阅读说明：本技术 一种利用gc-ms/ms高效检测并鉴别植物挥发物的方法 (Method for efficiently detecting and identifying plant volatile matters by using GC-MS/MS (gas chromatography-Mass Spectrometry/Mass Spectrometry) ) 是由 罗杰 袁弘伦 曹光平 刘贤青 候晓东 黄梦兰 杜鹏萌 于 2021-01-30 设计创作，主要内容包括：本发明公开了一种利用GC-MS/MS高效检测并鉴别植物挥发物的方法,其步骤是：首先利用全扫描模式对多物种植物挥发物进行检测,所得的数据解卷积后,对物质进行定性；其次是选取各物质的前体离子,利用产物离子模式检测各前体离子的产物离子,并优化碰撞电压；第三是将所得离子对整合至单个MRM(多反应监测模式)方法中；最后是建立适用于挥发物检测的校正模型。使用本方法,可以有效提高挥发物检测时的灵敏度、检测通量、可定性物质数量、定量准确性及方法重现性。本方法应用性强,显著提高了挥发物检测效果,对植物挥发物的研究有重大意义。(The invention discloses a method for efficiently detecting and identifying plant volatile matters by using GC-MS/MS, which comprises the following steps: firstly, detecting multi-species plant volatile matters by using a full scanning mode, and performing qualitative determination on substances after deconvolution of obtained data; secondly, selecting precursor ions of all substances, detecting the product ions of all the precursor ions by using a product ion mode, and optimizing collision voltage; third, the resulting ion pairs are integrated into a single MRM (multiple reaction monitoring mode) process; finally, a correction model suitable for volatile detection is established. By using the method, the sensitivity, the detection flux, the quantity of the qualitative substances, the quantitative accuracy and the method reproducibility in the volatile matter detection can be effectively improved. The method has strong applicability, remarkably improves the detection effect of the volatile matters, and has great significance for the research of plant volatile matters.)

1. A method for efficiently detecting and identifying plant volatile matters by utilizing GC-MS/MS comprises the following steps:

A. sample preparation:

(1) granular sample: husking the grains and then storing at normal temperature; powder sample: adding liquid nitrogen into the fresh sample in a mortar, grinding into powder, and storing at-80 ℃; liquid sample: squeezing fresh fruit juice with a juicer at 4 deg.C, and detecting within 12 hr;

(2) solution preparation: the internal standard liquid is V_{Water (W)}：V_TMP＝6×10⁶: 1; the powder sample extract is V_{Water (W)}：V_EDTA：V_TMP1000: 1: 0.001, V represents volume, TMP is 2,4, 6-trimethylpyridine, EDTA is ethylene diamine tetraacetic acid, water is deionized water, TMP and EDTA are analytical grade reagents;

(3) weighing a proper amount of sample, placing the sample in a headspace sampling bottle, and taking the granular sample according to the weight W_{Sample (I)}：W_{Internal standard liquid}Adding water in a ratio of 3: 1; powder sample according to W_{Sample (I)}：W_{Calcium chloride dihydrate}：W_{Extract liquid}Sequentially adding calcium chloride dihydrate and the extracting solution according to the proportion of 2:1: 2; liquid sample press W_{Sample (I)}：W_{Internal standard liquid}：W_{Sodium chloride}Sequentially adding the internal standard solution and sodium chloride according to the proportion of 1:0.1:0.1, and testing; v represents volume, W represents mass, and calcium chloride dihydrate and sodium chloride are analytical reagents;

B. the instrument conditions were as follows:

(1) injector conditions: an Agilent PAL RSI 120 automatic sample introduction system; solid phase micro-extraction probe: the solid phase microextraction probe comprises a DVB/CAR/PDMS solid phase microextraction probe in the family of chromatography, wherein DVB is divinylbenzene, CAR is a carbon molecular sieve, and PDMS is polydimethylsiloxane; according to different properties of samples, the temperature of a shaker is set to be 40-80 ℃, the shaking time is set to be 10-30 minutes, the adsorption time is set to be 20 minutes, and the desorption time is as follows: 2 minutes, aging temperature: 270 ℃, aging time: 5 minutes;

(2) chromatographic conditions are as follows: agilent 7890B chromatograph; a chromatographic column: agilent HP-5MS chromatographic column, 30m × 250 μm × 0.25 μm; temperature rising procedure: the initial temperature was 40 ℃ and held for 3 minutes; heating to 160 ℃ at the speed of 2 ℃/min; then raising the temperature to 300 ℃ at the speed of 50 ℃/min, and keeping the temperature for 3 min; the carrier gas is helium, and the flow rate is 1 mL/min;

(3) mass spectrum conditions: agilent 7000D triple quadrupole tandem mass spectrometer, transmission line temperature: 280 ℃; ion source temperature: 300 ℃; ionization mode: EI;

(4) full scan mode parameters: scanning range: 40-500 m/Z;

(5) product ion mode parameters: collision voltage gradient: 5 to 20 eV;

(6) MRM mode parameters: collision voltage gradient: 5-20 eV, selecting an optimal voltage according to a detection result of the product ion mode;

C. detecting plant volatile matters of different species by using a full scanning mode, deconvoluting the obtained data by using MSDIAL software, and comparing the data with a NIST17 database for qualification;

D. summarizing the obtained substances, manually removing repeated substances, detecting product ions of each precursor ion by using a product ion mode, and selecting the optimal product ions and collision voltages according to the results;

E. establishing a label metabolism database by using the obtained ion pairs;

F. and (3) taking a uniform sample of the same species for repeated sample injection, wherein the sample injection comprises multiple batches. And (3) respectively establishing a regression model between the content of each substance in each batch and the sampling times by using an algorithm, calculating the proportional relation between the models, and respectively correcting the errors between the substance batches and the batches by using the models.

Technical Field

The invention belongs to the field of analytical chemistry, and particularly relates to a method for efficiently detecting and identifying plant volatile matters by using GC-MS/MS (gas chromatography-Mass spectrometer), which can remarkably improve the detection effect of the plant volatile matters.

Background

Plant volatiles are a class of lipophilic liquids with high saturated vapor pressures. Over 1700 plant volatiles have been identified (Natalia Dudareva et al, Biosynthesis, function and metabolic engineering of plant volatile organic compounds, New Phytologist, 2013). Plant volatiles have important effects on both plants themselves and humans: in plants, volatiles play a role in interacting with the environment (Aino Kalske et al, interior manual selections for volatile-mediated plant-plant communication. Current Biology, 2019); plant volatiles provide food aroma to humans, and also may be used as spices, seasonings, pharmaceuticals, and the like (Eran Pichersky et al, biosyntheses of plant flavors: nature's diversity and inginuity. science, 2006). Therefore, plant volatiles have important research significance.

The method for studying plant volatiles at present is mainly a non-targeted metabonomics method based on Solid phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) (barbarbarbarbara Bojko et al, Solid-phase microextraction in metabolism. However, the non-target method has problems of low sensitivity, data convolution, inaccurate quantification, etc. (Tomas Cajka et al, means for measuring and target methods in mass spectrometry-based methods and lipidomics. analytical Chemistry, 2014). A liquid chromatography-mass spectrometry (LC-MS) based extensive targeted metabonomics method can effectively improve the sensitivity of the method, avoid data convolution and improve the quantitative capability (Chen wei et al, an integrated method for large-scale detection, identification and qualification of wide-target metabolites: application in the student of edge methodology. molecular Plant, 2013). Therefore, a broad-target idea can be applied to GC-MS, and the volatile matter detection efficiency is improved.

However, the detection flux and the quantity of the qualitative substances in the wide-target method are still limited by the non-target method, and the detection and qualitative flux of the volatile matter group cannot be effectively improved by directly transferring the wide-target method; in addition, the SPME-GC-MS based volatile group method has a problem of poor reproducibility of results. Aiming at the problems, according to the research background that the volatile synthesis path is limited and the volatile synthetases have the convergent evolution trend (Eran Pichia et al, Biosynthesis of plant vollatiles: nature's diversity and inguity. science, 2006), the method for detecting flux and the quantity of the qualitative substances are improved by integrating a multi-species volatile database, a regression model is utilized to establish a correction method suitable for SPME-GC-MS data, and the stability of the method is obviously improved. The invention establishes a wide-target metabolome method capable of efficiently detecting plant volatile matters, and has important significance for the research of the plant volatile matters.

Disclosure of Invention

The invention aims to provide a method for efficiently detecting and identifying plant volatile matters by using GC-MS/MS, which is mainly characterized in that compared with a non-target method applied at the present stage, the sensitivity, the detection flux, the quantity of qualitative substances, the quantitative accuracy and the method reproducibility of the method are obviously improved. By using the method, the research on plant volatile matters can be promoted.

In order to achieve the purpose, the invention adopts the following technical measures:

a method for efficiently detecting and identifying plant volatile matters by utilizing GC-MS/MS comprises the following steps:

1. sample preparation:

(1) granular sample: taking a proper amount of sample (more than 2g), shelling and then preserving at normal temperature; powder sample: taking a proper amount of fresh sample (more than 2g), adding liquid nitrogen into a mortar, grinding into powder, and preserving at-80 ℃; liquid sample: taking a proper amount of fresh sample (more than 4g), squeezing juice with a juicer, storing the juice at 4 ℃, and detecting within 12 hours;

(2) solution preparation: the internal standard liquid is V_{Water (W)}：V_TMP＝6×10⁶: 1; the powder sample extract is V_{Water (W)}：V_EDTA：V_TMP1000: 1: 0.001, V represents volume, TMP is 2,4, 6-trimethylpyridine, EDTA is ethylene diamine tetraacetic acid, water is deionized waterTMP and EDTA are analytical grade reagents (national pharmaceutical group chemical reagents, Inc.);

(3) weighing a proper amount of sample (more than 1g), placing the sample in a headspace sampling bottle, and taking the granular sample according to the weight W_{Sample (I)}：W_{Internal standard liquid}Adding water in a ratio of 3: 1; powder sample according to W_{Sample (I)}：W_{Calcium chloride dihydrate}：W_{Extract liquid}Sequentially adding calcium chloride dihydrate and the extracting solution according to the proportion of 2:1: 2; liquid sample press W_{Sample (I)}：W_{Internal standard liquid}：W_{Sodium chloride}Sequentially adding the internal standard solution and sodium chloride according to the proportion of 1:0.1:0.1, and testing; v represents volume, W represents mass, calcium chloride dihydrate and sodium chloride are analytical grade reagents (national pharmaceutical group chemical reagent Co., Ltd.);

2. the instrument conditions were as follows:

(1) sample injector conditions: an Agilent PAL RSI 120 automatic sample introduction system; solid phase micro-extraction probe: the solid phase microextraction probe comprises a DVB/CAR/PDMS solid phase microextraction probe in the family of chromatography, wherein DVB is divinylbenzene, CAR is a carbon molecular sieve, and PDMS is polydimethylsiloxane; according to different properties of samples, the temperature of a shaker is set to be 40-80 ℃, the shaking time is set to be 10-30 minutes, the adsorption time is set to be 20 minutes, and the desorption time is as follows: 2 minutes, aging temperature: 270 ℃, aging time: 5 minutes;

(2) chromatographic conditions are as follows: agilent 7890B chromatograph; a chromatographic column: agilent HP-5MS chromatographic column, 30m × 250 μm × 0.25 μm; temperature rising procedure: the initial temperature was 40 ℃ and held for 3 minutes; heating to 160 ℃ at the speed of 2 ℃/min; then raising the temperature to 300 ℃ at the speed of 50 ℃/min, and keeping the temperature for 3 min; the carrier gas is helium, and the flow rate is 1 mL/min;

(3) mass spectrum conditions: agilent 7000D triple quadrupole tandem mass spectrometer, transmission line temperature: 280 ℃; ion source temperature: 300 ℃; ionization mode: EI;

(4) full scan mode parameters: scanning range: 40-500 m/Z;

(5) product ion mode parameters: collision voltage gradient: 5 to 20 eV;

(6) MRM mode parameters: collision voltage gradient: 5-20 eV, selecting corresponding collision voltage for different substances according to the detection result of the product ion mode;

3. detecting different crop volatile matters by using a full scanning mode, deconvoluting the obtained data by using MSDIAL software, and comparing the data with a NIST17 database for qualification;

4. integrating the detected substances, manually removing repeated substances, detecting product ions of precursor ions of the substances after the duplication removal by using a product ion mode under different voltages, and selecting proper collision voltage according to the result;

5. establishing a label metabolism database by using the obtained substance ion pairs;

6. and (3) taking a uniform sample of the same species for repeated sample injection, wherein the sample injection comprises multiple batches. Taking a plurality of repeated mean values of each substance as the responsivity of the substance, taking a data set after the mean values as a training set, respectively establishing regression models between the content of each substance in each batch and the sampling times by using an algorithm, calculating the proportional relation between the models, and respectively correcting the errors between the substance in each batch and the material in each batch by using the models.

Compared with the prior art, the invention has the following advantages and effects:

(1) high flux. By integrating a multi-species volatile substance library and combining the MRM technology, the detection flux can be obviously improved when the plant volatile substances are detected. As shown in FIG. 1, the flux of volatile substances detected in rice seeds was increased from 69 to 174 in the non-target method, and the flux of volatile substances detected in passion fruit was increased from 115 to 196 in the non-target method.

(2) High sensitivity. A large amount of interfering ions are eliminated by utilizing the MRM technology through two-stage ion selection, so that the background interference of the mass spectrum is greatly reduced, and the signal-to-noise ratio of the object to be detected is obviously improved. As shown in FIG. 2, the sensitivity of guaiacol was improved by 4.59 times when detecting rice seeds.

(3) Good reproducibility. The data are corrected using a regression model, so that the method has good reproducibility. As shown in FIG. 3, after the volatile data of the rice seeds injected for 69 times are corrected, the coefficient of variation of hexanal is reduced from 0.51 to 0.08.

(4) The quantity of the qualitative substances is higher. The multi-species volatile library covers the main products in the volatile synthesis path, so the quantity of the qualitative substances in the detection result is higher. As shown in FIG. 4, the rice was originally without signal or with signal of unknown substance, and the signals were identified by volatile library and noted as 2-acetyl-1-pyrroline, 2-pentylfuran, indole, etc.

(5) Accurate quantification and easy integration of data. By using the MRM technology, the problem of convolution in data is avoided, and the quantitative accuracy of the method is improved; in addition, the method does not need a complex peak alignment step, so that different batches of data are easy to integrate.

Drawings

FIG. 1A is a graph showing the results of detection and identification of rice seed volatiles using a non-targeted method.

The peak intensity is 2.5X 10⁶-8.7×10⁷Between cps, 69 volatiles were exhibited.

FIG. 1B is a graph showing the results of high throughput detection and identification of rice seed volatiles using the present invention.

The peak intensity is 1.5X 10⁴-8.8×10⁵Between cps, 174 volatiles were exhibited.

FIG. 1C is a graph of the results of a non-targeted method for detecting and identifying passion fruit volatiles.

The peak intensity is 6.3X 10⁶-8.9×10⁸Between cps, 115 volatiles are exhibited.

FIG. 1D is a graph showing the results of high throughput detection and identification of passion fruit volatiles using the present invention.

The peak intensity is 2.1X 10⁴-2.6×10⁷Between cps, 196 volatiles were exhibited.

FIG. 2 is a comparison of results of detection of guaiacol in rice seeds by the non-targeted method and the method of the present invention.

The sensitivity is improved by 4.59 times.

FIG. 3 is a graph of the abundance of hexanal from 69 injections in a rice seed data corrected by the present invention.

The coefficient of variation is reduced from 0.51 to 0.08.

FIG. 4 is a qualitative result comparison of the non-target method and the method of the present invention for detecting rice seed

The method of the invention characterizes the non-signal substances and unknown substances in the non-targeting method.

Detailed Description

Example 1:

a method for efficiently detecting and identifying plant volatile matters by utilizing GC-MS/MS comprises the following steps:

1. sample preparation:

(1) and (6) sampling. Granular sample: respectively shelling 2.0g of Nipponbare rice seeds and 2.0g of Handan wheat 19 wheat seeds, storing at normal temperature, threshing 2.0g of Zhengdan 958 corn, and storing at normal temperature; powder sample: taking 2.0g of durian pulp and hot pepper fruit No. 1 in a mortar, adding liquid nitrogen, grinding into powder, storing with liquid nitrogen, and purchasing durian in local supermarket; liquid sample: 4mL of juice of tomatoes, oranges, apples, pears and passion fruits is squeezed by a juicer, the juice is stored at 4 ℃ and detected within 12 hours, and the tomatoes, the oranges, the apples, the pears and the passion fruits are purchased in a local supermarket;

(2) solution preparation: the internal standard liquid is V_{Water (W)}：V_TMP＝6×10⁶: 1; the powder sample extract is V_{Water (W)}：V_EDTA：V_TMP1000: 1: 0.001, V represents volume, TMP is 2,4, 6-trimethylpyridine, EDTA is ethylenediamine tetraacetic acid, water is deionized water, TMP and EDTA are analytical grade reagents (national pharmaceutical group chemical reagent, Inc.);

(3) granular and powdery samples: weighing 1.0g of sample, placing the sample in a headspace sample injection bottle, and adding 0.3mL of internal standard solution into the granular sample; adding 0.5g of calcium chloride dihydrate and 1mL of extracting solution into the powdery sample; liquid sample: taking 4mL of fruit juice, adding 0.4g of sodium chloride and 0.4mL of internal standard solution to be tested;

2. the instrument conditions were as follows:

(1) sample injector conditions: an Agilent PAL RSI 120 automatic sample introduction system; solid phase micro-extraction probe: the solid phase microextraction probe comprises a DVB/CAR/PDMS solid phase microextraction probe in the family of chromatography, wherein DVB is divinylbenzene, CAR is a carbon molecular sieve, and PDMS is polydimethylsiloxane; according to different properties of samples, the temperature of a shaker is set to be 40-80 ℃, the shaking time is set to be 10-30 minutes, the adsorption time is set to be 20 minutes, and the desorption time is as follows: 2 minutes, aging temperature: 270 ℃, aging time: 5 minutes;

(2) chromatographic conditions are as follows: agilent 7890B chromatograph; a chromatographic column: agilent HP-5MS chromatographic column, 30m × 250 μm × 0.25 μm; temperature rising procedure: the initial temperature was 40 ℃ and held for 3 minutes; heating to 160 ℃ at the speed of 2 ℃/min; then raising the temperature to 300 ℃ at the speed of 50 ℃/min, and keeping the temperature for 3 min; the carrier gas is helium, and the flow rate is 1 mL/min;

(3) mass spectrum conditions: agilent 7000D triple quadrupole tandem mass spectrometer, transmission line temperature: 280 ℃; ion source temperature: 300 ℃; ionization mode: EI;

(4) full scan mode parameters: scanning range: 40-500 m/Z;

(5) product ion mode parameters: collision voltage gradient: 5 to 20 eV;

(6) MRM mode parameters: collision voltage gradient: 5-20 eV, selecting corresponding collision voltage for different substances according to the detection result of the product ion mode;

3. different plant volatiles were detected using a full scan mode, and the resulting data were deconvoluted using MSDIAL software and compared with NIST17 database for characterization. (Please see Table I)

Table-partial results of full scan mode

Name of substance	Retention time (min)
		cis-3-Hexenal	5.6
1-Hexanol	8.58
		Styrene	9.38
2-Acetyl-1-pyrroline	11.33
		Camphene	12.46

4. Integrating the detected substances, manually removing repeated substances, detecting product ions of the removed substances by using a product ion mode under different voltages, and selecting proper collision voltage according to the result (see table II);

table two product ion mode partial results

5. Establishing a label metabolism database (please see table three) based on the optimized ion pairs and the collision voltage;

table three volatile label database partial results

6. Taking the same variety of rice seeds, dividing into 3 batches, and feeding 23 samples in each batch for 69 times. Taking 3 repeated mean values of each substance as the responsivity of the substance, taking a data set after the mean values as a training set, establishing a regression model for the training set by using an algorithm, optimizing a penalty factor parameter C and a kernel function parameter g in the model, and correcting errors in batches by using the regression model; subsequently, the ratio m between each batch was determined, and the abundance of the substance was normalized according to the ratio to correct the error between batches (see table four).

Table four part material correction model parameter