Method for rapidly identifying and comprehensively evaluating polygonatum cyrtonema provenance based on multi-character principal component analysis

Abstract

The invention discloses a rapid identification and comprehensive evaluation method for a polygonatum cyrtonema provenance, and belongs to the field of improved variety breeding and quality control of traditional Chinese medicinal materials. The method systematically measures a plurality of specific biological traits covering three dimensions of growth, physiology and medicine. And carrying out standardization processing on the measured multi-dimensional data, carrying out dimensionality reduction by using a principal component analysis model, constructing an evaluation model capable of comprehensively reflecting the comprehensive quality of the provenance, and calculating a quantitative comprehensive evaluation score. The score can objectively and scientifically divide different provenances into four different quality grades including excellent, good, common and to-be-improved quality grades. The method has the beneficial effects that the strong positive correlation between the easily measured growth traits of the overground part and the medicinal component content of the underground rhizome for determining the final quality of the medicinal material is established, so that the rapid, predictive and non-destructive early screening of the provenance quality is realized.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of medicinal material species identification, and particularly relates to a method for rapidly identifying and comprehensively evaluating the provenances of Polygonatum cyrtonema Hua based on multi-trait principal component analysis. BACKGROUND

[0002] Polygonatum cyrtonema Hua is an important medicinal and edible material, and its dried rhizomes play a crucial role in clinical application under the guidance of traditional Chinese medicine theory and in the modern health industry. It is rich in bioactive components such as polysaccharides, steroidal saponins, and flavonoids. Modern pharmacological studies have confirmed that it has a wide range of physiological activities such as enhancing immunity, antioxidant, anti-aging, regulating blood sugar and blood lipids. With the improvement of public health awareness and the continuous expansion of the market, the demand for high-quality Polygonatum cyrtonema is increasing, which has greatly promoted the transformation of its industrial model from traditional wild mining to large-scale and standardized artificial cultivation.

[0003] However, the current development of Polygonatum cyrtonema industry is facing a core bottleneck: the mixture of provenances and the uneven quality. Due to the long-term overuse of wild resources and the lag of systematic breeding, the genetic background of the provenances circulating in the market is extremely complex, leading to significant differences in yield potential, key active ingredient content, growth adaptability, and stress resistance of provenances from different geographical sources. This uneven quality not only seriously affects the stability and efficacy of clinical medication of traditional Chinese medicine, but also greatly restricts the standardized production of related health products and the sustainable development of the entire industry.

[0004] To address this challenge, various methods for identifying and quality controlling Polygonum multiflorum have been explored and applied in the field, but the existing technologies have different degrees of limitations: 1. Traditional experience identification method: mainly based on the macroscopic morphological characteristics of rhizome (such as "ginger-shaped Polygonum multiflorum", "chicken head Polygonum multiflorum"), texture, color and other evaluation. This method is highly dependent on the personal experience of the evaluator, with strong subjectivity, lack of quantitative standards, and unable to objectively reflect the chemical basis of the medicinal material, making it difficult to guarantee the objectivity, accuracy and repeatability of the evaluation results. 2. Single chemical index evaluation method: the current "Chinese Pharmacopoeia" takes the content of Polygonum multiflorum polysaccharide as the key quality control index, which is not less than 7.0% according to the dry product. This standard provides a basic threshold for the market access of Polygonum multiflorum medicinal materials, but its evaluation dimension is too single. A large number of studies have shown that the pharmacological effect of Polygonum multiflorum is a comprehensive reflection of the synergistic effect of multiple active ingredients, and only focusing on the content of polysaccharide while ignoring the contribution of other important active ingredients such as steroidal saponins, flavonoids, and total phenols cannot fully reflect the comprehensive medicinal value of the species. For example, the polysaccharide content of the excellent provenance from Zhejiang Songyang can be as high as 18.57%, far exceeding the pharmacopoeia standard, while other provenances may only hover around the pass line. Single index cannot effectively distinguish "qualified products" from "excellent products", limiting the precise selection of specific germplasm resources. 3. Modern chromatographic fingerprint and metabolomics technology: advanced analytical techniques such as high-performance liquid chromatography (HPLC), gas chromatography-mass spectrometry (GC-MS), and ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-Q-Orbitrap MS) can accurately and comprehensively analyze the chemical composition of medicinal materials, and have shown strong ability in species identification (e.g., distinguishing Polygonum multiflorum from other Polygonum plants or counterfeit products). For example, CN120044159 A discloses a method for identifying Polygonum multiflorum medicinal materials based on characteristic compounds and its application. However, these technologies usually require large equipment investment, complex operation procedures, and high data processing requirements. For intra-species (intra-species) rapid, intuitive, and comprehensive quality sorting and screening of a large number of different provenances, the application process is relatively cumbersome, and it is difficult to be widely used as a convenient tool for preliminary screening in the field or rapid quality inspection on the production line. 4. Molecular markers (DNA barcodes) technology: DNA sequencing-based molecular biology methods can accurately identify species and their geographical origin from the genetic level, providing decisive evidence for germplasm resource protection and product traceability. However, genetic markers mainly reflect the genetic identity of the species, and cannot directly reflect the actual accumulation level of secondary metabolites of plants in a specific growth environment, i.e., the genetic identity is not completely equivalent to the commodity quality of medicinal materials.

[0005] In summary, the prior art is either biased due to strong subjectivity, or one-sided due to single evaluation dimension, or difficult to popularize due to complex and expensive technology. There is an urgent need in the field to establish a scientific, systematic and efficient new method for screening excellent intra-species sources, which can comprehensively integrate multiple key quality dimensions, provide objective and quantitative evaluation results, and have the potential for rapid prediction, in order to guide the breeding of good varieties and standardized production of Polygonatum cyathopetalum, and promote industrial upgrading. SUMMARY

[0006] In order to overcome the above-mentioned deficiencies of the prior art, the present application provides a scientific, objective, comprehensive and efficient method for rapid identification and comprehensive evaluation of Polygonatum cyathopetalum sources based on multi-trait principal component analysis. This method aims to systematically solve the problem of inconsistent quality caused by mixed sources, and provides a reliable and feasible technical support system for the precise screening, genetic improvement and large-scale and standardized planting of excellent sources.

[0007] To achieve the above object and other related objects, the technical scheme adopted by the present application is as follows:

[0008] The method for rapid identification and comprehensive evaluation of Polygonatum cyathopetalum sources based on multi-trait principal component analysis comprises the following steps:

[0009] Step 1, sample preparation: preparing plant samples of the Polygonatum cyathopetalum sources to be tested;

[0010] Step 2, multi-dimensional trait index determination: determining the biological trait indexes of the plant samples; the biological trait indexes include growth trait indexes, physiological and biochemical trait indexes, and medicinal ingredient trait indexes;

[0011] Step 3, data standardization processing: performing Z-score standardization processing on the original data of the biological trait indexes to generate a standardized multi-dimensional data matrix;

[0012] Step 4, principal component analysis processing: applying a principal component analysis (PCA) model to perform dimension reduction processing on the standardized multi-dimensional data matrix, and extracting principal components with a cumulative variance contribution rate not less than 85% to ensure that the extracted principal components can represent most of the information of the original data;

[0013] Step 5, comprehensive score calculation: calculating a weighted comprehensive evaluation score (CES) based on the principal component variance contribution rate;

[0014] Step 6, source identification and quality grading: grading the quality of the Polygonatum cyathopetalum sources to be tested according to the weighted comprehensive evaluation score.

[0015] As a preferred embodiment of the present application, step 6, the weighted comprehensive score value is used to divide the multiple-flower polygonum sibiricum germplasm into one of at least four quality grades; wherein the quality grades are divided into an excellent grade, a good grade, a common grade, and an improvement-needed grade, and the grading standards are as follows: excellent grade: CES > 3.0; good grade: 1.0≤ CES < 3.0; common grade: -2.0≤ CES < 1.0; improvement-needed grade: CES < -2.0.

[0016] As a preferred embodiment of the present application, step 1 comprises:

[0017] (1) collecting multiple-flower polygonum sibiricum germplasms of different geographical origins;

[0018] (2) under unified and standardized cultivation management conditions, cultivating uniform two-year-old tubers to obtain plant samples of the multiple-flower polygonum sibiricum germplasms.

[0019] As a preferred embodiment of the present application, the cultivation management conditions are as follows: the understory canopy density is controlled at 0.7, the planting row spacing is 30 cm x 40 cm, and the planting depth is 8-10 cm.

[0020] As a preferred embodiment of the present application, the growth traits are used to reflect indicators of plant growth vigor and biomass accumulation capacity, including one or several of plant height, stem diameter, leaf length, leaf width, leaf area, leaf perimeter, and leaf shape index.

[0021] As a preferred embodiment of the present application, the physiological and biochemical traits are used to reflect indicators of plant photosynthetic efficiency, stress resistance, and physiological activity, including one or several of leaf chlorophyll content, peroxidase (POD) activity, superoxide dismutase activity (SOD), and malondialdehyde (MDA) content.

[0022] As a preferred embodiment of the present application, the chlorophyll content is determined by first treating with an ethanol-acetone mixture and then using a spectrophotometer; the malondialdehyde content is determined by the thiobarbituric acid colorimetric method; the peroxidase activity is determined by the guaiacol method; and the superoxide dismutase activity is determined by the nitro blue tetrazolium photoreduction method.

[0023] As a preferred embodiment of the present application, the medicinal component traits are used to reflect chemical indicators of the core medicinal value, including one or several of the following: polysaccharide content, total saponin content, total phenol content, and total flavonoid content in rhizomes.

[0024] As a preferred embodiment of the present application, the medicinal component is first treated as follows: the fresh rhizomes of the plant sample to be tested are washed, dried in an oven at 70 ℃ until constant weight, then crushed using a pulverizer and passed through a 60-mesh sieve.

[0025] As a preferred embodiment of the present application, the multiple-flowered polygonatum sources are selected from Anhui Jiuhua (AHJH), Zhejiang Linan (ZJLA), Zhejiang Longquan (ZJLQ), Zhejiang Jiangshan (ZJJS), Zhejiang Songyang (ZJSY), Zhejiang Yunhe (ZJYH), Fujian Yongan (FJYA), Fujian Jianou (FJJO), Jiangxi Jiujiang (JXJJ), Jiangxi Tonggu (JXTC), and Jiangxi Fuzhou (JXFZ).

[0026] As a preferred embodiment of the present application, step 3 aims to eliminate the influence of different dimensions and orders of magnitude between indicators, and ensure the comparability of data. The original data of the biological trait indicators are converted into dimensionless data with a mean of 0 and a standard deviation of 1 according to formula (1) to generate a standardized multi-dimensional data matrix.

[0027] (1) ;

[0028] wherein: z is the standardized value (Z-score);

[0029] x is a single original data point (i.e. the original measured value of a biological trait indicator of a source);

[0030] μ is the average value of the biological trait indicator in all sources;

[0031] σ is the standard deviation of the biological trait indicator in all sources.

[0032] As a preferred embodiment of the present application, step 4 includes the following steps:

[0033] (1) Selecting biological trait indicators with eigenvalues greater than 1 and cumulative variance contribution rates not less than 85% as principal components to determine the number of principal components;

[0034] (2) Explaining the biological significance of each principal component by analyzing the initial factor loading matrix to determine the variation information of the original traits represented by each principal component.

[0035] As a preferred embodiment of the present application, step 5 includes the following steps:

[0036] (1) Using the results of the principal component analysis (PCA) model to calculate the score of each source on each principal component extracted.

[0037] (2) ;

[0038] wherein: F ik : the score of the i th source on the k th principal component;

[0039] Z: the standardized value calculated by formula (1);

[0040] C kj : the score coefficient of the jth biological trait index on the kth principal component, the coefficient matrix is calculated by principal component analysis, which reflects the contribution weight of each original index to the principal component;

[0041] p: the total number of biological trait indexes;

[0042] (2) according to the variance contribution rate of each principal component, the score is weighted and summed to obtain the final comprehensive evaluation score (CES);

[0043] (3);

[0044] (4);

[0045] wherein, the weight W k The calculation formula is:

[0046] CES i : the final weighted comprehensive evaluation score of the ith provenance.

[0047] F ik : the score of the ith provenance on the kth principal component calculated by formula 2.

[0048] W k : the weight of the kth principal component.

[0049] P k : the variance contribution rate of the kth principal component (%).

[0050] : the cumulative variance contribution rate of all extracted principal components (%).

[0051] m: the total number of extracted principal components.

[0052] The application also provides a non-transitory computer readable storage medium, which stores computer program instructions, when the instructions are executed by a processor, the processor executes any one of the methods.

[0053] Compared with the prior art, the beneficial effects of the application are

[0054] (1)Comprehensiveness and objectivity: The method breaks through the limitations of traditional subjective evaluation and single index evaluation, and innovatively constructs a multi-dimensional comprehensive evaluation method including three key biological indexes of growth, physiology and medicinal value. The method converts complex multi-dimensional data into a single, quantitative weighted comprehensive evaluation score (CES) through a rigorous mathematical model, so as to comprehensively and objectively reflect the growth potential, environmental adaptability and intrinsic medicinal value of the seed source. The evaluation result is scientific and reliable, and has high comparability, which provides clear quantitative basis for the advantages and disadvantages of the seed source.

[0055] (2)Rapidness and predictability: Through correlation analysis, the method reveals and utilizes the extremely significant positive correlation between the aboveground growth traits (such as stem diameter, leaf area) which are easy to measure in the growing season and the underground root stem medicinal ingredient content which requires destructive sampling and complex analysis process. This key finding enables the effective prediction of the medicinal quality of the root stem after mature harvest by non-destructively measuring the aboveground traits of the plant in the early stage of growth (such as the vegetative growth period). This "phenotype predicting quality" mode greatly shortens the screening period, significantly reduces the identification cost, and realizes the "rapid" screening in a true sense, providing an unprecedented efficient tool for large-scale seed source survey and early directional breeding.

[0056] (3)Scientific breeding guidance value: The principal component analysis model constructed by the method is not only an evaluation tool, but also a breeding guidance tool with deep analysis function. Through the analysis of the factor load of each principal component, the core trait combination with the greatest contribution to the comprehensive quality can be accurately identified, which clearly and efficiently points out the breeding direction for the breeders, i.e. through breeding means to improve multiple traits highly related to the principal components, the comprehensive quality of the seed source can be most effectively improved, so that the breeding target is more targeted and the breeding process is more scientific. DETAILED DESCRIPTION

[0057] The embodiments of the present application will be described in detail below with specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the disclosure. The present application can also be implemented or applied in different specific embodiments, and various modifications or changes can be made to the details in the specification without departing from the spirit of the present application.

[0058] In the embodiments of the present application, the methods used are conventional methods unless otherwise specified, and the reagents used can be obtained from commercial channels. In the embodiments of the present application, the process equipment or device not specifically mentioned is the conventional equipment or device in the art. The present application will be further described below in conjunction with specific embodiments, but the protection scope of the present application is not limited to this:

[0059] Example 1: Test material preparation and multi-dimensional trait data collection

[0060] Step 1, sample preparation: prepare plant samples of the tested Polygonum bellum provenances;

[0061] The specific method is as follows:

[0062] 1. Collect 11 different geographical sources of Polygonum bellum provenances:

[0063] The provenance materials are selected from 11 different geographical sources of Polygonum bellum provenances from 4 provinces in China, which are: Anhui Jiuhua (AHJH), Zhejiang Lin'an (ZJLA), Zhejiang Longquan (ZJLQ), Zhejiang Jiangshan (ZJJS), Zhejiang Songyang (ZJSY), Zhejiang Yunhe (ZJYH), Fujian Yong'an (FJYA), Fujian Jianou (FJJO), and Jiangxi Jiujiang (JXJJ), Jiangxi Tonggu (JXTC), Jiangxi Fuzhou (JXFZ). Table 1 is the disclosure table of Polygonum bellum provenance genetic resources as follows:

[0064] Table 1. Disclosure table of Polygonum bellum provenance genetic resources

[0065] 2. Under the condition of unified and standardized cultivation management, the two-year-old tubers of 11 different geographical sources of Polygonum bellum provenances with uniform specifications are cultivated to obtain plant samples of Polygonum bellum provenances;

[0066] In order to maximize the exclusion of environmental heterogeneity on the performance of provenance traits and ensure that the observed differences are mainly due to genetic background, the two-year-old tubers of all 11 provenances with uniform specifications are subjected to multi-point repeated provenance comparison tests in the same simulated understory habitat. The cultivation conditions are strictly standardized: the understory canopy density is controlled at 0.7, the planting row spacing is 30 cm x 40 cm, and the planting depth is 8-10 cm. The field management measures (such as weeding, soil loosening, pest control, etc.) during the entire growth period are consistent.

[0067] Step 2, multi-dimensional trait index determination: determine the multiple biological trait indexes of the plant samples; the biological trait indexes include growth traits, physiological and biochemical traits, and medicinal ingredient traits;

[0068] Specifically, the present application accurately measures 15 key biological trait indexes according to the following standardized operation procedures when the plants grow to a certain period; wherein the certain period refers to the key time nodes with clear physiological characteristics or agronomic significance in the plant growth and development process;

[0069] Growth trait indexes (a total of 7, measured in the vigorous growth period of vegetative growth in June 2023):

[0070] Plant height: Measure the vertical height from the base of the plant to the terminal bud using a calibrated tape measure.

[0071] Stem diameter: Measure the diameter of the stem at 1 cm above the ground using a vernier caliper.

[0072] Leaf length, leaf width, leaf area, and leaf perimeter: Select 3-5 fully expanded and healthy mature leaves from the middle of the plant and scan them using a leaf area meter (LI-COR Biosciences, model LI-3000C) and read the data.

[0073] Leaf shape index: Calculate it by the formula "leaf length / leaf width".

[0074] Physiological and biochemical trait indicators (4 items, sampled from the leaves selected for growth trait determination):

[0075] Chlorophyll content: Extract it using an ethanol-acetone mixture, measure the absorbance at specific wavelengths (665 nm and 649 nm) using a spectrophotometer, and calculate it according to formula (4).

[0076] Specific operation process:

[0077] (1) Sampling: Accurately weigh about 0.1 g of fresh plant leaves, cut them into small pieces, and put them into a mortar.

[0078] (2) Grinding: Add a small amount of quartz sand, calcium carbonate powder, and 2-3 mL of extraction solution (ethanol:acetone = 1:2 by volume). Grind the leaves into a homogenate under dark conditions.

[0079] (3) Extraction: Transfer the homogenate into a 10 mL centrifuge tube, rinse the mortar and pestle several times with extraction solution, and combine the rinse solution into the centrifuge tube.

[0080] (4) Volumetric determination and centrifugation: Volumetrically determine the extraction solution to 10 mL, mix thoroughly, and stand for 3-5 minutes in the dark. Then centrifuge at 4000 r / min for 10 minutes.

[0081] (5) Measurement: Take the supernatant, use a spectrophotometer with extraction solution as blank control, and measure the absorbance values (A 665 and A 649 ) at 665 nm and 649 nm wavelengths, respectively.

[0082] Formulas (5)~(8):

[0083] Chlorophyll a concentration (Ca, mg / L):

[0084] (5);

[0085] Chlorophyll b concentration (Cb, mg / L):

[0086] (6);

[0087] Total chlorophyll concentration (CT, mg / L):

[0088] (7);

[0089] Chlorophyll content (mg / g FW):

[0090] (8);

[0091] Wherein: Vt: total volume of extract (L); W: fresh weight of sample (g).

[0092] Malondialdehyde (MDA) content: Determined by thiobarbituric acid (TBA) colorimetry, this index reflects the level of cell membrane lipid peroxidation and is an indicator of plant stress degree.

[0093] Specific operation process:

[0094] (1) Sample extraction: Accurately weigh 0.5 g of fresh leaves and add 5 mL of 10% trichloroacetic acid (TCA) solution for ice bath grinding. Centrifuge the grinding solution at 4000 r / min for 10 minutes.

[0095] (2) Reaction: Take 2 mL of supernatant and add 2 mL of 0.67% thiobarbituric acid (TBA) solution and mix well.

[0096] (3) Color development: Heat the mixed solution in a boiling water bath for 20 minutes, then quickly remove and cool in ice water.

[0097] (4) Determination: After cooling, centrifuge again, take the supernatant, and use a spectrophotometer to determine the absorbance values (A 532 , A 600 ) at 532 nm and 600 nm, respectively. Determine A 600 to correct non-specific absorption.

[0098] (5) Blank control: Replace the extraction supernatant with 2 mL of 10% TCA solution and perform the same operation.

[0099] (9);

[0100] Wherein: A 532 , A 600 : absorbance values at 532 nm and 600 nm, respectively.

[0101] Vt: total volume of extract (mL).

[0102] Vs: volume of extract taken for measurement (mL), in this case 2 mL.

[0103] W: fresh weight of sample (g).

[0104] ε: molar extinction coefficient of MDA-TBA complex, 1.55×105 L⋅mol -1 ⋅cm -1 .

[0105] d: optical path of cuvette (cm), usually 1 cm.

[0106] Peroxidase (POD) activity: determined using the guaiacol method. The definition of POD activity is usually: 0.01 of absorbance change per minute (ΔA) is defined as one unit of enzyme activity (U).

[0107] Specific operation process:

[0108] 1. Enzyme extraction: accurately weigh 0.5 g of fresh leaves, place in a pre-cooled mortar, and add 5 mL of phosphate buffer (usually pH 7.0-7.8) for ice bath grinding. Centrifuge the grinding liquid at 4°C, 12000 r / min for 20 minutes, and the supernatant is the crude enzyme solution of POD.

[0109] 2. Reaction system: add 2.9 mL of phosphate buffer, 1.0 mL of 2% H2O2 solution, and 1.0 mL of guaiacol solution in sequence in a test tube.

[0110] 3. Measurement: after adding 0.1 mL of crude enzyme solution, shake quickly and immediately pour into a cuvette. Use a spectrophotometer to read the absorbance value every 30 seconds at a wavelength of 470 nm, and continuously measure for 3 minutes.

[0111] 4. Blank control: replace the enzyme solution with an equal volume of phosphate buffer.

[0112] (10) ;

[0113] where ΔA 470 : change in absorbance at 470 nm within the reaction time.

[0114] V t : total volume of enzyme extraction solution (mL).

[0115] V s : volume of enzyme solution taken for measurement (mL), in this case 0.1 mL.

[0116] W: sample fresh weight (g).

[0117] t: reaction time (min).

[0118] SOD activity: measured by NBT photoreduction method. The definition of SOD activity is usually that the amount of enzyme required to inhibit 50% of NBT photoreduction reaction under specified conditions is one unit of enzyme activity (U).

[0119] Specific operation process:

[0120] 1. Enzyme extraction: the extraction method of SOD enzyme solution is the same as that of POD.

[0121] 2. Reaction system configuration: prepare two sets of test tubes, one set for determination tube (add enzyme solution), and one set for control tube (do not add enzyme solution, replace with buffer solution). Add phosphate buffer solution, methionine (Met) solution, NBT solution, and EDTA-Na2 solution in each test tube in turn.

[0122] 3. Sample addition and light protection: add 0.1 mL of enzyme solution in the determination tube, and add 0.1 mL of buffer solution in the control tube. Finally, add riboflavin solution. At the same time, set up a blank tube without enzyme solution and without light irradiation (dark control).

[0123] 4. Light irradiation reaction: place all test tubes (except dark control) under 4000 lx light irradiation for 20 minutes to fully color the control tube.

[0124] 5. Determination: after the reaction is completed, immediately measure the absorbance value of each tube at 560 nm wavelength, and set the dark control to zero. Record the absorbance of the control tube (A CK ) and the absorbance of the determination tube (A E ).

[0125] (11) ;

[0126] A CK : absorbance of control tube.

[0127] A E : absorbance of determination tube.

[0128] V t : total volume of enzyme extraction solution (mL).

[0129] V s : volume of enzyme solution taken for determination (mL), which is 0.1 mL in this example.

[0130] W: sample fresh weight (g).

[0131] I: represents the percentage of inhibition .

[0132] Pharmaceutical ingredient trait indicators (a total of 4, at the mature rhizome harvest period, rhizomes were dug in October-November 2023 to determine) :

[0133] The harvested fresh rhizomes were washed, dried to constant weight in an oven at 70°C, then crushed using a pulverizer and passed through a 60-mesh sieve.

[0134] To ensure standardization and repeatability of the determination method, the commercial test kit produced by Suzhou Keming Biotechnology Co., Ltd. was used, and the contents of polygonatum polysaccharide, total saponin, total phenol and total flavonoid in the rhizome powder were determined according to the instructions.

[0135] Step 3, data standardization processing: the original data of the biological trait indicators were subjected to Z-score standardization processing to generate a standardized multidimensional data matrix;

[0136] Specifically, the original data of the biological trait indicators were converted into dimensionless data with a mean of 0 and a standard deviation of 1 according to formula (1) to generate a standardized multidimensional data matrix;

[0137] 1. Data standardization and correlation analysis

[0138] The original data of 15 biological trait indicators of 11 provenances (average values of multiple repetitions of each provenance) were input into statistical analysis software (such as SPSS 26.0) for Z-score standardization processing, and the original data of all biological trait indicators were converted into dimensionless data with a mean of 0 and a standard deviation of 1 to eliminate the differences in the dimensions and orders of magnitude of the original data, laying a foundation for subsequent multivariate analysis.

[0139] Before building the model, Pearson correlation analysis was performed on the relationships between the biological trait indicators to verify the scientific basis of the "rapid prediction" feature proposed by the present application. The analysis results (shown in Table 2) clearly revealed the internal relationship between the aboveground growth traits and the underground medicinal quality.

[0140] Table 2: Correlation analysis of key growth trait indicators and pharmaceutical ingredient trait indicators Traits Polysaccharide content Total saponin content Total phenol content Total flavonoid content Plant height 0.875∗∗ 0.871∗∗ 0.862∗∗ 0.860∗∗ Stem diameter 0.887∗∗ 0.876∗∗ 0.845∗∗ 0.793∗∗ Leaf area 0.925∗∗ 0.918∗∗ 0.856∗∗ 0.839∗∗

[0141] Note: The data are derived from comprehensive determination of 11 provenances, and the correlation coefficient (r) value is 1; ** indicates that it reaches a highly significant correlation at the P<0.01 level.

[0142] As can be seen from Table 2, the correlation coefficient of stem diameter and polysaccharide content is as high as r = 0.887, and the correlation coefficient of leaf area and polysaccharide content is as high as r = 0.925. The data in Table 2 clearly confirms that the aboveground growth traits such as plant height, stem diameter and leaf area, which are easy to measure non-destructively in the early growth season, have a very significant strong positive correlation with the content of polysaccharide, saponin and other core medicinal ingredients in the root and rhizome which ultimately determine the quality of medicinal materials. This strong statistical evidence proves that by observing the growth of plants, the accumulation level of medicinal substances in the underground part can be effectively predicted, providing solid theoretical and data support for the rapid, early and non-destructive screening function of the present application.

[0143] Step 4, principal component analysis (PCA) processing: applying a principal component analysis model to reduce dimension processing on 15 standardized multidimensional data matrices, extracting principal components with cumulative variance contribution rate not less than 85%;

[0144] (1) The number of principal components is determined according to the principle that the eigenvalue is greater than 1. As shown in Table 3, the eigenvalues of the first two principal components are both greater than 1, and the cumulative variance contribution rate reaches 85.933%. This indicates that the two newly constructed comprehensive variables (principal components) which are not correlated with each other can already explain most of the information (more than 85%) contained in the original 15 variables. Therefore, it is reasonable, efficient and less information loss to select these two principal components (named F1 and F2) to replace all original traits for comprehensive evaluation.

[0145] Table 3: Eigenvalues and variance contribution rates of principal components Principal component Eigenvalue Contribution rate (%) Cumulative contribution rate (%) F1 11.553 77.019 77.019 F2 1.337 8.913 85.933 F3 0.858 5.720 91.653

[0146] (2) The biological significance of each principal component is explained by analyzing the initial factor loading matrix (as shown in Table 4), that is, understanding which original trait's variation information each principal component mainly represents.

[0147] Table 4: Initial factor loading coefficients of principal components Traits Loading coefficient (F1) Loading coefficient (F2) Plant height (x1) 0.916 -0.243 Stem diameter (x2) 0.922 0.171 Leaf area (x6) 0.955 -0.041 Chlorophyll (x8) 0.910 -0.004 MDA content (x9) -0.767 -0.037 POD activity (x10) 0.987 0.015 SOD activity (x11) 0.899 -0.029 Polysaccharide content (x12) 0.976 -0.035 Saponin content (x13) 0.963 0.106 Total phenol content (x14) 0.946 0.019 Flavonoid content (x15) 0.902 -0.022 Leaf shape index (x5) 0.277 0.952

[0148] Note: For clarity, only the loading coefficients of some representative traits are listed.

[0149] From the loading coefficients in Table 4, it can be clearly seen that:

[0150] The first principal component (F1): has very high positive load on almost all indexes representing excellent characteristics (such as growth traits like plant height, stem diameter, leaf area, and physiological activity indexes like chlorophyll, POD, SOD, and all medicinal component indexes like polysaccharide and saponin), and is strongly negatively correlated with the index MDA content representing stress damage. This shows that F1 is a composite factor that can comprehensively reflect the "comprehensive vitality and intrinsic quality" of the plant. If a provenance scores high on F1, it means that the plant is strong, has strong photosynthesis and stress resistance, and has high medicinal component accumulation, which is a direct manifestation of superior comprehensive performance.

[0151] The second principal component (F2): its main contribution comes from the leaf shape index, representing the variation of leaf morphological strategy of provenances, and is a secondary factor affecting comprehensive quality.

[0152] Step 5, comprehensive score calculation: calculate the weighted comprehensive score value based on the principal component variance contribution rate;

[0153] 1. Calculation of weighted comprehensive score value (CES)

[0154] According to the characteristic vector obtained by principal component analysis, the calculation formula of the score of each principal component can be constructed. Let {X1, X2,..., X15} represent the standardized values of the 15 indexes, and the calculation formula of the scores (Y1, Y2) of the two principal components is: Y1=0.269X1+0.271X2+0.264X3+0.259X4+0.081X5+0.281X6+0.210X7+0.268X8-0.226X9+0.290X10+0.264X11+0.287X12+0.283X13+0.278X14+0.265X15; Y2=-0.210X1+0.148X2+0.277X3-0.3579X4+0.823X5-0.035X6-0.196X7-0.003X8-0.032X9+0.013X10-0.025X11-0.030X12+0.092X13+0.016X14-0.019X15;

[0155] Then, according to the proportion of the variance contribution rate of each principal component in the total variance contribution rate of the extracted principal components as the weight, Y1 and Y2 are weighted and summed to calculate the final comprehensive evaluation score (CES):

[0156] CES=85.93377.019×Y1+85.9338.913×Y2=0.896×Y1+0.104×Y2 (a)

[0157] Step 6, source identification and quality grading: according to the weighted comprehensive score to measure the quality grade of the multiple-flowered polygonatum sources;

[0158] Specifically, the following steps are included:

[0159] The standardized data of 15 traits of 11 tested sources are substituted into formula (a) to calculate the Y1, Y2 scores and the final comprehensive evaluation score (CES) of each source, and ranking is performed, and the results are shown in Table 5.

[0160] Table 5: Principal component scores and comprehensive evaluation scores and rankings of 11 multiple-flowered polygonatum sources

[0161] The results of Table 5 clearly show the powerful application effect of the method of the application. It successfully condenses 165 complex data points (11 sources × 15 traits) of 11 sources into an intuitive, comparable and orderable comprehensive evaluation score.

[0162] 2. According to the score, the quality of the source can be objectively and scientifically identified and graded;

[0163] The quality grade is divided according to the following standards:

[0164] Excellent grade (first level): CES > 3.0. In this example, Zhejiang Songyang (4.839) and Zhejiang Yunhe (4.551) belong to this top level.

[0165] Good grade (second level): 1.0≤CES<3.0. In this example, Zhejiang Jiangshan (2.775) and Zhejiang Lin'an (1.222) belong to this level.

[0166] Common grade (third level): -2.0≤CES<1.0. In this example, Longquan, Tonggu, Jiujiang and Fuzhou belong to this level.

[0167] Improvement grade (fourth level): CES < -2.0. In this example, Jiuhua, Jian'ou and Yong'an belong to this level.

[0168] The evaluation results provide clear decision-making basis for breeders, agricultural producers and pharmaceutical production enterprises: high-quality sources such as Songyang and Yunhe with high comprehensive evaluation values should be prioritized, protected and promoted to achieve high-quality, high-yield and high-efficiency of the multiple-flowered polygonatum industry.

[0169] The present application establishes strong positive correlation between easily measured aboveground growth traits and underground rhizome medicinal ingredient content which determines the final quality of medicinal materials by statistical methods, thereby realizing rapid, predictive and non-destructive early screening of seed source quality. The method provides a strong technical tool for good seed selection, germplasm resource evaluation and quality control in large-scale production of Polygonatum cyrtonema Hua.

[0170] The above examples are intended to describe the preferred embodiments of the present application, and are not intended to limit the scope of the present application. Any modifications and improvements made by those skilled in the art to the technical solutions of the present application without departing from the design spirit of the present application shall fall within the protection scope of the present application.

Claims

1. A method for rapid identification and comprehensive evaluation of multiple-flower polygonatum provenances based on multi-trait principal component analysis, characterized in that, The method comprises the following steps: Step 1, sample preparation: preparing plant samples of the Polygonatum cyathopetalum provenance to be tested; Step 2, multi-dimensional trait index determination: determining a plurality of biological trait indexes of the plant samples; the biological trait indexes include growth trait indexes, physiological and biochemical trait indexes, and medicinal ingredient trait indexes; Step 3, data standardization processing: performing Z-score standardization processing on the original data of the biological trait indexes to generate a standardized multi-dimensional data matrix; Step 4, principal component analysis processing: performing dimension reduction processing on the standardized multi-dimensional data matrix by using a principal component analysis model to extract principal components with an accumulative variance contribution rate of not less than 85%; Step 5, comprehensive score calculation: calculating a weighted comprehensive score value based on the principal component variance contribution rate; Step 6, provenance identification and quality grading: grading the quality of the Polygonatum cyathopetalum provenance to be tested according to the weighted comprehensive score.

2. The method of claim 1, wherein, Step 6, the weighted comprehensive score value CES is used to divide the Polygonatum cyathopetalum provenance into one of at least four quality grades; wherein the quality grades are divided into an excellent grade, a good grade, a common grade, and an improvement-needed grade, and the grading standards are as follows: excellent grade: CES > 3.0; good grade: 1.0 ≤ CES < 3.0; common grade: -2.0 ≤ CES < 1.0; improvement-needed grade: CES < -2.

0.

3. The method of claim 1, wherein, Step 1 comprises: (1) collecting Polygonatum cyathopetalum provenances of different geographical origins to prepare two-year-old tubers with consistent specifications; (2) cultivating under unified and standardized cultivation management conditions to obtain plant samples of the Polygonatum cyathopetalum provenances.

4. The method of claim 1, wherein: The growth traits are used to reflect the indexes of plant growth vigor and biomass accumulation capacity, including one or several of plant height, stem diameter, leaf length, leaf width, leaf area, leaf perimeter, and leaf shape index.

5. The method of claim 1, wherein: The physiological and biochemical traits are used to reflect the indexes of photosynthetic efficiency, stress resistance, and physiological activity of plants, including one or several of leaf chlorophyll content, peroxidase activity, superoxide dismutase activity, and malondialdehyde content.

6. The method of claim 5, wherein: The chlorophyll content is determined by first treating with an ethanol-acetone mixed solution and then using a spectrophotometer; the malondialdehyde content is determined by using the thiobarbituric acid colorimetric method; the peroxidase activity is determined by using the guaiacol method; and the superoxide dismutase activity is determined by using the nitro blue tetrazolium photoreduction method.

7. The method of claim 1, wherein: The medicinal ingredient traits are used to reflect the chemical indexes of the core medicinal value, including one or several of the contents of Polygonatum sibiricum polysaccharide, total saponins, total phenols, and total flavonoids in the rhizomes.

8. The method of claim 7, wherein, The medicinal ingredient is subjected to the following treatment before the plant samples: washing the fresh rhizomes of the plant samples to be tested, drying them in an oven at 70 ℃ until the weight is constant, then crushing them using a crusher and passing them through a 60-mesh sieve.

9. The method of claim 1, wherein: The Polygonatum cyathopetalum provenances are selected from the group consisting of the Anhui Jiuhua provenance, the Zhejiang Lin'an provenance, the Zhejiang Longquan provenance, the Zhejiang Jiangshan provenance, the Zhejiang Songyang provenance, the Zhejiang Yunhe provenance, the Fujian Yong'an provenance, the Fujian Jianou provenance, the Jiangxi Jiujiang provenance, the Jiangxi Tonggu provenance, and the Jiangxi Fuzhou provenance.

Images