Method for analyzing phenolic substance components of highland barley with different grain colors under altitude gradient

Abstract

The invention relates to the technical field of bioengineering information, and particularly discloses a method for analyzing phenolic substance components of highland barley with different grain colors under an altitude gradient, which comprises the following steps of: establishing a metabolic response knowledge base of a mapping relationship between different environmental stress factors and standard highland barley grain metabolic relaxation characteristics through a controllable environment experiment; the highland barley data are converted through decoding, specific environment events experienced by each highland barley sample in the growth period are accurately judged, and specific environment growth records are reversely deduced, so that each batch of highland barley has a detailed growth file, reliable technical support is provided for production place authentication and quality traceability of high-quality highland barley, and the highland barley quality evaluation method is suitable for popularization and application. And meanwhile, a digital simulation process is combined to pre-judge how much antioxidation effect can be generated after the highland barley in a specific batch is prepared into a product, so that a using unit can scientifically select a highland barley raw material most suitable for a specific purpose, and the whole-course quality control is realized when the highland barley with the best anti-fatigue effect is selected for plateau troops to prepare marching solid food.

Description

Technical Field

[0001] This invention relates to the field of bioengineering information technology, specifically a method for analyzing the composition of phenolic substances in barley of different grain colors at different altitudes. Background Technology

[0002] Analysis of the phenolic composition of highland barley refers to the technical process of qualitatively and quantitatively determining the types, contents and distribution characteristics of phenolic compounds contained in highland barley grains through chemical or physicochemical methods. It is an important research tool for evaluating the nutritional quality and functional characteristics of highland barley.

[0003] Existing methods for analyzing phenolic compounds in highland barley commonly suffer from inaccurate results due to continuous changes in the metabolic state of samples after harvesting. These methods fail to accurately reflect the original quality characteristics of highland barley. Furthermore, traditional techniques cannot infer specific environmental stress events during the growth process from chemical components, lacking the ability to establish a correlation between growth history and quality. In addition, existing methods primarily rely on in vitro chemical assays, making it difficult to accurately predict the actual functional activity of highland barley products after human digestion, resulting in a disconnect between quality assessment and end-use effectiveness. Summary of the Invention

[0004] (a) Technical problems to be solved This invention provides a method for analyzing the composition of phenolic substances in barley of different grain colors under altitude gradients, which solves the problems mentioned in the background art.

[0005] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: a method for analyzing the composition of phenolic substances in barley of different grain colors under altitude gradients, comprising the following steps: S1. Construct a metabolic response knowledge base: A metabolic response knowledge base was established by conducting controlled environmental experiments to map the relationship between different environmental stress factors and the metabolic relaxation characteristics of standard highland barley grains. S2. Instantaneous sample fixation and particle color identification: At barley harvesting sites at different altitudes, whole barley grains of different colors were rapidly and deeply frozen in situ using freezing equipment within a very short time after the grains were removed from the grains. This was done to freeze the metabolic state of the grains at the moment of harvesting and to record the grain color and altitude information of each sample. S3, Dynamic Metabolic Sensing Detection: For single intact kernels treated with S2, a perturbation scanning of the kernel epidermal region was performed using a femtosecond laser and terahertz wave fusion detection system, and hyperspectral dynamic sequence signals during the perturbation relaxation process were acquired simultaneously. S4. Construction of metabolic relaxation map: Based on the hyperspectral dynamic sequence signal collected in S3, characteristic relaxation time parameters were extracted to construct a metabolic relaxation map characterizing the transient activity of the phenolic substance synthesis pathway. S5. Reverse Decoding of Growth History: The metabolic relaxation map constructed in S4 is matched with the metabolic response knowledge base constructed in S1, and an inverse decoding algorithm is used to invert and generate an environmental stress history report experienced by the barley grain in the later stage of growth. S6. Digital Twin Creation: The grain color information and altitude information recorded in S2, the metabolic relaxation map constructed in S4, and the environmental stress history report generated in S5 are used together as initial boundary conditions to create a virtual grain model of the barley sample. S7. Processing and digestion pathway simulation: The virtual seed model created in S6 is used to simulate subsequent processing technology and human gastrointestinal digestion process in sequence, and the transformation pathway and final product spectrum of phenolic substances in the above process are calculated. S8. Functional Potential Prediction and Assessment: Based on the final product spectrum obtained from the simulation in S7, theoretical bioactivity indicators are calculated, and a functional potential prediction report of the barley sample is generated, completing the evaluation from raw material to in vivo function.

[0006] Furthermore, S1 specifically includes: In the laboratory, specific environmental stress factors were coupled and applied to standard highland barley varieties in a multi-factor controllable environment growth chamber. These stress factors included UV-B radiation with a wavelength range of 280-315 nm, low-temperature cycling with a diurnal temperature range of -5 to 15 degrees Celsius, and atmospheric pressure simulating an altitude of 3000 to 5500 meters. Before and after the stress, dynamic spectral signals of the standard samples were collected using a femtosecond laser and terahertz wave fusion detection system. This led to the establishment of a metabolic response knowledge base that maps different environmental stress factors to the metabolic relaxation characteristics of standard highland barley grains.

[0007] Furthermore, S2 specifically includes: At barley harvesting sites at different altitudes, whole barley grains of different colors were rapidly and deeply frozen in situ within one minute of being removed from the grains. Specifically, a phosphate buffer solution with a pH of 5.5, pre-cooled with liquid nitrogen, was used for quick freezing to freeze the metabolic state of the barley grains at the moment of harvest. An image recognition system equipped with RGB and near-infrared spectral sensors was used to automatically identify and record the grain color information and altitude information of each barley sample. Subsequently, all samples were transported to the laboratory under a complete deep cold chain condition, with the cold chain temperature ranging from -196 to -80 degrees Celsius.

[0008] Furthermore, S3 specifically includes: In the laboratory, single, intact barley grains that had been processed by S2 and transported were subjected to perturbation scanning of a specific region on the barley grain epidermis using a fusion detection system integrating a femtosecond laser, a terahertz emission source, a hyperspectral imager, and a time-correlated single-photon counter. The system used femtosecond laser pulses with a repetition frequency of 1 kHz, a pulse energy of 10 microjoules, and a center wavelength of 800 nanometers. The hyperspectral dynamic sequence signal during the perturbation relaxation process was also acquired simultaneously.

[0009] Furthermore, S4 specifically includes: A wavelet transform-based denoising algorithm and a least squares-based multi-exponential fitting algorithm were used to process the hyperspectral dynamic sequence signal collected in S3, extracting at least three characteristic relaxation time constants and their corresponding fluorescence spectral intensities, and constructing a metabolic relaxation map characterizing the transient activity of the phenolic synthesis pathway. This metabolic relaxation map is a multidimensional data matrix containing the principal components of relaxation time, characteristic fluorescence lifetimes and their corresponding spectral peak positions.

[0010] Furthermore, S5 specifically includes: The metabolic relaxation map constructed in S4 is matched with the metabolic response knowledge base constructed in S1. The environmental stress history report experienced by barley grains in the later stage of grain growth is inverted and generated by the inverse decoding algorithm based on the Levenberg-Marquardt nonlinear least squares optimization algorithm. The environmental stress history report quantitatively describes the type, intensity and time window of the stress event. The grain color information recorded in S2 is used as a variety-specific correction factor and input into the inverse decoding algorithm.

[0011] Furthermore, S6 specifically includes: Using the grain color and altitude information recorded in S2, the metabolic relaxation map constructed in S4, and the environmental stress history report generated in S5 as initial boundary conditions, a virtual grain model of highland barley samples is created in a physicochemical model based on the law of conservation of mass and the biochemical reaction kinetic equation of phenolic substances.

[0012] Furthermore, S7 specifically includes: The virtual grain model created in S6 was used to simulate two subsequent processing techniques: solid-state fermentation and gradient heating baking. Then, the human gastrointestinal digestion process was simulated to calculate the transformation pathways and final product spectra of phenolic substances in the above processes.

[0013] Furthermore, S8 specifically includes: Based on the final product spectrum obtained from the S7 step simulation, a functional potential prediction report of the barley sample is generated by calculating the oxygen free radical absorption capacity value of the final product spectrum, the predicted value of cellular antioxidant activity based on the human colon cancer cell line Caco-2, and the weighted comprehensive score of the growth promotion index of Bifidobacterium and Lactobacillus, thus completing the evaluation from raw material to in vivo function.

[0014] (III) Beneficial Effects This invention provides a method for analyzing the composition of phenolic substances in barley at different grain colors under varying altitude gradients. It offers the following advantages: (I) The method for analyzing the phenolic composition of highland barley of different grain colors at this altitude gradient involves freezing the highland barley grains immediately at the harvest site, which instantly fixes their physiological state after they leave the parent plant. Then, using special preservation technology, the grains are transported to the laboratory. This method can completely preserve the true metabolic characteristics formed by highland barley in the high-altitude environment, ensuring that the subsequent analysis is based on the most original and authentic data. This lays a solid foundation for accurately assessing the quality of highland barley and avoids the problem of inaccurate data caused by the long time required for samples to return to the laboratory after harvesting in traditional techniques, during which the active ingredients inside the highland barley continue to change.

[0015] (II) The method for analyzing the composition of phenolic substances in highland barley of different grain colors at this altitude gradient uses specialized decoding technology to convert these chemical signals into specific environmental growth records. This allows for accurate determination of the specific environmental events experienced by each highland barley sample during its growth period, such as the specific day before harvest when it experienced ultraviolet radiation for how long and how strong, or the time period during which it encountered low temperature testing. This capability enables each batch of highland barley to have a detailed growth record, providing reliable technical support for the origin certification and quality traceability of high-quality highland barley.

[0016] (III) The method for analyzing the composition of phenolic substances in barley of different grain colors at this altitude gradient, through a computer simulation system, can predict in advance how much antioxidant effect a specific batch of barley will produce after being processed into products such as tsampa and biscuits and absorbed by the human body, or how much it will improve intestinal health. This allows users to scientifically select the most suitable barley raw materials for specific purposes, especially when selecting barley with the best anti-fatigue effect for high-altitude troops to make marching rations, thus achieving full quality control from the place of origin to the table. Attached Figure Description

[0017] Figure 1 This is the overall flowchart of the present invention. Detailed Implementation

[0018] The technical solutions of 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] like Figure 1 As shown, this invention provides a technical solution: a method for analyzing the composition of phenolic substances in barley of different grain colors under altitude gradients, comprising the following steps: S1. Construct a metabolic response knowledge base: A metabolic response knowledge base was established by conducting controlled environmental experiments to map the relationship between different environmental stress factors and the metabolic relaxation characteristics of standard highland barley grains. S2. Instantaneous sample fixation and particle color identification: At barley harvesting sites at different altitudes, whole barley grains of different colors were rapidly and deeply frozen in situ using freezing equipment within a very short time after the grains were removed from the grains. This was done to freeze the metabolic state of the grains at the moment of harvesting and to record the grain color and altitude information of each sample. S3, Dynamic Metabolic Sensing Detection: For single intact kernels treated with S2, a perturbation scanning of the kernel epidermal region was performed using a femtosecond laser and terahertz wave fusion detection system, and hyperspectral dynamic sequence signals during the perturbation relaxation process were acquired simultaneously. S4. Construction of metabolic relaxation map: Based on the hyperspectral dynamic sequence signal collected in S3, characteristic relaxation time parameters were extracted to construct a metabolic relaxation map characterizing the transient activity of the phenolic substance synthesis pathway. S5. Reverse Decoding of Growth History: The metabolic relaxation map constructed in S4 is matched with the metabolic response knowledge base constructed in S1. Through the reverse decoding algorithm, the environmental stress history report experienced by the barley grain in the later stage of growth is inverted and generated. S6. Digital Twin Creation: The grain color information and altitude information recorded in S2, the metabolic relaxation map constructed in S4, and the environmental stress history report generated in S5 are used together as initial boundary conditions to create a virtual grain model of the barley sample. S7. Processing and digestion pathway simulation: The virtual seed model created in S6 is used to simulate subsequent processing technology and human gastrointestinal digestion process in sequence, and the transformation pathway and final product spectrum of phenolic substances in the above process are calculated. S8. Functional Potential Prediction and Assessment: Based on the final product spectrum obtained from the simulation in S7, theoretical bioactivity indicators are calculated, and a functional potential prediction report of the barley sample is generated, completing the evaluation from raw material to in vivo function.

[0020] S1 specifically includes: In the laboratory, specific environmental stress factors were coupled and applied to a defined standard barley variety in a multi-factor controlled environment growth chamber. These stress factors included: UV-B radiation with a peak wavelength of 305 nm and an irradiance of 1.5 W / m² was continuously applied for 4 hours daily. Low-temperature cycling with day and night temperatures ranging from -5°C (for 8 hours) to 15°C (for 16 hours); A low-pressure environment was simulated at altitudes ranging from 3000 to 5500 meters (atmospheric pressure range of 70.1 kPa to 50.1 kPa). Each stress treatment lasted for a preset cycle of 5 days. Within 30 minutes before and after the start of each stress treatment, dynamic spectral signals of standard samples were collected using a femtosecond laser and terahertz wave fusion detection system. By performing principal component analysis and partial least squares regression analysis on all stress signal data, a quantitative and causal mapping database, namely a metabolic response knowledge base, was established between different environmental stress factors and the metabolic relaxation characteristics of standard highland barley grains. This database provides a unique comparison basis and judgment benchmark for subsequent interpretation of detection signals from actual samples.

[0021] S2 specifically includes: At barley harvesting sites at different altitudes, within one minute of the grains being removed from the barley, whole barley grains of different colors were subjected to rapid, in-situ deep freezing. Specifically, liquid nitrogen pre-cooling was used in an environment with a pH of 5.5 and containing 1% [unclear - possibly a specific ingredient or substance]. The barley grains were rapidly frozen in a phosphate buffer solution containing (w / v) polyvinylpyrrolidone and 1 mM ethylenediaminetetraacetic acid to quench enzyme activity and prevent the oxidation of phenolic substances, thus freezing the metabolic state of the grains at the moment of harvest. Simultaneously, an image recognition system equipped with RGB and near-infrared spectral sensors automatically identified and recorded the precise grain color information of each barley sample and the precise altitude information of the collection location calibrated by GPS and a barometer, based on a pre-set grain color standard card containing black, blue, white, and purple standard RGB and NIR reflectance ranges. Subsequently, all processed samples were transported to the laboratory within 72 hours under a complete cryogenic chain, and the transport containers were equipped with temperature recorders for verification. By rapidly freezing the samples at the production site, all biochemical reactions were effectively terminated, ensuring that the internal chemical composition of the samples did not change during the process from collection to laboratory analysis, thus preserving their true state in the original environment.

[0022] S3 specifically includes: In the laboratory, single, intact barley grains, processed and transported via S2, were manipulated using a fusion detection system integrating a femtosecond laser, a terahertz emission source, a hyperspectral imager, and a time-correlated single-photon counter. First, the grains were fixed with the endosperm side facing upwards under microscope assistance to avoid interference from the high autofluorescence of the germ. Then, a 5x5 dot matrix scan was performed on a specific area of ​​the barley grain epidermis using a femtosecond laser pulse with a repetition frequency of 1 kHz, a pulse energy of 10 microjoules, and a center wavelength of 800 nm. Each dot had a diameter of 10 μm. Simultaneously, hyperspectral dynamic sequence signals with a wavelength range of 400-1700 nm and a spectral resolution better than 5 nm were acquired. The entire acquisition process was conducted in an inert nitrogen atmosphere to minimize photo-oxidation effects. Without damaging the integrity of the sample, the original distribution and dynamic information of its internal chemical components were obtained, providing a high-quality data foundation for subsequent analysis.

[0023] S4 specifically includes: A wavelet transform-based denoising algorithm and a multi-exponential fitting algorithm based on the Levenberg-Marquardt nonlinear least squares optimization algorithm were used to process the hyperspectral dynamic sequence signal acquired in S3. At least three significant characteristic relaxation time constants (τ1, τ2, τ3) and their corresponding normalized amplitudes (A1, A2, A3) were extracted from the fitting results to construct a metabolic relaxation map characterizing the transient activity of the phenolic synthesis pathway. This metabolic relaxation map is a multidimensional data matrix containing the principal components of relaxation time, characteristic fluorescence lifetime, corresponding spectral peak positions, and the dynamic integral area of ​​the peak positions during the relaxation process. The complex raw signals obtained by detection are transformed into standardized index maps that can directly reflect the activity and content of different components through mathematical processing, making the chemical state of the sample measurable and comparable.

[0024] S5 specifically includes: The metabolic relaxation map constructed in S4 is matched with the metabolic response knowledge base constructed in S1. An inverse decoding algorithm based on the Levenberg-Marquardt nonlinear least squares optimization algorithm is used. This algorithm takes minimizing the sum of squared residuals between the measured map and the simulated map in the knowledge base as the objective function and introduces time continuity as a constraint to invert and generate an environmental stress history report experienced by highland barley grains in the later stage of growth. The report quantifies the type, intensity and most likely time window of the stress event in the form of a timeline. In addition, the decoding process uses a preset grain color-response coefficient lookup table and uses the grain color information recorded in S2 as a variety-specific correction factor to dynamically adjust the weight parameters in the inverse decoding algorithm. By intelligently comparing the sample's detection map with the standard database, the specific environmental conditions experienced by the sample during growth can be accurately inferred, realizing the purpose of inferring the growth history from its chemical characteristics.

[0025] S6 specifically includes: Using the grain color and altitude information recorded in S2, the metabolic relaxation map constructed in S4, and the environmental stress history report generated in S5 as initial boundary conditions, a virtual grain model of the barley sample was created in a physicochemical model based on the law of conservation of mass, Fick's second diffusion law, and the kinetic equation of phenolic specific biochemical reactions. This model clearly distinguishes the spatial structure of different tissues such as the epidermis, aleurone layer, and endosperm, and initializes the concentration and distribution of each phenolic substance pool. Integrating all known information about the sample, a highly realistic virtual model was constructed in the computer. This model completely reproduces the physical properties and chemical composition of the sample, providing a foundation for non-destructive simulation experiments.

[0026] S7 specifically includes: The virtual grain model created in step S6 was used to simulate two subsequent processing techniques: solid-state fermentation and gradient temperature baking. The solid-state fermentation simulation conditions were: temperature 30°C, humidity 70%, time 72 hours, inoculation with *Lactobacillus plantarum* CGMCC1.595 at a 5% inoculation rate, and baking ranging from 50°C to 180°C linearly over 20 minutes and maintained for 10 minutes. This was followed by simulation of the human gastrointestinal digestive process, including simulating the gastric phase environment at pH 2.0 containing 0.1% pepsin for 2 hours, and then... The intestinal environment was simulated for 2 hours in a pH 6.8 environment with 0.5% bile salts and 0.2% pancreatic enzymes. Finally, it was simulated for 24 hours in a colonic environment containing a core microbial community model composed of Bacteroides, Bifidobacterium, and Lactobacillus in a 4:3:3 ratio. By solving the differential equations in the model, the transformation pathways and final product spectra of phenolic substances in the above process were calculated. Using the virtual model, the entire process from processing and manufacturing to human ingestion was simulated in a computer, and the final form and content of the effective components in the sample after undergoing these processes were accurately predicted.

[0027] S8 specifically includes: Based on the final product spectrum obtained from the S7 simulation, a functional potential prediction report for highland barley samples is generated by calculating the oxygen free radical absorption capacity value of the final product spectrum, the predicted value of cellular antioxidant activity based on the human colon cancer cell line Caco-2, and the weighted comprehensive score of the growth promotion index of Bifidobacterium and Lactobacillus, with weights of 0.4, 0.35, and 0.25, respectively. This report is presented in a standardized percentage scoring format and includes a comparative analysis with the baseline sample, completing the assessment from raw material to in vivo function. The simulation data is transformed into intuitive functional rating indicators, generating an easy-to-understand value assessment report. This greatly facilitates non-professionals to quickly compare and make decisions on the health benefits of different highland barley samples, directly supporting the accurate selection and development of products.

[0028] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0029] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing the composition of different grain color highland barley phenolic compounds at different altitudes, characterized by: The method comprises the following steps: S1, constructing a metabolic response knowledge base: Through controllable environment experiments, a metabolic response knowledge base of the mapping relationship between different environmental stress factors and the standard barley grain metabolic relaxation characteristics is established; S2, sample instantaneous fixation and grain color identification: At the barley harvesting site at different altitudes, within a very short time after the grains are detached, the intact barley grains of different colors are rapidly and deeply frozen in situ by a freezing device to freeze the metabolic state at the time of grain harvesting, and the grain color information and altitude information of each sample are recorded; S3, dynamic metabolic sensing detection: For the single grain intact grains treated by S2, a femtosecond laser and terahertz wave fusion detection system is used to perform perturbation scanning in the grain epidermis region, and high spectral dynamic sequence signals in the perturbation relaxation process are synchronously collected; S4, metabolic relaxation spectrum construction: According to the high spectral dynamic sequence signals collected in S3, the characteristic relaxation time parameters are extracted, and a metabolic relaxation spectrum representing the transient activity of the phenolic substance synthesis pathway is constructed; S5, reverse decoding of growth history: The metabolic relaxation spectrum constructed in S4 is matched with the metabolic response knowledge base constructed in S1, and through a reverse decoding algorithm, the environmental stress history report experienced by the barley grain in the later growth period is inversely calculated and generated; S6, digital twin creation: The grain color information and altitude information recorded in S2, the metabolic relaxation spectrum constructed in S4, and the environmental stress history report generated in S5 are collectively used as initial boundary conditions to create a virtual grain model of the barley sample; S7, processing and digestion path simulation: The virtual grain model created in S6 is sequentially simulated for subsequent processing technology and human gastrointestinal digestion process, and the conversion path and final product spectrum of phenolic substances in the above process are calculated; S8, functional potential prediction and evaluation: Based on the final product spectrum simulated in S7, the theoretical biological activity index is calculated, the functional potential prediction report of the barley sample is generated, and the evaluation from raw material to in vivo function is completed.

2. The method according to claim 1, wherein the method is used for analyzing the composition of different grain color highland barley phenolic substances under altitude gradient. The S1 specifically comprises: By applying coupled environmental stress factors to the standard barley variety in a multi-factor controllable environment growth box, the stress factors include different waveband ultraviolet radiation, high and low temperature cycles, and simulated high altitude low pressure, and the dynamic spectrum signals of the standard sample are collected before and after the stress by using the femtosecond laser and terahertz wave fusion detection system, thereby establishing a metabolic response knowledge base of the mapping relationship between different environmental stress factors and the standard barley grain metabolic relaxation characteristics.

3. The method according to claim 2, wherein the method is used for analyzing the composition of different grain color highland barley phenolic compounds under altitude gradient. The S2 specifically comprises: At the barley harvesting site at different altitudes, within one minute after the grains are detached, the intact barley grains of different colors are rapidly and deeply frozen in situ by a freezing device, specifically using a liquid nitrogen pre-cooled inert buffer for quick freezing to freeze the metabolic state at the time of harvesting, and the grain color information and altitude information of each sample are recorded by an image recognition system.

4. The method according to claim 3, wherein the method is used for analyzing the composition of different grain color highland barley phenolic compounds under altitude gradient. The S3 specifically comprises: For the S2 treated single grain intact seeds, the femtosecond laser and terahertz wave fusion detection system is used to perform perturbation scanning on the epidermal region of the seeds at a repetition frequency of not less than 1 kHz and a pulse energy of microjoule level, and a high spectral dynamic sequence signal in the relaxation process is synchronously collected.

5. The method according to claim 4, wherein the method is used for analyzing the composition of different grain color highland barley phenolic compounds under altitude gradient. The S4 specifically comprises: The high spectral dynamic sequence signal collected in S3 is processed by using a wavelet transform and a multi-exponential fitting algorithm to extract a characteristic relaxation time parameter in the signal, and a metabolic relaxation atlas representing transient activity of a phenolic substance synthesis pathway is constructed, which is a multi-dimensional data set containing a relaxation time principal component, a characteristic fluorescence lifetime and a corresponding spectral peak position.

6. The method according to claim 5, wherein the method is used for analyzing the composition of different grain color highland barley phenolic compounds under altitude gradient. The S5 specifically comprises: The metabolic relaxation atlas constructed in S4 is matched with the metabolic response knowledge base constructed in S1, and an environmental stress history report experienced by the highland barley seeds in the later growth stage is inversely decoded and generated by using a reverse decoding algorithm based on a constraint optimization principle, which quantitatively describes the type, intensity and occurrence time window of the stress event, and the decoding process is associated with the grain color information recorded in S2 to correct the variety-specific response.

7. The method according to claim 6, wherein the method is used for analyzing the composition of different grain color highland barley phenolic compounds under altitude gradient. The S6 specifically comprises: The grain color information recorded in S2, the altitude information, the metabolic relaxation atlas constructed in S4 and the environmental stress history report generated in S5 are jointly used as initial boundary conditions to create a virtual grain model of the highland barley sample in a physical and chemical model containing biochemical reaction kinetics equations and mass balance equations.

8. The method according to claim 7, wherein the method is used for analyzing the composition of different grain color highland barley phenolic compounds under altitude gradient. The S7 specifically comprises: The virtual grain model created in S6 is sequentially simulated for solid-state fermentation, gradient temperature baking and simulation of a low-pH environment of gastric juice and simulation of intestinal flora enzymatic metabolism, and a conversion path and a final product spectrum of phenolic substances in the above processes are calculated.

9. The method according to claim 8, wherein the method is used for analyzing the composition of different grain color highland barley phenolic compounds under altitude gradient. The S8 specifically comprises: Based on the final product spectrum simulated in S7, a functional potential prediction report of the highland barley sample is generated by calculating an oxygen radical absorption capacity value, a cell antioxidant activity prediction value and a specific human intestinal flora growth promotion index, and an evaluation from raw materials to in-vivo function is completed.

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