CN114878724A - Method for distinguishing Chinese bee honey of different varieties and application - Google Patents
Method for distinguishing Chinese bee honey of different varieties and application Download PDFInfo
- Publication number
- CN114878724A CN114878724A CN202210812726.XA CN202210812726A CN114878724A CN 114878724 A CN114878724 A CN 114878724A CN 202210812726 A CN202210812726 A CN 202210812726A CN 114878724 A CN114878724 A CN 114878724A
- Authority
- CN
- China
- Prior art keywords
- honey
- bee honey
- chinese bee
- mass spectrum
- different
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/04—Preparation or injection of sample to be analysed
- G01N30/06—Preparation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/26—Conditioning of the fluid carrier; Flow patterns
- G01N30/28—Control of physical parameters of the fluid carrier
- G01N30/30—Control of physical parameters of the fluid carrier of temperature
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/26—Conditioning of the fluid carrier; Flow patterns
- G01N30/28—Control of physical parameters of the fluid carrier
- G01N30/32—Control of physical parameters of the fluid carrier of pressure or speed
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/26—Conditioning of the fluid carrier; Flow patterns
- G01N30/28—Control of physical parameters of the fluid carrier
- G01N30/34—Control of physical parameters of the fluid carrier of fluid composition, e.g. gradient
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/62—Detectors specially adapted therefor
- G01N30/72—Mass spectrometers
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/26—Conditioning of the fluid carrier; Flow patterns
- G01N30/28—Control of physical parameters of the fluid carrier
- G01N30/32—Control of physical parameters of the fluid carrier of pressure or speed
- G01N2030/324—Control of physical parameters of the fluid carrier of pressure or speed speed, flow rate
Abstract
The invention relates to the technical field of honey variety identification, and particularly discloses a method for distinguishing different varieties of Chinese bee honey and application thereof. The invention discloses a method for distinguishing bee honey of different varieties, which comprises the following steps: (1) pretreatment of a Chinese bee honey sample; (2) UHPLC-Q-active Orbitrap-MS data acquisition; (3) constructing a visual mass spectrum molecular network diagram; the pretreatment of the step (1) comprises the following steps: dissolving in water, extracting with acetonitrile, salting out and concentrating; the reagents used for salting out are sodium chloride and anhydrous magnesium sulfate; (4) and if the visual mass spectrum molecular network diagrams of different Chinese bee honey samples are the same, judging that the different Chinese bee honey samples are of the same variety, and if the visual mass spectrum molecular network diagrams are different, judging that the different Chinese bee honey samples are not of the same variety. The method can intuitively and quickly distinguish different varieties of Chinese bee honey, and can provide a new thought and method for the research of the authenticity of honey.
Description
Technical Field
The invention relates to the technical field of honey variety identification, in particular to a method for distinguishing bee honey of different varieties and application.
Background
Chinese bee honey (also called native honey) is made by collecting mountain plant nectar of bee variety "Chinese bee" and fully brewing, has higher medicinal value and biological activity than single flower Italian bee honey, and the price of Chinese bee honey in the market is usually three to five times of Italian bee honey due to lower yield and consumption preference. In addition, due to unique production area environment, climate conditions and honey source plants, different varieties of special Chinese bee honey with large differences in quality characteristics, nutritional functions and the like are formed, so that the market economic value difference of the special Chinese bee honey is large.
In recent years, compared with apis mellifera honey, related research on apis cerana honey has become an industrial hotspot. The quality characteristics of honey are closely related to the geographical source and honey source plants, and particularly the appearance, taste and nutritional characteristics of honey are often determined by the honey source plants, so that the important significance is realized by finding a new method for distinguishing the honey from different varieties and characteristics.
At present, traditional detection methods (such as pollen identification, sensory identification and physicochemical index identification) and various instruments (such as infrared spectrum, raman spectrum, nuclear magnetic resonance, gas chromatography, liquid chromatography, stable isotope mass spectrum and the like) combined with chemometrics methods (principal component analysis (PCA), Hierarchical Clustering Analysis (HCA), partial least squares discriminant analysis (PLS-DA), orthogonal partial least squares discriminant analysis (OPLS-DA), Random Forest (RF) and the like) are widely used for honey authenticity research, but the methods have certain limitations (complex data processing, large sample quantity and the like) in the aspect of honey variety identification.
The visualized mass spectrum molecular network technology is a method combined with gas, liquid or nuclear magnetism combined technology, and can realize the distinction of different samples according to the difference of visualized results. Natural product molecules with similar structures can generate similar mass spectrum fragment ions under the same mass spectrum separation condition, and the mass spectrum fragment ions are integrated into a visual mass spectrum molecular network diagram according to the similarity through database search and comparison. Because the chemical combination compositions of different samples have certain difference, the visualized molecular network diagram of the mass spectrum also has difference, and the different samples can be distinguished according to the difference of the visualized results.
The visualized mass spectrum molecular network technology is similar to other chemometrics methods such as PCA, HCA, PLS-DA, OPLS-DA and RF, but the chemometrics methods need to process samples firstly and then group the samples, and then realize the distinguishing of different groups of samples by combining different software through a dimension reduction technology, the data processing steps are complex, the number of required samples is large, and the obtained data result is not strong in visualization; the visualized mass spectrum molecular network technology can intuitively realize the distinction of different samples according to the visualized results of single or multiple different samples after treatment, the data processing steps are simple, and no model is required to be established. In recent years, a mass spectrum molecular network technology based on visualization of gas, liquid or nuclear magnetic combined technology is widely used for research of different fields such as microorganisms, medicines, plants and the like, but research and application of the technology in identification of honey varieties are not found.
Disclosure of Invention
The invention aims to provide a novel method for distinguishing bee honey of different varieties.
The invention provides a method for distinguishing Chinese bee honey with different varieties and characteristics by directly utilizing a visual mass spectrum molecular network technology. The method can visually and rapidly distinguish different varieties of special Chinese bee honey, and can provide a new idea and method for the research of the authenticity of honey.
In a visualized mass spectrum molecular network diagram, nodes represent fragment ions, connecting lines between the nodes represent correlation between the fragment ions, and mass spectrum fragments with similar structures can be gathered into clusters in a molecular network. Due to the difference among the varieties of Chinese bee honey samples, the number of nodes, the connection condition, the cluster quantity and the like of the visible mass spectrum molecular network diagram formed by the Chinese bee honey samples with different varieties and characteristics also have difference, so that the visible mass spectrum molecular network diagram of the Chinese bee honey with different varieties and characteristics can be established, the distinguishing of the Chinese bee honey with different varieties and characteristics can be intuitively and quickly realized according to the difference, and a new thought and method is further provided for the research of the authenticity of the honey.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a method of distinguishing bee honey in different breeds, comprising:
(1) pretreatment of a Chinese bee honey sample;
(2) UHPLC-Q-active Orbitrap-MS data acquisition;
(3) constructing a visual mass spectrum molecular network diagram;
(4) if the visual mass spectrum molecular network diagrams of different Chinese bee honey samples are the same, judging that the different Chinese bee honey samples are of the same variety, and if the visual mass spectrum molecular network diagrams of the different Chinese bee honey samples are different, judging that the different Chinese bee honey samples are not of the same variety;
the pretreatment of the step (1) comprises the following steps: dissolving in water, extracting with acetonitrile, salting out and concentrating; the reagents used for salting out are sodium chloride and anhydrous magnesium sulfate.
The invention firstly proposes a mass spectrum molecular network technology based on UHPLC-Q-active Orbitrap-MS combined visualization to realize the differentiation of different varieties of special Chinese bee honey; the original data of the Chinese bee honey sample obtained after mass spectrum scanning does not need any processing, the format is directly converted and uploaded to a database for identification, the data processing steps are simple, and data analysis and any discrimination model do not need to be carried out; the method has the advantages that no index is required to be measured, and the discrimination of different varieties of Chinese bee honey can be intuitively and quickly realized directly according to the difference of the visualized mass spectrum molecular network diagrams of different varieties of Chinese bee honey samples; the number of required samples is small, and a unique visual mass spectrum molecular network diagram can be formed by using single bee honey samples with different varieties and characteristics.
When the technology is applied, the specific reagent is added for salting out during pretreatment of the Chinese bee honey sample, so that the separation of a water phase and an organic phase can be effectively realized, analytes are more easily distributed in the organic phase, the extraction efficiency is improved, redundant water is removed, the subsequent concentration efficiency is improved, a more accurate and more comprehensive visual mass spectrum molecular network diagram can be constructed, and the accuracy of distinguishing different varieties of Chinese bee honey is improved.
In the method, the volume-to-mass ratio of the water added during water dissolving to the anhydrous magnesium sulfate added during salting out is 1 (0.5-0.7) mL/g, preferably 1:0.6 mL/g, so that the subsequent instrument detection is not influenced, and the improvement of the concentration efficiency and the cost control are both considered.
In the method of the present invention, the volume-to-mass ratio of the acetonitrile added during the extraction of acetonitrile to the sodium chloride added during the salting-out is 1 (0.1-0.15) mL/g, preferably 1:0.1 mL/g, and the extraction rate can be improved.
As a specific embodiment, the pretreatment method of the present invention is:
weighing a honey sample, adding water, and uniformly mixing; adding acetonitrile, and mixing uniformly; adding sodium chloride and anhydrous magnesium sulfate, and mixing; centrifuging, and concentrating all supernatant at about 40 deg.C to dryness; re-dissolving the honey sample with acetonitrile in water; after being filtered, the membrane is used for UHPLC-Q-active Orbitrap-MS on-machine analysis.
In the method of the present invention, in the chromatographic conditions for data collection in step (2), the mobile phase a is 0.1% formic acid aqueous solution, and the mobile phase B is methanol.
In the method of the present invention, the gradient elution procedure under the chromatographic conditions for data collection in step (2) is as follows:
the research of the invention finds that the selection of the mobile phase and the gradient elution procedure can be better combined with specific pretreatment conditions during the chromatographic detection, so that more analytes can be reserved, and comprehensive and accurate basic data can be provided for the subsequent analysis results.
In the method, in the chromatographic conditions during the data acquisition in the step (2), a chromatographic column is a full-porous silica gel-based reversed-phase high performance liquid chromatographic column; the column temperature is 38-42 ℃; the sample injection volume is 3-5 mu L; the flow rate is 0.25-0.35 mL/min.
Preferably, the chromatographic column is Agilent Eclipse Plus C18; the column temperature was 40 ℃; the sample injection volume is 5 mu L; the flow rate was 0.3 mL/min.
In the method of the present invention, the mass spectrometry conditions at the time of data acquisition in step (2) are: scanning mode: full MS/dd-MS 2 Positive ion mode; scanning range: 60-900m/z(ii) a Resolution ratio: 70000, dd-MS in Full MS mode 2 17500 in mode; spraying voltage: 3.5 kv; ion source temperature: 350 ℃; sheath gas and auxiliary gas: purity of>99.99% nitrogen at 35 arb and 10 arb, respectively.
In the method of the present invention, the specific operation method of step (3) is:
performing format conversion on raw data of a Chinese bee honey sample subjected to UHPLC-Q-active Orbitrap-MS analysis through MSconvert software, and setting parameters: the conversion format is mzXML, the screening condition is Peak packaging, and the MS grade is 1-2 grade;
uploading the obtained mzXML data to a GNPS database for analysis, and setting parameters: the cosine fraction threshold is 0.7, the minimum matching fragment ion is 6, the topK is 10, and the mass deviation of the parent ion and the daughter ion is 0.02 Da;
and then, processing the obtained data analysis result by using Cytoscape software to visualize the data analysis result.
Preferably, in the method of the invention, a visual mass spectrum molecular network diagram of Chinese bee honey is obtained, and the parameters are set as follows: the model is BioPAX _ STP, the fill color in the Node (Node) is black, the shape is circular, the label name is Parent mass, the line shape of the Edge (Edge) is Solid, the fill color is black, the transparency is 255, and the width is 4.0.
The technical scheme of the invention is schematically shown in figure 1.
The invention also provides an application of the method in Chinese bee honey variety (authenticity) identification and quality control.
When the method is applied, various Chinese bee honey samples can be firstly constructed by visual mass spectrum molecular network diagrams, and then the visible mass spectrum molecular network diagrams are distinguished by comparing the visual mass spectrum molecular network diagrams.
When the unknown honey sample is judged, a visual mass spectrum molecular network diagram can be constructed by the method, and the visual mass spectrum molecular network diagram is compared with the visual mass spectrum molecular network diagram of the known honey variety to judge the variety of the unknown honey sample.
The invention has the beneficial effects that:
according to the invention, the original data after mass spectrum scanning is obtained by directly preprocessing different varieties of special Chinese bee honey samples for the first time, the obtained original data is converted into a format and then directly uploaded to a corresponding database for analysis and comparison, a visual mass spectrum molecular network diagram of different varieties of special Chinese bee honey samples is formed, and the distinguishing of different varieties of special Chinese bee honey can be realized according to the difference of the visual mass spectrum molecular network diagram. Compared with other methods, the method disclosed by the invention has the advantages that the number of the required honey samples is small, any index does not need to be measured, the data processing step is simple, the data analysis and the establishment of any discrimination model are not needed, and the discrimination of the honey in different varieties and characteristics can be intuitively and quickly realized according to the difference of the visual mass spectrum molecular network diagrams.
Drawings
FIG. 1 is a technical route schematic diagram of a visual distinguishing method for Chinese bee honey with different varieties and characteristics.
FIG. 2 is a flow diagram of the original total ion flow of the Japanese cinnamon flower honey.
FIG. 3 is a visualized mass spectrum molecular network diagram of the osmanthus fragrans honey.
FIG. 4 is a graph of the original total ion flux of Melia azedarach.
FIG. 5 is a diagram of a mass spectrometric molecular network visualized with azadirachtin honey.
Fig. 6 is a flow chart of original total ion flow of idesia polycarpa honey.
FIG. 7 is a mass spectrum molecular network diagram visualized by idesia polycarpa honey.
Fig. 8 is a raw total ion flow graph of the osmanthus honey obtained in comparative example 1.
Fig. 9 is a raw total ion flow graph of the osmanthus honey obtained in comparative example 2.
Fig. 10 is a raw total ion flow graph of the osmanthus honey obtained in comparative example 3.
Detailed Description
Preferred embodiments of the present invention will be described in detail with reference to the following examples. It is to be understood that the following examples are given for illustrative purposes only and are not intended to limit the scope of the present invention. Various modifications and alterations of this invention will become apparent to those skilled in the art without departing from the spirit and scope of this invention.
The experimental procedures used in the following examples are all conventional procedures unless otherwise specified. Materials, reagents and the like used in the following examples are commercially available or can be prepared by a method conventional in the art unless otherwise specified.
The method comprises the steps of preprocessing three different varieties of special Chinese bee honey (wild osmanthus honey, meliacan honey and idesia honey, all from China Fujian province, and avoiding the difference among varieties caused by geographical environments), performing full scanning in a positive ion mode through UHPLC-Q-active Orbitrap-MS, converting scanned original raw data into data in an mzXML format through MSconvert software, uploading the converted data to a Global Natural Products Social network (GNPS) database for analysis, and visualizing the data by using Cytoscope software to form a visual mass spectrum Molecular network diagram of different varieties of special Chinese bee honey samples, thereby realizing the differentiation of different varieties of Chinese bee honey and further providing a new thought and method for honey variety identification.
Example 1
The embodiment provides a method for acquiring a visualized mass spectrum molecular network diagram of Chinese honey-Japanese cinnamon honey, which is produced in Fujian province of China. The method specifically comprises the following steps:
1. sample pretreatment
Weighing 2.0 g of wild osmanthus honey, adding 5 mL of water, and uniformly mixing for 2 min in a vortex manner; adding 10 mL of acetonitrile, and uniformly mixing for 2 min in a vortex manner; adding 1.0 g of sodium chloride and 3.0 g of anhydrous magnesium sulfate, and uniformly mixing for 2 min in a vortex manner; centrifuging at 12000 rpm for 10 min, collecting the whole supernatant to round bottom flask with corresponding volume, and concentrating at about 40 deg.C to dryness;the mixture was diluted with 1.5 mL of acetonitrile in water (2: 1,v/v) Redissolving the honey sample; after passing through a 0.22 um filter membrane, the sample is fed into a small sample injection bottle and is analyzed on a UHPLC-Q-active Orbitrap-MS machine.
2. UHPLC-Q-active Orbitrap-MS data acquisition
(1) Chromatographic conditions
A chromatographic column: agilent Eclipse Plus C18 (3.0X 150 mm, 1.8 um);
mobile phase: phase A: 0.1% aqueous formic acid; phase B: methanol (chromatographic grade, purity 99.9%);
gradient elution procedure: see table 1 below;
column temperature: 40 ℃;
sample introduction volume: 5 mu L of the solution;
flow rate: 0.3 mL/min.
Table 1 gradient elution procedure for honey samples
(2) Conditions of Mass Spectrometry
Scanning mode: full MS/dd-MS 2 Positive ion mode;
scanning range: 60-900 m/z；
Resolution ratio: 70000 (Full MS)/17500 (dd-MS) 2 ）；
Spraying voltage: 3.5 kv;
ion source temperature: 350 ℃;
sheath gas and auxiliary gas: nitrogen with a purity >99.99% with flow rates of 35 arb and 10 arb, respectively.
3. Construction of visual mass spectrum molecular network
Carrying out format conversion on raw data of the original wild osmanthus flower honey sample subjected to UHPLC-Q-active Orbitrap-MS analysis through MSconvert software, and setting parameters: the conversion format is mzXML, the screening condition is Peak packaging, and the MS grade is 1-2 grade; uploading the obtained mzXML data to a GNPS database for analysis, and setting parameters: the cosine fraction threshold is set to 0.7, the minimum matching fragment ion is 6, topK is set to 10, and the mass deviation of the parent and daughter ions is 0.02 Da; and then processing the obtained data result by using Cytoscape software to enable the data result to be visualized, and setting parameters: the model is BioPAX _ STP, the filling color in the Node is black, the shape is circular, the label name is Parent mass, the line shape of the Edge is Solid, the filling color is black, the transparency is 255, and the width is 4.0, and then the visualized mass spectrum molecular network diagram of the wild osmanthus honey is obtained.
The original total ion flowsheet (original. RAW data) of the wild osmanthus honey after the pretreatment steps of water dissolving, extracting, salting out, concentrating and the like and the detection on a UHPLC-Q-active Orbitrap-MS is shown in figure 2.
And then the data is converted into mzXML format data through MSconvert software, and a visualized mass spectrum molecular network diagram of the wild osmanthus honey is formed after GNPS data analysis (total 16173 ion fragments, wherein the number of primary mass spectrums is 2696, and the number of secondary mass spectrums is 13477) and Cytoscape software processing, and is shown in figure 3. The visualized mass spectrum molecular network diagram of the wild osmanthus honey contains 642 edges and 564 nodes (precursor ions), and comprises 38 clusters (the number of the nodes is more than or equal to 2) and 375 single nodes, and 413 connecting nodes are formed.
Example 2
This example provides a method for obtaining a visual mass spectrum molecular network diagram of Chinese bee honey-toosendanin honey, which is produced in Fujian province of China, and the specific method is the same as example 1.
Finally, the original total ion flow diagram (original. RAW data) of the melitensin honey after the above water-dissolving, extracting, salting out, concentrating and other pretreatment steps and UHPLC-Q-active Orbitrap-MS computer detection is shown in figure 4.
And then the data is converted into data in an mzXML format through MSconvert software, and a visualized mass spectrum molecular network diagram of the toosendanin honey is formed after GNPS data analysis (16058 ion fragments in total, wherein the number of primary mass spectrums is 2677, and the number of secondary mass spectrums is 13381) and Cytoscape software processing, and is shown in figure 5. The visualized mass spectrum molecular network diagram of the toosendanin honey contains 478 edges, 436 nodes (precursor ions), 31 clusters (the number of the nodes is more than or equal to 2) and 286 single nodes, and 318 connecting nodes are provided.
Example 3
The embodiment provides a method for acquiring a visualized mass spectrum molecular network diagram of Chinese bee honey-idesia polycarpa honey, wherein the idesia polycarpa honey is produced in Fujian province of China, and the specific method is the same as the embodiment 1.
Finally, the original total ion flow graph (original. RAW data) of the idesia polycarpa honey after the water-soluble, extraction, salting-out, concentration and other pretreatment steps are carried out on the idesia polycarpa honey, and UHPLC-Q-active Orbitrap-MS computer detection is shown in figure 6.
And then the data is converted into mzXML format data through MSconvert software, and a visualized mass spectrum molecular network diagram of the idesia polycarpa honey is formed after GNPS data analysis (16204 ion fragments in total, wherein the number of primary mass spectrums is 2701, and the number of secondary mass spectrums is 13503) and Cytoscape software processing, and is shown in figure 7. The visualized mass spectrum molecular network diagram of idesia polycarpa honey contains 572 sides and 561 nodes (precursor ions), wherein the diagram comprises 49 clusters (nodes are more than or equal to 2) and 329 single nodes, and 379 interconnecting nodes are provided.
The results of the above embodiment show that the visualized mass spectrum molecular network maps of different varieties of Chinese bee honey have specificity, the number of nodes and clusters in each visualized mass spectrum molecular network map have obvious difference, whether the visualized mass spectrum molecular network maps are the same or not can be judged by comparing a plurality of visualized mass spectrum molecular network maps, and whether the tested Chinese bee honey samples belong to the same kind of Chinese bee honey or not can be further judged.
Comparative example 1
The comparison example performs pretreatment on the honey sample in the example 1 and a UHPLC-Q-active Orbitrap-MS data acquisition step, and the specific method is the same as the example 1, but the difference is that in the sample pretreatment step, 2.0 g of wild osmanthus honey is weighed, 10 mL of methanol-water solution (containing 0.1% formic acid) is directly added as an extraction solvent, the mixture is uniformly mixed for 2 min by vortex, the mixture is centrifuged for 10 min at 12000 rpm, 1 mL of supernatant is taken to pass through a 0.22 mu m filter membrane to be fed into a sample vial, and the UHPLC-Q-active Orbitrap-MS is measured on a computer.
The results are shown in FIG. 8, which shows that less compound is eluted under the pretreatment conditions.
Comparative example 2
The comparison example performs pretreatment on the honey sample in the example 1 and UHPLC-Q-active Orbitrap-MS data acquisition steps, the specific method is the same as the example 1, and the difference is only that anhydrous sodium sulfate is used for replacing anhydrous magnesium sulfate in a salting-out step in the sample pretreatment step.
The results are shown in FIG. 9, and it can be seen from the TIC chart that the response of the eluted compounds after salting out by adding anhydrous sodium sulfate is lower than that by adding anhydrous magnesium sulfate.
Comparative example 3
The comparison example pretreats the honey sample in the example 1 and carries out the data acquisition steps of UHPLC-Q-active Orbitrap-MS, the specific method is the same as the example 1, and the difference is that in the data acquisition steps of the UHPLC-Q-active Orbitrap-MS, the mobile phase A is 0.1% formic acid water solution, and the mobile phase B is acetonitrile.
The results are shown in FIG. 10, from which it is clear that acetonitrile has a lower elution capacity than methanol for the sample after the specific pretreatment of the present invention.
Although the invention has been described in detail hereinabove with respect to a general description and specific embodiments thereof, it will be apparent to those skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (5)
1. A method for distinguishing bee honey in different varieties is characterized by comprising the following steps:
(1) pretreatment of a Chinese bee honey sample;
(2) UHPLC-Q-active Orbitrap-MS data acquisition;
(3) constructing a visual mass spectrum molecular network diagram;
(4) if the visual mass spectrum molecular network diagrams of different Chinese bee honey samples are the same, judging that the different Chinese bee honey samples are of the same variety, and if the visual mass spectrum molecular network diagrams of the different Chinese bee honey samples are different, judging that the different Chinese bee honey samples are not of the same variety;
the pretreatment of the step (1) comprises the following steps: dissolving in water, extracting with acetonitrile, salting out and concentrating; the reagents used for salting out are sodium chloride and anhydrous magnesium sulfate;
the volume mass ratio of the water added during water dissolving to the anhydrous magnesium sulfate added during salting out is 1 (0.5-0.7) mL/g; the volume mass ratio of the acetonitrile added during the extraction of the acetonitrile to the sodium chloride added during the salting-out is 1 (0.1-0.15) mL/g;
in the chromatographic conditions for data acquisition in the step (2), the gradient elution procedure is as follows:
in the chromatographic conditions during data acquisition in the step (2), the chromatographic column is a full-porous silica gel-based reversed-phase high performance liquid chromatographic column; the column temperature is 38-42 ℃; the sample injection volume is 3-5 mu L; the flow rate is 0.25-0.35 mL/min.
2. The method according to claim 1, wherein the volume mass ratio of the water added during water dissolution to the anhydrous magnesium sulfate added during salting out is 1:0.6 mL/g;
and/or the volume mass ratio of the acetonitrile added during the extraction of the acetonitrile to the sodium chloride added during the salting-out is 1:0.1 mL/g;
and/or, in the chromatographic conditions during the data acquisition of the step (2), the chromatographic column is Agilent Eclipse Plus C18; the column temperature was 40 ℃; the sample injection volume is 5 mu L; the flow rate was 0.3 mL/min.
3. The method of claim 1 or 2, wherein the mass spectrometry conditions at the time of data acquisition of step (2) are: scanning mode: full MS/dd-MS 2 Positive ion mode; scanning range: 60-900m/z(ii) a Resolution ratio: 70000, dd-MS in Full MS mode 2 17500 in mode; spraying voltage: 3.5 kv; ion source temperature: 350 ℃; sheath gas and auxiliary gas: purity of>99.99% nitrogen at 35 arb and 10 arb, respectively.
4. The method according to claim 1 or 2, wherein the specific operation method of step (3) is as follows:
performing format conversion on raw data of a Chinese bee honey sample subjected to UHPLC-Q-active Orbitrap-MS analysis through MSconvert software, and setting parameters: the conversion format is mzXML, the screening condition is Peak packaging, and the MS grade is 1-2 grade;
uploading the obtained mzXML data to a GNPS database for analysis, and setting parameters: the cosine fraction threshold is 0.7, the minimum matching fragment ion is 6, the topK is 10, and the mass deviation of the parent ion and the daughter ion is 0.02 Da;
and then, processing the obtained data analysis result by using Cytoscape software to visualize the data analysis result.
5. Use of the method of any one of claims 1-4 for variety discrimination and quality control of Chinese bee honey.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210812726.XA CN114878724B (en) | 2022-07-12 | 2022-07-12 | Method for distinguishing Chinese bee honey of different varieties and application |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210812726.XA CN114878724B (en) | 2022-07-12 | 2022-07-12 | Method for distinguishing Chinese bee honey of different varieties and application |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114878724A true CN114878724A (en) | 2022-08-09 |
| CN114878724B CN114878724B (en) | 2022-10-14 |
Family
ID=82683147
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210812726.XA Active CN114878724B (en) | 2022-07-12 | 2022-07-12 | Method for distinguishing Chinese bee honey of different varieties and application |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114878724B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115146131A (en) * | 2022-09-01 | 2022-10-04 | 中国农业科学院蜜蜂研究所 | Method for screening target active natural product and application thereof |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106855552A (en) * | 2017-01-23 | 2017-06-16 | 中国农业科学院蜜蜂研究所 | A kind of method for differentiating honey types using non-target metabonomic technology |
| US20180011990A1 (en) * | 2016-07-05 | 2018-01-11 | University Of Kentucky Research Foundation | Method and system for identification of metabolites |
| CN110531017A (en) * | 2019-09-12 | 2019-12-03 | 北方民族大学 | A kind of method of molecular networkization analysis root bark of Chinese wolf-berry compound structure |
| CN111398500A (en) * | 2020-03-19 | 2020-07-10 | 中国农业科学院蜜蜂研究所 | Method for identifying Chinese bee honey and Italian bee honey |
-
2022
- 2022-07-12 CN CN202210812726.XA patent/CN114878724B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180011990A1 (en) * | 2016-07-05 | 2018-01-11 | University Of Kentucky Research Foundation | Method and system for identification of metabolites |
| CN106855552A (en) * | 2017-01-23 | 2017-06-16 | 中国农业科学院蜜蜂研究所 | A kind of method for differentiating honey types using non-target metabonomic technology |
| CN110531017A (en) * | 2019-09-12 | 2019-12-03 | 北方民族大学 | A kind of method of molecular networkization analysis root bark of Chinese wolf-berry compound structure |
| CN111398500A (en) * | 2020-03-19 | 2020-07-10 | 中国农业科学院蜜蜂研究所 | Method for identifying Chinese bee honey and Italian bee honey |
Non-Patent Citations (4)
| Title |
|---|
| CHRISTINE M.FISHER 等: "Data processing strategies for non-targeted analysis of foods using liquid chromatography/high-resolution mass spectrometry", 《TRAC TRENDS IN ANALYTICAL CHEMISTRY》 * |
| 田尉婧 等: "基于质谱的蛋白质组学技术在食品真伪鉴别及品质识别方面的应用", 《色谱》 * |
| 程桃芳 等: "LC-MS/MS分子网络及其对中药研究的启发", 《中草药》 * |
| 谢博 等: "UPLC-Q-Exactive四极杆-静电场轨道阱高分辨质谱联用鉴别掺假蜂蜜", 《食品工业科技》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115146131A (en) * | 2022-09-01 | 2022-10-04 | 中国农业科学院蜜蜂研究所 | Method for screening target active natural product and application thereof |
| CN115146131B (en) * | 2022-09-01 | 2022-11-18 | 中国农业科学院蜜蜂研究所 | Method for screening target active natural product and application thereof |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114878724B (en) | 2022-10-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105574474A (en) | Mass spectrometry information-based biological characteristic image identification method | |
| CN111220763B (en) | Application of high-content DSM as characteristic marker of linden honey | |
| CN106645538B (en) | A kind of method for differentiating the acacia honey place of production using non-target metabonomic technology | |
| JP2003533672A (en) | Methods for untargeted complex sample analysis | |
| CN111721857A (en) | Method for identifying litchi varieties by using extensive targeted metabonomics technology | |
| CN114878724B (en) | Method for distinguishing Chinese bee honey of different varieties and application | |
| CN108535375B (en) | Method for analyzing maca marked metabolites based on LC-MS (liquid chromatography-mass spectrometry) metabonomics | |
| CN106855552A (en) | A kind of method for differentiating honey types using non-target metabonomic technology | |
| CN110702832B (en) | Application of high-content 4-hydroxyquinoline as characteristic marker of jujube honey | |
| CN112162054B (en) | Method for evaluating authenticity of sophora moorcroftiana honey | |
| Pluskal et al. | Metabolomics data analysis using MZmine | |
| CN108588210A (en) | Include hepatic injury biomarker, method and the application of biological micromolecule and gene | |
| CN111398499A (en) | Application of 3-amino-2-naphthoic acid in identifying apis cerana honey and apis mellifera honey | |
| CN107192770B (en) | Analytical method for identifying vitex negundo honey and syrup adulterated vitex negundo honey | |
| CN114113381B (en) | Syngnathus schutz characteristic polypeptide, application thereof and method for identifying comfortable Syngnathus schutz | |
| CN111337605B (en) | Method for evaluating authenticity of lotus bee pollen | |
| CN111487353B (en) | Application of high-content eupatorium adenophorum flavone-4', 7-diglucoside as characteristic marker of rose bee pollen | |
| CN106526040B (en) | Differentiate the method for fresh cocoon/dried cocoon raw silk based on GC or GC-MS technologies | |
| Vizcaino et al. | Secondary metabolic pathway-targeted metabolomics | |
| CN112285219B (en) | Authenticity evaluation method for acacia honey and application of authenticity evaluation method in adulteration identification | |
| CN115015462A (en) | Citrus huanglongbing detection method based on LC-MS (liquid chromatography-mass spectrometry) non-targeted analysis | |
| Selegato et al. | Application of feature-based molecular networking and MassQL for the MS/MS fragmentation study of depsipeptides | |
| CN115611840B (en) | Cold winter honey element and preparation method and application thereof | |
| CN102662011B (en) | Use of spermidine as anoxia preconditioning biomarker | |
| WO2019200946A1 (en) | Gc-q-orbitrap-based electronic identification database of pesticide compounds in edible agricultural products and detection method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |