Tea processing strategy determination method based on chemical and sensory characteristics and related device

Abstract

The invention provides a tea processing strategy determination method based on chemical and sensory characteristics and a related device, and relates to the technical field of tea processing. Obtaining a tea sample; carrying out sensory evaluation on the tea samples, wherein the sensory evaluation comprises subjective sensory scoring and objective electronic sensory analysis; carrying out chemical component analysis on the tea leaf sample, wherein the chemical component analysis comprises detection of non-volatile compounds and volatile compounds; on the basis of sensory evaluation and chemical analysis results, correlation chemical data and sensory attributes are analyzed through multivariate statistics to generate analysis results; optimized tea leaf processing parameters including time, temperature or intensity parameters of a processing technology are determined according to an analysis result, and the processing parameters are scientifically optimized by integrating sensory evaluation and chemical analysis and applying multivariate statistics to analyze associated data, so that the method has the advantages.

Description

Technical Field

[0001] This application relates to the field of tea processing technology, and in particular to a method and related apparatus for determining tea processing strategies based on chemical and sensory characteristics. Background Technology

[0002] As a vital global economic crop, tea's industry has seen continuous expansion, with global production reaching 7.05 million tons in 2024, an increase of over 70% compared to 2010. China, as the largest producer, leads the industry's development. Tea can be categorized into unfermented, semi-fermented, and fully fermented teas based on their fermentation level. Green tea, in particular, is widely popular in Asia and globally due to its unique flavor and health benefits, with renowned products like West Lake Longjing and Biluochun enjoying international acclaim. However, the production of traditional premium green teas heavily relies on spring raw materials. Summer and autumn tea resources, accounting for over 60% of the annual output, suffer from excessive accumulation of polyphenols and low amino acid content due to high temperatures and strong sunlight. This results in bitterness, a weak aroma, and other quality defects after processing, leading to significant resource waste and losses in industry profits.

[0003] Current tea quality evaluation systems primarily rely on sensory evaluation methods. While adhering to national standards and regulations, these methods are entirely based on the subjective judgment of tea tasters, involving scores for appearance, liquor color, aroma, taste, and infused leaf appearance. This process is susceptible to individual taster preferences, environmental interference, and fluctuations in physiological state, resulting in significant limitations in reproducibility. Even when conducted by professionally trained teams of tea tasters, long-term collaboration is required to maintain evaluation consistency, making objective quantification and standardized assessment difficult. In the field of chemical composition analysis, while techniques such as high-performance liquid chromatography and gas chromatography-mass spectrometry can accurately determine indicators such as catechins, caffeine, free amino acids, and volatile aroma components, the mapping relationship between chemical data and sensory quality has not been systematically elucidated. Significant gaps exist in our understanding of the molecular mechanisms underlying the transformation of non-volatile substances in key processes such as fixation and rolling, preventing chemical analysis results from effectively linking to actual sensory experiences. Processing technology optimization has long relied on trial-and-error methods, lacking a scientific framework for multi-process synergistic control, particularly in its adaptability to summer and autumn tea raw materials. While existing research has attempted to introduce electronic tongue, electronic nose, and metabolomics technologies, it has mostly focused on adjusting parameters of a single process and has failed to establish a deep correlation model between chemical data and sensory attributes. It has also failed to form a systematic optimization strategy covering the entire process of spreading, fixing, rolling, and drying, resulting in slow progress in the high-value utilization of low-quality raw materials. Summary of the Invention

[0004] This application provides a method and related apparatus for determining tea processing strategies based on chemical and sensory characteristics, which has the advantages of improving the objectivity, accuracy and resource utilization efficiency of tea processing strategy determination.

[0005] Firstly, the method for determining tea processing strategies based on chemical and sensory characteristics provided in this application adopts the following technical solution: A method for determining tea processing strategies based on chemical and sensory characteristics, comprising: Obtain tea samples; Sensory evaluation of tea samples was conducted, including subjective sensory scoring and objective electronic sensory analysis. Chemical analysis of tea samples was performed, including the detection of non-volatile and volatile compounds. Based on sensory evaluation and chemical analysis results, multivariate statistical analysis is used to correlate chemical data with sensory attributes to generate analytical results. Based on the analysis results, optimized tea processing parameters are determined, including processing time, temperature, or intensity parameters.

[0006] Optionally, the subjective sensory evaluation in the sensory evaluation is achieved by receiving subjective feedback through a preset port, which includes scoring the appearance, liquor color, aroma, taste, and infused leaves of the tea; the objective electronic sensory analysis uses electronic tongue and / or electronic nose devices to quantify the taste or aroma attributes of the tea extract, including bitterness, umami, sweetness, and astringency indicators.

[0007] Optionally, the detection of non-volatile compounds in the chemical composition analysis includes determining the content of catechin monomers, caffeine, and theanine using high-performance liquid chromatography, and quantitatively analyzing the composition of 17 free amino acids using an amino acid analyzer; the detection of volatile compounds uses headspace solid-phase microextraction-gas chromatography-mass spectrometry to identify and quantify aroma active ingredients, such as alcohols, aldehydes, and esters, and calculates their relative content using peak area normalization.

[0008] Optionally, the multivariate statistical analysis includes using partial least squares discriminant analysis or principal component analysis to visualize the clustering and differences of chemical data, and using the Mantel test or Spearman correlation coefficient to assess the association between chemical components and sensory scores; key differential components are screened based on variable importance projection values ​​during the analysis.

[0009] Optionally, the method further includes using broad-target metabolomics technology to analyze differential metabolites during tea processing and identifying key metabolic pathways through KEGG enrichment analysis; constructing a metabolite-sensory attribute association network based on weighted gene co-expression network analysis to verify the effectiveness of the processing strategy.

[0010] Optionally, the optimized tea processing parameters are determined for specific tea types: for yellow tea, the optimized parameters include withering temperature of 20-28°C and enzymatic oxidation time of the yellowing process; for green tea, the optimized parameters include fixation temperature of 250-310°C, rolling time of 10-15 minutes, and drying step temperature of 80→70→60°C.

[0011] Optionally, the method is applied to the high-value utilization of summer and autumn tea resources, improving the amino acid balance and reducing bitterness of summer tea through processing optimization; wherein the analysis results are used to guide the coordinated regulation of withering, fixation, rolling and drying processes.

[0012] Secondly, this application provides a system for determining tea processing strategies based on chemical and sensory characteristics, comprising: The acquisition module is used to acquire tea samples; The sensory evaluation module is used to evaluate tea samples, including subjective sensory scoring and objective electronic sensory analysis. The chemical composition analysis module is used to perform chemical analysis on tea samples, including the detection of non-volatile and volatile compounds; The analysis results module is used to generate analysis results based on sensory evaluation and chemical analysis results by linking chemical data and sensory attributes through multivariate statistical analysis. The output module is used to determine optimized tea processing parameters based on the analysis results, including processing time, temperature, or intensity parameters.

[0013] Thirdly, this application provides a computer device, the device comprising: a memory and a processor, wherein the processor, when executing computer instructions stored in the memory, performs the method described above.

[0014] Fourthly, this application provides a computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the method described above.

[0015] In summary, this application obtains tea samples; conducts sensory evaluation of the tea samples, including subjective sensory scoring and objective electronic sensory analysis; performs chemical analysis of the tea samples, including the detection of non-volatile and volatile compounds; generates analytical results by linking chemical data and sensory attributes through multivariate statistical analysis based on the sensory evaluation and chemical analysis results; and determines optimized tea processing parameters, including processing time, temperature, or intensity parameters, based on the analytical results. By integrating sensory evaluation and chemical analysis and applying multivariate statistical analysis to link the data, the processing parameters are scientifically optimized, thus possessing the aforementioned advantages. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the computer device structure of the hardware operating environment involved in the embodiments of this application; Figure 2 This is a flowchart illustrating the first embodiment of the method for determining tea processing strategies based on chemical and sensory characteristics according to this application. Figure 3This is a structural block diagram of the first embodiment of the tea processing strategy determination system based on chemical and sensory characteristics of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0018] Reference Figure 1 , Figure 1 This is a schematic diagram of the computer device structure of the hardware operating environment involved in the embodiments of this application.

[0019] like Figure 1 As shown, the computer device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk drive. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.

[0020] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the computer device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0021] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a tea processing strategy determination program based on chemical and sensory characteristics.

[0022] exist Figure 1In the computer device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in this application can be set in the computer device. The computer device calls the tea processing strategy determination program based on chemical and sensory characteristics stored in the memory 1005 through the processor 1001, and executes the tea processing strategy determination method based on chemical and sensory characteristics provided in the embodiments of this application.

[0023] This application provides a method for determining tea processing strategies based on chemical and sensory characteristics, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the method for determining tea processing strategies based on chemical and sensory characteristics according to this application.

[0024] In this embodiment, the method for determining tea processing strategies based on chemical and sensory characteristics includes the following steps: Step S10: Obtain tea samples; Step S20: Perform sensory evaluation on the tea samples, including subjective sensory scoring and objective electronic sensory analysis; Step S30: Perform chemical analysis on the tea sample, including the detection of non-volatile and volatile compounds; Step S40: Based on sensory evaluation and chemical analysis results, generate analysis results by linking chemical data and sensory attributes through multivariate statistical analysis; Step S50: Determine optimized tea processing parameters based on the analysis results, including processing time, temperature, or intensity parameters.

[0025] In traditional tea processing, quality evaluation relies excessively on subjective sensory assessment, lacking objective quantitative indicators, resulting in poor reproducibility. Furthermore, the correlation mechanism between chemical composition analysis and sensory attributes is unclear, and processing technology optimization often depends on experience, making it difficult to effectively improve the utilization value of low-quality raw materials such as summer and autumn teas, leading to resource waste. Existing research often focuses on single processes, lacking systematic solutions.

[0026] To address this, this embodiment proposes a method for determining tea processing strategies based on chemical and sensory characteristics. This method aims to overcome the limitations of traditional methods and achieve more scientific and precise tea processing. Specifically, it includes: acquiring tea samples; conducting sensory evaluation of the tea samples, including subjective sensory scoring and objective electronic sensory analysis; performing chemical analysis of the tea samples, including the detection of non-volatile and volatile compounds; generating analytical results based on the sensory evaluation and chemical analysis results by correlating chemical data with sensory attributes through multivariate statistical analysis; and determining optimized tea processing parameters based on these analytical results, including processing time, temperature, or intensity parameters.

[0027] For ease of understanding, the following explains some key terms in this embodiment: Tea samples refer to raw or semi-finished tea products used for sensory evaluation and chemical analysis. They may come from different origins, seasons, or processing stages.

[0028] Sensory evaluation refers to the process of assessing tea quality using human sensory organs or devices that simulate them. Subjective sensory evaluation is conducted by trained tasters, who evaluate the tea based on its appearance, liquor color, aroma, taste, and infused leaves. Objective electronic sensory analysis utilizes electronic sensory equipment to detect tea extracts or volatile gases, providing quantifiable taste or aroma data.

[0029] Chemical composition analysis refers to the qualitative or quantitative detection of chemical components in tea using physicochemical methods. Non-volatile compounds generally refer to substances in tea with larger molecular weights that are not easily volatilized. Volatile compounds, on the other hand, refer to substances in tea that are easily volatilized and constitute the main aroma of tea.

[0030] Multivariate statistical analysis refers to a class of statistical methods for processing and interpreting the relationships between multiple variables. It can reveal the interactions and potential patterns among variables in complex datasets.

[0031] The analysis results refer to the data models or conclusions obtained through multivariate statistical analysis regarding the relationship between chemical components and sensory attributes.

[0032] Optimized tea processing parameters refer to the processing conditions determined based on analysis results that can improve tea quality or meet specific quality requirements, such as the specific operational parameters of withering, fixation, rolling, fermentation, drying, etc., including time, temperature, or intensity.

[0033] The method of this embodiment first involves obtaining tea samples. Tea samples can be selected from different tea batches, processing stages, or geographical regions. For example, fresh tea leaves can be manually picked from tea gardens, or semi-finished tea leaves can be collected from various production stages in a tea processing plant. The quantity and variety of samples obtained should be sufficient to represent the research subject.

[0034] Next, sensory evaluation is conducted on the acquired tea samples. Sensory evaluation can include subjective sensory scoring and objective electronic sensory analysis. Subjective sensory scoring is performed by a group of basically trained tasters who assign corresponding ratings or scores based on the tea's appearance, the color of the brewed liquor, the aroma, the taste, and the condition of the infused leaves. Objective electronic sensory analysis utilizes commercially available electronic sensory equipment, such as electrochemical sensor arrays simulating taste receptors or gas sensor arrays simulating olfactory receptors, to detect tea extracts or volatile gases, thereby obtaining quantified taste or aroma data.

[0035] Subsequently, chemical analysis was performed on the tea samples. This analysis included the detection of both non-volatile and volatile compounds. Non-volatile compounds could be detected using conventional chemical analysis methods, such as determining the total polyphenol content using a spectrophotometer or the total nitrogen content using the Kjeldahl method. Volatile compounds could be detected using gas chromatography to separate and preliminarily identify the volatile substances in the tea.

[0036] Based on this, and using sensory evaluation and chemical analysis results, multivariate statistical analysis is employed to correlate chemical data with sensory attributes and generate analytical results. Multivariate statistical analysis can utilize various mathematical models and algorithms; for example, regression analysis can be used to establish a mathematical relationship between chemical components and sensory scores, or cluster analysis can be used to group tea samples with similar chemical components and sensory attributes. These analytical methods aim to reveal the intrinsic laws governing the influence of chemical components on sensory quality.

[0037] Finally, optimized tea processing parameters are determined based on the analysis results. The analysis results can indicate which chemical components are highly correlated with specific sensory properties and how these chemical components change under different processing conditions. For example, if the analysis results show that a certain processing parameter is significantly correlated with the bitterness and astringency of tea, this correlation can be used to adjust the processing parameter to reduce bitterness and astringency. Optimized processing parameters may include processing time, temperature, or intensity parameters, such as adjusting the withering time, the fixing temperature, the rolling intensity, or the drying temperature profile.

[0038] This embodiment establishes a quantitative correlation between tea chemical components and sensory quality by integrating sensory evaluation and chemical analysis data and employing multivariate statistical analysis. This overcomes the subjectivity of traditional sensory evaluation and provides a scientific basis for optimizing tea processing techniques. This method is particularly suitable for guiding the processing of raw materials such as summer and autumn teas. By precisely controlling processing parameters, it effectively improves problems such as bitterness, astringency, and weak aroma, thereby enhancing the overall quality and resource utilization value of tea.

[0039] In some of the above-mentioned implementation methods, sensory evaluation of tea samples is proposed, including subjective sensory scoring and objective electronic sensory analysis. However, if the specific methods and indicators of sensory evaluation are not clear and standardized enough, the evaluation results may be too subjective and have poor repeatability, making it difficult to accurately capture the true sensory characteristics of tea. This will affect the accuracy of subsequent correlation analysis between chemical data and sensory attributes, and consequently affect the determination of optimized processing parameters.

[0040] In this regard, this embodiment further proposes that the subjective sensory scoring in the sensory evaluation is achieved by receiving subjective feedback through a preset port, including scoring of the appearance, liquor color, aroma, taste and infused leaves of the tea; the objective electronic sensory analysis uses electronic tongue and / or electronic nose devices to quantify the taste or aroma attributes of the tea extract, including bitterness, umami, sweetness and astringency indicators.

[0041] Specifically, the implementation of subjective sensory scoring aims to obtain direct human sensory judgments on tea quality through a standardized process. The preset port can be a dedicated data entry system, such as a software interface based on a computer or tablet, or a standardized paper scoring sheet, ensuring that all sensory evaluators submit feedback within a unified framework. Subjective feedback covers multiple key sensory dimensions of tea, including appearance (such as the shape, color, and integrity of the tea leaves), liquor color (such as the brightness and depth of the tea liquor), aroma (such as the type, intensity, and persistence of the aroma), taste (such as the strength, richness, and aftertaste of the tea liquor), and infused leaves (such as the integrity, color, and softness of the tea leaves after brewing). By scoring these specific indicators, the sensory quality of the tea can be comprehensively and systematically evaluated. To ensure the reliability of the scoring, a review panel composed of professionally trained sensory evaluators is typically selected, and the evaluation is conducted in a controlled environment, such as a temperature-controlled, odor-free tasting room, using a blind tasting method to reduce the influence of external factors and subjective biases on the evaluation results.

[0042] Meanwhile, the objective electronic sensory analysis utilizes advanced instruments and equipment to quantify the taste and aroma attributes of tea. The electronic tongue device, through a sensor array simulating human taste buds, can detect various flavor compounds in tea extract and convert them into electrical signals, thereby quantifying basic taste indicators such as bitterness, umami, sweetness, and astringency. For example, the electronic tongue can include sensors sensitive to different tastes such as sour, sweet, bitter, salty, and umami. By analyzing the response patterns of the sensor array to the tea extract, a quantified taste spectrum is obtained. The electronic nose device, through a gas sensor array simulating the human olfactory system, identifies and quantifies volatile aroma components in tea extract or dry tea. For example, the electronic nose can include multiple metal oxide semiconductor sensors sensitive to different chemical substances. By analyzing the response patterns of the sensor array to tea aroma molecules, an aroma fingerprint spectrum is generated, thereby objectively evaluating aroma attributes. Before conducting electronic sensory analysis, the preparation of tea extract must strictly follow standardized procedures, such as uniform tea quantity, water temperature, and brewing time, to ensure sample consistency and comparability. These devices can provide highly repeatable and objective data, effectively compensating for the limitations of human sensory evaluation, such as fatigue and subjective differences.

[0043] Through the above technical solution, this embodiment can significantly improve the scientificity and accuracy of sensory evaluation. Specifically, by receiving subjective feedback at a preset port and scoring appearance, liquor color, aroma, taste, and infused leaf appearance, the subjective sensory evaluation process becomes more standardized and systematic, reducing variability among evaluators and ensuring the comprehensiveness and consistency of sensory data. Simultaneously, the use of electronic tongue and / or electronic nose devices to objectively quantify the taste or aroma attributes of tea extract, particularly the precise measurement of key indicators such as bitterness, umami, sweetness, and astringency, provides reliable and repeatable instrumental data for sensory evaluation. This combination of subjective evaluation and objective analysis not only overcomes the limitations of single evaluation methods, such as the variability of subjective evaluation and the inadequacy of objective analysis in capturing complex sensory experiences, but also reveals the sensory characteristics of tea more comprehensively and deeply. Therefore, the obtained sensory evaluation data is more accurate and reliable, providing high-quality input for subsequent multivariate statistical analysis based on sensory evaluation and chemical analysis results. This allows for a more accurate correlation between chemical data and sensory attributes, ultimately generating more instructive analytical results and determining more optimized tea processing parameters that better meet market or consumer preferences.

[0044] In some of the embodiments described above, chemical composition analysis of tea samples was proposed. However, in practice, if only general chemical composition detection is performed, it may be difficult to comprehensively and accurately reveal the key substances and their contents that affect the sensory quality of tea, thereby affecting the accuracy of subsequent processing strategy optimization.

[0045] In this regard, this embodiment further proposes that in the chemical composition analysis, the detection of non-volatile compounds includes using high performance liquid chromatography to determine the content of catechin monomers, caffeine and theanine, and using an amino acid analyzer to quantitatively analyze the composition of 17 free amino acids; the detection of volatile compounds uses headspace solid phase microextraction-gas chromatography-mass spectrometry to identify and quantify aroma active ingredients, such as alcohols, aldehydes and esters, and calculates the relative content by peak area normalization.

[0046] Specifically, in the detection of non-volatile compounds, high-performance liquid chromatography (HPLC), as a high-efficiency liquid chromatography technique, can effectively separate, identify, and quantify non-volatile, thermally unstable, or high-boiling-point compounds in complex mixtures. This method can accurately determine the content of key non-volatile components in tea, such as catechin monomers (e.g., epigallocatechin gallate, epicatechin gallate, epigallocatechin, epicatechin, etc.), caffeine, and theanine. These components are the main substances affecting the taste, bitterness, and freshness of tea. The process typically involves pretreatment steps such as grinding, extraction, and filtration of the tea sample, followed by injection of the extract into an HPLC system. Qualitative analysis is performed based on the retention time of the compounds by selecting appropriate chromatographic columns, mobile phases, and detectors (e.g., UV detectors), and quantification is achieved by comparing peak area or peak height with a standard curve. Furthermore, free amino acids, especially theanine, are important contributors to the freshness of tea. An amino acid analyzer can quantitatively analyze the composition of 17 free amino acids in tea, thereby comprehensively assessing the tea's potential for freshness and crispness. This analytical process typically involves hydrolyzing the sample or directly extracting the free amino acids, followed by separation via ion-exchange chromatography, reaction with chromogenic agents such as ninhydrin, and detection and quantification via colorimetric or fluorescence methods.

[0047] In the detection of volatile compounds, this embodiment employs headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME). HS-SPME is a highly efficient solvent-free or microsolvent-based sample pretreatment technique used to extract volatile or semi-volatile compounds from the headspace (gas phase) of a sample matrix. Gas chromatography-mass spectrometry (GC-MS) combines the high separation capability of gas chromatography with the powerful identification capability of mass spectrometry, enabling the separation, identification, and quantification of volatile components in complex mixtures. This technique can accurately identify and quantify aroma-active components in tea, such as alcohols, aldehydes, and esters, which are key to the unique aroma profile of tea. The process typically involves placing the tea sample in a sealed headspace vial, heating it until equilibrium is reached, allowing the volatile components to fully enter the headspace. Subsequently, solid-phase microextraction fibers were inserted into the headspace to adsorb volatiles. The fibers, now adsorbed with volatiles, were then inserted into the injection port of a gas chromatography-mass spectrometry (GC-MS) system for thermal desorption. The volatiles were then separated using a gas chromatography column and analyzed by mass spectrometry. Compound identification was performed by comparison with a standard library, and quantification was achieved using peak area. To more accurately compare the relative abundance changes of components in different samples, this embodiment further calculated the relative content of these aroma active ingredients using peak area normalization. Peak area normalization is a commonly used relative quantification method that represents the relative content of a component in a mixture by calculating the percentage of the peak area of ​​a single component relative to the total peak area of ​​all components.

[0048] Using the above technical solutions, high-performance liquid chromatography (HPLC) is employed to accurately determine the contents of catechin monomers, caffeine, and theanine, and an amino acid analyzer is used to comprehensively quantify the composition of 17 free amino acids. This allows for a deeper understanding of the impact of non-volatile components of tea on its flavor and freshness. Simultaneously, headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-MS / MS) combined with peak area normalization can accurately identify and quantify the relative contents of aroma-active components such as alcohols, aldehydes, and esters in tea, thus providing a comprehensive analysis of the aroma characteristics of tea. These refined chemical component analysis methods overcome the limitations of traditional generalized detection methods, providing more accurate and comprehensive data support for subsequent multivariate statistical analysis based on sensory evaluation and chemical analysis results. This makes the analysis results linking chemical data and sensory attributes more reliable, ultimately enabling more accurate determination of optimized tea processing parameters, thereby significantly improving the scientific rigor and effectiveness of tea processing strategies.

[0049] In some of the embodiments described above, a method was proposed to generate analytical results by linking chemical data and sensory attributes through multivariate statistical analysis based on sensory evaluation and chemical analysis results. However, in practice, if the multivariate statistical analysis method is not properly selected or is not refined enough, it may be difficult to accurately reveal the complex intrinsic relationship between chemical components and sensory attributes, resulting in insufficient accuracy in identifying key influencing factors, thereby affecting the effectiveness of subsequent processing parameter optimization.

[0050] To address this, this embodiment further proposes that the multivariate statistical analysis includes using partial least squares discriminant analysis or principal component analysis to visualize the clustering and differences of chemical data, and using the Mantel test or Spearman correlation coefficient to assess the association between chemical components and sensory scores; key differential components are screened based on variable importance projection values ​​during the analysis.

[0051] Specifically, the multivariate statistical analysis can employ Partial Least Squares Discriminant Analysis (PLS-DA) or Principal Component Analysis (PCA) to visualize the clustering and differences in chemical data. Partial Least Squares Discriminant Analysis is a supervised discriminant method whose main function is to maximize the differences between different groups (e.g., tea samples under different processing conditions) and identify the key variables leading to these differences. PLS-DA projects high-dimensional chemical data into a low-dimensional space, and the clustering and separation trends between different tea samples are visually displayed through score plots, thus clearly visualizing the clustering and differences in chemical data. Furthermore, loading plots can further identify which chemical components contribute most to this clustering and differences. Principal Component Analysis, on the other hand, is an unsupervised dimensionality reduction method. Its main function is to extract the principal components that best represent the data variation from high-dimensional data, thereby simplifying the data structure and revealing the underlying patterns within the data. PCA can transform complex chemical composition data into a few unrelated composite variables (principal components), and visually display the similarities or differences in the chemical composition of tea samples through score plots and loading plots, as well as which chemical components are the main factors causing these differences, thereby achieving the clustering and visualization of chemical data differences.

[0052] Building upon this, to assess the correlation between chemical components and sensory scores, the Mantel test or Spearman correlation coefficient can be used. The Mantel test is a nonparametric statistical method used to assess the correlation between two distance matrices. In tea analysis, a distance matrix for chemical components (e.g., based on Euclidean distance or correlation distance) and a distance matrix for sensory scores can be constructed. The Mantel test can then comprehensively assess the strength and significance of the correlation between the chemical component profile and the sensory attribute profile, thereby determining whether changes in chemical composition are highly consistent with changes in sensory quality. The Spearman correlation coefficient, on the other hand, is a nonparametric rank correlation coefficient used to assess the strength and direction of the monotonic relationship between two variables. In tea analysis, the Spearman correlation coefficient between the content of a single chemical component and the score of a single sensory attribute can be calculated to quantify the degree of their correlation. For example, the correlation between the content of a specific catechin and bitterness score, or the correlation between the content of a specific volatile compound and aroma intensity, can be assessed to identify chemical components that significantly affect specific sensory attributes.

[0053] Furthermore, the analysis uses variable importance projection values ​​(VIP values) to screen key differential components. Variable importance projection values ​​are an indicator used in partial least squares (PLS) models to measure the explanatory and predictive power of each independent variable (chemical component) on the dependent variable (sensory score). A higher VIP value indicates a greater contribution of that chemical component to the sensory attributes in the model, meaning it is a key differential component affecting sensory quality. By setting a threshold (e.g., a VIP value greater than 1), key components that significantly influence the sensory characteristics of tea can be screened from a large number of chemical components, thus providing clear targets for subsequent optimization of processing parameters.

[0054] By employing the aforementioned technical solutions, partial least squares discriminant analysis or principal component analysis in multivariate statistical analysis can effectively reduce the dimensionality and visualize complex chemical data, intuitively revealing the clustering and differences in chemical composition of tea samples under different processing conditions. This provides a clear view for understanding the impact of processing technology on chemical components. Simultaneously, the Mantel test or Spearman correlation coefficient can accurately quantify the strength and direction of the association between chemical components and sensory scores, ensuring that the identified associations are statistically significant. Furthermore, by screening key differential components based on variable importance projection values, the most significant chemical substances affecting the sensory quality of tea can be accurately identified from massive amounts of chemical data. This avoids blindly adjusting processing parameters, allowing subsequent processing strategy optimization to focus more on core influencing factors. Therefore, this embodiment significantly improves the accuracy and depth of the association analysis between chemical data and sensory attributes, providing a more scientific and reliable basis for determining optimized tea processing parameters. This, in turn, more effectively guides the refined control of tea processing technology to achieve the expected sensory quality goals.

[0055] In some of the embodiments described above, a method is proposed that, based on sensory evaluation and chemical analysis results, multivariate statistical analysis is used to correlate chemical data with sensory attributes to generate analytical results, and optimized tea processing parameters are determined based on the analytical results. However, in its implementation, correlation analysis alone may not be sufficient to deeply reveal the dynamic changes of metabolites during tea processing and the underlying mechanisms by which they affect sensory quality, and a systematic method is lacking to verify the effectiveness of the determined processing strategy.

[0056] In this regard, this embodiment further proposes that the method also includes using broad-target metabolomics technology to analyze differential metabolites in the tea processing process, and identifying key metabolic pathways through KEGG enrichment analysis; and constructing a metabolite-sensory attribute association network based on weighted gene co-expression network analysis to verify the effectiveness of the processing strategy.

[0057] Specifically, the use of broad-target metabolomics to analyze differential metabolites during tea processing refers to employing a technique that combines the comprehensiveness of non-targeted metabolomics with the quantitative accuracy of targeted metabolomics to perform high-throughput, high-sensitivity detection and relative or absolute quantification of as many known metabolites as possible in tea samples. This technique aims to comprehensively capture the changes in the types and contents of metabolites within tea at different processing stages or under different processing parameters, thereby identifying differential metabolites that change significantly during processing. These differential metabolites are often key substances affecting tea quality, especially sensory quality. In practice, samples can be collected at different stages of tea processing. After pretreatment such as grinding, extraction, and concentration, high-resolution mass spectrometry (such as UPLC-Q-TOF-MS or GC-MS) combined with chromatographic separation is used for detection. Identification and quantification are then performed by comparison with standard libraries or public databases, and finally, differential metabolites are screened using statistical methods.

[0058] Building upon this foundation, KEGG enrichment analysis identifies key metabolic pathways. This involves mapping a list of differentially metabolites obtained from broad-target metabolomics analysis to known metabolic or signaling pathways in the KEGG database, thereby identifying biochemical pathways that are significantly active or affected during specific tea processing. This analysis helps to gain a deeper understanding of how tea processing alters the chemical composition of tea by influencing specific metabolic pathways (e.g., flavonoid biosynthesis pathways, amino acid metabolism pathways, terpene synthesis pathways, etc.), thus affecting its sensory properties. In practice, the list of differentially metabolites can be input into the KEGG enrichment analysis tool. Based on preset statistical thresholds, the enrichment level of differentially metabolites in each metabolic pathway is calculated, and a list of significantly enriched metabolic pathways is output.

[0059] Meanwhile, constructing a metabolite-sensory attribute association network based on weighted gene co-expression network analysis refers to using a systems biology approach to analyze metabolite data to identify highly co-expressed metabolite modules and explore the associations between these modules and the sensory attributes of tea. This method can identify synergistically changing metabolite groups from complex metabolite data and quantify the association strength between these groups and tea sensory attributes (such as aroma and taste). This not only reveals the association between individual metabolites and sensory attributes but also provides a systemic understanding of how metabolite groups collectively influence sensory quality, thus providing a more comprehensive biological basis for optimizing processing strategies. In practice, quantitative data on tea metabolites and corresponding sensory scores under different processing conditions are first collected. Then, the WGCNA algorithm is used to analyze the metabolite data, construct a metabolite co-expression network, and identify modules. Finally, the correlation between each metabolite module and sensory attributes is calculated to identify modules highly correlated with specific sensory attributes.

[0060] The aforementioned technical solutions enable a deep, molecular-level understanding of the dynamic changes in metabolites during tea processing and their intrinsic mechanisms in shaping sensory quality. By comprehensively capturing metabolite changes during processing using broad-target metabolomics technology and combining this with KEGG enrichment analysis to identify key metabolic pathways, the biochemical processes influencing tea quality can be precisely located. Furthermore, constructing a metabolite-sensory attribute association network based on weighted gene co-expression network analysis not only provides a systematic understanding of how metabolite groups synergistically affect sensory characteristics but also offers a structured model for validating whether the determined processing strategies can effectively and stably improve specific sensory qualities of tea. This approach avoids the potential bias or instability of relying solely on statistical correlations, making the determined processing strategies more scientific and reliable, thereby ensuring the effectiveness of optimizing processing parameters.

[0061] In some of the above embodiments, a method for determining tea processing strategies based on chemical and sensory characteristics is proposed. This method generates analytical results through sensory evaluation and chemical analysis, combined with multivariate statistical analysis, to determine optimized tea processing parameters. However, in practical applications, different types of tea have unique processing techniques and quality characteristics. Providing only general optimization parameters may not fully realize the potential of various tea types, nor can it guide refined processing for specific tea types.

[0062] To address this, this embodiment further proposes that the optimized tea processing parameters be determined for specific tea types. Specifically, determining optimized processing parameters for a specific tea type means that after sensory evaluation and chemical analysis, and after generating analytical results through multivariate statistical analysis, the obtained processing parameters are not generalized, but rather refined and adjusted according to the type of tea to be processed (e.g., yellow tea, green tea, etc.). This customized approach ensures that the determined processing parameters are highly matched with the traditional processing characteristics, target quality and flavor, and biochemical properties of that tea type, thereby achieving more precise quality control and optimization.

[0063] For example, for yellow tea, optimized parameters include withering temperature (20-28°C) and enzymatic oxidation time during the yellowing process. The unique quality of yellow tea hinges on the "yellowing" process. Therefore, optimized processing parameters pay particular attention to withering temperature and enzymatic oxidation time during yellowing. Withering temperature (20-28°C) is a crucial range for controlling moisture loss and enzyme activity activation in yellow tea processing. Within this temperature range, the permeability of tea cell membranes increases moderately, laying the foundation for subsequent enzymatic reactions. Enzymatic oxidation time during the yellowing process refers to the time and humidity controlled during the yellowing process to promote the slight oxidation of endogenous enzymes (such as polyphenol oxidase) under specific conditions, accompanied by non-enzymatic browning, resulting in the characteristic yellow pigment and mellow flavor of yellow tea. Precise control of this time is crucial for the formation of the "yellow liquor and yellow leaves" quality of yellow tea; too short or too long a time will affect its flavor and color.

[0064] For example, for green tea, optimized parameters include a fixation temperature of 250-310°C, a rolling time of 10-15 minutes, and a drying temperature gradient of 80→70→60°C. The core of green tea processing lies in rapidly deactivating enzymes through high temperatures to maintain the green color and fresh flavor of the tea leaves. Optimized processing parameters include a fixation temperature of 250-310°C, a rolling time of 10-15 minutes, and a drying temperature gradient of 80→70→60°C. A fixation temperature of 250-310°C refers to subjecting the tea leaves to high-temperature treatment for a short period to quickly destroy enzyme activity, prevent the oxidation of polyphenols, and simultaneously evaporate some moisture, softening the tea leaves and making them easier to roll. A rolling time of 10-15 minutes refers to mechanically pressing the tea leaves after fixation, breaking down the tea cell structure, allowing tea juice to seep out, facilitating the dissolution of internal components during brewing, and forming the unique strip shape of the tea leaves. The drying step temperature of 80→70→60°C is a staged drying method that aims to gradually reduce the moisture content of tea leaves, fix the quality of tea leaves, prevent tea leaves from scorching at high temperatures or from being incompletely dried at low temperatures, and thus ensure that the aroma, taste and color of green tea reach their best state.

[0065] The aforementioned technical solutions identify optimized processing parameters for different tea types, overcoming the limitations of generic processing parameters that cannot meet the unique quality requirements of various teas. Specifically, for yellow tea, precise control of withering temperature and enzymatic oxidation time in the yellowing process better promotes the formation of its unique yellow pigment and mellow flavor, while avoiding over-oxidation or under-fermentation. For green tea, optimizing fixation temperature, rolling time, and drying temperature gradients more effectively deactivates enzyme activity, preserving the freshness, green color, and aroma of the tea leaves, and forming an ideal leaf shape. This customized parameter determination method allows tea processing strategies to more accurately adapt to the biochemical characteristics and target quality requirements of different tea varieties, thereby significantly improving the processing quality and market competitiveness of specific tea types.

[0066] This embodiment proposes a method for determining tea processing strategies based on chemical and sensory characteristics. It involves acquiring tea samples, conducting sensory evaluation and chemical composition analysis, and generating analytical results based on multivariate statistical analysis to determine optimized tea processing parameters. However, in practical applications, tea resources from specific seasons, such as summer and autumn teas, often exhibit significant differences in quality characteristics compared to spring teas, generally exhibiting relatively lower amino acid content and stronger bitterness. Using only general processing strategies may fail to fully realize the potential value of summer and autumn teas or effectively improve their inherent quality defects, thus limiting the effective utilization of these resources.

[0067] In this regard, this embodiment further proposes to apply the above method to the high-value utilization of summer and autumn tea resources, and to improve the amino acid balance and reduce bitterness of summer tea through processing optimization; wherein, the analysis results are used to guide the coordinated regulation of withering, fixation, rolling and drying processes.

[0068] This method is specifically designed for summer and autumn tea resources, aiming to achieve high-value utilization through refined processing and quality improvement. Summer and autumn teas typically have larger yields, but due to factors such as the growing season and climate, their internal composition differs from spring teas. They often exhibit relatively higher polyphenol content and relatively lower amino acid content, resulting in a bitter and astringent taste and insufficient freshness. This method allows for targeted optimization of processing techniques, transforming summer and autumn teas, which might otherwise be used as low-end raw materials or extracts, into finished teas with higher sensory quality and market value, or for developing specialty tea products, thereby enhancing their overall economic benefits.

[0069] One of the core objectives of this method is to improve the intrinsic quality of summer tea by optimizing processing parameters. Enhancing amino acid balance involves regulating enzymatic reactions during processing to promote protein hydrolysis into free amino acids and adjusting the proportions of different types of amino acids to enhance the freshness and fullness of the tea. For example, this can be achieved by controlling the temperature and humidity during withering or by introducing microorganisms at specific fermentation stages to influence the generation and transformation of amino acids. Reducing bitterness is mainly achieved by controlling the degree of oxidation and polymerization of tea polyphenols or promoting the transformation and degradation of bitter substances. For example, during the fixation process, precise control of temperature and time can effectively deactivate polyphenol oxidase activity, reducing the oxidation of tea polyphenols; during rolling or fermentation, mechanical or microbial action can also promote the transformation of some bitter substances into other flavor compounds.

[0070] The sensory evaluation and chemical analysis results obtained by this method, along with the correlation data generated based on multivariate statistical analysis, are directly applied to guide key processes in tea processing. Specifically, for the withering process, the analysis results can guide the adjustment of the withering thickness, time, and ventilation conditions to control the rate of water loss and enzyme activity, creating optimal conditions for the transformation of internal components in subsequent processes. For example, if the analysis shows that excessively high enzyme activity in the fresh leaves may lead to undesirable flavor, the withering conditions can be adjusted to inhibit their activity. For the fixation process, the analysis results can guide the precise control of the fixation temperature, time, and method (such as drum fixation or steam fixation) to quickly inactivate enzyme activity, fix the green color of the tea leaves, evaporate some moisture, and reduce the formation of bitter substances. For example, if the analysis shows a high content of tea polyphenols, the fixation temperature can be appropriately increased or the fixation time extended to more thoroughly inactivate enzymes. For the rolling process, the analysis results can guide the adjustment of the rolling pressure, time, and number of rolling cycles to moderately disrupt the tea cell structure, promote the exudation of tea juice, accelerate the mixing and transformation of internal components, and form the specific shape of the tea leaves. For example, if analysis shows that there is room for improvement in amino acid content, the rolling intensity can be appropriately increased to promote cell damage and accelerate the release of amino acids. For the drying process, the analysis results can guide the phased control of drying temperature, airflow, and time to effectively remove excess moisture, fix tea quality, form unique aromas and flavors, and further reduce bitterness. For example, if analysis shows that certain volatile aroma compounds are easily volatilized, low-temperature slow drying or multi-stage drying can be used to better preserve the aroma. Synergistic regulation means that these process parameters are not adjusted in isolation, but are comprehensively optimized and coordinated according to overall quality goals (such as improving amino acid balance and reducing bitterness). For example, if enzyme activity does not reach the expected level after withering, stronger treatment may be needed during fixation; if bitterness is still heavy after fixation, rolling and drying may need to further promote the transformation or degradation of bitter substances. This data-driven synergistic regulation ensures that the entire processing can dynamically adapt to the characteristics of summer and autumn tea raw materials, achieving precise control over the quality of the final product.

[0071] By specifically applying the aforementioned methods for determining tea processing strategies based on chemical and sensory characteristics to summer and autumn tea resources, and with the clear objective of improving the amino acid balance and reducing bitterness in summer tea, the problems of relatively low quality and strong bitterness in summer and autumn tea can be addressed in a targeted manner. Specifically, by conducting sensory evaluation and chemical composition analysis on summer and autumn tea samples, and combining the analytical results generated by multivariate statistical analysis, key chemical components and sensory attributes affecting the quality of summer and autumn tea can be accurately identified. These analytical results can then directly guide the synergistic regulation of key processing techniques such as withering, fixation, rolling, and drying, making the adjustment of processing parameters no longer empirical but based on scientific data and quality objectives. For example, optimizing withering conditions to regulate enzyme activity, precisely controlling fixation temperature and time to deactivate polyphenol oxidase and reduce the formation of bitter substances, adjusting rolling intensity to promote the release and conversion of amino acids, and using segmented drying to fix aroma and further reduce bitterness. This synergistic regulation mechanism ensures that the entire processing can dynamically adapt to the characteristics of summer and autumn tea raw materials, thereby effectively improving the types and proportions of amino acids, enhancing freshness, and significantly reducing bitterness caused by tea polyphenols, ultimately realizing the high-value utilization of summer and autumn tea resources, transforming them from low-priced raw materials into high-quality products with higher market competitiveness.

[0072] The following example will provide a more detailed explanation of the above technical solution: At a tea production base, the primary goal is to enhance the utilization value of summer and autumn tea resources and address the common problems of bitterness and weak aroma in these teas. The base decided to adopt a tea processing strategy determination method based on chemical and sensory characteristics to systematically optimize its green tea processing techniques.

[0073] First, the base obtains tea samples from summer and autumn tea gardens. These samples include fresh leaves from different batches and at different harvest times, as well as semi-finished tea that has undergone preliminary processing but has not yet reached the ideal quality.

[0074] Next, sensory evaluations were conducted on these tea samples. The sensory evaluation consisted of two parts: On one hand, a team of rigorously trained professional tea tasters conducts subjective sensory evaluations. The tasters receive subjective feedback at pre-set points, providing detailed scores for the tea's appearance, liquor color, aroma, taste, and infused leaves. For example, they record the bitterness, freshness, aroma type, and persistence of the tea liquor. Unlike traditional sensory evaluations that rely entirely on experience, this scoring system is more standardized, but still retains a degree of subjectivity.

[0075] On the other hand, to introduce objective quantitative indicators, objective electronic sensory analysis equipment is used. The electronic tongue device quantifies the taste attributes of tea extract, detecting and outputting numerical values ​​for indicators such as bitterness, umami, sweetness, and astringency. Simultaneously, the electronic nose device quantifies the aroma attributes of tea, capturing and identifying patterns of volatile aroma components. This objective analysis method compensates for the limitations of subjective sensory evaluation and provides repeatable quantitative data.

[0076] Subsequently, chemical analysis was performed on the same batch of tea samples. The chemical analysis also consisted of two parts: For the detection of non-volatile compounds, high-performance liquid chromatography (HPLC) was used to accurately determine the contents of catechin monomers, caffeine, and theanine. Simultaneously, an amino acid analyzer was used to quantitatively analyze the composition of 17 free amino acids in tea leaves to assess their nutritional and flavor quality.

[0077] For the detection of volatile compounds, headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) was employed. This technique can identify and quantify the active aroma components in tea, such as alcohols, aldehydes, and esters. The relative contents of these volatile components were calculated using peak area normalization, thereby constructing an aroma profile of tea.

[0078] After obtaining sensory evaluation and chemical analysis data, multivariate statistical analysis was performed. This analysis included visualizing the chemical data using partial least squares discriminant analysis (PLS-DA) or principal component analysis (PCA) to reveal clustering and difference patterns among different tea samples. For example, significant differences in certain chemical components between summer / autumn tea samples and spring tea could be observed. Next, the association between chemical components and sensory scores was assessed using Mantel tests or Spearman correlation coefficients. For instance, high levels of specific catechin monomers were found to be positively correlated with bitterness scores, while high levels of theanine were positively correlated with freshness scores. In the analysis, the most critical differential components affecting sensory attributes were identified based on variable importance projection (VIP) values.

[0079] Furthermore, to gain a deeper understanding of the quality formation mechanisms during tea processing, this method also employs broad-target metabolomics to analyze differentially expressed metabolites during tea processing. Metabolomics analysis of samples from different processing stages identified metabolites that underwent significant changes during key processes such as withering, fixation, rolling, and drying. Subsequently, KEGG enrichment analysis identified key metabolic pathways involved in these differentially expressed metabolites, such as pathways related to amino acid metabolism and flavonoid biosynthesis. A metabolite-sensory attribute association network was constructed based on weighted gene co-expression network analysis (WGCNA) to verify the impact and effectiveness of specific processing strategies on flavor compound formation. For example, it was found that adjusting the fixation temperature can regulate the transformation of certain bitter and astringent precursors, thereby reducing the bitterness of the final product.

[0080] Finally, optimized tea processing parameters were determined based on the analysis results. Considering the characteristics of summer and autumn green tea, the analysis results may indicate the need to adjust the fixation temperature, rolling time, and drying temperature gradient to improve the amino acid balance and reduce bitterness. For example, the analysis results might suggest adjusting the fixation temperature from the traditional 280°C to 290°C to more effectively deactivate enzyme activity and reduce polyphenol oxidation; shortening the rolling time from 15 minutes to 10 minutes to avoid excessive cell damage and the release of more bitter substances due to over-rolling; and adjusting the drying temperature gradient to 80°C→70°C→60°C to better preserve aroma compounds and stabilize quality. These optimized parameters are used to guide the coordinated control of processes such as withering, fixation, rolling, and drying.

[0081] Through this systematic approach, the base overcomes the limitations of traditional experience-based processing, avoiding blind trial and error. Instead, it precisely adjusts processing techniques based on scientific data and quantitative analysis. This method organically combines the intuitiveness of sensory evaluation, the precision of chemical analysis, and the correlation of multivariate statistical analysis, providing a scientific basis and technical support for the high-value utilization of summer and autumn tea resources. This results in the production of high-quality green tea products with better amino acid balance, lower bitterness, and superior aroma.

[0082] The above description is merely an embodiment of this practice and is not intended to limit the scope of protection of this embodiment. For those skilled in the art, this embodiment can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this embodiment should be included within the scope of protection of this embodiment.

[0083] Furthermore, embodiments of this application also propose a computer-readable storage medium storing a program for determining a tea processing strategy based on chemical and sensory characteristics. When the program for determining a tea processing strategy based on chemical and sensory characteristics is executed by a processor, it implements the steps of the method for determining a tea processing strategy based on chemical and sensory characteristics as described above.

[0084] Reference Figure 3 , Figure 3 This is a structural block diagram of the first embodiment of the system for determining tea processing strategies based on chemical and sensory characteristics according to this application.

[0085] like Figure 3 As shown in the embodiments of this application, the tea processing strategy determination system based on chemical and sensory characteristics includes: Acquisition module 10 is used to acquire tea samples; Sensory evaluation module 20 is used to perform sensory evaluation on tea samples, including subjective sensory scoring and objective electronic sensory analysis; The chemical composition analysis module 30 is used to perform chemical composition analysis on tea samples, including the detection of non-volatile compounds and volatile compounds; The analysis results module 40 is used to generate analysis results based on sensory evaluation and chemical analysis results by linking chemical data and sensory attributes through multivariate statistical analysis. Output module 50 is used to determine optimized tea processing parameters based on the analysis results, including processing time, temperature, or intensity parameters.

[0086] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solution of this application. In specific applications, those skilled in the art can make settings as needed, and this application does not impose any restrictions on this.

[0087] This embodiment obtains tea samples; performs sensory evaluation on the tea samples, including subjective sensory scoring and objective electronic sensory analysis; performs chemical analysis on the tea samples, including the detection of non-volatile and volatile compounds; based on the sensory evaluation and chemical analysis results, multivariate statistical analysis is used to correlate chemical data with sensory attributes to generate analytical results; and optimizes tea processing parameters, including processing time, temperature, or intensity parameters, is determined based on the analytical results. By integrating sensory evaluation and chemical analysis and applying multivariate statistical analysis to correlate data, processing parameters are scientifically optimized, thus possessing the aforementioned advantages.

[0088] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this application. In practical applications, those skilled in the art can select some or all of it to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0089] In addition, for technical details not described in detail in this embodiment, please refer to the method for determining tea processing strategies based on chemical and sensory characteristics provided in any embodiment of this application, which will not be repeated here.

[0090] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system 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 system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0091] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0092] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application. The above are only preferred embodiments of this application and do not limit the patent scope of this application. All equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for determining tea processing strategies based on chemical and sensory characteristics, characterized in that, include: Obtain tea samples; Sensory evaluation of tea samples was conducted, including subjective sensory scoring and objective electronic sensory analysis. Chemical analysis of tea samples was performed, including the detection of non-volatile and volatile compounds. Based on sensory evaluation and chemical analysis results, multivariate statistical analysis is used to correlate chemical data with sensory attributes to generate analytical results. Based on the analysis results, optimized tea processing parameters are determined, including processing time, temperature, or intensity parameters.

2. The method according to claim 1, characterized in that, The subjective sensory evaluation involves receiving subjective feedback through a preset port, including ratings of the tea's appearance, liquor color, aroma, taste, and infused leaves. The objective electronic sensory analysis uses electronic tongue and / or electronic nose devices to quantify the taste or aroma attributes of the tea extract, including bitterness, umami, sweetness, and astringency indicators.

3. The method according to claim 1, characterized in that, The detection of non-volatile compounds in the chemical composition analysis includes determining the content of catechin monomers, caffeine, and theanine using high-performance liquid chromatography, and quantitatively analyzing the composition of 17 free amino acids using an amino acid analyzer; the detection of volatile compounds uses headspace solid-phase microextraction-gas chromatography-mass spectrometry to identify and quantify aroma active ingredients, such as alcohols, aldehydes, and esters, and calculates their relative content using peak area normalization.

4. The method according to claim 1, characterized in that, The multivariate statistical analysis includes using partial least squares discriminant analysis or principal component analysis to visualize the clustering and differences in chemical data, and using the Mantel test or Spearman correlation coefficient to assess the association between chemical components and sensory scores; key differential components are screened based on variable importance projection values ​​during the analysis.

5. The method according to claim 1, characterized in that, The method also includes using broad-target metabolomics technology to analyze differential metabolites during tea processing and identifying key metabolic pathways through KEGG enrichment analysis; and constructing a metabolite-sensory attribute association network based on weighted gene co-expression network analysis to verify the effectiveness of the processing strategy.

6. The method according to claim 1, characterized in that, The optimized tea processing parameters are determined for specific tea types: for yellow tea, the optimized parameters include withering temperature of 20-28°C and enzymatic oxidation time for the yellowing process; for green tea, the optimized parameters include fixation temperature of 250-310°C, rolling time of 10-15 minutes, and drying step temperature of 80→70→60°C.

7. The method according to claim 1, characterized in that, The method is applied to the high-value utilization of summer and autumn tea resources, and improves the amino acid balance and reduces bitterness of summer tea through processing optimization; wherein, the analysis results are used to guide the coordinated regulation of withering, fixation, rolling and drying processes.

8. A system for determining tea processing strategies based on chemical and sensory characteristics, characterized in that, include: The acquisition module is used to acquire tea samples; The sensory evaluation module is used to evaluate tea samples, including subjective sensory scoring and objective electronic sensory analysis. The chemical composition analysis module is used to perform chemical analysis on tea samples, including the detection of non-volatile and volatile compounds; The analysis results module is used to generate analysis results based on sensory evaluation and chemical analysis results by linking chemical data and sensory attributes through multivariate statistical analysis. The output module is used to determine optimized tea processing parameters based on the analysis results, including processing time, temperature, or intensity parameters.

9. A computer device, characterized in that, The device includes a memory and a processor, wherein the processor, when executing computer instructions stored in the memory, performs the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Includes instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1 to 7.

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