A method for detecting components applicable to plant extracts

Abstract

The present invention discloses a method for detecting components applicable to plant extracts, which relates to the technical field of plant extract detection and comprises the following steps: S1. Using biphasic liquid-liquid extraction for enrichment treatment of target components; S2. Using the initial enrichment solution for multi-dimensional attribute mapping and component factor construction; S3. Using the target component response tensor for associated feature constraint calculation; S4. Using the spectral feature set to be compared for ideal spectrum construction and response difference analysis; S5. Using the ideal and actual spectrum residual signals for mapping analysis; S6. Using the standardized comparison spectrum set for spectrum correction; By setting biphasic solvent-liquid extraction, the selectivity of target component enrichment is improved, and efficient extraction of target components in the complex system of plant extracts is realized. Compared with the existing single-solvent extraction or solid-phase extraction, this method regulates the interaction between solvents to enable the target components to form an optimal distribution between liquid phases and reduce the co-extraction of non-target components.

Description

Technical Field

[0001] The present invention relates to the technical field of plant extract detection, and specifically provides a method for detecting the components of 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 accurately identifying and quantifying target components to ensure product quality and biological activity. Existing component detection technologies mainly rely on chromatography, spectroscopy, and mass spectrometry analysis, but there are still many challenges in terms of sample complexity, environmental interference, and detection accuracy. Plant extracts usually contain various chemical components, including polyphenols, flavonoids, alkaloids, etc., with complex components and large content variations.

[0003] In the prior art, existing methods for detecting plant extracts usually use single-solvent extraction or solid-phase extraction. However, these methods have insufficient selectivity for target components in complex systems, easily causing co-extraction of non-target components, which increases the difficulty of subsequent purification and detection. In addition, the partition coefficient of a single solvent is greatly affected by polarity, making it difficult to meet the requirements of simultaneous detection of multiple components, resulting in low enrichment efficiency of target components and thus reducing detection sensitivity. Summary of the Invention

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

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present invention provides a method for detecting the components of plant extracts, including the following steps:

[0007] S1. Use biphasic liquid-liquid extraction for enrichment treatment of target components to obtain an initial enriched solution;

[0008] S2. Use the initial enriched solution for multi-dimensional attribute mapping and component factor construction to obtain a target component response tensor;

[0009] S3. Use the target component response tensor for associated feature constraint calculation to obtain a set of spectral features to be compared;

[0010] S4. Use the set of spectral features to be compared for ideal spectrum construction and response difference analysis to obtain an ideal spectrum function of the target component;

[0011] S5. Use the residual signals between the ideal and actual spectra for mapping analysis to obtain a standardized set of comparison spectra;

[0012] S6. Use a standardized comparison spectral set for spectral correction to obtain clear signals of the components of the plant extract.

[0013] To further optimize this technical solution, in step S2, a biphasic solvent - liquid - liquid extraction method is adopted to enrich the target components of the sample from the complex matrix. In the biphasic solvent - liquid - liquid extraction, the distribution of the target components depends on their partition coefficients in the two - phase solvents. :

[0014] ;

[0015] where, represents the concentration of the target component in the organic phase;

[0016] represents the concentration of the target component in the aqueous phase.

[0017] To further optimize this technical solution, in step S2, a solvent polarity dynamic regulation model is introduced, and the solvent polarity matching factor is defined as:

[0018] ;

[0019] where, is the polarity parameter of the target component;

[0020] represents the polarity parameter of the organic phase;

[0021] represents the polarity parameter of the aqueous phase;

[0022] is the volume fraction of the organic phase in the extraction system;

[0023] When is the smallest, it indicates that the solvent system has the best polarity match for the target component. At this time, the partition coefficient is the largest, and the enrichment efficiency of the target component is the highest.

[0024] To further optimize this technical solution, in step S2, the migration of the target molecules in the two - phases is not only affected by the partition coefficient but also controlled by factors such as the solvent mixing time and the interfacial diffusion rate. The distribution kinetic equation of the components is as follows:

[0025] ;

[0026] where, represents the solvent interfacial diffusion rate;

[0027] represents the reverse diffusion coefficient, that is, the rate at which the target component returns from the organic phase to the aqueous phase;

[0028] At the equilibrium state ( ), the concentration of the target component in the organic phase at the equilibrium state can be obtained:

[0029] .

[0030] To further optimize this technical solution, in step S2, in order to quantify the enrichment effect of the target component, an enrichment factor is defined as:

[0031] ;

[0032] where and represent the volumes of the organic phase and the aqueous phase respectively;

[0033] represents the concentration of the target component in the organic phase at the equilibrium state;

[0034] If , the target component is effectively enriched into the organic phase, and the extraction is successful. Moreover, the larger it is, the higher the extraction efficiency;

[0035] If , the concentrations of the target component in the aqueous phase and the organic phase do not change, and the extraction is ineffective;

[0036] If , the target component tends to remain in the aqueous phase, and the extraction fails or has extremely low efficiency, and the extraction system needs to be optimized.

[0037] To further optimize this technical solution, in step S4, a non - linear spectral signal de - convolution method is adopted to eliminate spectral peak overlap and improve the spectral resolution of the target component. The spectral signal of the target component is . Due to trace impurity and solvent interference, the actually measured signal can be expressed as:

[0038] ;

[0039] where represents the remaining matrix interference signal that needs to be removed;

[0040] is the measurement noise, assuming it follows a Gaussian distribution ;

[0041] Non - linear kernel transform de - convolution is used to separate and :

[0042] ;

[0043] Among them, the convolution kernel function adopts a non-linear function based on Fourier transform:

[0044] ;

[0045] Among them, controls the separation degree of spectral peaks, with the range ;

[0046] controls the distribution in the Fourier frequency domain and improves the high-frequency noise suppression ability.

[0047] To further optimize this technical solution, in step S4, the usage process of the non-linear spectral signal deconvolution method includes:

[0048] Spectral denoising: Calculate through kernel transformation to remove noise and weak interference;

[0049] Background signal subtraction: Use to perform matrix interference subtraction and improve the purity of the target component signal;

[0050] Spectral resolution improvement: Used to separate peak overlap situations and improve the specific recognition ability of target components.

[0051] To further optimize this technical solution, a time-frequency domain joint spectral analysis method is introduced in step S4. The spectral signals of the target component at different wavelengths are time series , and short-time Fourier transform (STFT) is used for time-frequency joint analysis:

[0052] ;

[0053] Among them, is the time-frequency domain spectral distribution function of the target component;

[0054] is the window function, and a Gaussian window is selected to smooth the signal;

[0055] is the frequency component in the Fourier transform.

[0056] To further optimize this technical solution, the time-frequency joint analysis process in step S4 includes:

[0057] Spectral component identification: The spectra of different components may be similar in the time domain, but have obvious distinguishability in the frequency domain. Use for high-dimensional feature extraction, which helps to separate similar compounds;

[0058] Stability analysis: By analyzing at different time pointsThe change situation is evaluated to assess the spectral stability of the target component and determine the degree of its influence by external factors;

[0059] Key absorption peak screening: Calculate the position of the characteristic peak of the target component using frequency domain features to avoid misjudgment caused by peak overlap and improve the analysis accuracy.

[0060] Further optimize the technical solution. In step S4, the time-frequency domain feature vector is used for quantitative calculation of the target component, and the concentration of the target component is , and its spectral absorption intensity has the following relationship with the concentration:

[0061] ;

[0062] wherein, is the linear correction coefficient fitted by experiments;

[0063] is the spectrometer system error.

[0064] In a second aspect, an embodiment of the present invention provides a computer device, including a memory and a processor. The memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of a method for detecting components of plant extracts as described in the first aspect of the present invention are implemented.

[0065] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, wherein: when the computer program instructions are executed by the processor, the steps of a method for detecting components of plant extracts as described in the first aspect of the present invention are implemented.

[0066] Compared with the prior art, the present invention provides a method for detecting components of plant extracts, which has the following beneficial effects:

[0067] The component detection method applicable to 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 can be effectively unified in structure and semantically reconstructed, enabling the characteristic responses of target components to be accurately extracted from complex backgrounds. 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 resolvability after component enrichment are significantly improved, enhancing the adaptability of the method to multi-component systems. Compared with the existing single-solvent system dominated by polarity differences, this method does not rely on chemical extraction means for primary selection, but completes the spectroscopic separation of component differences in an algorithmic manner, improving the enrichment efficiency of target components and the final detection sensitivity while maintaining the detection throughput. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0069] Figure 1 It is a schematic structural diagram of a component detection method applicable to plant extracts proposed by the present invention;

[0070] Figure 2 It is a schematic flow diagram of a non-linear spectral signal deconvolution method for a component detection method applicable to plant extracts proposed by the present invention;

[0071] Figure 3 It is a schematic flow diagram of a time-frequency joint analysis for a component detection method applicable to plant extracts proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0072] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention is provided in conjunction with the drawings in the specification.

[0073] In the following description, many specific details are set forth to facilitate a thorough understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0074] Second, the "one embodiment" or "embodiment" referred to herein means a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is separate or selectively exclusive of other embodiments.

[0075] Embodiment 1:

[0076] Referring to Figures 1 to 3 , this is the first embodiment of the present invention. This embodiment provides a method for detecting the components of plant extracts, including the following steps:

[0077] S1. Use biphasic liquid-liquid extraction to enrich the target components and obtain an initial enriched solution;

[0078] First, perform sample pretreatment on the plant extract to ensure the accuracy of subsequent detection. Take an appropriate amount of plant extract (usually powder, oily or viscous liquid), and select an appropriate solvent for dissolution according to the sample characteristics. For water-soluble extracts, dissolve them with ultrapure water and filter through a 0.22 μm filter membrane to remove insoluble particles. For fat-soluble extracts, dissolve them with methanol, ethanol or ethyl acetate, and use centrifugation (rotation speed ≥ 10,000 rpm, time ≥ 10 min) to remove the precipitate. For viscous liquid samples, use ultrasonic-assisted dissolution (40 kHz, 30 min) to improve the solubility. Finally, transfer the treated solution to a glass chromatography vial, seal it and store it in a 4°C refrigerator for short-term storage to prevent component degradation.

[0079] S2. Use the initial enriched solution for multi-dimensional attribute mapping and component factor construction to obtain a target component response tensor;

[0080] Adopt the biphasic solvent-liquid-liquid extraction method to enrich the target components of the sample from the complex matrix. In the biphasic solvent-liquid-liquid extraction, the distribution of the target components depends on their distribution coefficients in the two-phase solvents :

[0081] ;

[0082] Among them, represents the concentration of the target component in the organic phase;

[0083] represents the concentration of the target component in the aqueous phase;

[0084] Introduce a solvent polarity dynamic regulation model, and the calculation process of the solvent polarity matching factor is as follows:

[0085] ;

[0086] Among them, is the polarity parameter of the target component;

[0087] represents the polarity parameter of the organic phase;

[0088] represents the polarity parameter of the aqueous phase;

[0089] is the volume fraction of the organic phase in the extraction system;

[0090] Solvent polarity matching factor is inversely proportional to the distribution coefficient When approaches 0, the polarity of the target component is closest to the overall polarity of the solvent system. 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, thus making the distribution coefficient reach the maximum and the enrichment efficiency of the target component is the highest;

[0091] The migration of the target molecule between the two phases is not only affected by the distribution coefficient, but also controlled by factors such as solvent mixing time and interfacial diffusion rate. The distribution kinetic equation of the component is as follows:

[0092] ;

[0093] Among them, represents the solvent interfacial diffusion rate;

[0094] represents the reverse diffusion coefficient, that is, the rate at which the target component returns from the organic phase to the aqueous phase;

[0095] At the equilibrium state ( ), the concentration of the target component in the organic phase at the equilibrium state can be obtained:

[0096] .

[0097] This kinetic model can be used to optimize the extraction time and the number of extraction times , for example:

[0098] Determine the optimal extraction time: Measure the value at different time points through experiments and fit the curve of to find the shortest time to reach 95% of the equilibrium concentration, so as to improve the experimental efficiency;

[0099] Optimize the number of extractions : If a single extraction cannot achieve a sufficiently high concentration of the target component, predict the cumulative enrichment effect after multiple extractions based on the kinetic equation and adjust the extraction strategy;

[0100] To quantify the enrichment effect of the target component, the enrichment factor is calculated as follows:

[0101] ;

[0102] where and represent the volumes of the organic phase and the aqueous phase, respectively;

[0103] represents the concentration of the target component in the organic phase at equilibrium;

[0104] If , the target component is effectively enriched into the organic phase, and the extraction is successful. Moreover, the larger it is, the higher the extraction efficiency;

[0105] If , the concentrations of the target component in the aqueous phase and the organic phase remain unchanged, and the extraction is ineffective;

[0106] If , the target component is more inclined to remain in the aqueous phase, the extraction fails or the efficiency is extremely low, and the extraction system needs to be optimized;

[0107] The formula in step S2 includes the following when used:

[0108] Parameter initialization

[0109] Preset the polarity of the target component , select a typical organic / aqueous phase system and give ;

[0110] Obtain the original concentration of through experiments or databases;

[0111] Set the extraction solvent ratio , and calculate ;

[0112] Optimize the extraction system

[0113] Traverse (or solvent components) to search for the configuration that minimizes ;

[0114] At this time reaches the maximum, and subsequent kinetic modeling is performed;

[0115] Dynamically simulate the enrichment process

[0116] At the minimum, combined with the initial value and model parameters , solve to obtain the equilibrium state concentration ;

[0117] Output the response tensor of the target component

[0118] Organize the values obtained under multiple optimization conditions into a three-dimensional tensor according to different solvent components, time steps, and sample dimensions :

[0119] ;

[0120] : Different target components or sample batches;

[0121] : Different set conditions;

[0122] : Extraction time step or number of experiments;

[0123] 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 at the same time reduce the burden of subsequent purification steps. The existing technology usually relies on the polarity difference of solvents for simple distribution, while this method realizes more precise component enrichment through the synergistic regulation between solvents and is applicable to the separation process of complex plant extracts.

[0124] S3. Use the response tensor of the target component to perform associated feature constraint calculations to obtain the spectral feature set to be compared;

[0125] To meet the spectral difference identification requirements in step S4, it is necessary to perform feature constraint processing on the response data in the tensor to screen out the target index regions closely related to the spectral absorption characteristics. During the feature constraint processing, the associated feature mapping function is used to extract the joint feature projection value with the maximum polarity contribution gradient and steady-state stability index from the response tensor, and its function is:

[0126] ;

[0127] Among them, represents the change gradient of concentration with the polarity matching factor and is used to capture the polarity dependence of component response;

[0128] It represents the concentration increment per unit time and depicts the speed change of the system entering the steady state process;

[0129] is the th group of joint characteristic quantities under the condition in the spectral feature set to be compared;

[0130] This mapping process is used to highlight the dominant influence mechanism of the target response on spectral changes, so that in the subsequent S4, based on the spectral signal of the target component and the actually measured signal When calculating the difference, it can locate the influence area of the core components in terms of physical meaning, 、 and have the following relationship;

[0131] ;

[0132] Among them, is a normalized spectral deviation function, which is used to evaluate the difference degree between the actual spectrum and the ideal spectrum at a specific wavelength and provides a validity screening mechanism for sub-bands;

[0133] Logically, S3 is an essential constraint calculation process between the numerical tensor of S2 and the signal comparison of S4, which is used to complete the matching and bridging between the response physical quantity and the spectral signal dimension. Through this joint feature set constructed based on the gradient and dynamic steady-state contribution, the accuracy and pertinence of subsequent anomaly recognition and comparison are improved.

[0134] S4. Use the spectral feature set to be compared to construct the ideal spectrum and analyze the response difference to obtain the ideal spectrum function of the target component;

[0135] In the step S4, the nonlinear spectral signal deconvolution method is adopted to eliminate spectral peak overlap and improve the spectral resolution of the target component. The spectral signal of the target component is , based on the interference of trace impurities and solvents, the actually measured signal can be expressed as:

[0136] ;

[0137] Among them, represents the remaining matrix interference signal that needs to be removed;

[0138] is the measurement noise, which follows a Gaussian distribution ;

[0139] This formula combines the normalized spectral deviation function , provide the judgment criteria for the effectiveness of component response:

[0140] When , it indicates that , and the response of the target component in this wavelength region is good;

[0141] When , it shows that there are obvious interferences or structural offsets in this band, marking the invalid area of the component;

[0142] When , it means that the response in this band is insufficient, and there may be weak spectral response, interference absorption, or systematic underestimation of the instrument, and this band should be excluded;

[0143] Adopt non - linear kernel transform deconvolution to separate and :

[0144] ;

[0145] Among them, the convolution kernel function adopts a non - linear function based on Fourier transform:

[0146] ;

[0147] Among them, controls the separation degree of spectral peaks, with the range ;

[0148] controls the distribution in the Fourier frequency domain and improves the ability to suppress high - frequency noise;

[0149] In the step S4, the usage process of the non - linear spectral signal deconvolution method includes:

[0150] Spectral denoising: Calculate through kernel transform to remove noise and weak interferences;

[0151] Background signal subtraction: Use to subtract matrix interferences and improve the purity of the target component signal;

[0152] Spectral resolution improvement: Used to separate peak overlaps and improve the specific recognition ability of the target component;

[0153] In the step S4, when introducing the time - frequency domain joint spectral analysis method, the spectral signals of the target component at different wavelengths are time series , and short - time Fourier transform (STFT) is used for time - frequency joint analysis:

[0154] ;

[0155] Among them, is the time-frequency domain spectral distribution function of the target component, that is, the ideal spectral function of the target component;

[0156] is the window function, and a Gaussian window is selected to smooth the signal;

[0157] is the frequency component in the Fourier transform;

[0158] The time-frequency joint analysis process in step S4 includes:

[0159] Spectral component identification: The spectra of different components may be similar in the time domain, but have obvious distinctiveness in the frequency domain. Using for high-dimensional feature extraction helps to separate similar compounds;

[0160] Stability analysis: By analyzing the variation at different time points, evaluate the spectral stability of the target component and judge the degree of its influence by external factors;

[0161] Key absorption peak screening: Use the frequency domain features to calculate the position of the characteristic peaks of the target component, avoid misjudgment caused by peak overlap, and improve the analysis accuracy;

[0162] In step S4, the time-frequency domain feature vector is used for quantitative calculation of the target component, and the concentration of the target component is , and its spectral absorption intensity The relationship with the concentration is:

[0163] ;

[0164] Among them, is the linear correction coefficient fitted by the experiment;

[0165] is the spectrometer system error;

[0166] Existing technologies usually rely on single wavelength or linear models for spectral determination of target components. However, this method combines multivariate analysis with non-linear factors to make the extraction of spectral signals more accurate. Compared with traditional methods, this technology can effectively distinguish the spectral signal offsets under different environmental conditions and provide a more accurate target component recognition ability, improving the reliability of spectral analysis.

[0167] S5. Use the ideal and actual spectral residual signals for mapping analysis to obtain a standardized comparison spectral set;

[0168] To achieve a structured comparison of the potential component differences in the spectra of different samples, in , and Based on this, a mapping quantization system is established. By projecting the components of each sample in the frequency domain onto the spectral space characterized by , a standardized comparison spectrum of each sample under the same reference framework is constructed, that is:

[0169] ;

[0170] The standardization operation is used to eliminate the interference caused by the background noise and baseline shift of different samples, so that the comparison of the target component-related features between different samples has a unified standard. The output constitutes a standardized comparison spectrum set, which will be directly used as the input basis for the difference identification and quantitative estimation analysis in step S6.

[0171] S6. Use the standardized comparison spectrum set to perform spectral correction to obtain a clear signal of the components of the plant extract;

[0172] In step S6, a dynamic error correction function is constructed to perform non-linear drift correction, so that the corrected signal is closer to . The corrected signal is obtained by correcting the original signal with the standardized comparison spectrum set to remove the influence of background noise and target components, so as to obtain a clear signal, that is . The relationship between the corrected signal and the dynamic error correction function is:

[0173] ;

[0174] Among them, ;

[0175] and are correction coefficients, which can be obtained by training experimental data to ensure the best correction effect;

[0176] is dynamically adjusted according to the known experimental environment parameters to correct the spectral drift error in real time;

[0177] The corrected spectral signal approximates and is used for the final quantitative analysis of the target component;

[0178] The formulas in the steps include:

[0179] Pre-experiment calibration stage

[0180] The reference data normalized through step S5 is used to calculate and measurement noise ;

[0181] Training error correction parameters , and determine the optimal correction model; 

[0182] Experimental determination stage

[0183] During each determination, the experimental environment parameters are obtained in real time , and calculate ;

[0184] The measured signal is error-corrected to obtain the final corrected signal ; 

[0185] Using to perform quantitative analysis of the target component, and obtain the component information of the plant extract.

[0186] The prior art usually adopts a fixed linear correction method or empirical drift compensation, which is difficult to adapt to complex environmental changes. This method uses the dynamic change information of the experimental parameters to establish a more adaptable non-linear error correction model, so that the final measured signal is closer to the ideal spectral signal of the target component , and improves the accuracy and stability of the measurement of the target component.

[0187] Embodiment 2:

[0188] This embodiment also provides a computer device, which is applicable to a situation of a method for detecting the components of a plant extract, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement a method for detecting the components of a plant extract as proposed in the above embodiment.

[0189] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by the processor, it implements a method for detecting the components of a plant extract as proposed in the above embodiment.

[0190] The computer device may be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via 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 computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, carrier networks, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, trackball, or touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0191] If a 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 such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, etc., which are various media that can store program codes.

[0192] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

[0193] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which a program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0194] 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, the 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 in hardware, as in another embodiment, any one or a combination of the following techniques known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0195] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for detecting components of plant extracts, characterized in that, Including the following steps: S1. Use biphasic liquid-liquid extraction to enrich the target component to obtain an initial enriched solution; S2. Use the initial enrichment solution for multi-dimensional property mapping and component factor construction to obtain the target component response tensor. Specifically, adopt the biphasic solvent-liquid-liquid extraction method to enrich the target components of the sample from the complex matrix. In the biphasic solvent-liquid-liquid extraction, the distribution of the target components depends on their partition coefficients in the two-phase solvents. Introduce the solvent polarity dynamic regulation model and define the solvent polarity matching factor. When is the smallest, it indicates that the solvent system has the best polarity matching for the target components. At this time, the partition coefficient is the largest and the enrichment efficiency of the target components is the highest. Optimize the extraction system: traverse the solvent components and search for the configuration that makes the smallest. At this time reaches the maximum, and then perform subsequent kinetic modeling. Dynamically simulate the enrichment process: when is the smallest, obtain the equilibrium state concentration . Organize the values obtained under multiple optimization conditions into a three-dimensional tensor according to different solvent components, time steps, and sample dimensions . S3. Use the response tensor of the target component to perform correlation feature constraint calculation to obtain a spectral feature set to be compared; S4. Use the spectral feature set to be compared to construct an ideal spectrum and analyze the response difference to obtain the ideal spectrum function of the target component; S5. Use the residual signals between the ideal and actual spectra to perform mapping analysis to obtain a standardized comparison spectrum set; S6. Use the standardized comparison spectrum set to perform spectral correction to obtain a clear signal of the components of the plant extract.

2. The component detection method for plant extracts according to claim 1, characterized in that, The partition coefficient Specifically: ； Among them, represents the concentration of the target component in the organic phase; Indicates the concentration of the target component in the aqueous phase.

3. The component detection method for plant extracts according to claim 2, characterized in that, The solvent polarity matching factor is: ； Among them, is the polarity parameter of the target component; Represents the polarity parameter of the organic phase; represents the polar parameter of the aqueous phase; is the volume fraction of the organic phase in the extraction system.

4. The component detection method applicable to plant extracts according to claim 3, wherein, In step S2, the migration of the target molecule in the two phases is not only affected by the partition coefficient, but also controlled by factors such as the solvent mixing time and the interfacial diffusion rate. The component distribution kinetic equation is as follows: ； Among them represents the solvent interface diffusion rate; Denotes the reverse diffusion coefficient, i.e., the rate at which the target component returns from the organic phase to the aqueous phase; At equilibrium the concentration of the target component in the organic phase at equilibrium can be obtained: 。 5. A method for detecting components of a plant extract according to claim 4, characterized in that, In step S2, to quantify the enrichment effect of the target component, an enrichment factor is defined : ； Among them, and respectively represent the volumes of the organic phase and the aqueous phase; represents the concentration of the target component in the organic phase under the equilibrium state; If , the target component is effectively enriched in the organic phase, and the extraction is successful. Moreover the larger it is, the higher the extraction efficiency is; If , the concentrations of the target component in the aqueous phase and the organic phase do not change, and the extraction is ineffective; If , the target component is more likely to remain in the aqueous phase, resulting in failed extraction or extremely low efficiency, and the extraction system needs to be optimized.

6. The component detection method for plant extracts according to claim 1, wherein In the step S4, a non-linear spectral signal deconvolution method is adopted to eliminate spectral peak overlap and improve the spectral resolution of the target component. The spectral signal of the target component is , and based on the interference of trace impurities and solvents, the actually measured signal is expressed as: ； Among them, represents the remaining matrix interference signal that needs to be removed; To measure noise, it is assumed to follow a Gaussian distribution ; Use non-linear kernel transform deconvolution to separate and : ； Among them, the convolution kernel function adopts a non-linear function based on Fourier transform: ； Among them, control the separation degree of spectral peaks, with a range ; Control the distribution in the Fourier frequency domain and improve the high-frequency noise suppression ability.

7. The component detection method applicable to 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: Calculate through kernel transformation , removing noise and weak interference; Background signal subtraction: Using to perform matrix interference subtraction and improve the purity of the target component signal; Spectral resolution improvement: used to separate peak overlap situations and improve the specific recognition ability of the target component.

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

9. The component detection method applicable to plant extracts according to claim 8, wherein In step S4, the time-frequency joint analysis process includes: Spectral component identification: The spectra of different components may be similar in the time domain, but are significantly distinguishable in the frequency domain. Using for high-dimensional feature extraction helps to separate compounds of the same type; Stability analysis: By analyzing the changes at different time points, evaluate the spectral stability of the target component and determine the degree of its influence by external factors; Key absorption peak screening: Use frequency domain characteristics to calculate the characteristic peak positions of the target component, avoid misjudgment caused by peak overlap, and improve the analysis accuracy.

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