Partial quantitative evaluation method for plant-derived food materials and application of partial quantitative evaluation method

Abstract

The invention discloses a partial quantitative evaluation method for plant-derived food materials and belongs to the field of traditional Chinese medicine modernization and food science. The method aims at solving the problems that traditional bias evaluation is subjective and fuzzy, and the bias of a compound product is uncontrollable. The core of the method is to construct an environmental habit-metabolic characteristic-growth cycle three-dimensional collaborative evaluation model, parameters of a target food material in the three dimensions are obtained, scores of all the dimensions are calculated, then quantitative partial scores are obtained through weighted calculation, and traditional qualitative description of'cold, hot, warm and cool 'is converted into objective numerical values. The method also comprises a score correction rule for special groups of plant-derived food materials. On the basis, the invention provides a compound product design, intelligent food therapy recommendation and plant directional cultivation method. According to the method, standardization and calculable evaluation of plant-derived food material bias are achieved, and a scientific quantification tool is provided for precise diet therapy, compound research and development and genuine medicinal material cultivation.

Description

Technical Field

[0001] This invention relates to the fields of modernization of traditional Chinese medicine, food science, and agricultural information technology, specifically to a method for quantitatively evaluating the properties (i.e., cold, hot, warm, and cool attributes) of plant-derived ingredients based on multi-dimensional parameters. Furthermore, this invention also relates to the application of this method in compound product design, intelligent dietary therapy recommendations, and targeted plant cultivation. Background Technology

[0002] The properties of plant-based foods (also known as "food properties" or "medicinal properties") are core concepts in traditional Chinese medicine (TCM) dietary therapy and herbal medicine theory, serving as the fundamental basis for guiding "differentiated dietary therapy" and "principal, assistant, adjuvant, and guide" combinations. However, traditional property evaluation systems have significant limitations: First, they are subjective and ambiguous, relying mainly on qualitative descriptions (such as "warm" and "cold") from the Compendium of Materia Medica and the Pharmacopoeia of the People's Republic of China, failing to quantify the intensity gradient within the same attribute (for example, ginger and jujube are both "warm," but their warming intensity differs greatly); second, they lack rules for special groups, lacking evaluation logic tailored to the characteristics of groups with unique biological features, such as fungi, parasitic plants, and high-altitude plants; third, they neglect the dynamic influence of habitat, as the metabolic components and properties of the same species can change significantly due to different growth environments (such as light, humidity, and altitude), and traditional systems struggle to quantify and predict such changes.

[0003] Existing quantitative studies often focus on a single dimension, such as relying solely on the content of a certain metabolic component (e.g., flavonoids, volatile oils) or simply considering environmental factors (e.g., soil pH) for simple correlations. They fail to integrate the systemic mechanisms of the synergistic effects of "environment-metabolism-growth," and thus cannot construct universal, accurate, and interpretable quantitative models. This results in a lack of reliable, calculable underlying tools in areas such as personalized health management, quality control of authentic medicinal materials, and scientific research and development of compound products. Summary of the Invention

[0004] The technical problem to be solved by this invention is to overcome the shortcomings of the prior art and to provide a quantifiable, calculable and reproducible method for evaluating the bias of plant-derived ingredients, and to extend its application to high value-added scenarios.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows: Firstly, a method for quantitatively evaluating the bias of plant-derived ingredients is provided. The core of this method lies in constructing a three-dimensional collaborative evaluation model of "environmental habits (fixed direction) - metabolic characteristics (fixed intensity) - growth cycle (fine-tuning)". This method includes: acquiring parameter data of the target plant-derived ingredient in three dimensions: environmental habits, metabolic characteristics, and growth cycle; calculating the score for each dimension based on predetermined scoring rules; and weighting the scores of the three dimensions to obtain a comprehensive quantitative bias score, which objectively characterizes the intensity and direction of the ingredient's cold / hot, warm / cool properties.

[0006] Preferably, in the weighted calculation, the metabolic characteristic dimension is assigned a weight no less than that of the environmental habit dimension, to reflect the core role of secondary metabolites in determining the strength of bias. When the evaluation directions of the environmental and metabolic dimensions conflict, the weight of the metabolic characteristic dimension can be further increased for arbitration.

[0007] Furthermore, the method also includes a step of adaptively modifying the scores for specific groups (such as fungi, parasitic plants, and high-altitude plants) to improve the model's universality.

[0008] Secondly, this involves a design method for compound products. This method uses the aforementioned evaluation method to calculate the skewness quantitative score of each plant-based ingredient (which may be combined with animal-based ingredients) in the formula, and then weights and sums them according to their dosage proportions to obtain the comprehensive skewness score of the compound product, which is used for efficacy prediction, formula optimization, and compatibility evaluation.

[0009] Thirdly, an intelligent dietary therapy recommendation method is also provided. This method receives individual user status information (such as constitution, season, and region), determines the target bias suitability range, and selects suitable ingredients or recipes from the food database based on the above evaluation method to generate personalized recommendations.

[0010] Fourthly, this paper provides a biased and targeted cultivation method for plant-derived food ingredients. Based on the inverse correlation model established by the aforementioned evaluation methods, this method achieves targeted optimization and stable production of biased attributes of plant-derived food ingredients by controlling cultivation parameters (environment, management, and induction).

[0011] The beneficial effects of this invention are as follows: It achieved a paradigm shift from qualitative to quantitative: transforming traditional empirical descriptions into objective ratings based on multidimensional data and algorithms, significantly improving the scientific rigor and consistency of the evaluation.

[0012] A universal and refined evaluation system has been constructed: by introducing a three-dimensional collaborative model and special group correction rules, it can cover a wide range of plant groups, from everyday vegetables to rare medicinal materials, and the evaluation results are highly consistent with traditional understanding.

[0013] It has created application value throughout the industry chain: the established quantitative system can be directly used as an underlying tool to serve multiple high-value fields such as intelligent health management, modern Chinese medicine research and development, and precision agriculture planting, and promote the transformation of related industries from experience-driven to data-driven. Attached Figure Description

[0014] Figure 1 This is a schematic diagram illustrating the principle of the three-dimensional quantitative evaluation system for plant-derived food bias provided in this embodiment of the invention.

[0015] Figure 2This is a logical diagram illustrating the dynamic adjustment law of multiple factors in the intelligent dietary therapy recommendation method provided in this embodiment of the invention.

[0016] Figure 3 This is a schematic diagram illustrating the dosage and compatibility rules of traditional Chinese medicine prescriptions provided in the embodiments of the present invention, used to explain the design method of compound products.

[0017] Figure 4 This is a schematic diagram illustrating a case of targeted regulation of plant-derived food attributes in planting, as provided in an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the present invention clearer, 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.

[0019] This embodiment combines Figure 1 The specific implementation methods described in claims 1-3 and 7 are explained in detail. As shown in Table 1, the core of this evaluation system lies in listing the core evaluation indicators, scoring rules, and weighting configurations for each dimension.

[0020] Table 1: Core Indicators and Rules for Three-Dimensional Bias Quantitative Evaluation Note: The base score for each dimension is the sum of its full score (e.g., the environmental dimension is 4+3+1=8 points). The directional coefficient is used to calculate the bias direction in the final calculation. Weighting: When the directions are consistent, the base weight (environmental habits 40% + metabolic characteristics 40% + growth cycle 20%) is used; when the directions of the environmental and metabolic dimensions conflict and the score difference is ≥3 points, the modified weight (environmental habits 30% + metabolic characteristics 50% + growth cycle 20%) is used.

[0021] Scoring Process: First, calculate the scores for each indicator according to the rules in Table 1, and sum them to obtain the initial scores for each dimension. Then, determine the "direction" of the environment and metabolism dimensions (based on the comprehensive determination of the direction coefficients of each indicator). If the directions are consistent, calculate the total score using the basic weighting; if the directions are opposite and the score difference is ≥3 points, trigger conflict arbitration and use adjusted weights. The final total score falls between -10 and +10, with >+3 indicating a positive (warm) orientation, <-3 indicating a negative (cool) orientation, and -3 to +3 indicating a neutral orientation.

[0022] Special group modification example: Fungi (such as Ganoderma lucidum): Their metabolism is mainly polysaccharides and triterpenes. The weights are adjusted to (environment 25%, metabolism 50%, growth 25%), and the polysaccharide content is adjusted to a coefficient of 0.5 before being included in the metabolic score.

[0023] High-altitude plants (such as Fritillaria cirrhosa): flavonoid synthesis is enhanced under the influence of strong UV radiation. The weights are adjusted to (35%, 45%, 20%), and the calculated coolness score is multiplied by the altitude correction factor (1.05 for every 1000 meters increase in altitude).

[0024] Parasitic plants (such as Cistanche deserticola): Their habitat is highly dependent on the host. The weights are adjusted to (20%, 60%, 20%), and their environmental habit score is calculated as "host habitat score × 0.7 + their own microhabitat score × 0.3".

[0025] To verify the feasibility and accuracy of the method of the present invention, the following explanation uses typical warming ingredients (ginger) and cooling ingredients (winter melon) as examples.

[0026] Ginger: Soil moisture 18% (sunny side, +4), annual sunshine 2600h (sunny side, +3), habitat pH 8.0 (slightly alkaline, +1), environmental dimension score = 8, mean directional coefficient (+1). Volatile oil 2.2% (sunny side, +2), flavonoids 0.9% (sunny side, +2), photosynthetic type C3 (neutral, +1), metabolic dimension score = 8, mean directional coefficient (+1). Economic maturity period 90 days (sunny side, +2), monthly weight gain 10% (sunny side, +1), specific leaf area 120 (sunny side, +1), growth dimension score = 8, mean directional coefficient (+1). Both environment and metabolism are warm-biased; the total score calculated using basic weights is: 8 × 40% + 8 × 40% + 8 × 20% = +8.0 points (strongly warm-biased).

[0027] Winter melon: Soil moisture 45% (shade, +4), annual sunshine 2000h (shade, +3), habitat pH 6.5 (neutral, +1), environmental dimension score = 8, mean directional coefficient (-1). Flavonoids 2.5% (shade, +2), volatile oil 0.2% (shade, +2), photosynthetic type C3 (neutral, +1), metabolic dimension score = 8, mean directional coefficient (-1). Economic maturity period 120 days (neutral, +2), monthly weight gain rate 5% (neutral, +1), specific leaf area 280 (neutral, +1), growth dimension score = 8, mean directional coefficient (0). Both environment and metabolism are slightly cool, calculated total score: 8 × 40% + 8 × 40% + 8 × 20% = -3.2 points (slightly cool).

[0028] This embodiment combines Figure 3 The quantitative compatibility model shown further illustrates the compound product design method described in claims 4, 5, and 9. Specifically, taking the implementation of a kidney-tonifying and essence-nourishing compound with a comprehensive bias of 'slightly warm to weakly warm' (+1.0 ~ +4.0 points) as an example, the design steps are as follows: Selected ingredients and their properties: Plant sources: Cuscuta (using the method of Example 1, the biased score was calculated to be +3.2), Lycium barbarum (+1.2).

[0029] Animal source: mutton (using the patented method with application number 2025113337562, bias score +6.0).

[0030] Design and Calculation: The recipe is set as follows: 30g dodder seed, 20g goji berries, and 50g mutton, totaling 100g. Calculate the weight percentage of each ingredient: dodder seed 30%, goji berries 20%, mutton 50%.

[0031] Overall bias score = (3.2 * 30%) + (1.2 * 20%) + (6.0 * 50%) = +3.9 points.

[0032] Results and Fit: The calculated score of +3.9 falls within the target range (+1.0 ~ +4.0), indicating that the formula meets the slightly warming requirements common in "kidney-tonifying and essence-boosting" products. If "stewing" is used for cooking (estimated to reduce the overall bias score by 1 point), the adjusted score is +2.9, still within the fit range.

[0033] This embodiment combines Figure 2 The intelligent dietary therapy recommendation logic shown further illustrates the method described in claims 6 and 10. This system receives individual user status information, and its recommendation process is as follows: Input: The user's TCM constitution is "Yang deficiency", the current season is "winter", and the location is "northern".

[0034] Determine the target range: The system rule base sets the appropriate range for "Yang deficiency constitution" to +2.0 to +8.0 points, "winter" to +2.0 to +6.0 points, and "northern arid region" to +2.0 to +5.0 points. The system takes the intersection and determines the final target bias appropriate range to be +2.0 to +5.0 points.

[0035] Screening and Verification: The system filters ingredients from the ingredient database that have a bias score between +2.0 and +5.0, such as pumpkin (+2.2), mutton (+6.0, but after "stewing" correction, it is approximately +5.0), and walnuts (+3.5). The user selects "Pumpkin and Mutton Soup" (200g pumpkin, 100g mutton). The system calculates the overall bias score of the raw ingredients for this recipe and corrects it using the rule that "stewing reduces the yang attribute by 1 point," resulting in a final evaluation score of +4.1. The system then outputs a suggestion of "high suitability, warming and tonifying the spleen and kidneys" along with a detailed recipe.

[0036] like Figure 4As shown, the directional cultivation process using dandelion as an example includes the following, and this embodiment is used to further illustrate the method described in claim 8. Objective: To adjust the properties of medicinal dandelion from "slightly cooling" (-5.0 points) to "near neutral" (-2.0 points), which is more suitable for daily consumption.

[0037] Target analysis: The overall bias score needs to be increased by approximately 3.0 points. Based on the model's reverse derivation, the cooling components (such as some flavonoids) need to be reduced, while the milder components need to be increased.

[0038] Cultivation regulation: Environment: Adjusted from moderate shading (2100h of sunlight) to full sun (≥2500h); soil moisture decreased from 40% to 25-30%.

[0039] Metabolic induction: Increase the application of phosphorus and potassium fertilizers (150 kg / ha) and reduce nitrogen fertilizers to promote root development and polysaccharide accumulation, and inhibit excessive flavonoid synthesis in the aboveground parts.

[0040] Growth management: Harvest during the vigorous vegetative growth period (90 days after sowing), instead of the traditional medicinal flowering period (150 days).

[0041] Results Verification: After the above-mentioned targeted regulation, the test results showed that the flavonoid content of the new batch of dandelion decreased from 3.5% to 2.0%, while the polysaccharide content increased. Re-evaluation using the method of Example 1 showed that its bias score increased to -2.0, achieving the predetermined target.

[0042] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for quantitatively evaluating the bias of plant-derived food materials, characterized by, Includes the following steps: S1. Obtain parameter data for the target plant-based food in three dimensions: environmental habits, metabolic characteristics, and growth cycle; S2. Based on predetermined scoring rules, calculate the first score for the environmental behavior dimension, the second score for the metabolic characteristics dimension, and the third score for the growth cycle dimension, respectively. S3. The first score, the second score, and the third score are weighted and calculated to obtain the biased quantitative score of the target plant-based food ingredient, so as to quantitatively characterize its cold, hot, warm, and cool properties.

2. The method according to claim 1, characterized in that, In step S3, the weighting calculation uses the following weight configuration: the weight of the metabolic feature dimension is greater than or equal to the weight of the environmental behavior dimension.

3. The method according to claim 1 or 2, characterized in that, The method also includes a step of correcting the scores of special groups of plant-derived ingredients, the special groups including at least one of fungi, parasitic plants, and high-altitude plants.

4. A design method for a compound product, characterized in that, The compound product contains at least one plant-based ingredient, and the method includes: Using the method described in any one of claims 1 to 3, determine the bias quantification score of each plant-derived ingredient in the compound product; Based on the biased quantitative scores of each plant-based ingredient and their proportions in the compound product, the comprehensive biased quantitative score of the compound product is calculated.

5. The design method according to claim 4, characterized in that, The compound product also contains at least one animal-derived ingredient; The method further includes: using the method described in the patent application number 2025113337562 to determine the bias quantification score of each animal-derived food ingredient; The steps for calculating the comprehensive bias quantification score include: weighting the scores based on the bias quantification scores of each plant-based ingredient and each animal-based ingredient and their total proportion in the compound product.

6. A smart dietary therapy recommendation method, characterized in that, include: Receive individual status information input by the user; Based on the individual state information, the target bias adaptation interval is determined; Using the method described in any one of claims 1 to 3, ingredients whose bias quantification scores fall within the target bias adaptation range are selected from a pre-stored plant-based ingredient database, and a recommendation list is generated and output.

7. The method according to claim 1, characterized in that, In step S2, the parameters for scoring the metabolic characteristic dimension include the photosynthetic type of the target plant-derived food ingredient.

8. A method for selectively cultivating plant-based food ingredients, characterized in that, include: Determine the expected bias quantification score for the target plant-based food ingredients; Based on the evaluation method of any one of claims 1 to 3, a correlation model between the expected bias quantitative score and the target cultivation parameters is established; The cultivation process is adjusted according to the association model to cultivate plant-based ingredients with the desired bias quantification score.

9. The design method according to claim 5, characterized in that, It also includes the step of: correcting the comprehensive bias quantitative score according to the cooking and processing method to be used for the compound product.

10. The intelligent dietary therapy recommendation method according to claim 6, characterized in that, The individual status information is automatically generated based on the results of the physical fitness questionnaire input by the user or the physical sign data collected by the system.

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