Component detection method suitable for plant extract

Abstract

The invention discloses a component detection method suitable for a plant extract, and relates to the technical field of plant extract detection, and the component detection method comprises the following steps: S1, carrying out enrichment treatment on a target component by using two-phase liquid-liquid extraction; s2, performing multi-dimensional attribute mapping and component factor construction by using the initial enrichment liquid; s3, performing correlation feature constraint calculation by using the target component response tensor; s4, performing ideal spectrum construction and response difference analysis by using the to-be-compared spectrum feature set; s5, performing mapping analysis by using ideal and actual spectrum residual signals; s6, performing spectrum correction by using the standard comparison spectrum set; the selectivity of target component enrichment is improved by setting biphasic solvent-liquid-liquid extraction, efficient extraction of target components in a plant extract complex system is achieved, compared with existing single solvent extraction or solid-phase extraction, the method has the advantages that the target components are optimally distributed among liquid phases by regulating and controlling interaction among solvents, and the extraction efficiency is improved. And co-extraction of non-target components is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of plant extract detection, in particular to a component detection method suitable for plant extracts. Background Art

[0002] The component detection of plant extracts has important application value in many fields such as food, medicine, and cosmetics. The core lies in the accurate identification and quantification of target components to ensure product quality and biological activity. Existing component detection technologies mainly rely on chromatography, spectroscopy, and mass spectrometry, but there are still many challenges in terms of sample complexity, environmental interference, and detection accuracy. Plant extracts usually contain a variety of chemical components, including polyphenols, flavonoids, alkaloids, etc., with complex components and large variations in content.

[0003] In the prior art, existing plant extract detection methods usually use single solvent extraction or solid phase extraction, but these methods are not selective enough for target components in complex systems, and are prone to co-extraction of non-target components, increasing the difficulty of subsequent purification and detection. In addition, the distribution coefficient of a single solvent is greatly affected by polarity, which makes it difficult to meet the needs of simultaneous detection of multiple components, resulting in low enrichment efficiency of target components, thereby reducing detection sensitivity. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides a component detection method suitable for plant extracts to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides a method for detecting components of a plant extract, comprising the following steps: S1. Using two-phase liquid-liquid extraction to enrich the target component to obtain an initial enriched solution; S2. Use the initial enrichment solution to perform multidimensional attribute mapping and component factor construction to obtain the target component response tensor; S3, using the target component response tensor to perform correlation feature constraint calculation to obtain a spectral feature set to be compared; S4, using the spectral feature set to be compared to construct an ideal spectrum and analyze the response difference to obtain an ideal spectral function of the target component; S5, using the ideal and actual spectrum residual signals to perform mapping analysis to obtain a standardized comparison spectrum set; S6. Spectral correction was performed using the standardized comparison spectrum set to obtain clear signals of the plant extract components.

[0006] Further optimizing the technical solution, the step S2 adopts a two-phase solvent-liquid-liquid extraction method to enrich the target component of the sample from the complex matrix. In the two-phase solvent-liquid-liquid extraction, the distribution of the target component depends on its distribution coefficient in the two-phase solvent. : ; in, Indicates the concentration of the target component in the organic phase; Indicates the concentration of the target component in the aqueous phase.

[0007] To further optimize the technical solution, a solvent polarity dynamic control model is introduced in step S2 to define a solvent polarity matching factor: ; in, is the polarity parameter of the target component; Indicates the polarity parameter of the organic phase; Represents the polarity parameter of the water phase; is the volume fraction of the organic phase in the extraction system; when When it is the smallest, it means that the solvent system has the best polarity match for the target component, and the distribution coefficient is The largest, target component enrichment efficiency is the highest.

[0008] To further optimize the technical solution, the migration of the target molecule in the two phases in step S2 is not only affected by the distribution coefficient, but also controlled by factors such as solvent mixing time and interface diffusion rate. The distribution kinetic equation of the component is as follows: ; in, represents the solvent interface diffusion rate; represents the reverse diffusion coefficient, i.e., the rate at which the target component returns from the organic phase to the aqueous phase; When reaching equilibrium ( ), the target component concentration in the organic phase at equilibrium can be obtained: .

[0009] Further optimizing the technical solution, in step S2, in order to quantify the enrichment effect of the target component, an enrichment factor is defined : ; in, and represent the volumes of the organic and aqueous phases, respectively; It represents the concentration of target component in the organic phase at equilibrium; like , the target component was effectively enriched in the organic phase, the extraction was successful, and The larger it is, the higher the extraction efficiency; like , the concentration of the target component in the aqueous phase and the organic phase did not change, and the extraction was invalid; like , the target components tend to remain in the water phase, the extraction fails or the efficiency is extremely low, and the extraction system needs to be optimized.

[0010] Further optimizing the technical solution, the nonlinear spectral signal deconvolution method is used in step S4 to eliminate spectral peak overlap and improve the spectral resolution of the target component. The spectral signal of the target component is , based on trace impurities and solvent interference, the actual measured signal It can be expressed as: ; in, Represents the remaining matrix interference signal, which needs to be removed; To measure noise, we assume a Gaussian distribution ; Use nonlinear kernel transformation to deconvolve and separate and : ; Among them, the convolution kernel function Using a nonlinear function based on Fourier transform: ; in, Control the separation degree and range of spectral peaks ; Control the distribution of Fourier frequency domain and improve the ability to suppress high-frequency noise.

[0011] To further optimize the technical solution, in step S4, the use process of the nonlinear spectral signal deconvolution method includes: Spectral denoising: calculation via kernel transform , remove noise and weak interference; Background signal subtraction: using Perform matrix interference subtraction to improve the purity of the target component signal; Improved spectral resolution: used to separate peak overlaps and improve the specific identification of target components.

[0012] To further optimize the technical solution, the time-frequency domain joint spectral analysis method is introduced in step S4, and the spectral signals of the target component at different wavelengths are time series , short-time Fourier transform (STFT) is used for time-frequency joint analysis: ; in, is the time-frequency domain spectral distribution function of the target component; As the window function, a Gaussian window is selected to smooth the signal; is the frequency component in Fourier transform.

[0013] To further optimize the technical solution, the time-frequency joint analysis process in step S4 includes: Spectral component identification: Spectra of different components may be similar in the time domain, but they are clearly distinguishable in the frequency domain. Perform high-dimensional feature extraction to help separate similar compounds; Stability analysis: By analyzing the Changes, evaluate the spectral stability of the target component, and determine the degree to which it is affected by external factors; Key absorption peak screening: Use frequency domain characteristics to calculate the characteristic peak position of the target component to avoid misjudgment caused by peak overlap and improve analysis accuracy.

[0014] To further optimize the technical solution, the time-frequency domain feature vector is used in step S4 Quantitative calculation of the target component is performed, and the concentration of the target component is , its spectral absorption intensity The relationship with concentration is: ; in, is the linear correction coefficient of the experimental fit; is the systematic error of the spectrometer.

[0015] In a second aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of a component detection method suitable for plant extracts as described in the first aspect of the present invention are implemented.

[0016] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of a component detection method applicable to plant extracts as described in the first aspect of the present invention are implemented.

[0017] Compared with the prior art, the present invention provides a component detection method suitable for plant extracts, which has the following beneficial effects: This component detection method suitable for plant extracts avoids the problems of poor selectivity of target components and co-extraction interference caused by single solvent extraction in traditional methods by setting up a target component response model based on tensor mapping and a spectral consistency discrimination mechanism. By constructing a three-dimensional response tensor, the spectral data of plant extracts under multiple platforms and conditions are effectively structurally unified and semantically reconstructed, so that the characteristic response of the target component can be accurately extracted from a complex background. At the same time, by introducing a spectral analysis model based on ideal spectral matching and error function correction, the specificity of signal recognition and the distinguishability after component enrichment are significantly improved, and the adaptability of the method to multi-component systems is enhanced. Compared with the existing single solvent system dominated by polarity differences, this method does not rely on chemical extraction methods for primary selection, but completes the spectral separation of component differences in an algorithmic manner, while maintaining the detection flux, it improves the enrichment efficiency of the target component and the final detection sensitivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 This is a schematic diagram of the structure of a component detection method applicable to plant extracts proposed by the present invention; Figure 2 A schematic diagram of a flow chart of a nonlinear spectral signal deconvolution method for a component detection method of plant extracts proposed by the present invention; Figure 3 This is a schematic diagram of the time-frequency joint analysis process of a component detection method suitable for plant extracts proposed in the present invention. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0023] Embodiment 1: Reference Figure 1 to Figure 3 , which is the first embodiment of the present invention, provides a component detection method suitable for plant extracts, comprising the following steps: S1. Using two-phase liquid-liquid extraction to enrich the target component to obtain an initial enriched solution; First, the plant extracts were pre-treated to ensure the accuracy of subsequent testing. Take an appropriate amount of plant extract (usually powder, oil or viscous liquid) and choose an appropriate solvent to dissolve it according to the characteristics of the sample. For water-soluble extracts, ultrapure water was used for dissolution and filtered through a 0.22 μm filter membrane to remove insoluble particles. For fat-soluble extracts, methanol, ethanol or ethyl acetate were used for dissolution, and the precipitate was removed by centrifugation (speed ≥10,000 rpm, time ≥10 min). For viscous liquid samples, ultrasonic assisted dissolution (40 kHz, 30 min) was used to improve solubility. Finally, the treated solution was transferred to a glass chromatography vial, sealed and stored in a 4°C refrigerator for short-term storage to prevent component degradation.

[0024] S2, using the initial enriched solution to perform multidimensional attribute mapping and component factor construction to obtain the target component response tensor; The target components of the sample are enriched from the complex matrix by using a biphasic solvent-liquid extraction method. In the biphasic solvent-liquid extraction, the distribution of the target components depends on their distribution coefficients in the two phases of solvent. : ; in, Indicates the concentration of the target component in the organic phase; Indicates the concentration of the target component in the aqueous phase; The dynamic regulation model of solvent polarity is introduced, and the calculation process of solvent polarity matching factor is as follows: ; in, is the polarity parameter of the target component; Indicates the polarity parameter of the organic phase; Represents the polarity parameter of the water phase; is the volume fraction of the organic phase in the extraction system; Solvent polarity matching factor and the partition coefficient The polarity matching determines the preferred distribution of the solute between the two phases. When it approaches 0, the polarity of the target component is closest to the overall polarity of the solvent system, and the solute molecules tend to be stable in energy. Due to the close polarity, the solubility of the target component in this phase increases, resulting in an increase in concentration. At the same time, the solubility in the other phase decreases, and the concentration decreases, which makes the distribution coefficient Reach the maximum, the target component enrichment efficiency is the highest; The migration of the target molecule in the two phases is not only affected by the distribution coefficient, but also controlled by factors such as solvent mixing time and interface diffusion rate. The distribution kinetic equation of the component is as follows: ; in, represents the solvent interface diffusion rate; represents the reverse diffusion coefficient, i.e., the rate at which the target component returns from the organic phase to the aqueous phase; When reaching equilibrium ( ), the target component concentration in the organic phase at equilibrium can be obtained: .

[0025] This kinetic model can be used to optimize the extraction time and extraction times ,For example: Determine the optimal extraction time: Experimentally measure the value, and fit right curve, find the shortest time to reach 95% equilibrium concentration , to improve experimental efficiency; Optimize extraction times : If a single extraction cannot achieve a sufficiently high concentration of the target component, the cumulative enrichment effect after multiple extractions is predicted based on the kinetic equation and the extraction strategy is adjusted; To quantify the enrichment effect of the target component, the enrichment factor The calculation formula is as follows: ; in, and represent the volumes of the organic and aqueous phases, respectively; It represents the concentration of target component in the organic phase at equilibrium; like , the target component was effectively enriched in the organic phase, the extraction was successful, and The larger it is, the higher the extraction efficiency; like , the concentration of the target component in the aqueous phase and the organic phase did not change, and the extraction was invalid; like , the target components tend to be retained in the aqueous phase, the extraction fails or the efficiency is extremely low, and the extraction system needs to be optimized; The formula in step S2, when used, includes: Parameter initialization Preset target component polarity , select a typical organic / aqueous phase system and give ; Obtained through experiments or databases The original concentration of Set the extraction solvent ratio ,calculate ; Optimize the extraction system Traversal (or solvent component), search using Minimal configuration; at this time Reach the maximum value and conduct subsequent kinetic modeling; Dynamic simulation of enrichment process exist At minimum, combined with the initial Values ​​and model parameters , solve , and the equilibrium concentration is obtained ; Output target component response tensor Multiple Under optimized conditions Values, organized as three-dimensional tensors according to different solvent components, time steps, and sample dimensions : ; : Different target components or sample batches; : Different Set conditions; : Extract the time step or number of experiments; Compared with the existing mature liquid-liquid extraction technology, this method can effectively reduce the co-extraction of non-target components during the extraction process, increase the concentration ratio of target components, and reduce the burden of subsequent purification steps. The existing technology usually relies on the polarity difference of solvents for simple distribution, while this method achieves more precise component enrichment through the synergistic effect between solvents, which is suitable for the separation process of complex plant extracts.

[0026] S3, using the target component response tensor to perform correlation feature constraint calculation to obtain a spectral feature set to be compared; In order to meet the spectral difference recognition requirements in step S4, the response data in the tensor needs to be processed with feature constraints to screen out the target index area closely related to the spectral absorption characteristics. The associated feature mapping function is used in the feature constraint processing process. Extract the joint feature projection value with the maximum polar contribution gradient and steady-state stability index from the response tensor, and its function is: ; in, represents the gradient of concentration variation with the polarity matching factor, which is used to capture the polarity dependence of the component response; It represents the concentration increment per unit time, and describes the change in the speed of the system entering the steady state process; is the first Group in The joint feature quantity under the condition; This mapping process is used to highlight the dominant influencing mechanism of the target response on the spectral change, so that the subsequent S4 The actual measured signal When calculating the difference between , and Have the following relationships; ; in, is the normalized spectral deviation function used to evaluate the The difference between the actual spectrum and the ideal spectrum under the condition of the spectral band is measured, providing a sub-band effectiveness screening mechanism; S3 is logically a necessary constraint calculation process between the S2 numerical tensor and the S4 signal comparison, which is used to complete the matching bridge between the response physical quantity and the spectral signal dimension. Through this joint feature set based on gradient and dynamic steady-state contribution, the accuracy and pertinence of subsequent anomaly identification and comparison are improved.

[0027] S4, using the spectral feature set to be compared to construct an ideal spectrum and analyze the response difference to obtain an ideal spectral function of the target component; In step S4, a nonlinear spectral signal deconvolution method is used to eliminate spectral peak overlap and improve the spectral resolution of the target component. The spectral signal of the target component is , based on trace impurities and solvent interference, the actual measured signal It can be expressed as: ; in, Represents the remaining matrix interference signal, which needs to be removed; is the measurement noise, which follows a Gaussian distribution ; This formula combines the normalized spectral deviation function , providing component response effectiveness judgment criteria: when ,illustrate , the target component responds well in this wavelength region; when When , it indicates that there is obvious interference or structural shift in the band, which is used to mark the component failure area; when When , it means that the response of this band is insufficient, there may be weak spectral response, interference absorption, or systematic underestimation of the instrument, and this band should be excluded; Use nonlinear kernel transformation to deconvolve and separate and : ; Among them, the convolution kernel function Using a nonlinear function based on Fourier transform: ; in, Control the separation degree and range of spectral peaks ; Control the distribution of Fourier frequency domain to improve the ability to suppress high-frequency noise; In step S4, the use process of the nonlinear spectral signal deconvolution method includes: Spectral denoising: calculation via kernel transform , remove noise and weak interference; Background signal subtraction: using Perform matrix interference subtraction to improve the purity of the target component signal; Improved spectral resolution: used to separate peak overlaps and improve the specific identification of target components; In step S4, a time-frequency domain joint spectral analysis method is introduced, and the spectral signals of the target component at different wavelengths are time series. , short-time Fourier transform (STFT) is used for time-frequency joint analysis: ; in, is the time-frequency domain spectral distribution function of the target component, that is, the ideal spectral function of the target component; As the window function, a Gaussian window is selected to smooth the signal; is the frequency component in Fourier transform; The time-frequency joint analysis process in step S4 includes: Spectral component identification: Spectra of different components may be similar in the time domain, but they are clearly distinguishable in the frequency domain. Perform high-dimensional feature extraction to help separate similar compounds; Stability analysis: By analyzing the Changes, evaluate the spectral stability of the target component, and determine the degree to which it is affected by external factors; Key absorption peak screening: Use frequency domain characteristics to calculate the characteristic peak position of the target component to avoid misjudgment caused by peak overlap and improve analysis accuracy; In step S4, the time-frequency domain feature vector is used Quantitative calculation of the target component is performed, and the concentration of the target component is , its spectral absorption intensity The relationship with concentration is: ; in, is the linear correction coefficient of the experimental fit; is the spectrometer system error; Existing technologies usually rely on single wavelength or linear models to perform spectral measurement of target components. However, this method combines multivariate analysis with nonlinear factors to make spectral signal extraction more accurate. Compared with traditional methods, this technology can effectively distinguish spectral signal deviations under different environmental conditions, and provide more accurate target component identification capabilities, thereby improving the reliability of spectral analysis.

[0028] S5, using the ideal and actual spectrum residual signals to perform mapping analysis to obtain a standardized comparison spectrum set; In order to achieve a structured comparison of the potential component differences in the spectra of different samples, , and A mapping quantification system is established based on The components are projected in the frequency domain to The spectral space represented by the method constructs the standardized comparison spectrum of each sample in the same reference frame. ,Right now: ; The standardization operation is used to eliminate the interference caused by background noise and baseline offset of different samples, so that the comparison of target component-related features between different samples has a unified standard. The standardized comparison spectrum set will be directly used as the input basis for the difference identification and quantitative estimation analysis in step S6.

[0029] S6. Spectral calibration is performed using a standardized comparison spectrum set to obtain clear signals of plant extract components; In step S6, a dynamic error correction function is constructed Perform nonlinear drift correction so that the corrected signal Closer , the corrected signal is obtained by dividing the original signal by Compare spectra with the standard Correction is performed to remove the influence of background noise and target components, so as to obtain a clear signal, that is, , the corrected signal Dynamic Error Correction Function The relationship between them is: ; in, ; and is the correction coefficient, which can be obtained by training experimental data to ensure the best correction effect; Through the known experimental environment parameters Perform dynamic adjustments to correct spectral drift errors in real time; Corrected spectral signal Approach , used for the final quantitative analysis of target components; The formula in the step includes: Pre-experiment calibration phase The normalized benchmark data in step S5 is used to calculate the specific experimental environment. and measurement noise ; Training error correction parameters , determine the optimal correction model; Experimental determination stage Acquire experimental environment parameters in real time during each measurement ,calculate ; The measured signal Perform error correction to obtain the final correction signal ; use Carry out quantitative analysis of target components to obtain information on plant extract components.

[0030] Existing technologies usually use fixed linear correction methods or empirical drift compensation, which are difficult to adapt to complex environmental changes. Dynamic change information of the signal is used to establish a more adaptive nonlinear error correction model. Closer to the ideal target component spectral signal , improving the accuracy and stability of target component measurement.

[0031] Embodiment 2: This embodiment also provides a computer device, which is applicable to a method for detecting components of plant extracts, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a method for detecting components of plant extracts as proposed in the above embodiment.

[0032] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a component detection method applicable to plant extracts as proposed in the above embodiment is implemented.

[0033] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0034] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0035] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0036] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk case (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0037] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0038] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for detecting components of plant extracts, characterized in that: The following steps are involved: S1. Using two-phase liquid-liquid extraction to enrich the target component to obtain an initial enriched solution; S2. Use the initial enrichment solution to perform multidimensional attribute mapping and component factor construction to obtain the target component response tensor; S3, using the target component response tensor to perform correlation feature constraint calculation to obtain a spectral feature set to be compared; S4, using the spectral feature set to be compared to construct an ideal spectrum and analyze the response difference to obtain an ideal spectral function of the target component; S5, using the ideal and actual spectrum residual signals to perform mapping analysis to obtain a standardized comparison spectrum set; S6. Spectral correction was performed using the standardized comparison spectrum set to obtain clear signals of the plant extract components.

2. A component detection method suitable for plant extracts according to claim 1, characterized in that: In step S2, a two-phase solvent-liquid-liquid extraction method is used to enrich the target components of the sample from the complex matrix. In the two-phase solvent-liquid-liquid extraction, the distribution of the target components depends on their distribution coefficients in the two-phase solvent. : ； in, Indicates the concentration of the target component in the organic phase; Indicates the concentration of the target component in the aqueous phase.

3. A component detection method suitable for plant extracts according to claim 2, characterized in that: In step S2, a solvent polarity dynamic control model is introduced to define the solvent polarity matching factor: ; in, is the polarity parameter of the target component; Indicates the polarity parameter of the organic phase; Represents the polarity parameter of the water phase; is the volume fraction of the organic phase in the extraction system; when When it is the smallest, it means that the solvent system has the best polarity match for the target component, and the distribution coefficient is The largest, target component enrichment efficiency is the highest.

4. A component detection method suitable for plant extracts according to claim 3, characterized in that: The migration of the target molecule in the two phases in step S2 is not only affected by the distribution coefficient, but also controlled by factors such as solvent mixing time and interface diffusion rate. The distribution kinetic equation of the component is as follows: ； in represents the solvent interface diffusion rate; represents the reverse diffusion coefficient, i.e., the rate at which the target component returns from the organic phase to the aqueous phase; In reaching equilibrium When , the target component concentration in the organic phase at equilibrium can be obtained: 。 5. A component detection method suitable for plant extracts according to claim 4, characterized in that: In step S2, the enrichment factor is defined to quantify the enrichment effect of the target component. : ； in, and represent the volumes of the organic and aqueous phases, respectively; It represents the concentration of target component in the organic phase at equilibrium; like , the target component was effectively enriched in the organic phase, the extraction was successful, and The larger it is, the higher the extraction efficiency; like , the concentration of the target component in the aqueous phase and the organic phase did not change, and the extraction was invalid; like , the target components tend to be retained in the water phase, the extraction fails or the efficiency is extremely low, and the extraction system needs to be optimized.

6. A component detection method suitable for plant extracts according to claim 1, characterized in that: In step S4, a nonlinear spectral signal deconvolution method is used to eliminate spectral peak overlap and improve the spectral resolution of the target component. The spectral signal of the target component is , based on trace impurities and solvent interference, the actual measured signal It is expressed as: ； in, Represents the remaining matrix interference signal, which needs to be removed; To measure noise, we assume a Gaussian distribution ; Use nonlinear kernel transformation to deconvolve and separate and : ； Among them, the convolution kernel function Using a nonlinear function based on Fourier transform: ； in, Control the separation degree and range of spectral peaks ; Control the distribution of Fourier frequency domain and improve the ability to suppress high-frequency noise.

7. A component detection method suitable for plant extracts according to claim 6, characterized in that: In step S4, the use process of the nonlinear spectral signal deconvolution method includes: Spectral denoising: calculation via kernel transform , remove noise and weak interference; Background signal subtraction: using Perform matrix interference subtraction to improve the purity of the target component signal; Improved spectral resolution: used to separate peak overlaps and improve the specific identification of target components.

8. A component detection method suitable for plant extracts according to claim 6, characterized in that: In step S4, a time-frequency domain joint spectral analysis method is introduced, and the spectral signals of the target component at different wavelengths are time series. , short-time Fourier transform is used for time-frequency joint analysis: ； in, is the time-frequency domain spectral distribution function of the target component; As the window function, a Gaussian window is selected to smooth the signal; is the frequency component in Fourier transform.

9. A component detection method suitable for plant extracts according to claim 6, characterized in that: The time-frequency joint analysis process in step S4 includes: Spectral component identification: Spectra of different components may be similar in the time domain, but they are clearly distinguishable in the frequency domain. Perform high-dimensional feature extraction to help separate similar compounds; Stability analysis: By analyzing the Changes, evaluate the spectral stability of the target component, and determine the degree to which it is affected by external factors; Key absorption peak screening: Use frequency domain characteristics to calculate the characteristic peak position of the target component to avoid misjudgment caused by peak overlap and improve analysis accuracy.

10. A component detection method suitable for plant extracts according to claim 6, characterized in that: In step S4, the time-frequency domain feature vector is used Quantitative calculation of the target component is performed, and the concentration of the target component is , its spectral absorption intensity The relationship with concentration is: ； in, is the linear correction coefficient of the experimental fitting; is the systematic error of the spectrometer.

Images