Method for identifying production places of Chinese wolfberry fruits

Abstract

The invention provides a Chinese wolfberry production place identification method which comprises the following steps: measuring rare earth elements and / or stable isotopes; and substituting the rare earth element value and / or the stable isotope into the discrimination model to obtain the producing area information. Wherein the Chinese wolfberry fruits are from Xinjiang, Gansu or Ningxia. According to the method, the origin of Chinese wolfberry can be identified to identify the authenticity of the geographically identified Chinese wolfberry product.

Description

Technical Field

[0001] The invention relates to a method for identifying wolfberry origin, in particular to a method for identifying wolfberries produced in Xinjiang, Ningxia and Gansu. Background Art

[0002] The chemical and biological fingerprint characteristics in organisms are based on the natural fractionation effect caused by the environment, climate and biological metabolism during the material exchange between organisms and the external environment, resulting in differences in the composition and content of biological and chemical fingerprints of products from different sources. This difference carries information about environmental factors and is not affected by chemical additives. Therefore, it can be used as a "natural fingerprint" of substances to identify the origin of agricultural products and confirm the authenticity of agricultural products.

[0003] Fingerprint analysis technologies such as stable isotope omics, element omics, and organic nutrient composition omics are among the technical means currently used most in the identification of the origin of plants, especially agricultural products, both at home and abroad. However, there is currently no specific identification method for wolfberry in the western region, especially in Xinjiang, Gansu, and Ningxia. Summary of the invention

[0004] Due to the special soil, climate and sunshine conditions, the wolfberries from Jinghe County and Ningxia Zhongning County have formed unique qualities related to the geographical location of their origins, making them well-known geographical indication products. However, driven by economic interests, wolfberries from Gansu are often passed off as Jinghe County or Zhongning County wolfberries, which damages the brand image of geographical indication wolfberries and infringes on the rights of consumers.

[0005] Among the existing plant origin identification analysis methods, some methods use elements such as Na, K, Ca, Fe, and Mg for analysis. Although these elements are usually present in higher concentrations and are easier to detect, they are also common elements in fertilizers and are easily affected by chemical fertilizers, resulting in inaccurate origin identification results. In particular, different years and different amounts of fertilizer applied will affect the accuracy of identification.

[0006] In order to develop an identification method that can eliminate the interference of chemical fertilizers, the inventors of the present invention studied methods for distinguishing stable isotopes, rare earth elements, and the coupling of stable isotopes and rare earth elements, optimized the method for distinguishing wolfberries from three northwestern regions, and thus completed the present invention.

[0007] According to the present invention, a method for identifying the origin of wolfberry is provided, comprising: determining rare earth elements and / or stable isotopes; and substituting the rare earth element values ​​and / or stable isotopes into a discrimination model to obtain origin information.

[0008] According to the identification method of the present invention, the rare earth elements are La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Sc and Y.

[0009] The preferred rare earth elements are Lu, Tb and Y, as these three elements contribute more to the identification of the origin of the product. The preferred stable isotope is δ 13 C value, δ 2 H value and δ 18 O value, compared with other stable isotopes, δ 13 C value, δ 2 H value and δ 18 The O value contributes more to the identification of origin.

[0010] According to the origin identification method of the present invention, the wolfberry comes from Xinjiang, Gansu or Ningxia.

[0011] According to the identification method of the present invention, the origin discrimination model based on stable isotopes is:

[0012] Y 新疆 =0.602761δ 13 C+0.05148δ 15 N-0.426128δ 2 H+0.204932δ 18 O+0.0755416 87 Sr / 86 Sr+0.71002;

[0013] Y 甘肃 =-0.0155681δ 13 C+0.0591107δ 15 N+0.0990225δ 2 H+0.843377δ 18 O+0.0284561 87 Sr / 86 Sr+0.683337;

[0014] Y 宁夏 =-0.587394δ 13 C-0.109828δ 15 N+0.328383δ 2 H-1.03743δ 18 O-0.10363 87 Sr / 86 Sr+0.71002.

[0015] Through this model, we can use δ 13 C.δ 2 H, δ 18O, δ 15 N and 87 Sr / 86 Sr was used to identify the origins of wolfberries in three places. According to the verification of the inventors, the model had a 100% accuracy rate in detecting wolfberries in Jinghe County, Xinjiang and Gansu, and a 98% accuracy rate in Zhongning County, Ningxia.

[0016] The present invention also preferably uses the following stable isotope origin discrimination model for origin identification:

[0017] Y 新疆 =0.692776δ 13 C-0.317903δ 2 H+0.198361δ 18 O+0.71002;

[0018] Y 甘肃 =-0.0922948δ 13 C+0.00903286δ 2 H+0.870558δ 18 O+0.683337;

[0019] Y 宁夏 =-0.601672δ 13 C+0.308987δ 2 H-1.05769δ 18 O+0.71002.

[0020] This discriminant model is compared with δ 13 C.δ 2 H, δ 18 O, δ 15 N and 87 Sr / 86 The Sr model can achieve the same accuracy as the model of five stable isotopes, but only three isotopes need to be measured, reducing the detection cost and process.

[0021] According to the present invention, the following origin discrimination model based on rare earth elements can be used for origin identification:

[0022] Y 新疆=-0.024058La-0.147008Ce-0.121567Pr+0.0619063Nd-0.0906707Sm-0.0027872Eu-0.0303543Gd-0.0358994Tb-0. 00411465Dy+0.189365Ho+0.0997961Er+0.120016Tm+0.101285Yb-0.0231351Lu+0.0154276Sc+0.71977Y+0.71002;

[0023] Y 甘肃 =0.0497651La+0.147028Ce+0.137043Pr-0.105211Nd+0.131981Sm+0.0407894Eu+0.0139378Gd-0.421184Tb+0.0 180229Dy-0.196832Ho-0.161144Er-0.188737Tm-0.102415Yb-0.487103Lu-0.0534498Sc-0.651967Y+0.683337;

[0024] Y 宁夏 =-0.0250648La+0.00187691Ce-0.0137081Pr+0.0419476Nd-0.0396071Sm-0.0374759Eu+0.0165964Gd+0.451648Tb-0.0 136757Dy+0.00492702Ho+0.0592682Er+0.0662856Tm-0.000191392Yb+0.503952Lu+0.0373325Sc-0.0762164Y+0.71002.

[0025] According to the model, 16 kinds of rare earth elements were used for origin identification, which could accurately detect wolfberries from three places, with an overall accuracy rate of 96%. The accuracy rate for wolfberries from Gansu and Zhongning County, Ningxia reached 100%.

[0026] According to the identification method of the present invention, the origin discrimination model based on rare earth elements can also be:

[0027] Y 新疆 =0.0999531Tb-0.140226Lu+0.805123Y+0.71002;

[0028] Y 甘肃 =-0.440778Tb-0.506137Lu-0.76638Y+0.683337;

[0029] Y 宁夏 =0.335137Tb+0.639832Lu-0.0486325Y+0.71002.

[0030] The overall accuracy of the rare earth element model reached 93%, and the accuracy of wolfberry identification in Gansu and Ningxia Zhongning County reached 100%. However, since the number of elements to be measured was reduced, the detection cost could be greatly reduced.

[0031] According to the identification method of the present invention, the origin is identified by the origin discrimination model of rare earth elements coupled with stable isotopes, and the identification model is:

[0032] Y 新疆 =0.346275δ 13 C+0.0291651δ 15 N-0.22693δ 2 H-0.150639δ 18 O-

[0033] 0.0132136 87 Sr / 86 Sr-0.0533608La-0.0139608Ce-0.00481796Pr+0.0038911Nd-0.0272418Sm+0.00697476Eu+0.0226768Gd-0.183189Tb- 0.0185625Dy+0.087613Ho-0.0360274Er+0.00707545Tm+0.0398149Yb-0.164816Lu+0.123955Sc+0.274727Y+0.71002;

[0034] Y 甘肃 =-0.237233δ 13 C+0.0228562δ 15 N+0.19455δ 2 H+0.377517δ 18 O+0.00842259 87 Sr / 86Sr+0.0568585La+0.0159567Ce-0.0288812Pr-0.0340728Nd+0.0252398Sm+0.0219686Eu-0.0286401Gd-0.167262Tb+ 0.0109809Dy-0.0343567Ho-0.028689Er-0.0432511Tm-0.015011Yb-0.236558Lu-0.17339Sc-0.151528Y+0.683337;

[0035] Y 宁夏 =-0.112104δ 13 C-0.0517264δ 15 N+0.034891δ 2 H-0.222007δ 18 O, +0.00489966 87 Sr / 86 Sr-0.00276408La-0.00178999Ce+0.0333265Pr+0.029742Nd+0.00232771Sm-0.0286599Eu+0.00559374Gd+0.348293Tb+ 0.0077233Dy-0.0536996Ho+0.0643462Er+0.0356175Tm-0.0249976Yb+0.398321Lu+0.0471968Sc-0.125154Y+0.71002.

[0036] The accuracy of the model for wolfberries from three places reached 100%, which is higher than the model that only uses isotopes or rare earth elements.

[0037] According to the identification method of the present invention, the origin discrimination model based on rare earth element coupled stable isotopes can also be:

[0038] Y 新疆 =0.281675δ 13 C-0.187649δ 2 H-0.19517δ 18 O-0.175965Tb-0.153032Lu+0.375149Y+0.71002;

[0039] Y 甘肃 =-0.19221δ 13 C+0.108865δ 2 H+0.469427δ 18O-0.186447Tb-0.25605Lu-0.25101Y+0.683337;

[0040] Y 宁夏 =-0.0919452δ 13 C+0.0801883δ 2 H-0.268199δ 18 O+0.360007Tb+0.405778Lu-0.127378Y+0.71002.

[0041] The accuracy of the discriminant model for wolfberries from the three regions was 100%, which is higher than the verification model that uses only isotopes or rare earth elements. In addition, since the types of elements that need to be measured are reduced, the detection cost can be greatly reduced.

[0042] According to the identification method of the present invention, the stable isotope δ 2 H value and δ 18 The O value is determined by vacuum extracting the water in wolfberry. Since wolfberry has a high sugar content, it easily absorbs water from the air, resulting in large errors in the test results. Vacuum extraction can greatly improve the accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is the isotope loadings diagram of the three production areas of wolfberry.

[0044] Figure 2 This is the isotope VIP map of the three producing areas of wolfberry.

[0045] Figure 3 This is the isotope score map of three wolfberry production areas.

[0046] Figure 4 This is the loading chart of rare earth elements in the three producing areas of wolfberry.

[0047] Figure 5 This is the VIP map of rare earth elements from the three producing areas of wolfberry.

[0048] Figure 6 This is the rare earth element score chart of the three wolfberry producing areas.

[0049] Figure 7 This is the isotope and rare earth element loadings diagram of the three producing areas of wolfberry.

[0050] Figure 8 This is the VIP chart of isotopes and rare earth elements of three producing areas of wolfberry.

[0051] Fig. 9 This is the isotope and rare earth element score chart of three wolfberry production areas.

[0052] Fig.10 Based on δ 13 C.δ 2 H, δ 18 O Loadings diagram of the validation model of three origins of wolfberry constructed with three stable isotopes.

[0053] Fig.11 Based on δ 13 C.δ 2 H, δ 18 O Score chart of the validation model of wolfberry from three production areas constructed with three stable isotopes.

[0054] Fig.12 This is the loading diagram of the wolfberry three-origin verification model based on the three rare earth elements Lu, Tb and Y.

[0055] Fig.13 This is the score chart of the wolfberry three-origin verification model based on the three rare earth elements Lu, Tb, and Y.

[0056] Fig.14 Based on δ 13 C.δ 2 H, δ 18 Loadings diagram of the three-origin verification model of wolfberry constructed by coupling three stable isotopes of O, Lu, Tb and Y, three rare earth elements.

[0057] Fig.15 Based on δ 13 C.δ 2 H, δ 18 O Score chart of the validation model of wolfberry's three origins constructed by coupling three stable isotopes of Lu, Tb and Y, three rare earth elements. DETAILED DESCRIPTION

[0058] The present invention is further described below in conjunction with specific examples, and the methods are conventional methods unless otherwise specified. The examples of instrument models and numerical ranges in the embodiments are only used for illustrative purposes and are not intended to limit the scope of protection of the present invention. It is obvious to those skilled in the art to make various modifications and changes within the scope of the present invention. The scope of protection of the present invention shall be subject to the claims.

[0059] For example, the embodiments illustrate the model and specifications of the instrument, the quantity, quality, and processing time of the samples, but those skilled in the art will appreciate that other models, specifications, quantities, and processing times can also meet the same requirements.

[0060] Materials and methods:

[0061] 1. Sample Collection

[0062] 1.1 Sample distribution

[0063] We selected wolfberries from different production areas, including Xinjiang Jinghe wolfberry, Ningxia Zhongning wolfberry, and Gansu wolfberry, for field investigation, and conducted sampling based on the area of ​​each production area.

[0064] 1.2 Collection of wolfberry samples

[0065] In each orchard in the main wolfberry producing areas, at least 5-8 sampling points (distributed in a "plum blossom shape") were selected to collect wolfberry samples. 1 kg of sample was collected at each point, and the fresh wolfberry fruit samples from the entire orchard were mixed as one sample (about 5-8 kg). All sampling points were located using GPS.

[0066] Through field research and investigation, combined with the distribution characteristics of local wolfberry gardens, samples were collected in the area within 100km around the target site.

[0067] 2. Sample Pretreatment

[0068] The samples collected in step “1. Sample collection” were homogenized using a fruit wall breaking machine to obtain homogenized samples.

[0069] 2.1 Water extraction method

[0070] About 2.5 mL of the fresh wolfberry homogenate sample was taken out and placed in a glass sample bottle (8 mL, diameter * height 1.30 * 6.00 cm) and frozen (-18 ° C). After freezing for 24 h, absorbent cotton was plugged into the glass sample bottle and water was extracted at 105 ° C for 2 h in a vacuum water extraction device until the water was completely extracted. The collected water was transferred to a 2 mL gas phase vial for δ 2 H value and δ 18 O value determination.

[0071] 2.2 Preparation method of Lycium barbarum fruit freeze-dried powder

[0072] The homogenate samples were placed in a vacuum freeze dryer for vacuum freeze drying for 72 hours, then taken out, crushed into powder with a rolling pin in a ziplock bag, and placed in a brown sample bottle for testing.

[0073] 3. Stable isotope detection and analysis

[0074] (1)δ 2 H value and δ 18 O value determination method

[0075] The δ 2 H, δ 18 O value was measured. 0.1 μL of water sample was sent into the element analyzer by automatic sample injector, and high temperature cracking was performed to generate CO and H 2, passes through a dilution instrument, and finally enters an isotope ratio mass spectrometer for detection.

[0076] (2)δ 13 C.δ 15 N value determination method

[0077] Total δ in Lycium barbarum fruit freeze-dried powder 13 C and δ 15 The determination method of N isotope ratio is elemental analyzer-isotope-ratio mass spectrometer (EA-IRMS). Lycium barbarum freeze-dried powder sample is wrapped in a tin cup and placed in the automatic sample tray of the element analyzer. It is burned at 960℃ and the Cr 2 O 3 Oxidized to CO 2 and nitrogen oxides, which are reduced to N by copper 2 , that is, the C and N elements in the sample are converted into CO 2 and N 2 , and under the action of the carrier gas He flow, N 2 and CO 2 The isotope ratio mass spectrometer was used to measure the isotope ratio, and the USGS40 was used as the standard material. The test results were calibrated by the single-point calibration method.

[0078] (3) Calculation method of stable isotope ratio:

[0079] The stable C, N, H, and O isotope ratios are expressed as δ 13 C‰, δ 15 N‰, δ 2 H‰ and δ 18 O‰ indicates that δ 13 The relative standard for C is V-PDB, δ 15 The relative standard of N is Air, δ 18 O and δ 2 The relative standard of H‰ is V-SMOW. The calculation formula is as follows:

[0080] δ(‰)=(R 样品 / R 标准 -1)×1000(‰)

[0081] 4. Detection and analysis of rare earth elements

[0082] (1) Microwave digestion method: Weigh the wolfberry powder sample to about 0.200 g. After weighing, carefully transfer the sample to the microwave digestion tube using a weighing paper tube to avoid sticking to the wall. If there is a phenomenon of sticking to the wall, nitric acid can be used to completely rinse the sample on the inner wall of the Teflon digestion tube to the bottom of the digestion tube during the acid addition process. The acid system is 8 mL HNO 3 The heating program, cooling program and digestion conditions are as follows: microwave power is 1600W, digestion temperature is 180℃,

[0083] Heating procedure: Step 1: 0-120°C (8 min), hold for 2 min;

[0084] Step 2: 120-160℃ (5min), keep for 5min;

[0085] Step 3: 160-180℃ (5min), keep for 15min;

[0086] Cooling procedure: Step 4: Cool for 20 minutes.

[0087] (2) Determination method of rare earth elements: The contents of rare earth elements La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Sc, and Y are determined by ICP-MS. The internal standard method is used to ensure the stability of the instrument, and Li, Ge, Y, In, Tb, and Bi are selected as internal standard elements; the external standard method is used for quantitative analysis. In order to ensure the reliability of the determination results, the content of rare earth elements is analyzed and controlled using standard substances during the determination process. The standard substances are determined once for every 10 samples.

[0088] ICP-MS working conditions:

[0089] RF power: 1350W Atomization chamber temperature: 2℃ Sampling depth: 7.5mm

[0090] Cooling gas flow rate: 15L·min -1 Auxiliary gas flow rate: 1.0L min -1 Carrier gas speed: 1.14L·min -1

[0091] Oxide index: CeO + / Ce + <1.5% Double charge indicator: Ce 2+ / Ce + <3%.

[0092] 5. 87 Sr / 86 Sr isotope value determination method

[0093] Weigh 150 mg of sample, add it to a 15 mL Savillex digestion tank, add 1 mL of concentrated nitric acid and 1 mL of perchloric acid, seal and heat overnight, evaporate to dryness, then add concentrated nitric acid and evaporate to dryness three times. Take the dissolved sample and add a few drops of concentrated HNO 3 After evaporation, add 1.5 mL 3.5 mol / L HNO 3 , and then be separated on the column. Sr isotope separation was performed using Sr special resin (produced by Triskem, 100-150 μm) with 7 mL 3.5 mol / L HNO 3 The purified Sr was evaporated to dryness, and then evaporated to dryness again with a few drops of concentrated nitric acid, and then 1 mL of 2% HNO 3 , to be tested. The Sr isotope composition test was carried out on MC-ICP-MS (Neptune plus, ThermoFisher, USA). The Sr isotope instrument fractionation correction used an exponential equation, 88 Sr / 86 Sr=8.375209 is corrected.

[0094] For the determination of Sr isotope composition, all static methods were used, and the structural parameters of the Faraday cup are shown in Table 1. 83 Kr is for monitoring 84 Kr, 86 Kr 84 Sr. 86 Sr interference, measurement 85 Rb is for monitoring 87 Rb 87 Interference of Sr. Before testing the sample, NIST 987 200μg / L standard solution was used to optimize the parameters of Neptune Plus, including the plasma part (parameters such as the position of the torque tube and the carrier gas flow rate) and the ion lens parameters to achieve maximum sensitivity. The operating condition parameters of the instrument are shown in Table 2.

[0095] Table 1 Faraday cup structural parameters for Neptune plus MC-ICP-MS measurement of Sr isotopes

[0096]

[0097]

[0098] The samples after chemical separation were washed with 2% HNO 3 The solution is introduced into the mass spectrometer, making 88 The signal intensity of Sr is about 8V (the concentration of Sr in the solution is about 200μg / L), and the free nebulizer injection method is used. After the sample test is completed, 2% HNO3 The solution cleans the injection system and then the measurement of the next sample begins. 87 Sr / 86 Sr ratio is adopted 88 Sr / 86 Sr = 8.375209 was used for exponential normalization correction.

[0099] Table 2 Instrument test parameters

[0100]

[0101] 6. Analysis on the authenticity of the origin of Jinghe wolfberry, Zhongning wolfberry and Gansu wolfberry

[0102] According to the isotopic characteristic values ​​detected in the previous steps, some characteristic values ​​were selected and the origin was determined by using the orthogonal corrected partial least squares discriminant analysis (OPLS-DA) model and the SIMCA version 14.1 software using the OPLS-DA discriminant model.

[0103] Based on the data, a discriminant model was established by combining multiple parameters such as stable isotope omics, rare earth element omics, stable isotope combined with rare earth element omics, etc. The discriminant model was used to determine the origin of wolfberry, and the discriminant accuracy of the discriminant model was analyzed.

[0104] Examples 1 to 3 are based on the analysis of the origin of wolfberry fruit in Jinghe, Xinjiang, Zhongning, Ningxia, and Gansu

[0105] According to the sample collection method in the Materials and Methods section, wolfberry samples were collected within an area within 50 km around Jinghe County, Xinjiang, and a total of about 20 valid samples were collected.

[0106] Sample pretreatment, sample preparation, stable isotope and rare earth element detection and analysis were carried out according to the Materials and Methods section.

[0107] Examples 4 to 6 respectively establish three discrimination models based on the data in Examples 1 to 3

[0108] According to the isotope characteristic values ​​and rare earth element characteristic values ​​detected in the previous steps, stable isotopes, rare earth elements, and stable isotopes and rare earth elements characteristic values ​​were selected respectively, and the origin was determined by using the orthogonal corrected partial least squares discriminant analysis (OPLS-DA) model and the SIMCA version 14.1 software using the OPLS-DA discriminant model.

[0109] Example 4: Stable isotope discrimination model

[0110] Figure 1This is a loadings diagram that analyzes the correlation between stable isotopes and wolfberry production areas. △ represents the three production areas of Gansu (Gan), Xinjiang (Xin), and Ningxia (Nin), and □ represents stable isotopes. As can be seen from the figure, δ 13 C contributes more to the identification of Xinjiang origin, while δ 18 O and δ 2 H makes a great contribution to distinguishing Xinjiang from Gansu and Ningxia.

[0111] Figure 2 This is the isotope VIP diagram of the three wolfberry production areas. The indicators with VIP values ​​greater than 1 can be screened out as the characteristic indicators of the model. From this figure, we can see that the characteristic indicator of the model is δ 18 O and δ 13 C.

[0112] The stable isotope origin discrimination model established using the OPLS-DA discrimination model is as follows:

[0113] Y 新疆 =0.602761δ 13 C+0.05148δ 15 N-0.426128δ 2 H+0.204932δ 18 O+

[0114] 0.0755416 87 Sr / 86 Sr+0.71002;

[0115] Y 甘肃 =-0.0155681δ 13 C+0.0591107δ 15 N+0.0990225δ 2 H+0.843377

[0116] δ 18 O+0.0284561 87 Sr / 86 Sr+0.683337;

[0117] Y 宁夏 =-0.587394δ 13 C-0.109828δ 15 N+0.328383δ 2 H-1.03743δ 18 O

[0118] -0.10363 87 Sr / 86 Sr+0.71002.

[0119] Figure 3 This is the isotope score diagram of wolfberry from three production areas. ○ represents samples from Xinjiang, △ represents samples from Ningxia, and □ represents samples from Gansu. Figure 3 The model shows that wolfberry produced in Gansu is clearly distinguished from those in the other two places, but there is a small overlap in wolfberry samples from Jinghe County, Xinjiang and Zhongning County, Ningxia, and they cannot be completely distinguished.

[0120] Table 3 is based on the stable isotope model (δ 13 C.δ 15 N, δ 2 H, δ 18 O. 87 Sr / 86 The results of the stable isotope model for the discrimination of wolfberries from three production areas: Jinghe County, Xinjiang, Zhongning County, Ningxia, and Gansu Province. Table 3 shows that the accuracy of the stable isotope model for wolfberry detection in Jinghe County, Xinjiang and Gansu reached 100%, and the accuracy in Zhongning County, Ningxia reached 98%.

[0121] Table 3

[0122]

[0123] Example 5: Rare Earth Element Discrimination Model

[0124] Figure 4 This is a loadings diagram that analyzes the correlation between stable isotopes and wolfberry origins, where △ represents Gansu (Gan), Xinjiang (Xin), and Ningxia (Nin), and □ represents stable isotopes. It can be seen from the figure that the Y element has a greater contribution to the identification of Xinjiang origin, while Tb and Lu have a greater contribution to the identification of Ningxia origin.

[0125] Figure 5 This is the isotope VIP diagram of three wolfberry producing areas. The indicators with VIP values ​​greater than 1 can be screened out as the characteristic indicators of the model. From this figure, we can see that the characteristic indicators of the model are Lu, Tb and Y elements.

[0126] The origin discrimination model based on 16 rare earth elements established by using the OPLS-DA discrimination model is as follows:

[0127] Y 新疆=-0.024058La-0.147008Ce-0.121567Pr+0.0619063Nd-0.0906707Sm-0.0027872Eu-0.0303543Gd-0.0358994Tb-0. 00411465Dy+0.189365Ho+0.0997961Er+0.120016Tm+0.101285Yb-0.0231351Lu+0.0154276Sc+0.71977Y+0.71002,

[0128] Y 甘肃 =0.0497651La+0.147028Ce+0.137043Pr-0.105211Nd+0.131981Sm+0.0407894Eu+0.0139378Gd-0.421184Tb+0.0 180229Dy-0.196832Ho-0.161144Er-0.188737Tm-0.102415Yb-0.487103Lu-0.0534498Sc-0.651967Y+0.683337,

[0129] Y 宁夏 =-0.0250648La+0.00187691Ce-0.0137081Pr+0.0419476Nd-0.0396071Sm-0.0374759Eu+0.0165964Gd+0.451648Tb-0.0 136757Dy+0.00492702Ho+0.0592682Er+0.0662856Tm-0.000191392Yb+0.503952Lu+0.0373325Sc-0.0762164Y+0.71002.

[0130] Figure 6 This is the isotope score diagram of wolfberry from three production areas. ○ represents samples from Xinjiang, △ represents samples from Ningxia, and □ represents samples from Gansu. Figure 6 The model shows that wolfberries produced in Ningxia can be clearly distinguished, but there is some overlap between wolfberry samples from Jinghe County, Xinjiang and Gansu, and they cannot be completely distinguished.

[0131] Table 4 shows the discriminant analysis results of wolfberry from three production areas, namely, Jinghe County, Xinjiang, Zhongning County, Ningxia, and Gansu Province, based on the rare earth element model (La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Sc, and Y values). As can be seen from Table 4, the overall discrimination accuracy of the rare earth element model reached 96%, and the discrimination rate of wolfberry from Gansu and Zhongning County, Ningxia reached 100%.

[0132] Table 4

[0133]

[0134] Example 6: Origin discrimination model based on rare earth element coupled stable isotopes

[0135] Figure 7 This is a loadings diagram that analyzes the correlation between stable isotopes and wolfberry production areas. △ represents the three production areas of Gansu (Gan), Xinjiang (Xin), and Ningxia (Nin), and □ represents stable isotopes. As can be seen from the figure, δ 2 H, Tb and Lu made great contributions to the identification of Ningxia origin.

[0136] Figure 8 This is the isotope VIP diagram of three wolfberry production areas. The indicators with VIP values ​​greater than 1 can be selected as the characteristic indicators of the model. Figure 8 It shows that the characteristic index of this model is δ 18 O, Lu, Tb, δ 13 C, Y, and δ 2 H.

[0137] The origin discrimination model based on rare earth element coupled stable isotopes established using the OPLS-DA discrimination model is as follows:

[0138] Y 新疆 =0.346275δ 13 C+0.0291651δ 15 N-0.22693δ 2 H-0.150639δ 18 O-

[0139] 0.0132136 87 Sr / 86 Sr-0.0533608La-0.0139608Ce-0.00481796Pr+0.0038911Nd-0.0272418Sm+0.00697476Eu+0.0226768Gd-0.183189Tb -0.0185625Dy+0.087613Ho-0.0360274Er+0.00707545Tm+0.0398149Yb-0.164816Lu+0.123955Sc+0.274727Y+0.71002

[0140] Y 甘肃 =-0.237233δ 13 C+0.0228562δ 15 N+0.19455δ 2H+0.377517δ 18 O

[0141] +0.00842259 87 Sr / 86 Sr+0.0568585La+0.0159567Ce-0.0288812Pr-0.0340728Nd+0.0252398Sm+0.0219686Eu-0.0286401Gd-0.167262Tb +0.0109809Dy-0.0343567Ho-0.028689Er-0.0432511Tm-0.015011Yb-0.236558Lu-0.17339Sc-0.151528Y+0.683337

[0142] Y 宁夏 =-0.112104δ 13 C-0.0517264δ 15 N+0.034891δ 2 H-0.222007

[0143] δ 18 O, +0.00489966 87 Sr / 86 Sr-0.00276408La-0.00178999Ce+0.0333265Pr+0.029742Nd+0.00232771Sm-0.0286599Eu+0.00559374Gd+

[0144] 0.348293Tb+0.0077233Dy-0.0536996Ho+0.0643462Er+0.0356175Tm-0.0249976Yb+0.398321Lu+0.0471968Sc-0.125154Y+0.71002

[0145] Fig. 9 This is the isotope and rare earth element score chart of three wolfberry production areas. Fig. 9 It is shown that the model can clearly distinguish wolfberries from three production areas.

[0146] Table 5 shows the discriminant analysis results of isotopes and rare earth elements of wolfberry from three production areas based on the isotope-coupled rare earth element model. From Table 5, it can be seen that the isotope-coupled rare earth element model has an accuracy rate of 100% for wolfberry from the three production areas, which is higher than the model that uses isotopes or rare earth elements alone.

[0147] Table 5

[0148]

[0149] Examples 7 to 9: Verification of indicators after discriminant model screening based on the data in Examples 1 to 6

[0150] According to the isotope values ​​and rare earth element values ​​detected in the previous steps, the eigenvalues ​​were screened out according to the constructed discriminant model, the number of elements with little influence on the discrimination results was reduced, the parameters were optimized, and the OPLS-DA model was established again. The accuracy of the model constructed by these eigenvalues ​​was verified by using the OPLS-DA discriminant model using SIMCA version 14.1 software.

[0151] Example 7: Origin optimization discrimination model based on stable isotopes

[0152] according to Figure 1 Loadings diagram, δ 13 C.δ 18 O and δ 2 H has a great influence on the identification of wolfberries from three places. We try to optimize the parameters and use δ 13 C.δ 18 O and δ 2 H three kinds of element component discrimination model. The constructed model results are as follows:

[0153] Y 新疆 =0.692776δ 13 C-0.317903δ 2 H+0.198361δ 18 O+0.71002;

[0154] Y 甘肃 =-0.0922948δ 13 C+0.00903286δ 2 H+0.870558δ 18 O+0.683337;

[0155] Y 宁夏 =-0.601672δ 13 C+0.308987δ 2 H-1.05769δ 18 O+0.71002.

[0156] Fig.10 Based on δ 13 C.δ 2 H, δ 18 O Loadings diagram of the validation model of three origins of wolfberry constructed with three stable isotopes. Fig.10 Display, δ 2 H and δ 18 O makes the greatest contribution to distinguishing the origin of Ningxia.

[0157] Fig.11 Based on δ 13 C.δ 2 H, δ 18 O Score diagram of the validation model of wolfberry from three production areas constructed with three stable isotopes, Fig.11 The results show that the model can clearly distinguish wolfberries produced in Gansu, but there is a slight overlap between wolfberries produced in Xinjiang and Ningxia.

[0158] According to the verification model established in Example 6, wolfberry samples from three production areas, namely, Jinghe County, Xinjiang, Zhongning County, Ningxia, and Gansu Province, were identified to determine the accuracy rate.

[0159] Table 6 is based on the stable isotope validation model (δ 13 C.δ 2 H, δ 18 The stable isotope validation model has a detection accuracy of 100% for wolfberry in Jinghe County, Xinjiang, Zhongning County, Ningxia, and Gansu Province, and a detection accuracy of 98% for wolfberry in Zhongning County, Ningxia.

[0160] Table 6

[0161]

[0162] Example 8: Origin optimization discrimination model based on rare earth elements

[0163] according to Figure 4 The Loadings diagram shows that the three elements Tb, Lu, and Y have a great influence on the identification of wolfberries from the three regions. We try to optimize the parameters and use the three elements Tb, Lu, and Y to construct the discrimination model. The results of the constructed model are as follows:

[0164] Origin optimization discrimination model based on rare earth elements:

[0165] Y 新疆 =0.0999531Tb-0.140226Lu+0.805123Y+0.71002;

[0166] Y 甘肃 =-0.440778Tb-0.506137Lu-0.76638Y+0.683337;

[0167] Y 宁夏 =0.335137Tb+0.639832Lu-0.0486325Y+0.71002.

[0168] Fig.12 This is the loading diagram of the wolfberry three-origin verification model based on the three rare earth elements Lu, Tb and Y. Fig.12It shows that Y contributes more to distinguishing the origin of Ningxia.

[0169] Fig.13 This is the score chart of the validation model of wolfberry's three production areas based on the three stable isotopes of Lu, Tb and Y. Fig.13 The results show that the model can effectively distinguish wolfberries produced in Ningxia, but there is some overlap between wolfberries produced in Xinjiang and Gansu, which may lead to misjudgment.

[0170] According to the verification model established in Example 8, wolfberry samples from three production areas, namely, Jinghe County in Xinjiang, Zhongning County in Ningxia, and Gansu Province, were identified to determine the accuracy rate.

[0171] Table 7 shows the discriminant analysis results of wolfberries from three production areas, namely, Jinghe County, Xinjiang, Zhongning County, Ningxia, and Gansu Province, based on the rare earth element verification model (Yb, Lu, and Y values). As can be seen from Table 7, the overall discrimination accuracy of the rare earth element model reached 93%, and the discrimination rate for wolfberries from Gansu and Zhongning County, Ningxia reached 100%.

[0172] Table 7

[0173]

[0174] Example 9: Optimized origin discrimination model based on rare earth element coupled stable isotopes

[0175] According to the factors that have a greater impact on the origin, the 13 C.δ 2 H, δ 18 O three stable isotopes coupled Lu, Tb, Y elements, try to optimize the parameters, and rebuild the discrimination model. The results of the constructed model are as follows:

[0176] Optimized origin identification model based on rare earth element coupled stable isotopes:

[0177] Y 新疆 =0.281675δ 13 C-0.187649δ 2 H-0.19517δ 18 O-0.175965Tb-

[0178] 0.153032Lu+0.375149Y+0.71002;

[0179] Y 甘肃 =-0.19221δ 13 C+0.108865δ 2 H+0.469427δ 18 O-0.186447Tb-

[0180] 0.25605Lu-0.25101Y+0.683337;

[0181] Y 宁夏 =-0.0919452δ 13 C+0.0801883δ 2 H-0.268199δ 18 O+0.360007Tb

[0182] +0.405778Lu-0.127378Y+0.71002.

[0183] Fig.15 Based on δ 13 C.δ 2 H, δ 18 The score diagram of the validation model of wolfberry from three production areas constructed by coupling three stable isotopes of O, Lu, Tb, and Y. Fig.15 It can be seen that the model can be used to effectively identify wolfberry samples from Jinghe County, Xinjiang, Zhongning County, Ningxia and Gansu.

[0184] Table 8 shows the discriminant analysis results of wolfberry from three production areas, namely, Jinghe County, Xinjiang, Zhongning County, Ningxia, and Gansu Province, based on the optimized stable isotope-coupled rare earth element verification model. As can be seen from Table 8, the accuracy of the isotope-coupled rare earth element model for wolfberry from the three production areas is 100%, which is higher than the verification model using only isotopes or rare earth elements. In addition, since the types of elements that need to be measured are reduced, the detection cost can be greatly reduced.

[0185] Table 8

[0186]

[0187] Embodiments 1 to 3 measured the characteristic values ​​of stable isotopes and rare earth elements in Xinjiang, Gansu, and Ningxia. Embodiments 4 to 9 established a discrimination model based on the measured characteristic values ​​and optimized the discrimination model. According to the present invention, the origin of wolfberry in Jinghe County, Xinjiang, Gansu, and Zhongning County, Ningxia can be identified by using a stable isotope model, a rare earth element model, and a stable isotope-coupled rare earth element model, wherein the model of stable isotope-coupled rare earth elements has a higher identification accuracy than a single isotope model or a rare earth element model. Different models can be selected according to experimental conditions, actual sample conditions, and detection accuracy requirements.

[0188] Although specific embodiments of the present invention have been illustrated and described, it is obvious to those skilled in the art that various other changes and modifications may be made, and the embodiments may be combined, without departing from the general scope of the present disclosure. Therefore, all changes, modifications and combinations that do not depart from the scope of the technical concept of the present invention belong to the scope of the present invention.

Claims

1. A method for identifying the origin of wolfberry, characterized in that: include: Determination of rare earth elements and / or stable isotopes; as well as Substitute rare earth element values ​​and / or stable isotopes into the discrimination model to obtain origin information.

2. The identification method according to claim 1, wherein: The rare earth elements are La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Sc and Y, preferably Lu, Tb and Y; the stable isotope is preferably δ 13 C value, δ 2 H value and δ 18 O value.

3. The method for identifying the origin of the product according to claim 1 or 2, wherein: The wolfberry comes from Xinjiang, Gansu or Ningxia.

4. The method for identifying the origin of the product according to claim 1 or 2, wherein: The origin discrimination model based on stable isotopes is: Y 新疆 =0.602761δ 13 C+0.05148δ 15 N-0.426128δ 2 H+0.204932δ 18 O+0.0755416 87 Sr / 86 Sr+0.71002; Y 甘肃 =-0.0155681δ 13 C+0.0591107δ 15 N+0.0990225δ 2 H+0.843377δ 18 O+0.0284561 87 Sr / 86 Sr+0.683337; Y 宁夏 =-0.587394δ 13 C-0.109828δ 15 N+0.328383δ 2 H-1.03743δ 18 O-0.10363 87 Sr / 86 Sr+0.71002。 5. The method according to claim 1 or 2, wherein: The origin discrimination model based on stable isotopes is: Y 新疆 =0.692776d 13 C-0.317903δ 2 H+0.198361d 18 O+0.71002; Y 甘肃 =-0.0922948d 13 C+0.00903286d 2 H+0.870558d 18 O+0.683337; Y 宁夏 =-0.601672δ 13 C+0.308987d 2 H-1.05769δ 18 O+0.71002.

6. The method according to claim 1 or 2, wherein: Origin discrimination model based on rare earth elements: Y 新疆 =-0.024058La-0.147008Ce-0.121567Pr+0.0619063Nd-0.0906707Sm-0.0027872Eu-0.0303543Gd-0.0358994Tb-0.00411465Dy+0.189365Ho+0.0997961Er+0.120016Tm+0.101285Yb- 0.0231351Lu+0.0154276Sc+0.71977Y+0.71002; Y 甘肃 =0.0497651La+0.147028Ce+0.137043Pr-0.105211Nd+0.131981Sm+0.0407894Eu+0.0139378Gd-0.421184Tb+0.0180229Dy-0.196832Ho-0.161144Er-0.188737Tm-0.102415Yb-0.487103Lu-0.0534498Sc-0.651967Y+0.683337; <h2 style=";text-align:left;direction:ltr">Y<h2 style=";text-align:left;direction:ltr"> 宁夏 <h2 style=";text-align:left;direction:ltr"> =-0.0250648La+0.00187691Ce-0.0137081Pr+0.0419476Nd-0.0396071Sm-0.0374759Eu+0.0165964Gd+0.451648Tb-0.0 136757Dy+0.00492702Ho+0.0592682Er+0.0662856Tm-0.000191392Yb+0.503952Lu+0.0373325Sc-0.0762164Y+0.71002。 7. The identification method according to claim 1 or 2, wherein: The origin discrimination model based on rare earth elements is: Y 新疆 =0.0999531Tb-0.140226Lu+0.805123Y+0.71002; <h2 style=";text-align:left;direction:ltr">Y<h2 style=";text-align:left;direction:ltr"> 甘肃 <h2 style=";text-align:left;direction:ltr"> =-0.440778Tb-0.506137Lu-0.76638Y+0.683337; <h2 style=";text-align:left;direction:ltr">Y<h2 style=";text-align:left;direction:ltr"> 宁夏 <h2 style=";text-align:left;direction:ltr"> = 0.335137Tb+0.639832Lu=0.0486325Y+0.71002.

8. The identification method according to claim 1 or 2, wherein the origin discrimination model based on rare earth element coupled stable isotopes is: Y 新疆 =0.346275δ 13 C+0.0291651δ 15 N-0.22693δ 2 H-0.150639δ 18 O-0.0132136 87 Sr / 86 Sr-0.0533608La-0.0139608Ce-0.00481796Pr+0.0038911Nd-0.0272418Sm+0.00697476Eu+0.0226768Gd-0.183189Tb-0.0185625Dy+0.087613Ho-0.0360274Er+0.00707545Tm+0.0398149Yb-0.164816Lu+0.123955Sc+0.274727Y+0.71002; Y 甘肃 =-0.237233δ 13 C+0.0228562δ 15 N+0.19455δ 2 H+0.377517δ 18 O+0.00842259 87 Sr / 86 Sr+0.0568585La+0.0159567Ce-0.0288812Pr-0.0340728Nd+0.0252398Sm+0.0219686Eu-0.0286401Gd-0.167262Tb+0.0109809Dy-0.0343567Ho-0.028689Er-0.0432511Tm-0.015011Yb-0.236558Lu-0.17339Sc-0.151528Y+0.683337; Y 宁夏 =-0.112104δ 13 C-0.0517264δ 15 N+0.034891δ 2 H-0.222007δ 18 O+0.00489966 87 Sr / 86 Sr-0.00276408La-0.00178999Ce+0.0333265Pr+0.029742Nd+0.00232771Sm-0.0286599Eu+0.00559374Gd+0.348293Tb+0.0077233Dy-0.0536996Ho+0.0643462Er+0.0356175Tm- 0.0249976Yb+0.398321Lu+0.0471968Sc-0.125154Y+0.71002.

9. The identification method according to claim 1 or 2, wherein: The origin discrimination model based on rare earth element coupled stable isotopes is: Y 新疆 =0.281675d 13 C-0.187649δ 2 H-0.19517δ 18 O-0.175965Tb- 0.153032Lu+0.375149Y+0.71002; Y 甘肃 =-0.19221δ 13 C+0.108865δ 2 H+0.469427δ 18 O-0.186447Tb- 0.25605Lu-0.25101Y+0.683337; Y 宁夏 =-0.0919452d 13 C+0.0801883d 2 H-0.268199δ 18 O+0.360007Tb +0.405778Lu-0.127378Y+0.71002.

10. The identification method according to any one of claims 1 to 3, wherein: Stable isotope δ in wolfberry 2 H value and δ 18 The O value was determined by vacuum extraction of water from wolfberries.

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