerprint matching sub-step: The relative abundance distribution of each component in the measured data vector is calculated, and the cosine similarity of the relative abundance distribution with the standard fingerprint spectra of commonly used solvents stored in the internal database is calculated. If the similarity is higher than the preset matching threshold, the corresponding solvent type prompt is output.

Images