A method for distinguishing the centroid of different medicinal parts of Yebazi based on GC-MS

Abstract

The present invention analyzes samples containing medicinal parts of wild basil by GC-MS, and identifies wild basil flowers, wild basil leaves or wild basil stems by establishing a discriminant function and a centroid equation; the discriminant function (X, Y) of the wild basil flowers, wild basil leaves or wild basil stems is calculated to the centroid (X, Y) of different wild basil parts. i ,Y i ) distance, and the part corresponding to the smallest value among the three is the medicinal part of Yebazi in this sample.

Description

Technical Field

[0001] The present invention relates to a method for detecting different medicinal parts of Yebazi, and in particular to a method for distinguishing the centroids of different medicinal parts of Yebazi based on GC-MS. Background Art

[0002] Yebazi is a medicinal and edible plant with a special aroma. It is mainly distributed in southwest China, especially in Yunnan Province. In Yunnan, it is used as a herbal tea, herbal medicine, and nectar plant. Local ethnic minorities such as the Yi and Bai use it to treat colds, fevers, influenza, and diarrhea. Studies have reported that many compounds isolated from Yebazi have antiviral, anticancer, and anti-inflammatory activities, and can even relieve symptoms of Alzheimer's disease. In addition, as a characteristic natural plant with a strong regionality, the extract of Yebazi has the application effects of enhancing flavor, improving quality, and correcting flavor in cigarettes. It has the characteristics of an excellent natural tobacco flavor and is a potential resource for natural flavors added to Chinese cigarettes.

[0003] Different parts of Yebazi have different efficacy results. The relative content of bioactive substances in Yebazi flowers is the highest, followed by leaves, and the relative content of stems is the lowest. At the same time, the yield of extracts from different parts and the effect of adding them to cigarettes are also very different. Driven by commercial interests, many illegal persons use Yebazi stem and leaf products to impersonate or adulterate Yebazi flower products to obtain illegal profits. Therefore, it is necessary to identify and distinguish different parts of Yebazi flowers, such as stems and leaves.

[0004] In order to solve the above problems, the present invention is proposed. Summary of the invention

[0005] The purpose of the present invention is achieved through the following technical solutions.

[0006] The present invention provides a linear discrimination method of different medicinal parts of Yebazi based on GC-MS, which comprises the following steps:

[0007] Step (1): analyzing a sample containing the medicinal part of Yebazi using GC-MS;

[0008] Step (2): identifying the wild basil flower, wild basil leaf or wild basil stem based on the following discriminant function and centroid equation;

[0009] Discriminant function:

[0010] X=-27.379C1-46.145C2+14.698C3+56.639C4+0.229C5+9.015C6-153.053;

[0011] Y=0.603C1-2.949C2+0.330C3+13.730C4+0.052C5-0.073C6-6.772;

[0012] Wherein: C1 is the content of caryophyllene oxide μg / g, C2 is the content of 18-octadecane-9-ene lactone μg / g, C3 is the content of 1-(2-methoxyvinyl)cyclopropane μg / g, C4 is the content of neophytadiene μg / g, C5 is the content of hexadecane μg / g, and C6 is the content of oleamide μg / g;

[0013] The center of mass equations corresponding to different medicinal parts of Yebazi are as follows:

[0014]

[0015] According to the formula:

[0016] Calculate (X,Y) to different wild dam parts (X i ,Y i ) distance, and the part corresponding to the smallest value among the three is the medicinal part of Yebazi in this sample.

[0017] Preferably, step (1) may specifically include the following steps:

[0018] (11) Sample collection

[0019] Collect a certain amount of samples to be tested, divide them evenly into several portions, take samples of a certain weight from each portion, and put them into bags for testing;

[0020] (12) Sample pretreatment

[0021] Weigh 0.1 g of sample in a 10 ml centrifuge tube, add 1.5 ml of 1 μg / ml deuterated toluene-ethyl acetate solution, sonicate the mixture for 10 minutes, centrifuge at 4000 rpm for 8 minutes, filter, take 500 μ L of supernatant, transfer to a chromatographic injection bottle, and prepare for GC-MS analysis.

[0022] (13) GC-MS detection

[0023] The sample solution obtained in step (12) is subjected to GC-MS detection by direct injection.

[0024] (14) Data processing

[0025] Normalization: Quantification was performed using the internal standard method. The content of each volatile component was calculated as follows:

[0026]

[0027] Where: C i Indicates the content of volatile components measured; A iis the peak area of ​​the corresponding compound; A0 is the peak area of ​​the internal standard; M0 is the mass of the internal standard; M is the mass of the sample taken. The data are then used for statistical analysis.

[0028] The volatile component content value is the arithmetic mean of multiple tests.

[0029] Of course, step (1) may also use other sampling conditions, sample pre-treatment conditions or data processing methods, as long as they can comply with the detection principle and detect the content of each substance.

[0030] Preferably, the sample taken in step (1) is divided into not less than 6 portions on average, and each portion weighs not less than 0.1 g.

[0031] Preferably, in step (1), the instrument analysis parameters are as follows:

[0032] Chromatographic conditions:

[0033] Mass spectrometry conditions: The chromatographic column was a DB-35GC chromatographic column (30m×0.25mm×0.25μm) (Agilent, USA). The injection port temperature was 280°C. The carrier gas was helium with a flow rate of 1 ml / min. The injection volume was 1.0 μL, split injection, and the split ratio was 10:1. The initial temperature was 80°C, heated to 275°C at a rate of 60°C / min, then increased to 295°C at a rate of 1°C / min and maintained for 1 minute.

[0034] The temperatures of the transmission line and ion source are 300°C and 280°C respectively; the scanning mode is full scan mode; the ionization mode is 70ev electron impact; and the solvent delay time is 2min. Of course, other instrument analysis parameters can also be used in step (1), as long as the content of the characteristic substance concerned by the present invention can be measured.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. The present invention realizes accurate identification of medicinal parts and products of different wild basil leaves through GC-MS analysis for the first time, and at the same time, can perform preliminary analysis of the volatile medicinal components of the products.

[0037] 2. The present invention is the first to identify different medicinal parts of wild basil by establishing a centroid equation. The method is simple and only requires substituting the caryophyllene oxide content, 18-octadecane-9-ene lactone content, 1-(2-methoxyvinyl)cyclopropane content, neophytadiene content, hexadecane content, and oleamide content into the discriminant function in step (2) to obtain the coordinates (X, Y), and then calculating the centroid distance from (X, Y) to different medicinal parts of wild basil according to the distance formula, and comparing the centroid distances of different wild basil. The part corresponding to the one with the smallest centroid distance is the medicinal part of the wild basil in the sample. The result is reliable and can be widely used in real life. DETAILED DESCRIPTION

[0038] The present invention is described below in conjunction with specific examples, but the embodiments of the present invention are not limited thereto. Experimental methods without specific conditions in the examples are usually carried out according to conventional conditions and conditions described in the manual, or according to the conditions recommended by the manufacturer. The general equipment, materials, reagents, etc. used can be obtained from commercial channels unless otherwise specified.

[0039] Example 1

[0040] According to the above step (1), four samples of Yebazi cotyledon 1, Yebazi stem 1, Yebazi flower 1 and Yebazi cotyledon 2 were taken and divided into six parts each for analysis and processing, and the corresponding GC-MS data were obtained as shown in Table 1:

[0041] Table 1 GC-MS data of samples from different wild dams

[0042]

[0043]

[0044]

[0045] ND means that the ingredient was not detected and its content was calculated as 0 μg / g.

[0046] The corresponding discriminant function indicator components are shown in Table 2:

[0047] Table 2 Discriminant function index content of different samples

[0048]

[0049]

[0050] Substituting the corresponding component content values ​​into step (2), the results are shown in Table 3:

[0051] Table 3 Sample discriminant equation values ​​and discriminant results

[0052] ID <![CDATA[D 叶 ]]> <![CDATA[D 茎 ]]> <![CDATA[D 花 ]]> Identification results Yebaziye1 6.14 68.10 411.43 Yebaziye Wild bazi stem 1 61.24 7.81 476.08 Wild basil stem Wild Bazi Flower 1 416.86 476.48 0.53 Wild damselfly Yebaziye2 2.22 59.92 417.43 Yebaziye

[0053] The experimental results show that the determination results of this determination method are consistent with the actual location of the sample, indicating that the method of the present invention has good reliability.

[0054] In order to further verify the reliability of the method, 4 samples of wild bazi stems, 4 wild bazi leaves and 4 wild bazi flowers were selected respectively. The samples themselves could not be distinguished visually after sampling and sample preparation. Then, without informing the test personnel of the specific source of the samples, the test personnel were asked to perform part discrimination on each sample purely based on the method of the present invention. The discrimination results are shown in Table 4 below:

[0055] Table 4 Verification results of the method

[0056]

[0057] The identification results obtained based on the method of the present invention are completely consistent with the source of the samples. As shown in Table 4, the prediction accuracy of the method of the present invention for the stems, leaves and flowers of Yebazi is 100%, which can be used for the accurate prediction of the medicinal parts of Yebazi.

Claims

1. A method for distinguishing the centroid of different medicinal parts of Yebazi based on GC-MS, characterized in that: The following steps are involved: Step (1): analyzing samples containing the medicinal parts of Yebazi; Step (2): Identify the wild bazi flower, wild bazi leaf or wild bazi stem based on the following discriminant function and centroid equation; Discriminant function: X=-27.379C1-46.145C2+14.698C3+56.639C4+0.229C5+9.015C6-153.053; Y =0.603C1-2.949C2+0.330C3+13.730C4+0.052C5-0.073C6-6.772; Wherein: C1 is the content of caryophyllene oxide μg / g, C2 is the content of 18-octadecane-9-ene lactone μg / g, C3 is the content of 1-(2-methoxyvinyl)cyclopropane μg / g, C4 is the content of neophytadiene μg / g, C5 is the content of hexadecane μg / g, and C6 is the content of oleamide μg / g; The center of mass equations corresponding to different medicinal parts of Yebazi are as follows: ； According to the formula: ; Calculate (X, Y) to different wild dam locations ( , ), the part with the smallest value is the medicinal part of the wild bazi in the sample; The step (1) may specifically include the following steps: (11) Sample collection Collect a certain amount of samples to be tested, divide them into several portions, take samples of a certain weight from each portion, and put them into bags for testing; (12) Sample pretreatment Weigh 0.1 g of sample in a 10 ml centrifuge tube and add 1.5 ml of 1 μg / ml deuterated toluene-ethyl acetate solution; sonicate the mixture for 10 min, centrifuge at 4000 rpm for 8 min, filter, and take 500 μL of the supernatant and transfer it to a chromatographic injection bottle for GC-MS analysis; (13) GC-MS detection The sample solution obtained in step (12) is subjected to GC-MS detection by direct injection; (14) Data processing Normalization: Quantification was performed using the internal standard method; the content of each volatile component was calculated as follows: ； Where: C i Indicates the content of volatile components measured; A i is the peak area of ​​the corresponding compound; A0 represents the peak area of ​​the internal standard; M0 represents the mass of the internal standard; M is the mass of the sample taken; the data are then used for statistical analysis; The volatile component content value is the arithmetic mean of multiple tests; The instrument analysis parameters in step (1) are as follows: Chromatographic conditions: The chromatographic column was a DB-35 GC column; the injection port temperature was 280°C; the carrier gas was helium with a flow rate of 1 ml / min; the injection volume was 1.0 μL, split injection, and the split ratio was 10:1; the initial temperature was 80°C, heated to 275°C at a rate of 60°C / min, and then increased to 295°C at a rate of 1°C / min and maintained for 1 minute; Mass spectrometry conditions: The temperatures of the transfer line and ion source were 300°C and 280°C, respectively; Scan mode: full scan mode; Ionization mode: 70ev electron impact; Solvent delay time: 2min.

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