CN116381085A - Mung bean origin tracing method based on characteristic difference metabolites - Google Patents

Mung bean origin tracing method based on characteristic difference metabolites Download PDF

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CN116381085A
CN116381085A CN202310356301.7A CN202310356301A CN116381085A CN 116381085 A CN116381085 A CN 116381085A CN 202310356301 A CN202310356301 A CN 202310356301A CN 116381085 A CN116381085 A CN 116381085A
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mung bean
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lysophosphatidylcholine
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陈颖
何磊
于宁
张九凯
邢冉冉
邓婷婷
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Abstract

The invention relates to a mung bean origin tracing method based on characteristic difference metabolites, which is used for detecting mung bean samples of different origins based on a non-targeted metabonomics technology of ultra-high performance liquid chromatography-time-of-flight mass spectrometry, obtaining metabolite data of the mung bean samples of different origins and screening characteristic difference metabolites of the mung bean samples of different origins by using a chemometric method; based on the screened characteristic difference metabolites, a mung bean origin tracing classification model is constructed through machine learning, so that precise tracing of the mung bean origin is realized. The mung bean origin tracing method has the advantages of accuracy, reliability, stable tracing model and the like, and can provide technical support for mung bean origin identification.

Description

Mung bean origin tracing method based on characteristic difference metabolites
Technical Field
The invention relates to the field of agricultural product origin tracing, in particular to a mung bean origin tracing method.
Background
Mung bean (Vigna radiata L.) is a seed of mung bean of Vigna of subfamily Papilionaceae of Leguminosae, is rich in dietary protein, essential amino acids, dietary fiber, vitamins and minerals, and has effects of clearing heat, detoxicating, and relieving swelling. Researches show that mung beans also contain active ingredients such as flavonoid, phenolic acid, polysaccharide and the like, and have the functions of resisting bacteria, diminishing inflammation, resisting oxidation, protecting nerves and the like. So far, over 5000 mung bean germplasm resources are saved in China, mung beans are planted in multiple provinces in China, and the main production places are Hebei, shanxi, anhui, inner Mongolia, jilin, shandong, jiangsu, shanxi, xinjiang, yunnan, henan and the like. The weather conditions and the geographic conditions of the producing areas are greatly different, the quality of mung beans in each producing area is obviously different, and the development of the tracing research of mung bean producing areas is particularly important to protect mung bean brands in each producing area, so that counterfeit and inferior products can be prevented from entering the market, and high-quality mung beans and mung bean products are provided for consumers.
At present, classification analysis is carried out on mung beans in four different production areas such as Hebei, guangdong, fujian, gansu and the like by combining near infrared spectrum, raman spectrum technology and chemometrics, but the method cannot obtain the whole profile information of metabolites in the mung beans.
Non-targeted metabonomics technology allows for the simultaneous analysis of thousands of known and unknown metabolites, which has been used by an increasing number of research in recent years for traceable research into food and agricultural product origin. At present, no relevant report for tracing mung bean origin based on non-targeted metabonomics technology is known. To remedy this gap, there remains a need in the art to establish a mung bean origin identification method based on characteristic differential metabolites.
Disclosure of Invention
The invention aims to provide a mung bean origin tracing method based on characteristic difference metabolites, which is based on a non-targeted metabonomics analysis technology, acquires metabolite data of mung beans in different origins by utilizing ultra-high performance liquid chromatography-time-of-flight mass spectrometry, screens mung bean characteristic difference metabolites in each origin by chemometrics, and builds a mung bean origin tracing classification model by utilizing a machine learning classification algorithm so as to realize accurate tracing of mung bean origins.
In order to achieve the above object, the present invention adopts the following technical scheme:
the first aspect of the invention provides a mung bean origin tracing method based on characteristic difference metabolites, which comprises the following steps:
(1) Collecting mung bean samples of different production places, respectively removing impurities, freeze-drying, grinding to powder, adding an extracting solution, extracting, centrifuging, collecting supernatant, blow-drying with nitrogen, re-dissolving, and filtering to obtain filtrate of the sample to be detected;
(2) Performing non-targeted metabonomics analysis on the filtrate of the mung beans to be detected in different producing areas obtained in the step (1) by adopting an ultra-high performance liquid chromatography tandem time-of-flight mass spectrometer to obtain metabolite data of different mung bean samples, and processing the metabolite data by mass spectrometry data analysis software;
(3) Adopting orthogonal partial least squares discriminant analysis (OPLS-DA) to analyze the metabolic data of mung bean samples of different producing areas, and constructing an OPLS-DA model; screening out characteristic difference metabolites of mung beans in different producing areas according to the VIP value of the OPLS-DA model and the p value of the ANOVA, carrying out qualitative identification on the screened characteristic difference metabolites in the producing areas, carrying out molecular prediction by using software, and determining the structure of the screened characteristic difference metabolites;
(4) And (3) utilizing the screened mung bean characteristic difference metabolite data, and adopting a machine learning algorithm to establish a mung bean origin prediction model so as to realize accurate tracing of the mung bean origin.
Preferably, the different producing areas in the step (1) are selected from two or more of Hebei, shanxi, anhui, inner Mongolia, jilin, shandong, jiangsu, shanxi, xinjiang, yunnan and Henan.
More preferably, the different producing areas in the step (1) are 11 producing areas in total of Hebei, shanxi, anhui, inner Mongolia, jilin, shandong, jiangsu, shanxi, xinjiang, yunnan and Henan.
Preferably, the volume ratio of the extracting solution and the redissolved solution used in the step (1) is 60:40 in a methanol/water mixed solvent.
Preferably, the chromatographic conditions detected in the step (2) by using an ultra-high performance liquid chromatography-tandem time-of-flight mass spectrometer are as follows: the chromatographic column is Phenomenex Kinetex C and 18; mobile phase A is an aqueous solution containing 0.1% v/v formic acid, mobile phase B is an acetonitrile solution containing 0.1% v/v formic acid; gradient elution procedure: 0-13min,1% B to 99% B;13-15min,99% B;15-16min,99% B to 1% B;16-20min,1% B; the flow rate was 0.3mL/min.
Preferably, the mass spectrometry conditions detected in step (2) using an ultra performance liquid chromatography tandem time of flight mass spectrometer are as follows: and adopting an electrospray ionization ion source ESI to acquire data in positive ion mode and negative ion mode respectively, wherein the ion source temperature is 550 ℃, the collision energy is 35eV, the collision energy distribution is 15eV, the positive ion mode spray voltage is +5500V, and the negative ion mode is-4500V.
Preferably, in the step (3), metabolites of VIP >1 and p <0.05 are selected as characteristic differential metabolites for distinguishing mung beans in different production places according to VIP values of the OPLS-DA model and p values of ANOVA.
More preferably, in the step (3), metabolites of VIP >5 and p <0.05 are selected as characteristic differential metabolites for distinguishing mung beans in different producing areas.
Further preferably, metabolites of VIP >10 and p <0.05 are selected as characteristic differential metabolites for distinguishing mung beans from different producing regions in the step (3).
Preferably, in the step (3), the molecular prediction is performed by using MS-FINDER software and PeakView software.
Preferably, the characteristic differential metabolite selected in step (3) is selected from the group consisting of 9, 10-dihydroxyoctadecenoic acid, glycerophosphorylcholine, lysophosphatidylcholine 1 (C) 26 H 50 NO 7 P), lysophosphatidylcholine 2 (C) 24 H 50 NO 7 P), lysophosphatidylcholine 3 (C) 26 H 48 NO 7 P), lysophosphatidylethanolamine 1 (C) 23 H 44 NO 7 P), lysophosphatidylethanolamine 4 (C) 21 H 44 NO 7 P), lysophosphatidylinositol (C) 27 H 49 O 12 P) isoorientin, vitexin, isovitexin, luteolin-6-C-glucoside, indole, 3-indolecarboxaldehyde, 3-indoleacrylic acid, indoline, gamma-glutamylleucine, gamma-glutamyl-S-methylcysteine, arginine, tryptophan, methyl cysteine, N-acetyltryptophan, citric acid, malic acid, aconitic acid, 1-naphthylisocyanate, 1-menylamine, adenosine, xanthosine, adenosine-3' -phosphate, oxypurinol, choline, phthalic anhydride, soyasaponin I, tetraose (C) 26 H 45 NO 20 ) Two or more of them.
More preferably, the characteristic differential metabolites comprise isovitexin, 3-indoleacrylic acid, gamma-glutamyl-S-methyl cysteine and citric acid.
Further preferred, the characteristic differential metabolites comprise lysophosphatidylcholine 1, lysophosphatidylcholine 2, lysophosphatidylinositol, vitexin, isovitexin, indole, 3-indolecarboxaldehyde, 3-indoleacrylic acid, indoline, gamma-glutamyl-S-methylcysteine, citric acid and phthalic anhydride.
Further preferably, the characteristic differential metabolite comprises 9, 10-dihydroxyoctadecenoic acid, glycerophosphorylcholine, lysophosphatidylinositol (C 27 H 49 O 12 P) isoorientin, vitexin, isovitexin, luteolin-6-C-glucoside, indole, 3-indolecarboxaldehyde, 3-indoleacrylic acid, indoline, gamma-glutamylleucine, gamma-glutamyl-S-methylcysteine, arginine, tryptophan, methyl cysteine, N-acetyltryptophan, citric acid, malic acid, aconitic acid, 1-naphthylisocyanate, 1-menylamine, adenosine, xanthosine, adenosine-3' -phosphate, oxypurinol, choline, phthalic anhydride, soyasaponin I and tetraose (C) 26 H 45 NO 20 )。
Most preferably, the characteristic differential metabolite comprises 9, 10-dihydroxyoctadecenoic acid, glycerophosphorylcholine, lysophosphatidylcholine 1 (C 26 H 50 NO 7 P), lysophosphatidylcholine 2 (C) 24 H 50 NO 7 P), lysophosphatidylcholine 3 (C) 26 H 48 NO 7 P), lysophosphatidylethanolamine 1 (C) 23 H 44 NO 7 P), lysophosphatidylethanolamine 4 (C) 21 H 44 NO 7 P), lysophosphatidylinositol (C) 27 H 49 O 12 P) isoorientin, vitexin, isovitexin, luteolin-6-C-glucoside, indole, 3-indolecarboxaldehyde, 3-indoleacrylic acid, indoline, gamma-glutamylleucine, gamma-glutamyl-S-methylcysteine, arginine, tryptophan, methyl cysteine, N-acetyltryptophan, citric acid, malic acid, aconitic acid, 1-naphthylisocyanate, 1-menylamine, adenosine, xanthosine, adenosine-3' -phosphate, oxypurinol, choline, phthalic anhydride, soyasaponin I and tetraose (C) 26 H 45 NO 20 )。
Preferably, the programming language adopted by the machine learning algorithm in the step (4) is R language, the random forest and the support vector machine are analyzed by using R packages "random forest" and "e1071", respectively, and all mung bean samples are split randomly into a training set and a testing set for model construction, tuning and performance evaluation.
Preferably, in the step (4), the discrimination accuracy of the random forest and the support vector machine model is higher than 90% through establishing and optimizing the classification model.
More preferably, in the step (4), the discrimination accuracy of the random forest and the support vector machine model is higher than 93% by establishing and optimizing the classification model.
Further preferably, in the step (4), the discrimination accuracy of the random forest and the support vector machine model is higher than 93% and higher than 98% respectively through the established and optimized classification model.
Second aspect of the invention providesA characteristic differential metabolite composition for mung bean origin tracing is provided, wherein the characteristic differential metabolite composition is selected from 9, 10-dihydroxyoctadecenoic acid, glycerophosphorylcholine, lysophosphatidylcholine 1 (C) 26 H 50 NO 7 P), lysophosphatidylcholine 2 (C) 24 H 50 NO 7 P), lysophosphatidylcholine 3 (C) 26 H 48 NO 7 P), lysophosphatidylethanolamine 1 (C) 23 H 44 NO 7 P), lysophosphatidylethanolamine 4 (C) 21 H 44 NO 7 P), lysophosphatidylinositol (C) 27 H 49 O 12 P) isoorientin, vitexin, isovitexin, luteolin-6-C-glucoside, indole, 3-indolecarboxaldehyde, 3-indoleacrylic acid, indoline, gamma-glutamylleucine, gamma-glutamyl-S-methylcysteine, arginine, tryptophan, methyl cysteine, N-acetyltryptophan, citric acid, malic acid, aconitic acid, 1-naphthylisocyanate, 1-menylamine, adenosine, xanthosine, adenosine-3' -phosphate, oxypurinol, choline, phthalic anhydride, soyasaponin I, tetraose (C) 26 H 45 NO 20 ) Two or more of them.
More preferably, the characteristic differential metabolite composition comprises isovitexin, 3-indoleacrylic acid, gamma-glutamyl-S-methyl cysteine and citric acid.
Further preferred, the characteristic differential metabolite composition comprises lysophosphatidylcholine 1, lysophosphatidylcholine 2, lysophosphatidylinositol, vitexin, isovitexin, indole, 3-indolecarboxaldehyde, 3-indoleacrylic acid, indoline, gamma-glutamyl-S-methylcysteine, citric acid and phthalic anhydride.
Further preferred, the characteristic differential metabolite composition comprises 9, 10-dihydroxyoctadecenoic acid, glycerophosphorylcholine, lysophosphatidylinositol (C 27 H 49 O 12 P) isoorientin, vitexin, isovitexin, luteolin-6-C-glucoside, indole, 3-indolecarboxaldehyde, 3-indoleacrylic acid, indoline, gamma-glutamylleucine, gamma-glutamyl-S-methylcysteine, arginine, tryptophanCysteine methyl ester, N-acetyl tryptophan, citric acid, malic acid, aconitic acid, 1-naphthyl isocyanate, 1-methyl naphthylamine, adenosine, xanthosine, adenosine-3' -phosphoric acid, oxypurinol, choline, phthalic anhydride, soyasaponin I and tetrasaccharide (C) 26 H 45 NO 20 )。
Most preferably, the characteristic differential metabolite composition comprises 9, 10-dihydroxyoctadecenoic acid, glycerophosphorylcholine, lysophosphatidylcholine 1 (C 26 H 50 NO 7 P), lysophosphatidylcholine 2 (C) 24 H 50 NO 7 P), lysophosphatidylcholine 3 (C) 26 H 48 NO 7 P), lysophosphatidylethanolamine 1 (C) 23 H 44 NO 7 P), lysophosphatidylethanolamine 4 (C) 21 H 44 NO 7 P), lysophosphatidylinositol (C) 27 H 49 O 12 P) isoorientin, vitexin, isovitexin, luteolin-6-C-glucoside, indole, 3-indolecarboxaldehyde, 3-indoleacrylic acid, indoline, gamma-glutamylleucine, gamma-glutamyl-S-methylcysteine, arginine, tryptophan, methyl cysteine, N-acetyltryptophan, citric acid, malic acid, aconitic acid, 1-naphthylisocyanate, 1-menylamine, adenosine, xanthosine, adenosine-3' -phosphate, oxypurinol, choline, phthalic anhydride, soyasaponin I and tetraose (C) 26 H 45 NO 20 )。
In a third aspect, the present invention provides the use of a characteristic differential metabolite composition selected from the group consisting of 9, 10-dihydroxyoctadecenoic acid, glycerophosphorylcholine, lysophosphatidylcholine 1 (C) 26 H 50 NO 7 P), lysophosphatidylcholine 2 (C) 24 H 50 NO 7 P), lysophosphatidylcholine 3 (C) 26 H 48 NO 7 P), lysophosphatidylethanolamine 1 (C) 23 H 44 NO 7 P), lysophosphatidylethanolamine 4 (C) 21 H 44 NO 7 P), lysophosphatidylinositol (C) 27 H 49 O 12 P), isoorientin, vitexin, isovitexin, oleaceaeoxazin-6-C-glucoside, indole, 3-indolecarboxaldehyde, 3-indoleacrylic acid, indoline, gamma-glutamylleucine, gamma-glutamyl-S-methylcysteine, arginine, tryptophan, cysteine methyl ester, N-acetyltryptophan, citric acid, malic acid, aconitic acid, 1-naphthylisocyanate, 1-menylamine, adenosine, xanthosine, adenosine-3' -phosphate, oxypurinol, choline, phthalic anhydride, soyasaponin I, tetrasaccharide (C 26 H 45 NO 20 ) Two or more of them.
More preferably, the characteristic differential metabolite composition comprises isovitexin, 3-indoleacrylic acid, gamma-glutamyl-S-methyl cysteine and citric acid.
Further preferred, the characteristic differential metabolite composition comprises lysophosphatidylcholine 1, lysophosphatidylcholine 2, lysophosphatidylinositol, vitexin, isovitexin, indole, 3-indolecarboxaldehyde, 3-indoleacrylic acid, indoline, gamma-glutamyl-S-methylcysteine, citric acid and phthalic anhydride.
Further preferred, the characteristic differential metabolite composition comprises 9, 10-dihydroxyoctadecenoic acid, glycerophosphorylcholine, lysophosphatidylinositol (C 27 H 49 O 12 P) isoorientin, vitexin, isovitexin, luteolin-6-C-glucoside, indole, 3-indolecarboxaldehyde, 3-indoleacrylic acid, indoline, gamma-glutamylleucine, gamma-glutamyl-S-methylcysteine, arginine, tryptophan, methyl cysteine, N-acetyltryptophan, citric acid, malic acid, aconitic acid, 1-naphthylisocyanate, 1-menylamine, adenosine, xanthosine, adenosine-3' -phosphate, oxypurinol, choline, phthalic anhydride, soyasaponin I and tetraose (C) 26 H 45 NO 20 )。
Most preferably, the characteristic differential metabolite composition comprises 9, 10-dihydroxyoctadecenoic acid, glycerophosphorylcholine, lysophosphatidylcholine 1 (C 26 H 50 NO 7 P), lysophosphatidylcholine 2 (C) 24 H 50 NO 7 P), lysophosphatidylcholine 3 (C) 26 H 48 NO 7 P)，Lysophosphatidylethanolamine 1 (C) 23 H 44 NO 7 P), lysophosphatidylethanolamine 4 (C) 21 H 44 NO 7 P), lysophosphatidylinositol (C) 27 H 49 O 12 P) isoorientin, vitexin, isovitexin, luteolin-6-C-glucoside, indole, 3-indolecarboxaldehyde, 3-indoleacrylic acid, indoline, gamma-glutamylleucine, gamma-glutamyl-S-methylcysteine, arginine, tryptophan, methyl cysteine, N-acetyltryptophan, citric acid, malic acid, aconitic acid, 1-naphthylisocyanate, 1-menylamine, adenosine, xanthosine, adenosine-3' -phosphate, oxypurinol, choline, phthalic anhydride, soyasaponin I and tetraose (C) 26 H 45 NO 20 )。
The invention has the beneficial effects that:
the mung bean origin tracing method based on the characteristic difference metabolites can comprehensively obtain mung bean metabolite information, can screen the characteristic difference metabolites of mung beans in different origins by utilizing orthogonal partial least squares discriminant analysis, and can realize accurate tracing of mung bean origins based on a machine learning classification model of the characteristic difference metabolites.
Drawings
FIG. 1 is an OPLS-DA score map (A) and a substitution test map (B) for distinguishing mung beans of different origin;
fig. 2 is a diagram of an confusion matrix of random forest (a) and support vector machine (B).
Detailed Description
The invention is further illustrated below with reference to specific examples, which are to be construed as merely illustrative of the invention and not limiting the scope of the invention, which is defined by the appended claims.
Example 1, mung bean origin tracing method based on characteristic difference metabolites
(1) 43 mung bean samples (Table 1) were collected from 11 producing areas of Hebei, shanxi, anhui, inner Mongolia, jilin, shandong, jiangsu, shanxi, xinjiang, yunnan and Henan, washed to remove impurities, and lyophilized for 72 hours. Grinding the freeze-dried mung bean sample to powder under the protection of liquid nitrogen, and storing the powder in a refrigerator at the temperature of-20 ℃ for later use. Accurately weighing 1g of mung bean powder samples of different production places, placing the mung bean powder samples in a 45mL centrifuge tube, and adding 5mL of methanol: the water (60:40, v/v) extract was vortexed for 60s, sonicated in cold water for 1 hour, then centrifuged at 12000 Xg for 10 minutes at 4 ℃, the supernatant collected, blow-dried with nitrogen, and 1mL of methanol was added: the water (60:40, v/v) extract was reconstituted. The compound solution is filtered by a hydrophilic polytetrafluoroethylene filter membrane with the diameter of 0.22 mu m to obtain filtrate, namely the detection sample of the machine. The quality control QC sample is prepared by mixing the extracts of all samples in equal quantity.
TABLE 1 43 mung bean samples from 11 producing sites
Sequence number Production area Resources/varieties Year of year Sequence number Production area Variety of species Year of year
1 Hebei river Giant deer Bai Jiamao mung bean 2021 23 (Jilin) Ji Lv No. 10 2021
2 Hebei river Ji Lv 19 2021 24 (Jilin) White green No. 8 2021
3 Hebei river Ji Lu 22 # 2021 25 (Jilin) White green No. 12 2021
4 Hebei river Mung bean with pargo 2021 26 (Jilin) White green No. 16 2021
5 Shanxi style food And green No. 11 2021 27 (Jilin) White green No. 17 2021
6 Shanxi style food Jinphasu No. 8 2021 28 Shandong province Green No. 7 2021
7 Shanxi style food Black pearl 2021 29 Shandong province Green number 8 2021
8 Shanxi style food Jinphasu No. 7 2021 30 Shandong province Green No. 9 2021
9 Shanxi style food And green No. 9 2021 31 Jiangsu Su green No. 1 2021
10 Shanxi style food And green No. 18 2021 32 Jiangsu Su green No. 2 2021
11 Shanxi style food And green No. 19 2021 33 Jiangsu Su green No. 3 2021
12 Shanxi style food And green No. 20 2021 34 Jiangsu Su green No. 7 2021
13 Shanxi style food And green No. 21 2021 35 Shanxi province Elm green No. 1 2021
14 Shanxi style food 1508-5-4-1 2021 36 Shanxi province Seide mung bean 2021
15 (Anhui) Ming Lv series No. 1 2021 37 Shanxi province Fuxian mung bean 2021
16 (Anhui) Zhongqing No. 5 2021 38 Xinjiang Zhongqing No. 5 2021
17 (Anhui) Anhuiaceae green No. 3 2021 39 Xinjiang Zhongqing No. 9 2021
18 Inner Mongolia Tianshan mountain Daming green 2021 40 Yunnan (Yunnan) province Gong Shan mung bean 2021
19 Inner Mongolia Red green No. 1 2021 41 Henan province Winding green number 2 2021
20 Inner Mongolia Mung bean for treating summer sleeping 2021 42 Henan province Zhongqing No. 5 2021
21 (Jilin) Ji Lv No. 7 2021 43 Henan province Zheng Lu 8 No. Zheng Lu 2021
22 (Jilin) Ji Lv No. 9 2021
(2) The mass spectrum information of mung bean samples of different producing areas is acquired by adopting an ultra-high performance liquid chromatography tandem time-of-flight mass spectrometer, and the conditions are as follows:
chromatographic conditions: the chromatographic column was a Phenomenex Kinetex C18 chromatographic column (2.1 mm. Times.100 mm,2.6 μm); mobile phase A is aqueous solution containing 0.1% (v/v) formic acid, mobile phase B is acetonitrile solution containing 0.1% (v/v) formic acid; gradient elution procedure: 0-13min,1% B to 99% B;13-15min,99% B;15-16min,99% B to 1% B;16-20min,1% B; the flow rate was 0.3mL/min.
Mass spectrometry conditions: and adopting an electrospray ionization ion source ESI to acquire data in positive ion mode and negative ion mode respectively, wherein the ion source temperature is 550 ℃, the collision energy is 35eV, the collision energy distribution is 15eV, the positive ion mode spray voltage is +5500V, and the negative ion mode is-4500V. The mass spectrum adopts a full scanning mode, and an information association acquisition method is established to be combined with automatic dynamic background subtraction. The MS mass detection range is m/z 100-1000, and the MS/MS mass detection range is m/z 50-1000. After 5 samples are collected, an automatic correction liquid transmission system is utilized to execute one external accurate mass correction, so that the high mass accuracy of the mass spectrometer in the data collection process is ensured.
The data acquisition uses an analysis to acquire in real time, and adopts marker view software to perform deconvolution, peak extraction, peak alignment and other data processing on the raw data obtained by the mass spectrum to obtain a data matrix containing retention time, mass-to-charge ratio and ion abundance information for subsequent chemometric analysis.
(3) And analyzing the metabolic data of mung bean samples of different producing areas by adopting orthogonal partial least squares discriminant analysis (OPLS-DA) to construct an OPLS-DA model. FIG. 1 shows the score map (A) and the substitution test map (B) of mung bean OPLS-DA at different production sites. From the score plot, it can be seen that the 11-origin mung beans based on all metabolites were not completely separated, R2 and Q2 were lower than the right original point in the displacement test, and the left Q2 point was lower than zero in the OPLS-DA model, indicating that the model was efficient and reliable.
VIP is selected according to the VIP value of the OPLS-DA model and the p value of ANOVA>1 and p<0.05 as a characteristic differential metabolite distinguishing mung beans from different origin (Table 2). And qualitatively identifying the screened characteristic difference metabolites of the production place by combining the database with the standard substances and comparing the accurate mass number, the isotope abundance distribution and the secondary ion fragment information of the metabolites. Molecular prediction using MS-FINDER software and PeakView software, screening 35 different metabolites including 9, 10-dihydroxyoctadecenoic acid, glycerophosphorylcholine, lysophosphatidylcholine 1 (C) 26 H 50 NO 7 P), lysophosphatidylcholine 2 (C) 24 H 50 NO 7 P), lysophosphatidylcholine 3 (C) 26 H 48 NO 7 P), lysophosphatidylethanolamine 1 (C) 23 H 44 NO 7 P), lysophosphatidylethanolamine 4 (C) 21 H 44 NO 7 P), lysophosphatidylinositol (C) 27 H 49 O 12 P), isoorientin, vitexin, isovitexin, luteolin-6-C-glucoside, indole, 3-indolecarboxaldehyde, 3-indoleacrylic acid, indoline, gamma-glutamylleucine, gamma-glutamyl-S-methylcysteine, arginine, tryptophan, methyl cysteine, N-acetyltryptophan,citric acid, malic acid, aconitic acid, 1-naphthyl isocyanate, 1-methyl naphthylamine, adenosine, xanthosine, adenosine-3' -phosphate, oxypurinol, choline, phthalic anhydride, soyasaponin I, tetrasaccharide (C) 26 H 45 NO 20 ，Alpha-Tetrasaccharide，59957-92-5)。
TABLE 2 VIP values of characteristic differential metabolites of mung beans at different production sites
Figure BDA0004163381570000081
Figure BDA0004163381570000091
Figure BDA0004163381570000101
(4) And further establishing a mung bean origin prediction model by using the screened characteristic difference metabolite data of mung beans in each origin and adopting a machine learning algorithm, so as to realize accurate tracing of mung bean origins.
The programming language adopted by the machine learning algorithm is R language (version 4.0.2), the random forest and the support vector machine are respectively analyzed by using R packages of "random forest" and "e1071", and all mung bean samples are randomly split into a training set (70% of a data matrix) and a testing set (30% of the data matrix) for model construction, tuning and performance evaluation. By establishing and optimizing the classification model, the discrimination accuracy of the random forest and the support vector machine model is 93.15% (figure 2A) and 98.72% (figure 2B) respectively, excellent discrimination performance is shown in the construction of the mung bean origin tracing model, and particularly the discrimination accuracy of the support vector machine model reaches 98.72%, so that the method can be used for precise tracing of the mung bean origin and provides technical support for mung bean authenticity evaluation.
The results show that the method for tracing the green beans to the origin based on the characteristic difference metabolites combined with chemometric screening and the machine learning algorithm has the advantages of accurate experimental results and high feasibility. The non-targeted metabonomics technology comprehensively obtains metabolite information of mung beans in different producing areas, and chemometric means screen out characteristic difference metabolites affecting the distinguishing between producing areas; the mung bean origin tracing model is successfully constructed by further combining a machine learning classification algorithm, and has important reference significance for mung bean and other beans tracing.
Although specific embodiments of the invention have been described, those skilled in the art will recognize that many changes and modifications may be made thereto without departing from the scope or spirit of the invention. Accordingly, the present invention is intended to embrace all such alterations and modifications that fall within the scope of the appended claims and equivalents thereof.

Claims (10)

1. The mung bean origin tracing method based on the characteristic difference metabolites is characterized by comprising the following steps of:
(1) Collecting mung bean samples of different production places, respectively removing impurities, freeze-drying, grinding to powder, adding an extracting solution, extracting, centrifuging, collecting supernatant, blow-drying with nitrogen, re-dissolving, and filtering to obtain filtrate of the sample to be detected;
(2) Performing non-targeted metabonomics analysis on the filtrate of the mung beans to be detected in different producing areas obtained in the step (1) by adopting an ultra-high performance liquid chromatography tandem time-of-flight mass spectrometer to obtain metabolite data of different mung bean samples, and processing the metabolite data by mass spectrometry data analysis software;
(3) Adopting orthogonal partial least squares discriminant analysis (OPLS-DA) to analyze the metabolic data of mung bean samples of different producing areas, and constructing an OPLS-DA model; screening out characteristic difference metabolites of mung beans in different producing areas according to the VIP value of the OPLS-DA model and the p value of the ANOVA, carrying out qualitative identification on the screened characteristic difference metabolites in the producing areas, carrying out molecular prediction by using software, and determining the structure of the screened characteristic difference metabolites;
(4) And (3) utilizing the screened mung bean characteristic difference metabolite data, and adopting a machine learning algorithm to establish a mung bean origin prediction model so as to realize accurate tracing of the mung bean origin.
2. The mung bean origin tracing method according to claim 1, wherein the chromatographic conditions detected by the ultra-high performance liquid chromatography-tandem time-of-flight mass spectrometer in the step (2) are as follows: the chromatographic column is Phenomenex Kinetex C and 18; mobile phase A is an aqueous solution containing 0.1% v/v formic acid, mobile phase B is an acetonitrile solution containing 0.1% v/v formic acid; gradient elution procedure: 0-13min,1% B to 99% B;13-15min,99% B;15-16min,99% B to 1% B;16-20min,1% B; the flow rate was 0.3mL/min.
3. The mung bean origin tracing method according to claim 1, wherein the mass spectrometry conditions detected by using an ultra-high performance liquid chromatography tandem time-of-flight mass spectrometer in the step (2) are as follows: and adopting an electrospray ionization ion source ESI to acquire data in positive ion mode and negative ion mode respectively, wherein the ion source temperature is 550 ℃, the collision energy is 35eV, the collision energy distribution is 15eV, the positive ion mode spray voltage is +5500V, and the negative ion mode is-4500V.
4. The method according to claim 1, wherein in the step (3), metabolites of VIP >1 and p <0.05 are selected as characteristic differential metabolites for distinguishing mung beans in different production areas according to VIP values of OPLS-DA models and p values of ANOVA.
5. The method according to claim 1, wherein in the step (3), the molecular prediction is performed by using MS-filter software and PeakView software.
6. The method according to claim 1, wherein the characteristic differential metabolites selected in step (3) are selected from the group consisting of 9, 10-dihydroxyoctadecenoic acid, glycerophosphorylcholine, lysophosphatidylcholine 1 (C) 26 H 50 NO 7 P), lysophosphatidylcholine 2 (C) 24 H 50 NO 7 P), lysophosphatidylcholine 3 (C) 26 H 48 NO 7 P), lysophosphorusFatty acyl ethanolamine 1 (C) 23 H 44 NO 7 P), lysophosphatidylethanolamine 4 (C) 21 H 44 NO 7 P), lysophosphatidylinositol (C) 27 H 49 O 12 P) isoorientin, vitexin, isovitexin, luteolin-6-C-glucoside, indole, 3-indolecarboxaldehyde, 3-indoleacrylic acid, indoline, gamma-glutamylleucine, gamma-glutamyl-S-methylcysteine, arginine, tryptophan, methyl cysteine, N-acetyltryptophan, citric acid, malic acid, aconitic acid, 1-naphthylisocyanate, 1-menylamine, adenosine, xanthosine, adenosine-3' -phosphate, oxypurinol, choline, phthalic anhydride, soyasaponin I, tetraose (C) 26 H 45 NO 20 ) Two or more of them.
7. The method according to claim 1, wherein the programming language adopted by the machine learning algorithm in the step (4) is R language, the random forest and the support vector machine are analyzed by using R packages "randomForest" and "e1071", respectively, and all mung bean samples are split randomly into training sets and test sets for model construction, tuning and performance evaluation.
8. The mung bean origin tracing method according to claim 1, wherein in the step (4), the discrimination accuracy of the random forest and the support vector machine model is higher than 90% through the established and optimized classification model.
9. A characteristic differential metabolite composition for mung bean origin tracing, characterized in that the characteristic differential metabolite composition is selected from 9, 10-dihydroxyoctadecenoic acid, glycerophosphorylcholine, lysophosphatidylcholine 1 (C) 26 H 50 NO 7 P), lysophosphatidylcholine 2 (C) 24 H 50 NO 7 P), lysophosphatidylcholine 3 (C) 26 H 48 NO 7 P), lysophosphatidylethanolamine 1 (C) 23 H 44 NO 7 P), lysophosphatidylethanolamine 4 (C) 21 H 44 NO 7 P), lysophosphatidylinositol (C) 27 H 49 O 12 P) isoorientin, vitexin, isovitexin, luteolin-6-C-glucoside, indole, 3-indolecarboxaldehyde, 3-indoleacrylic acid, indoline, gamma-glutamylleucine, gamma-glutamyl-S-methylcysteine, arginine, tryptophan, methyl cysteine, N-acetyltryptophan, citric acid, malic acid, aconitic acid, 1-naphthylisocyanate, 1-menylamine, adenosine, xanthosine, adenosine-3' -phosphate, oxypurinol, choline, phthalic anhydride, soyasaponin I, tetraose (C) 26 H 45 NO 20 ) Two or more of them.
10. The application of a characteristic difference metabolite composition in the aspect of tracing mung bean origin is characterized in that the characteristic difference metabolite composition is selected from 9, 10-dihydroxyoctadecenoic acid, glycerophosphorylcholine and lysophosphatidylcholine 1 (C) 26 H 50 NO 7 P), lysophosphatidylcholine 2 (C) 24 H 50 NO 7 P), lysophosphatidylcholine 3 (C) 26 H 48 NO 7 P), lysophosphatidylethanolamine 1 (C) 23 H 44 NO 7 P), lysophosphatidylethanolamine 4 (C) 21 H 44 NO 7 P), lysophosphatidylinositol (C) 27 H 49 O 12 P) isoorientin, vitexin, isovitexin, luteolin-6-C-glucoside, indole, 3-indolecarboxaldehyde, 3-indoleacrylic acid, indoline, gamma-glutamylleucine, gamma-glutamyl-S-methylcysteine, arginine, tryptophan, methyl cysteine, N-acetyltryptophan, citric acid, malic acid, aconitic acid, 1-naphthylisocyanate, 1-menylamine, adenosine, xanthosine, adenosine-3' -phosphate, oxypurinol, choline, phthalic anhydride, soyasaponin I, tetraose (C) 26 H 45 NO 20 ) Two or more of them.
CN202310356301.7A 2023-04-06 2023-04-06 Mung bean origin tracing method based on characteristic difference metabolites Pending CN116381085A (en)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106645538A (en) * 2017-01-23 2017-05-10 中国农业科学院蜜蜂研究所 Method for identifying acacia honey producing place by non-target metabonomics technology
CN112986431A (en) * 2021-02-18 2021-06-18 中国农业科学院农业质量标准与检测技术研究所 Method for identifying organic milk and conventional milk based on metabonomics
CN114924002A (en) * 2022-05-12 2022-08-19 中国检验检疫科学研究院 Method for identifying NFC and FC orange juice by combining metabonomics technology and machine learning
CN115015460A (en) * 2022-06-01 2022-09-06 中国检验检疫科学研究院 Method for identifying producing area of cordyceps sinensis by using extensive targeted metabonomics technology
CN115586283A (en) * 2022-11-04 2023-01-10 南京中医药大学 Dried ginger producing area tracing method based on characteristic metabolites

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106645538A (en) * 2017-01-23 2017-05-10 中国农业科学院蜜蜂研究所 Method for identifying acacia honey producing place by non-target metabonomics technology
CN112986431A (en) * 2021-02-18 2021-06-18 中国农业科学院农业质量标准与检测技术研究所 Method for identifying organic milk and conventional milk based on metabonomics
CN114924002A (en) * 2022-05-12 2022-08-19 中国检验检疫科学研究院 Method for identifying NFC and FC orange juice by combining metabonomics technology and machine learning
CN115015460A (en) * 2022-06-01 2022-09-06 中国检验检疫科学研究院 Method for identifying producing area of cordyceps sinensis by using extensive targeted metabonomics technology
CN115586283A (en) * 2022-11-04 2023-01-10 南京中医药大学 Dried ginger producing area tracing method based on characteristic metabolites

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