Method for screening production places of Chinese wolfberry fruits applied to development of health-care wine

Abstract

The invention discloses a fructus lycii producing area screening method applied to development of health care wine. The fructus lycii producing area screening method comprises the following steps: 1) establishing an analysis method to determine the contents of 12 functional substances and 38 inorganic elements in multiple batches of fructus lycii in different producing areas; 2) performing multivariate statistical analysis on the content data of the functional substances and the inorganic elements to find key indexes influencing the quality of the Chinese wolfberry fruits; and 3) establishing a robot learning algorithm by using the key indexes influencing the quality of the Chinese wolfberry fruits, and establishing a Chinese wolfberry fruit discrimination model suitable for health wine development. The method can meet the functional substance content requirement of health care wine development, and meanwhile, the inorganic element content is determined by measuring inorganic elements.

Description

Technical Field

[0001] The invention relates to the technical field of medicinal material origin identification, and in particular to a method for screening wolfberry origin used in the development of health wine. Background Art

[0002] Traditional Chinese medicines (TCMs) are often added to health wines. Due to the diverse origins and complex functional components of these herbs, precipitation and gloss loss often occur within the wine, impacting quality and becoming an industry challenge hindering product improvement. Current methods commonly used in the industry include repeated filtration, aging, and resin treatment, but these methods fail to address the problem at the source of the herbs.

[0003] Goji berries, the dried, mature fruit of the Ningxia wolfberry (Lycium barbarum L.) plant of the Solanaceae family, are a berry rich in nutrients and containing a variety of bioactive compounds. They have been consumed in my country for thousands of years. Goji berries are often used in winemaking. Goji berry wine, combining the berries' nutrients with the flavor of wine, is an alcoholic beverage with health benefits.

[0004] In recent years, significant progress has been made in the application of chemometrics and machine learning techniques to the classification of food, agricultural products, and traditional Chinese medicines, using functional compounds and inorganic elements as characteristic components. These technologies, combined with spectral analysis, image processing, and data modeling, provide efficient and accurate solutions for plant classification. Wolfberry, a commonly used traditional Chinese medicine, has also been reported in the literature on the identification of wolfberry origin using analytical techniques such as stable isotope omics, element omics, and organic nutrient fingerprinting. However, currently, there has been no effort to combine multiple functional substances with inorganic elements to analyze the differences in wolfberries from different origins, particularly for screening raw materials for the development of health wines.

[0005] Goji berries suitable for the development of health wines must not only meet the requirements of multi-index functional substances, but also meet the problems of precipitation and loss of gloss during the blending and aging of the wine. The inorganic elements in goji berries usually have a huge impact on the stability of the wine, especially when combined with functional substances, which can accelerate the formation of precipitation. The functional substances and inorganic elements in goji berries suitable for the development of health wines are not the higher the better, and should have complex correlations based on the specific role played by different indicators in the wine. For example, relatively high levels of ions such as Ca and Mg that are prone to precipitation can quickly precipitate in the early stages of wine blending and aging, and will not affect the stability of the wine later. On the contrary, low levels will interact with functional substances during the subsequent shelf life to produce precipitation, which does not meet quality control requirements. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for screening the origin of wolfberry for use in the development of health wine. The method screens wolfberry raw materials for the development of health wine from the source of wolfberry medicinal materials, which can not only meet the functional substance content requirements for the development of health wine, but also determine the inorganic element content by measuring inorganic elements.

[0007] A method for screening the origin of wolfberries for use in the development of health wine comprises the following steps:

[0008] 1) Establish an analytical method to determine the contents of 12 functional substances and 38 inorganic elements in multiple batches of wolfberry from different origins;

[0009] 2) Perform multivariate statistical analysis on the functional substance and inorganic element content data to identify key indicators affecting wolfberry quality;

[0010] 3) Establish a robot learning algorithm for the key indicators that affect the quality of wolfberries and build a wolfberry discrimination model suitable for the development of health wine.

[0011] The 12 functional substances are p-hydroxybenzoic acid (4-HC), chlorogenic acid, scopolamine lactone, 4-coumaric acid (4-CA), ferulic acid, rutin, kaempferol, betaine, citric acid (AA-2βG), lycium barbarum polysaccharides (PS), total phenols (TF), and total flavonoids (TP).

[0012] The p-hydroxybenzoic acid, chlorogenic acid, scopolamine lactone, 4-coumaric acid, ferulic acid, rutin, and kaempferol were determined using UHPLC, and the specific conditions were as follows:

[0013] (1) Chromatographic conditions: Octadecylsilane bonded silica gel as filler; Agilent 1290 Infinity II LC chromatography system, Acquity UPLC BEH Shield RP18 column (2.1×150 mm, 1.7 μm, 30°C); acetonitrile (A) and 0.2% phosphoric acid-water (B) as mobile phases, gradient elution conditions: 0–5 min, 98% B; 5–9 min, 98%–89% B; 9–20 min, 89%–85% B; 20–30 min, 84% B; 30–34 min, 84%–60% B; 34–42 min, 60% B; the flow rate was set at 0.3 mL / min, and the detection wavelength was 230 nm.

[0014] (2) Preparation of reference solution: Take appropriate amount of p-hydroxybenzoic acid, chlorogenic acid, scopolamine lactone, 4-coumaric acid, ferulic acid, rutin, and kaempferol reference substances, accurately weigh them, and add methanol to prepare reference solution;

[0015] (3) Preparation of test solution: 2.5 g of the test sample powder was decocted with 25 mL of water for 1 hour, and then shaken and extracted twice with 20 mL of ethyl acetate. The combined extracts were added with water and allowed to stand for 15 minutes, and the aqueous layer was discarded. The ethyl acetate layer was dried and dissolved in 2 mL of 50% methanol. The solution was then filtered using a 0.22 μm syringe, and the filtrate was obtained.

[0016] (4) Determination method: Accurately aspirate 1 μl of the reference solution and the test solution respectively, inject them into the liquid chromatograph, determine, and record the chromatogram.

[0017] The betaine, lycium barbarum polysaccharides are determined with reference to the current edition of the "Chinese Pharmacopoeia" and the national standard method for wolfberry formula granules; the total flavonoids are determined with reference to the "Technical Specifications for Inspection and Evaluation of Health Foods" (2003 edition), and the total phenol content is determined with reference to T / NAIA 097-2021 "Spectrophotometric Method for Determination of Total Phenol Content in Wolfberry".

[0018] The 38 inorganic elements are Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Ag, Cd, Ba, Pb, K, Ca, Na, Mg, Li, Sr, U, Sc, Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, and Th. The determination is carried out using the ICP-MS method with reference to the current edition of the Chinese Pharmacopoeia. The specific conditions are as follows:

[0019] (1) Preparation of standard solution: Accurately pipette appropriate amount of standard solution of each element and dilute with (5+95) nitric acid solution to prepare standard solvent;

[0020] (2) Preparation of internal standard solution: Accurately measure appropriate amounts of single element standard solutions of germanium, indium, rhodium, and bismuth and dilute them with (5+95) nitric acid solution to make a mixed solution containing 100 ng of each element per 1 mL. All or some of the elements can be selected as the internal standard solution and added online;

[0021] (3) Preparation of test solution: Grind the test sample into coarse powder, take about 0.5g, weigh accurately, place in a pressure-resistant and high-temperature microwave digestion tank, add 5mL of nitric acid, seal and digest according to the corresponding requirements of each microwave digestion instrument and a certain digestion procedure. Process according to the following digestion procedure: first, heat from room temperature to 80℃ for 10 minutes and keep the temperature constant for 3 minutes; then heat to 130℃ for 5 minutes and keep the temperature constant for 3 minutes; then heat to 180℃ for 8 minutes and keep the temperature constant for 15 minutes; after complete digestion, cool to room temperature, remove the digestion tank after cooling, transfer the digestion solution to a 50mL volumetric flask, wash the digestion tank with a small amount of water 3 times, combine the washing solution in the volumetric flask, dilute with water to the scale, shake well, and obtain the test sample working solution (for elements with higher content, dilute the working solution to a certain amount and use it as the test sample solution); prepare the reagent blank solution in the same way;

[0022] (4) ICP-MS measurement conditions: The instrument parameters are as follows: high-frequency plasma power of 1550W, high-purity argon as carrier gas, plasma gas, nebulizing gas and collision gas flow rates of 0.8L / min, 1.03L / min and 14L / min respectively, the nebulizing chamber temperature is 2.70℃, the acquisition method is peak jump mode, 3 sampling points and 3 sampling repetitions are used; the instrument RF power is 1550W, the nebulizing pump is 40r / min, the sampling depth is 5mm, the cooling gas flow rate is 14.0L / min, the nebulizing gas flow rate is 1.14L / min, the auxiliary gas flow rate is 0.80L / min, and the CCT collision gas flow rate is 4.40mL / min, repeated 3 times.

[0023] The multivariate statistical analysis includes one or more of cluster analysis polar coordinate heat map clustering, PCA, and OPLS-DA, and screens key indicators with VIP greater than 1.

[0024] Preferably, the key indicators affecting the quality of wolfberry include wolfberry polysaccharide, rutin, betaine, citric acid, Li, Ca, Pb, Zn, Mg, Cr, Cu, Ba, Pr, Nd, Sc, Sr, Al, La, U, and Gd.

[0025] Preferably, the key indicators affecting the quality of wolfberries include wolfberry polysaccharides, rutin, and betaine. These substances have high VIP values ​​and have a greater impact on the quality of wolfberries.

[0026] Preferably, the key element indicators affecting the quality of wolfberry include Li, Ca, Pb, Zn, and Mg. The VIP values ​​of these elements are relatively high, and their contribution to the production area of ​​wolfberry is greater.

[0027] The robotic learning algorithms include one or more of backpropagation (BP), convolutional neural networks (CNN), random forests (RF), and support vector machines (SVM). First, goji berry samples from each production area were grouped into a training set:test set ratio of 8:2 using the Kennard-Stone (KS) algorithm. All machine learning methods then underwent hyperparameter screening using a grid search strategy. We systematically evaluated model performance under different hyperparameter combinations, using a 5-fold cross-validation approach to identify the optimal machine learning parameter configuration. For the BP network, we set a learning rate of 0.001 and a target error of 0.0001. For the CNN, we adjusted the maximum number of training epochs to 500, the initial learning rate to 0.001, and the L2 regularization to 0.0001. For the SVM, we set the regularization parameter and kernel function parameter to 1. For the RF, we optimized the number of trees to 50 and the maximum depth to 1. Hyperparameter screening ensured that each method was compared and evaluated at its optimal state. Accuracy, precision, recall, and F1 score were used to evaluate model performance. At the same time, ROC curve analysis was used to further verify the classification ability of the model.

[0028] The wolfberry production areas screened by the method are Ningxia, Gansu and Qinghai.

[0029] The health-care wine is wolfberry wine.

[0030] Compared with the existing technology, the present invention has the following beneficial effects: it successfully establishes a wolfberry discrimination model suitable for the development of health wine, and can screen out medicinal raw materials that meet the requirements of wine development from the source of medicinal materials, thereby eliminating the need for repeated filtration to remove wine sediment during subsequent wine blending and aging, which can not only ensure the clarity and transparency of the wine, but also reduce production costs and have a benefit effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0032] Figure 1 The chromatograms for the determination of the functional substance content in the examples of the present invention are shown in Figures S1, S3, and S5. The chromatograms of the reference substance are shown in Figures S2, S4, and S6. The chromatograms of the sample substance are shown in Figures S2, S4, and S6. Peaks 1 to 9 represent hydroxybenzoic acid, chlorogenic acid, scopolamine lactone, 4-coumaric acid, ferulic acid, rutin, kaempferol, betaine, and citric acid, respectively.

[0033] Figure 2Polar coordinate heat map of 12 functional substances in 90 batches of wolfberry samples in the embodiment of the present invention;

[0034] Figure 3 This is the PCA score diagram of 12 functional substances in 90 batches of wolfberry samples in the embodiment of the present invention;

[0035] Figure 4 This is the OPLS-DA score diagram of 12 functional substances in 90 batches of wolfberry samples in the embodiment of the present invention;

[0036] Figure 5 This is the VIP score diagram of 12 functional substances in 90 batches of wolfberry samples in the embodiment of the present invention;

[0037] Figure 6 This is the OPLS-DA score diagram of 38 inorganic elements in 90 batches of wolfberry samples in the embodiment of the present invention;

[0038] Figure 7 VIP score diagram of 38 inorganic elements in 90 batches of wolfberry samples in the embodiment of the present invention;

[0039] Figure 8 This is the OPLS-DA score diagram of key indicators of 90 batches of wolfberry samples in the embodiment of the present invention;

[0040] Figure 9 The confusion matrix diagrams of the four robot learning methods in the embodiments of the present invention are shown in Figure 1, where A is the training set and B is the test set. DETAILED DESCRIPTION

[0041] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which embodiments of the present invention belong. If the definitions set forth in this section are contrary to or otherwise inconsistent with definitions set forth in the patents, patent applications, published patent applications, and other publications incorporated herein by reference, the definitions listed in this section take precedence over the definitions incorporated herein by reference.

[0043] Unless otherwise specified, the methods used in the following examples are conventional methods. The materials, reagents, and instruments used are conventional materials, reagents, and instruments in the art, unless otherwise specified, and can be obtained commercially by those skilled in the art.

[0044] When an amount, concentration or other value or parameter is expressed as a range, a preferred range or a range defined by a series of upper preferred values ​​and lower preferred values, this should be understood as specifically disclosing all ranges formed by any pairing of any upper range limit or preferred value with any lower range limit or preferred value, regardless of whether the range is disclosed alone. For example, when a range "1 to 5" is disclosed, the described range should be interpreted as including the ranges "1 to 4", "1 to 3", "1 to 2", "1 to 2 and 4 to 5", "1 to 3 and 5", etc. When a numerical range is described herein, unless otherwise stated, the range is intended to include its endpoints and all integers and fractions within the range. In the present specification and claims, range definitions may be combined and / or interchanged, and if not otherwise stated, such ranges include all subranges contained therein.

[0045] Example 1 Determination and multivariate statistical analysis of 12 functional substances in wolfberry

[0046] 90 batches of wolfberry samples were collected from Ningxia, Qinghai, Gansu and other major distribution areas suitable for the development of health wine, including 20 batches from Baiyin City, Gansu Province (GS-BY), 20 batches from Jiuquan City, Gansu Province (GS-JQ), 25 batches from Ningxia (NX), and 25 batches from Qinghai (QH). The contents of 12 functional substances were determined according to the method in the manual. The chromatograms of the samples and the results are shown in the following table. Figure 1 The average content results are shown in Table 1 below.

[0047] Table 1 Average contents of 12 functional components in wolfberries from different origins

[0048]

[0049]

[0050] With the exception of 4-ACA, there is no significant difference in the content of phenolic acids from different production areas. Phenolic compounds are a large class of plant secondary metabolites, indicating that the production area of ​​wolfberry has little impact on them. Qinghai has the highest rutin content, which may be due to the high ultraviolet rays promoting the synthesis of kaempferol and rutin. The polysaccharide content of the two production areas in Gansu is significantly lower than that of other areas. Ningxia has the highest betaine content, followed by Baiyin City, Gansu, which may be related to the close geographical location of the two. Betaine is the characteristic active ingredient of wolfberry, which is also an important indicator of the advantage of Ningxia wolfberry. As a representative water-soluble component, the content of lycium barbarum in Jiuquan, Gansu is significantly higher than that of the other three places.

[0051] Cluster analysis of 90 batches of wolfberry samples was conducted with the contents of 12 functional substances as variables. The cluster diagram is shown in Figure 2 The results showed that the samples from different sources were close to each other and it was impossible to accurately distinguish the characteristics of wolfberries from different origins. PCA biplots were drawn using PC1, PC2, PC3 and component loading values ​​(e.g. Figure 3There was no obvious distinction between the samples from the four origins, and the confidence intervals had a large overlap, so the overall distinction could not be achieved. OPLS-DA analysis was further performed on the four groups of wolfberry samples. The results showed that the two groups of samples from Gansu Province had obvious overlap (e.g. Figure 4 There is also a small overlap between NX and QH, indicating that the 12 functional substances cannot effectively distinguish wolfberries from different origins. Using VIP>1, we screened out wolfberry polysaccharides, rutin, betaine, and lycium barbarum as key indicator components of wolfberry (e.g. Figure 5 shown).

[0052] Example 2 Determination and multivariate statistical analysis of 38 inorganic elements in wolfberry

[0053] The contents of 38 inorganic elements in wolfberry were determined according to the method in the manual. The average content results are shown in Table 2.

[0054] Table 2 Average contents of 38 inorganic elements in wolfberries from different origins

[0055]

[0056]

[0057]

[0058] The 38 inorganic elements measured were categorized into three levels based on their concentration per kilogram: grams, milligrams, and micrograms. K, Na, Mg, and Ca were at the gram level, representing the highest concentrations. Fifteen elements, including Fe, Al, Zn, Mn, Cu, Sr, Li, Ni, Ba, Cr, Pb, Co, Ce, Cd, and Se, were at the milligram level. GSBY and NX were generally found to have significantly higher concentrations of these elements, such as Al, Mn, Fe, Ba, and Co. These elements are considered beneficial or important, and this may explain the differences between NX goji berries and those from other regions. GSBY and NX are geographically adjacent, so their mineral element content trends are similar. Li was significantly higher in GS-JQ than in other regions, while Ba, Fe, and Al were the lowest. Several heavy metals, such as Ag, Ni, and As, were found to be the lowest in QH. U and 17 rare earth elements were found at the microgram level, with Ce, La, Nd, and Y being the four highest. Rare earth elements often coexist in the same mineral due to their similar atomic structures and chemical properties. Therefore, their overall distribution patterns are similar in the four production areas, with GS-BY generally having the highest content and GS-BY being relatively low.

[0059] OPLS-DA analysis showed that samples from different origins could not be effectively distinguished (e.g. Figure 6As shown in the figure, it is impossible to effectively distinguish wolfberries from different origins by simply using 38 inorganic elements. Li, Ca, Pb, Zn, Mg, Cr, Cu, Ba, Pr, Nd, Sc, Sr, Al, La, U, and Gd were selected as the key inorganic elements of wolfberry by using VIP>1 in the OPLS-DA model (as shown in the figure). Figure 7 shown).

[0060] Example 3: OPLS-DA production area classification based on key indicators affecting wolfberry quality

[0061] The indicators with VIP>1 screened out in Examples 1 and 2, including Lycium barbarum polysaccharides, rutin, betaine, citric acid, Li, Ca, Pb, Zn, Mg, Cr, Cu, Ba, Pr, Nd, Sc, Sr, Al, La, U, and Gd, were subjected to OPLS-DA analysis. The results showed that OPLS-DA achieved excellent differentiation of samples from the four origins (e.g. Figure 8 The fit indices were excellent, with R²X = 0.810, R²Y = 0.910, and Q² = 0.888. 200 permutation tests revealed no overfitting in the model (R² < 0.3, Q² < 0.05). This demonstrates that OPLS-DA based on key indicators can achieve regional differentiation. It is important to note that indicators such as Lycium barbarum polysaccharides, rutin, betaine, Li, Ca, Pb, Zn, and Mg have high VIP values, indicating a more prominent role.

[0062] Example 4: Verification of the effect of production area classification based on robot learning of key indicators affecting wolfberry quality

[0063] In order to verify the accuracy and reliability of the chemometric analysis results and achieve more efficient classification of wolfberry origin, we further developed four machine learning algorithms, BP, CNN, SVM, and RF, as described in the main text of the manual. The results show that BP, CNN, SVM, and RF all performed very well in distinguishing the origin of wolfberries in the classification of key quality indicators of wolfberries. The performance indicators of the four models all reached 100.00% on the training set and test set, and the AUC value was stable at 1.0000, which was very stable. Confusion matrix diagram (such as Figure 9 None of the four models (shown in Figure 2) found misclassification, further demonstrating the high accuracy of these models. These findings suggest that the combination of key quality indicators (KQIs) and machine learning has strong applicability for classifying goji berry samples by origin and can be applied to goji berry origin traceability.

[0064] Example 5 Wolfberry Quality Assessment Based on Robot Learning Results

[0065] Three batches of wolfberry samples were collected from Ningxia, Qinghai and other places other than the model of the present invention. The quality was first evaluated based on the results of robot learning (any of the four models that did not meet the requirements were identified as non-compliant). Then, health wine was formulated according to our company's wolfberry wine production process, the functional components in the wolfberry wine were determined, and the stability of the wine was observed for 12 months (the wine was filtered once using our company's membrane filtration technology in the first month). The results showed that the functional substances in the wolfberry wines from Ningxia and Qinghai that met the model all met the quality standard requirements, while some functional substances in the wines that did not meet the model or other places of origin did not meet the quality standard requirements. Although the wines that met the model showed precipitation within 1 month, after filtration, the subsequent wine stability was good. The wines that did not meet the model or other places of origin still showed precipitation after filtration, indicating that the wolfberry origin model established by the present invention is particularly suitable for the development and application of health wines. The results are shown in Table 3 below.

[0066] Table 2 Average contents of 38 inorganic elements in wolfberries from different origins

[0067]

[0068] Note: √ indicates that the requirements are met, and × indicates that the requirements are not met.

Claims

1. A method for screening the origin of wolfberry fruit for use in the development of health wine, characterized in that: The screening method comprises the following steps: 1) Establish an analytical method to determine the contents of 12 functional substances and 38 inorganic elements in multiple batches of wolfberry from different origins; 2) Perform multivariate statistical analysis on the functional substance and inorganic element content data to identify key indicators affecting wolfberry quality; 3) Establish a robot learning algorithm for the key indicators that affect the quality of wolfberries and build a wolfberry discrimination model suitable for the development of health wine.

2. The method for screening the origin of wolfberry fruit for use in the development of health wine according to claim 1, wherein: The 12 functional substances are p-hydroxybenzoic acid, chlorogenic acid, scopolamine lactone, 4-coumaric acid, ferulic acid, rutin, kaempferol, betaine, citric acid, lycium barbarum polysaccharide, total phenols, and total flavonoids; the 38 inorganic elements are Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Ag, Cd, Ba, Pb, K, Ca, Na, Mg, Li, Sr, U, Sc, Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, and Th.

3. The method for screening the origin of wolfberry fruit for use in the development of health wine according to claim 1, wherein: The multivariate statistical analysis includes one or more of polar coordinate heat map clustering, PCA, and OPLS-DA.

4. The method for screening the origin of wolfberry fruit for use in the development of health wine according to claim 1, wherein: The method for screening key indicators affecting wolfberry quality is to screen out indicators with VIP>1 through the established OPLS-DA model.

5. The method for screening the origin of wolfberry fruit for use in the development of health wine according to claim 1, wherein: The key indicators affecting the quality of wolfberry include wolfberry polysaccharide, rutin, betaine, citric acid, Li, Ca, Pb, Zn, Mg, Cr, Cu, Ba, Pr, Nd, Sc, Sr, Al, La, U, and Gd.

6. The method for screening the origin of wolfberry fruit for use in the development of health wine according to claim 5, characterized in that: The key indicators affecting the quality of wolfberry include wolfberry polysaccharide, rutin, betaine, Li, Ca, Pb, Zn, and Mg.

7. The method for screening the origin of wolfberry fruit for use in the development of health wine according to claim 1, wherein: The robot learning algorithm includes one or more of back propagation algorithm (BP), convolutional neural network (CNN), random forest (RF) and support vector machine (SVM).

8. The method for screening the origin of wolfberry fruit for use in the development of health wine according to claim 1, wherein: The wolfberry production areas screened by the method are Ningxia, Gansu and Qinghai.

9. The method for screening the origin of wolfberry fruit for use in the development of health wine according to claim 1, wherein: The health-care wine is wolfberry wine.

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