Method for identifying Phyllanthus emblica from different origins by using H NMR

Abstract

The invention relates to the technical field of nuclear magnetic resonance detection technology, and discloses a method for identifying emblica from different origins by using nuclear magnetic resonance hydrogen spectrum. The method for identifying emblica from different origins by using nuclear magnetic resonance hydrogen spectrum is as follows: taking emblica samples from different origins, adding 50% methanol to weigh the weight after treatment, heating and reflux in a constant temperature water bath, supplementing the lost weight with 50% methanol, centrifuging again, evaporating and drying the obtained upper clear liquid, adding deuterated methanol to dissolve, and then taking samples for nuclear magnetic resonance detection, analyzing the nuclear magnetic resonance hydrogen spectrum fingerprint spectrum obtained by the detection, establishing an artificial neural network model, and identifying emblica from different origins. The method of the invention consumes less deuterated reagents and has low cost; the processing method of the emblica sample to be identified is flexible, simple and efficient; by setting the relevant measurement parameters of the nuclear magnetic resonance instrument, observing and analyzing the obtained spectrum, the emblica from different origins can be intuitively and accurately distinguished.

Description

Technical Field

[0001] The invention relates to the technical field of nuclear magnetic resonance detection, and in particular to a method for identifying emblica emblica from different origins by utilizing nuclear magnetic resonance hydrogen spectrum. Background Art

[0002] Phyllanthus emblica L. is a plant of the genus Phyllanthus in the Euphorbiaceae family. Its dried mature fruits have been used since ancient times to treat diseases such as asthma, cough, inflammation, diabetes, constipation and jaundice, and the effect is very good. As a Chinese medicine with both medicinal and edible properties, Phyllanthus emblica is widely used in the healthcare and cosmetics industries. It is also an important ingredient in many desserts, snacks, beverages and other meals. Phyllanthus emblica is widely distributed, and the climate, temperature, rainfall, altitude and other growing environments in different production areas have obvious differences. Globally, Phyllanthus emblica is widely distributed in subtropical regions, from southern Sichuan, China in the north to Indonesia in the south, mainly including southern China, central and southern India, Thailand, Vietnam, Laos, Malaysia and other countries and regions. In China, Phyllanthus emblica is distributed in dry and hot river valleys, relatively humid areas and humid areas, mainly including Yunnan, Sichuan, Guizhou, Guangxi, Guangdong, Fujian and Hainan.

[0003] The pharmacological activity and nutritional quality of Phyllanthus emblica are mainly attributed to its rich vitamins, minerals and various metabolites, such as polyphenols, flavonoids, terpenes, sterols, vitamins and amino acids. However, due to differences in origin, the growth and material accumulation of Phyllanthus emblica may be different, resulting in uneven quality of medicinal materials, which in turn affects the safety and effectiveness of clinical medication and makes it difficult to achieve standardization of commercial medicinal materials. This has become a major problem restricting the development of the Phyllanthus emblica industry. Studies have shown that the Phyllanthus emblica in Yunnan has a higher content of gallic acid, methyl gallate, corilagin, chebulic acid and ellagic acid, and it is speculated that it may be more suitable for medicinal use. At present, the technical means used for the quality evaluation of Phyllanthus emblica mainly focus on high performance liquid chromatography (HPLC), ultra-high performance liquid chromatography (UPLC) and chromatography-mass spectrometry (LC-MS). These methods are usually limited by laboratory conditions, cumbersome, time-consuming, and costly, and it is difficult to achieve rapid identification of Phyllanthus emblica from different origins.

[0004] Nuclear magnetic resonance (NMR) is one of the widely used metabolomics tools in the field of food science. It has the advantages of no selectivity for samples, simple pretreatment, short detection time and good reproducibility. The chemical composition of Phyllanthus emblica is complex and diverse. Traditional analytical techniques may only detect one type of compound. So far, there has been no report on the application of NMR in the identification of Phyllanthus emblica from different origins. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method for identifying emblica emblica from different origins by using nuclear magnetic resonance hydrogen spectrum, comprising the following steps:

[0006] S1. Weigh the sample groups of Phyllanthus emblica from Yunnan, Guangdong and Guangxi, with a mass of 1.0 g for each group of samples, crush and dry them, sieve them, and then add 50 mL of 50% methanol to each group of samples and weigh them;

[0007] S2. Heat the mixed system obtained in S1 in a constant temperature water bath for 1 h, cool it, weigh it again, make up the lost weight with 50% methanol, and shake it well;

[0008] S3, centrifuging the reaction mixture obtained in S2 at 25° C. and 3000 rpm for 5 min;

[0009] S4, take 2.5 mL of the supernatant after centrifugation of S3, evaporate to dryness, add to 1 mL of deuterated methanol, dissolve, and transfer 540 μL to the NMR tube with a pipette;

[0010] S5. The sample in the NMR tube in S4 was measured using a nuclear magnetic resonance instrument. The measurement conditions were: acquisition frequency 400 MHz, Fourier transform 0.122 Hz, pulse sequence zg30, pulse interval 1.00 s, temperature 288.0 K, sampling time 4.09 s, scanning number 16 times, spectrum width 8012.82 Hz, exponential line broadening function 0.3 Hz;

[0011] S6. Use MestReNova 6.0.1 to perform segmented integration, baseline correction and calibration on the NMR spectrum obtained in S5 to obtain the NMR hydrogen spectrum fingerprint. The Yunnan samples have higher peak intensities at δ7.00-6.50 and δ6.00-5.50, the Guangdong samples have higher peak intensities at δ4.50-3.50 and δ3.50-3.00, and the Guangxi samples have higher peak intensities at δ3.50-3.00.

[0012] S7. Establishment of artificial neural network model: A three-layer neural network was established based on the H NMR fingerprint in S6 for identification of Phyllanthus emblica samples from three different geographical origins.

[0013] Furthermore, in step S1, the number of samples from each origin is not less than 15 groups, and the samples need to be rinsed with tap water and dried in the sun until they reach a constant weight before use.

[0014] Furthermore, in step S1, the mesh size of the sieving is 80 meshes.

[0015] Furthermore, in step S1, 50% methanol is obtained by adding water to 50 mL of methanol and diluting the mixture to 100 mL.

[0016] Furthermore, in step S6, the residual signals of the nuclear magnetic resonance hydrogen spectrum fingerprint in the range of δ0.50-9.50ppm, the spectrum width of 0.04ppm, δ3.28-3.36ppm and δ4.68-5.08ppm are excluded respectively, and each spectrum width region is integrated and normalized before analyzing the nuclear magnetic resonance data.

[0017] Furthermore, in step S7, 70.6% of the samples are randomly selected as a training set, and the remaining 29.4% of the samples are used as a validation set.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] (1) The method provided by the present invention consumes less deuterated reagent and has low cost.

[0020] (2) In the method provided by the present invention, the processing method of the Phyllanthus emblica sample to be identified is flexible, simple and efficient.

[0021] (3) By setting the relevant measurement parameters of the nuclear magnetic resonance instrument and observing and analyzing the obtained spectra, the emblica of different origins can be distinguished intuitively and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is the Yunnan emblica 1 H-NMR spectrum;

[0023] Figure 2 Phyllanthus emblica 1 H-NMR spectrum;

[0024] Figure 3 It is the Phyllanthus emblica of Guangxi 1 H-NMR spectrum;

[0025] Figure 4 The samples of Phyllanthus emblica from different origins were in the range of δ7.00 to 6.50. 1 H-NMR spectrum;

[0026] Figure 5 The samples of Phyllanthus emblica from different origins were 1 H-NMR spectrum;

[0027] Figure 6 The results are as follows: 1 H-NMR spectrum;

[0028] Figure 7 The samples of Phyllanthus emblica from different origins have a δ value of 3.50 to 3.00. 1H-NMR spectrum. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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 work are within the scope of protection of the present invention.

[0030] Example

[0031] This embodiment discloses a method for identifying Phyllanthus emblica from different origins using nuclear magnetic resonance hydrogen spectroscopy:

[0032] 1. Main instruments

[0033] Electronic balance (AX224ZH / E, Ohaus Instruments Changzhou Co., Ltd.), sample pipette (Aibend GmbH, Germany), desktop high-speed centrifuge (H2-16K, Hunan Kecheng Instrument Equipment Co., Ltd.), electric constant temperature water bath (DKS-28, Zhongxin Medical Instrument Co., Ltd., Jiaxing City, Zhejiang Province), nuclear magnetic resonance spectrometer (BrukerAscendTM 400MHz, Bruker Corporation, Germany).

[0034] 2. Reagents

[0035] Deuterated methanol (L890V03, Hebei Bailingwei Ultrafine Materials Co., Ltd.), methanol (analytical grade, 2211081, Sichuan Xilong Science Co., Ltd.).

[0036] 3. Sample collection

[0037] S1. 51 batches of fresh Phyllanthus emblica L. were collected in Yunnan (n=17), Guangdong (n=17) and Guangxi (n=17). The samples were rinsed with tap water and then dried in the sun until a constant weight was reached. The dried samples were crushed, passed through an 80-mesh sieve, and stored in a desiccator. 1.0 g of the dried powder of Phyllanthus emblica L. was accurately weighed and placed in a 150 mL stoppered conical flask. 50 mL of 50% methanol was accurately added and the weight was determined.

[0038] S2. Heat the mixed system obtained in S1 in a constant temperature water bath for 1 h, cool it, weigh it again, make up the lost weight with 50% methanol, and shake it well;

[0039] S3, centrifuging the reaction mixture obtained in S2 at 25° C. and 3000 rpm for 5 min;

[0040] S4, take 2.5 mL of the supernatant after centrifugation and evaporate it in an evaporating dish, add 1 mL of deuterated methanol to dissolve, and use a pipette to transfer 540 μL to the NMR tube;

[0041] S5. Nuclear magnetic resonance hydrogen spectrum acquisition: BrukerAscendTM 400MHz nuclear magnetic resonance spectrometer was used to collect the hydrogen spectrum of each sample. 1 H-NMR spectrum, the condition parameters are: acquisition frequency 400MHz, Fourier transform 0.122Hz, pulse sequence zg30, pulse interval 1.00s, temperature 288.0K, sampling time 4.09s, scan number 16 times, spectrum width 8012.82Hz. Exponential line broadening function is 0.3Hz;

[0042] S6. All NMR spectra were segmented, integrated, baseline corrected, and calibrated using MestReNova 6.0.1 (Mestrelabs Research SL, Santiago de Compostella, Spain). 3 OD at δ3.31ppm); 1 The H-NMR spectrum was within the range of δ0.50-9.50 ppm, with a spectral width of 0.04 ppm. The residual signals at δ3.28-3.36 ppm and δ4.68-5.08 ppm were excluded respectively; before analyzing the NMR data, each spectral width region was integrated and normalized.

[0043] 1 H-NMR spectrum analysis

[0044] 1 H-NMR spectra can generally be divided into three regions: aromatic region (δ9.50~6.00ppm), mainly aromatic compounds; sugar region (δ6.00~3.50ppm), mainly sugar substances; amino acid region (δ3.50~0.50ppm), mainly amino acids and small molecule organic acid components. The fingerprints of the three origins of Phyllanthus emblica are similar in composition, including metabolites such as amino acids, organic acids, monosaccharides, oligosaccharides, and polysaccharides. The peak intensities of the Phyllanthus emblica samples from Yunnan are higher in the aromatic region (δ7.00~6.50ppm) and sugar region (δ6.00~5.50ppm); on the contrary, the peak intensities of the Phyllanthus emblica samples from Guangdong are higher in the sugar region (δ4.50~3.50ppm) and amino acid region (δ3.50~3.00ppm), while the peak intensity of the amino acid region (δ3.50~3.00ppm) of the Phyllanthus emblica samples from Guangxi is higher than that of other regions.

[0045] S7. ANN analysis

[0046] based on 1A three-layer neural network was established based on H-NMR spectra for identification of Phyllanthus emblica samples from three different geographical origins. 70.6% of the samples were randomly selected as the training set, that is, 36 samples were randomly selected from 51 samples as the training set, and the remaining 29.4% of the samples were used as the validation set.

[0047] Experimental example

[0048] based on 1 A three-layer neural network was established using H-NMR spectra to identify 51 groups of samples from three different geographical sources in the embodiment. The harvest period was November 2021. The sample information is shown in Table 1:

[0049] Table 1

[0050]

[0051] 70.6% of the samples are randomly selected as the training set, that is, 36 samples are randomly selected from 51 samples as the training set, and the remaining 29.4% of the samples are used as the validation set. The artificial neural network recognition results are shown in Table 2:

[0052] Table 2

[0053]

[0054] From the test results in Table 2, it can be seen that all samples were successfully identified, and the recognition ability was 100%. In the validation set, the correct percentage of the three origins was 100%, indicating that the artificial neural network (ANN) model of the present invention has a good origin identification ability.

[0055] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for identifying emblica buds from different origins using nuclear magnetic resonance proton spectroscopy, characterized in that: The following steps are involved: S1. Weigh the sample groups of Phyllanthus emblica from Yunnan, Guangdong and Guangxi, with a mass of 1.0 g for each group of samples, crush and dry them, sieve them, and then add 50 mL of 50% methanol to each group of samples and weigh them; S2. Heat the mixed system obtained in S1 in a constant temperature water bath for 1 h, cool it, weigh it again, make up the lost weight with 50% methanol, and shake it well; S3, centrifuging the reaction mixture obtained in S2 at 25° C. and 3000 rpm for 5 min; S4, take 2.5 mL of the supernatant after centrifugation of S3, evaporate to dryness, add to 1 mL of deuterated methanol, dissolve, and transfer 540 μL to the NMR tube with a pipette; S5. The sample in the NMR tube in S4 was measured using a nuclear magnetic resonance instrument. The measurement conditions were: acquisition frequency 400 MHz, Fourier transform 0.122 Hz, pulse sequence zg30, pulse interval 1.00 s, temperature 288.0 K, sampling time 4.09 s, scanning number 16 times, spectrum width 8012.82 Hz, exponential line broadening function 0.3 Hz; S6. Use MestReNova 6.0.1 to perform segmented integration, baseline correction and calibration on the NMR spectrum obtained in S5 to obtain the NMR hydrogen spectrum fingerprint. The Yunnan samples have higher peak intensities at δ7.00-6.50 and δ6.00-5.50, the Guangdong samples have higher peak intensities at δ4.50-3.50 and δ3.50-3.00, and the Guangxi samples have higher peak intensities at δ3.50-3.

00. S7. Establishment of artificial neural network model: A three-layer neural network was established based on the H NMR fingerprint in S6 for identification of Phyllanthus emblica samples from three different geographical origins.

2. The method for identifying emblica buds from different origins using proton nuclear magnetic resonance spectroscopy according to claim 1, characterized in that: In step S1, the number of samples from each origin is not less than 15 groups, and the samples need to be rinsed with tap water and dried in the sun until they reach a constant weight before use.

3. The method for identifying Phyllanthus emblica from different origins by utilizing proton nuclear magnetic resonance spectroscopy according to claim 1, characterized in that: In the step S1, the mesh size of the sieving is 80 meshes.

4. The method for identifying Phyllanthus emblica from different origins by utilizing H NMR spectrum according to claim 1, characterized in that: In the step S1, 50% methanol is obtained by adding water to 50 mL of methanol and diluting the mixture to 100 mL.

5. The method for identifying Phyllanthus emblica from different origins by utilizing H NMR spectrum according to claim 1, characterized in that: In step S6, the residual signals of the nuclear magnetic resonance hydrogen spectrum fingerprint in the range of δ0.50-9.50ppm, the spectrum width of 0.04ppm, δ3.28-3.36ppm and δ4.68-5.08ppm are excluded respectively, and each spectrum width region is integrated and normalized before analyzing the nuclear magnetic resonance data.

6. The method for identifying Phyllanthus emblica from different origins by utilizing H NMR spectrum according to claim 1, characterized in that: In step S7, 70.6% of the samples are randomly selected as the training set, and the remaining 29.4% of the samples are used as the validation set.

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