CN114778653A - Metabolism marker combination for tracing mutton producing area, screening method and application - Google Patents
Metabolism marker combination for tracing mutton producing area, screening method and application Download PDFInfo
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Abstract
The invention relates to the technical field of food authenticity detection and identification, in particular to a metabolic marker combination for mutton production area tracing, a screening method and application. The metabolic marker combination for tracing the mutton producing area is obtained by combining metabonomics data and a chemometrics method, provides scientific basis for mutton producing area tracing research, authenticity identification and quality safety control, has strong universality and is suitable for popularization and application. The mutton producing area traceability detection method provided by the invention is simple, rapid, accurate and stable, and is applied to the mutton producing area traceability field by a non-target metabonomics technology based on a real-time direct analysis ion source and quadrupole-flight time high-resolution mass spectrometry combined technology for the first time.
Description
Technical Field
The invention relates to the technical field of food authenticity detection and identification, in particular to a metabolic marker combination for mutton production area tracing, a screening method and application.
Background
With the improvement of economic living standard, the quality requirement of consumers on agricultural products is higher and higher. Mutton, as an excellent source of animal protein, is becoming increasingly popular with consumers. The geographical marked mutton and domestic and foreign high-quality mutton have excellent quality and specific flavor, so that the purchasing willingness of consumers is higher. The first 100 geotagged products in the european mutual identity catalog included mutton. Most consumers who buy mutton are most concerned about the authenticity information of agricultural products, because the authenticity information not only relates to the price of the mutton, but also relates to the quality and safety of the mutton. Authenticity tags can affect the consumer's willingness to purchase mutton, particularly high value geotagged mutton. Under the stimulation of huge economic profit, illegal merchants frequently carry out mutton production and counterfeiting and are good. The method mainly comprises the step of putting low-grade mutton or mutton in other producing areas on the market after being labeled with geographical marking mutton or high-quality mutton. This behavior has already constituted food fraud. Food fraud not only causes safety accidents to agricultural products, but also causes significant economic losses to the agricultural animal husbandry. The global economic loss caused by food fraud is about $ 150 billion. Therefore, ensuring the authenticity of agricultural products is an urgent problem to be solved at present. Traceability systems are important means of ensuring the authenticity of agricultural products, and their main purpose is to provide continuous monitoring of agricultural products throughout the supply chain, which can be summarized as "from farm to dining table". Therefore, in order to ensure the effectiveness of the tracing system, an agricultural product origin tracing detection technology must be developed, which is beneficial to realizing that the government strengthens the supervision of agricultural products in the market, quickly implements the recall of products after the safety accident of the agricultural products occurs, maintains the rights and benefits of consumers, can realize the brand protection of high-quality products such as geographical signs and the like, and provides technical support for the international market for the Chinese geographical sign products.
At present, the detection technology for tracing the origin of mutton mainly comprises a stable isotope method, a mineral element method, a nuclear magnetic resonance spectroscopy method, an infrared spectrum fingerprint method, an organic component fingerprint analysis method, other methods (a molecular biological method and sensory analysis) and the like. However, these conventional mutton origin tracing detection technologies have the disadvantages of complex pretreatment or large sampling amount, and there is an urgent need to develop a quick and reliable mutton origin tracing method with small sampling amount to maintain fair market and protect consumers' rights.
In view of this, the invention is particularly proposed.
Disclosure of Invention
The invention aims to provide a metabolic marker combination which can be used for tracing mutton in different producing areas so as to realize low sampling amount and simple treatment and realize quick tracing detection of mutton producing areas.
Another object of the present invention is to provide a method for screening the metabolic marker composition, which can optimize a metabolic marker combination for mutton of different origin to obtain a more suitable metabolic marker combination.
The invention also aims to provide a detection method for tracing mutton producing areas by using the metabolic marker combination, wherein the metabolic marker combination used in the detection method is obtained based on non-targeted metabonomics screening, has better universality and is suitable for popularization.
The invention is realized by the following steps:
in a first aspect, the present invention provides a metabolic marker combination for tracing mutton origins, which is characterized in that the metabolic marker combination comprises at least 3 of the following compounds:
l-citrulline, 3-aminoisobutyric acid, 1, 5-diaminopentane, 3-hydroxyaspartic acid, spermine, palmitic acid, glyceric acid, nervonic acid, tetracosanoic acid, alpha-linolenic acid, (9Z,11E) -13-oxo-9, 11-octadecadienoic acid, behenic acid, glycerol monolinoleate, glycerol monooleate, cholesterol, dihydrothymine, 2-deoxyguanosine, 2 '-deoxycytidine-5' -monophosphate, 2 '-deoxyadenosine-5' -monophosphate, glycolipids, L-carnosine, anserine, L-tyrosyl-tyrosine, nicotinamide, nicotinic acid or vitamin D3.
In a second aspect, the invention provides a screening method for a mutton producing area tracing metabolic marker combination according to the foregoing embodiment, the screening method includes using a real-time direct analysis ion source and a quadrupole-time-of-flight mass spectrometer to perform metabonomic data acquisition on mutton samples from different producing areas, performing chemometric analysis on the metabonomic data, establishing a discrimination model, extracting characteristic information to obtain a difference factor after significant difference analysis, and comparing the difference factor with a metabonomic database to obtain a metabolic marker combination with difference.
In alternative embodiments, the samples of mutton from different origins include Ningxia Tan mutton samples, Gansu black goat mutton samples, New Zealand lamb mutton samples or Anhui Hu mutton samples.
In an optional embodiment, the mutton samples from different producing areas are crushed and extracted by an extracting solution to obtain a metabonomics sample, and the metabonomics sample is subjected to data acquisition by using a real-time direct analysis ion source and a quadrupole-time-of-flight mass spectrometer.
Preferably, the extraction liquid comprises a polar extraction liquid and a non-polar extraction liquid.
Preferably, the polar extraction solution contains at least one of water, methanol or acetonitrile;
preferably, the nonpolar extracting solution contains at least one of n-hexane, cyclohexane, petroleum ether or ethyl acetate;
preferably, the polar extracting solution is water, and the nonpolar extracting solution is n-hexane;
preferably, the volume ratio of the water to the n-hexane is 1-1.2: 1.
The screening method according to claim 2, wherein the detection parameters of the real-time direct analysis ion source are: the gas source is selected from helium or nitrogen, the gas temperature is 400-500 ℃, the carrier gas pressure is 0.50-0.55 Mpa, and the sample injection speed is 1-2 mm/s.
In an alternative embodiment, the detection parameters of the quadrupole-time-of-flight mass spectrometer are as follows: the temperature of a transmission line is 150 ℃, the cluster removing voltage is 60V, the collision energy is 10V, a metabonomics sample is analyzed by adopting a positive ion mode and a negative ion mode, the primary collection quality range is 50-1000, the secondary collection quality range is 50-1000 by adopting an IDA mode.
In an alternative embodiment, the real-time direct analysis ion source is combined with a quadrupole-time-of-flight mass spectrometer, and the air mass calibration method is used to calibrate the instrument, wherein the [ M + H ] of the reference ion is used for calibration]+Peak locations include 149.02333, 279.15909, 391.28429, 462.14659, 536.16538, and 610.18417.
In a third aspect, the present invention provides an application of the metabolic marker combination described in the foregoing embodiment or the screening method described in the foregoing embodiment in tracing the production area of mutton.
In a fourth aspect, the invention provides a detection method for tracing the origin of mutton, which is characterized in that a discrimination model is established by applying the metabolic marker combination described in the embodiment, the mutton sample to be detected is subjected to metabonomic detection, and the metabonomic detection result is substituted into the discrimination model to trace the origin of the mutton; the discriminant model comprises at least one of a PCA model, an LDA model, a PLS-DA model or an OPLS-DA model.
In an alternative embodiment, the discriminant models are LDA and OPLS-DA models, which satisfy the condition R2Y > Q2> 0.50.
Preferably, a quality control sample is used in the detection process, wherein the quality control sample is a mixed solution of all samples to be detected in equal volume.
The invention has the following beneficial effects:
the metabolic marker combination for tracing the mutton producing area is obtained by combining metabonomics data and a chemometrics method, provides scientific basis for mutton producing area tracing research, authenticity identification and quality safety control, has strong universality and is suitable for popularization and application.
The mutton producing area traceability detection method provided by the invention is simple, rapid, accurate and stable, and is applied to the mutton producing area traceability field by a non-target metabonomics technology based on a real-time direct analysis ion source and quadrupole-flight time high-resolution mass spectrometry combined technology for the first time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
FIG. 1 is a discrimination model diagram of a polar metabolite OPLS-DA in a positive ion mode;
FIG. 2 is a diagram of a discrimination model of a polar metabolite OPLS-DA in a negative ion mode;
FIG. 3 is a model diagram of discrimination of a nonpolar metabolite OPLS-DA in a positive ion mode;
FIG. 4 is a discrimination model diagram of a nonpolar metabolite OPLS-DA in a negative ion mode.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. The examples, in which specific conditions are not specified, were conducted under conventional conditions or conditions recommended by the manufacturer. The reagents or instruments used are not indicated by the manufacturer, and are all conventional products available commercially.
In a specific embodiment, in a first aspect, the present invention provides a metabolic marker combination for mutton origin tracing, which is characterized in that the metabolic marker combination comprises at least 3 of the following compounds:
l-citrulline, 3-aminoisobutyric acid, 1, 5-diaminopentane, 3-hydroxyaspartic acid, spermine, palmitic acid, glyceric acid, nervonic acid, tetracosanoic acid, alpha-linolenic acid, (9Z,11E) -13-oxo-9, 11-octadecadienoic acid, behenic acid, glycerol monolinoleate, glycerol monooleate, cholesterol, dihydrothymine, 2-deoxyguanosine, 2 '-deoxycytidine-5' -monophosphate, 2 '-deoxyadenosine-5' -monophosphate, glycolipids, L-carnosine, anserine, L-tyrosyl-tyrosine, nicotinamide, nicotinic acid or vitamin D3.
The method screens 26 compounds from a plurality of compounds to be used for carrying out source tracing analysis on mutton from different producing areas, and the original verification rate can reach 95.8 percent and the cross verification rate can reach over 86.7 percent through verification.
Different metabolic marker combinations can be suitable for tracing mutton from different producing areas, different metabolic marker combinations can be formed by selecting different numbers of the metabolic markers, and low-cost and efficient tracing detection is realized.
In a second aspect, the invention provides a screening method for a mutton source tracing metabolic marker combination according to the foregoing embodiment, the screening method includes collecting metabonomic data of mutton samples from different sources by using a real-time direct analysis ion source and a quadrupole-time-of-flight mass spectrometer, performing chemometric analysis on the metabonomic data, establishing a discrimination model, extracting characteristic information to obtain a difference factor after significant difference analysis, and comparing the difference factor with a metabonomic database to obtain a metabolic marker combination with difference.
The invention is applied to the field of mutton place tracing by using a non-targeted metabonomics technology based on a real-time direct analysis ion source and quadrupole-time-of-flight high-resolution mass spectrometry combined technology for the first time, and successfully obtains a combination containing at most 26 metabolic markers.
In alternative embodiments, the samples of mutton from different origins include Ningxia Tan mutton samples, Gansu black goat meat samples, New Zealand lamb meat samples or Anhui Hu mutton samples. The existing tracing method can only trace the source of single type of mutton sample, and the traditional tracing method such as a stable isotope method, a mineral element method, organic component fingerprint analysis and the like is time-consuming and labor-consuming. The mutton origin tracing detection method provided by the invention is simple, rapid, accurate and stable, and is applied to the mutton origin tracing field by a non-targeted metabonomics technology based on a real-time direct analysis ion source and quadrupole-flight time high-resolution mass spectrometry combined technology for the first time.
In an optional embodiment, after the mutton samples from different producing areas are crushed, the mutton samples are extracted through an extracting solution to obtain a metabonomics sample, and a real-time direct analysis ion source and a quadrupole-time-of-flight mass spectrometer are used for carrying out data acquisition on the metabonomics sample.
Preferably, the extraction liquid comprises a polar extraction liquid and a non-polar extraction liquid.
Preferably, the polar extraction solution contains at least one of water, methanol or acetonitrile;
preferably, the nonpolar extracting solution contains at least one of n-hexane, cyclohexane, petroleum ether or ethyl acetate;
preferably, the polar extracting solution is water, and the nonpolar extracting solution is n-hexane;
preferably, the volume ratio of the water to the n-hexane is 1-1.2: 1.
In an alternative embodiment, the detection parameters of the real-time direct analysis ion source are: the gas source is selected from helium or nitrogen, the gas temperature is 400-500 ℃, the carrier gas pressure is 0.50-0.55 Mpa, and the sample injection speed is 1-2 mm/s.
In an alternative embodiment, the detection parameters of the quadrupole-time-of-flight mass spectrometer are: the temperature of a transmission line is 150 ℃, the cluster removing voltage is 60V, the collision energy is 10V, a metabonomics sample is analyzed by adopting a positive ion mode and a negative ion mode, the primary collection quality range is 50-1000, the secondary collection quality range is 50-1000 by adopting an IDA mode.
In an alternative embodiment, the real-time direct analysis ion source is combined with a quadrupole-time-of-flight mass spectrometer, and the air mass calibration method is used to calibrate the instrument, wherein the [ M + H ] of the reference ion is used for calibration]+Peak locations include 149.02333, 279.15909, 391.28429, 462.14659, 536.16538, and 610.18417.
Typically, a real-time direct analysis ion source is used in conjunction with quadrupole-time-of-flight mass spectrometryThe data obtained by the instrument detection needs to be preprocessed, and the preprocessing comprises common data processing means such as baseline correction, peak extraction, peak correction, normalization and normalization. Performing OPLS-DA analysis on the obtained initial differential compounds, and screening out VIP value>1. And the remaining compounds were analyzed by Kruskal-Wallis H test to screen for P-values<0.01 of a compound. Analyzing the detection data of samples from different producing areas in pairs to screen out maximum difference multiple/minimum difference multiple FC(Max/min)>2 as a differentiating factor.
In a third aspect, the present invention provides the use of the metabolic marker combination of the previous embodiments or the screening method of the previous embodiments in tracing the production area of mutton.
In a fourth aspect, the invention provides a detection method for tracing the source of a mutton producing area, which is characterized in that a discrimination model is established by applying the metabolic marker combination described in the embodiment, the metabonomics detection is carried out on a mutton sample to be detected, and the metabonomics detection result is substituted into the discrimination model to trace the source of the mutton producing area; the discriminant model comprises at least one of a PCA model, an LDA model, a PLS-DA model or an OPLS-DA model.
In an alternative embodiment, the discriminant models are LDA and OPLS-DA models, which satisfy the condition R2Y > Q2> 0.50.
The term "PCA model" is an abbreviation of principal component analysis (principal component analysis), which is a multidimensional data statistical analysis method without supervision of pattern recognition. The model can reveal the internal structure among a plurality of variables through a few principal components, thereby simplifying the analysis process. The method for decomposing the variables is to perform dimension reduction processing on the data, thereby realizing the removal of noise and redundancy and revealing a simple structure hidden behind complex data.
The term "LDA model" is short for a linear discriminant analysis model (linear discriminant analysis), and the model ensures that data of the same category are close to each other and data of different categories are separated as much as possible after projection by projecting historical data, and generates a linear discriminant model to separate and predict newly generated data.
The term "PLS-DA model" is short for partial least squares discriminant analysis model (partial least-squares discriminant analysis). The model is a partial least square method analysis method with a supervision mode, and when data are analyzed, the grouping relation of samples is known, so that the characteristic variables of each group can be better selected and distinguished, and the relation between the samples is determined. The model uses a partial least square regression method, establishes a regression model while reducing the dimension of data, and performs discriminant analysis on a regression result.
The term "OPLS-DA model" is a short term of the full-name orthogonal partial least squares discriminant analysis (orthogonal partial least squares-discriminant analysis), combines an Orthogonal Signal Correction (OSC) method and a PLS-DA method, can decompose an X matrix (variable observation matrix) into two types of information which are related to and unrelated to a Y matrix (sample class attribution matrix), screens a differential variable by removing irrelevant differences, filters noise which is unrelated to classification information, and improves the analytic ability and effectiveness of the model.
Preferably, a quality control sample is used in the detection process, wherein the quality control sample is a mixed solution of all samples to be detected in equal volume.
And for the quality control samples, analyzing the detection rate and the relative standard deviation of the total metabolite spectrum data, deleting the compounds with the detection rate of less than or equal to 80% or the relative standard deviation of more than or equal to 30% in the same group, and filling the rest missing values by adopting 1/10 which is the minimum value of the characteristic value in all samples.
The features and properties of the present invention are described in further detail below with reference to examples.
Example 1
The embodiment provides a method for screening a metabolic marker combination, which includes the steps of adopting a real-time direct analysis ion source and a quadrupole-time-of-flight mass spectrometer (DART-TOFMS) to conduct metabonomic data acquisition on mutton sample metabolites in a four-producing area, conducting chemometric analysis on detection data, extracting characteristic information, establishing an analysis model, and determining the differential metabolites of the mutton in the four-producing area, wherein the method comprises the following steps:
1. mutton sample source
According to the embodiment, Ningxia Tan mutton samples, Gansu black goat meat samples, New Zealand lamb meat samples and Anhui Hu mutton samples are selected for metabolic marker screening, 30 lambs in four producing areas and 6-8 months old lambs in total are collected respectively, and detailed information is shown in Table 1. The mixed breed lambs and their ewes were fed together until they reached weaning weight. The butterfly raft muscle of the lamb is selected as the sampling part. The samples were placed in a-20 ℃ freezer for use.
Table 1 mutton sample information in example 1
2. Metabolic sample preparation
Weighing 0.3g of the mutton sample into a 2mL centrifuge tube, adding 1 ceramic proton and 1.2mL of water, homogenizing for 40s (60Hz) by using a high-flux tissue grinder, standing for 4min, centrifuging (14680rpm, 1min), and taking supernatant (polar metabolite) to be detected; adding 1mL of n-hexane into the residue, homogenizing with a high-throughput tissue grinder (60HZ) for 40s, standing for 4min, centrifuging (14680rpm, 2min), and collecting the supernatant (nonpolar metabolite) of n-hexane. Quality control samples (QC) are prepared by mixing all samples to be tested in the same volume of liquid, and the QC samples are used to monitor the state of the instrument and the stability of data during the analysis of the instrument.
Wherein, direct analysis of ion source (DART) ion source parameters in real time: helium temperature of 400 deg.C (aqueous phase) and 500 deg.C (organic phase), carrier gas (He and N)2) The pressure is 0.50-0.55 MPa, and the moving speed is 1.5 mm/s.
Wherein, the parameters of the high resolution quadrupole-time of flight mass spectrometer (TOFMS): the ion source is an electrospray ion source (ESI), the transmission line temperature is 150 ℃, the declustering voltage (DP) is 60V, the Collision Energy (CE) is 10V, and a mutton sample is analyzed in a positive ion mode and a negative ion mode. The first-stage collection mass range is 50-1000 Da, the second-stage collection is carried out in an IDA mode, the mass range is 50-1000 Da, and dynamic background subtraction is carried out.
Wherein, the ion source and quadrupole-time-of-flight mass spectrometer (DART-TOFMS) are directly analyzed in real time, and the reference ion [ M + H ] used for calibration is calibrated by adopting an air mass correction method]+Mainly 149.02333, 279.15909, 391.28429, 462.14659, 536.16538, 610.18417 and the like.
3. Differential factor screening
The raw data were pre-processed using msdail 4.60 software, including baseline correction, peak extraction, peak correction, normalization and normalization, to obtain the relative peak area and retention time for all metabolites, and the fold of maximum/minimum difference results for each compound. The processed data were imported into Excel and the total metabolite profile data were analyzed for detection rate and relative standard deviation based on quality control group samples (QC), with the same group of compounds with detection rate < 80% or relative standard deviation > 30% being deleted and the remaining missing values being filled with 1/10, the minimum of this characteristic value among all samples.
And (3) importing the data into SIMCA14.1 software for visual analysis, and carrying out OPLS-DA analysis on the obtained initial differential compound to judge whether the four groups of samples have differences.
As shown in fig. 1-4, the four groups of samples had better degrees of separation, with all samples falling within the 95% confidence interval. In the model, R2X (cum) and R2Y (cum) respectively represent the interpretability of the model on X and Y matrixes, Q2Y (cum) represents the forecasting capability of the model, and when R2X is smaller, R2Y and Q2Y are larger and approach to 1, the model is more stable and reliable. In the established OPLS-DA model, four groups of least square method discrimination models Q2 are all larger than 0.5, the intercept of the displacement test (validation test) Q2 for 200 times is all smaller than 0.05, and the P value of the analysis of variance (CV-ANOVA) of interactive verification is all smaller than 0.05, which shows that no overfitting (Over-fitting) phenomenon occurs in the OPLS-DA model, namely the model is relatively stable and has good prediction capability. Compounds with VIP value >1 were screened. The pre-processed data were analyzed by Kruskal-Wallis H test with the aid of SPSS22.0 software and screened for compounds with a P-value < 0.01. And (4) carrying out pairwise difference multiple analysis on the four groups of data, and screening out the compound with maximum difference multiple/minimum difference multiple FC (Max/min) > 2. Compounds satisfying the above bars were used as differential factors.
4. Metabolic marker combinatorial screening
Performing molecular formula matching on the accurate m/z value through MSDIAL software, and performing inferential identification on ions; then, MS/MS analysis was performed, and the obtained MS2 fragments were compared with fragment information in public metabolome databases (Massbank, GNPS, HMDB, and FOODB) using MS-FINDER 3.50software to confirm the compounds. By identifying the difference factors, 26 difference markers are identified, and the detailed information is shown in table 2.
TABLE 2 Metabolic marker combinations obtained in example 1
Example 2
In this example, the metabolic marker combination provided in example 1 is used to perform traceability analysis on four groups of samples in example 1, and the combination of 26 compounds obtained in example 1 is used to perform differential analysis on the four sources of mutton transcripts selected in example 1.
The LDA model constructed with 26 compounds for different sample sets was as follows:
anhui sample group:
Y1＝-31.192+X1×0.003+X2×0.00002454+X3×0+X4×0.001+X5×0+X6×0-X7×0.002+X8×0.001+X9×0.00003464+X10×0.002-X11×0.00001767+X12×0.00002281+X13×0.000009942+X14×0.002+X15×0+X16×0.014-X17×0.001+X18×0.003+X19×0+X20×0+X21×0.00004071+X22×0.001-X23×0.00003265+X24×0+X25×0.001+X26×0.002。
new zealand sample set:
Y2＝-43.451+X1×0.002+X2×0.000001113+X3×0+X4×0.001+X5×0+X6×0-X7×0.001+X8×0.001+X9×0.00006291+X10×0.001-X11×0.00003369+X12×0.00002101+X13×0.000007056+X14×0.005-X15×0.001+X16×0.017-X17×0.001+X18×0.008+X19×0-X20×0.001-X21×0.00003491+X22×0.002+X23×0+X24×0.001+X25×0.001+X26×0.001。
ningxia sample group:
Y3＝-36.45+X1×0.002+X2×0.00003934+X3×0+X4×0.001+X5×0-X6×0.00001638-X7×0.002+X8×0.001+X9×0.00002869+X10×0.001-X11×0.00005158+X12×0.00001936+X13×0.00001976+X14×0.005-X15×0.001+X16×0.017-X17×0.001+X18×0.002+X19×0+X20×0+X21×0+X22×0.001+X23×0.00005476+X24×0.001+X25×0.001+X26×0.001。
gansu sample group:
Y4＝-16.81+X1×0.001+X2×0+X3×0+X4×0.001+X5×0+X6×0.001-X7×0.002+X8×0+X9×0.00001074+X10×0-X11×0.00001789+X12×0.000004173+X13×0.00001347+X14×0.002+X15×0+X16×0.01-X17×0.001+X18×0.001+X19×0.00006599-X20×0.00008123+X21×0+X22×0.002+X23×0+X24×0+X25×0+X26×0.001。
R2Y and Q2 of the OPLS-DA model of the polar positive ion mode are 0.969 and 0.736 respectively;
the polar negative ion mode OPLS-DA model has R2Y and Q2 of 0.978 and 0.654, respectively;
R2Y and Q2 of the OPLS-DA model of the non-polar positive ion mode are 0.978 and 0.784 respectively;
the OPLS-DA model of the non-polar negative ion mode has R2Y and Q2 of 0.949 and 0.799, respectively.
Wherein X1-X26 respectively correspond to 26 compounds in four samples and directly analyze the detection results of the ion source and the quadrupole-time-of-flight mass spectrometer in real time.
The LDA classification results of the four-produced-area mutton based on the differential marker metabolites show that the primary validation rate is 95.8% and the cross validation rate is 86.7%.
According to the method, origin tracing is carried out on mutton in four production areas by combining non-targeted metabonomics with chemometrics analysis; screening for significant differential p-value<0.01, VIP value>1 and fold of maximum difference/fold of minimum difference FC in fold of pairwise difference analysis(Max/min)>2 as a differentiating factor. And 26 differential marker metabolites were identified by alignment to the public metabolite database. And the four groups of samples have better separation degree by carrying out OPLS-DA analysis on the metabolites with the difference markers. The LDA classification result of the mutton of the four-generation area based on the differential marker metabolites shows that the original validation rate is 95.8 percent, and the cross validation rate is 86.7 percent. Therefore, the method can realize the rapid and effective tracing of the mutton produced in four seasons.
Comparative example
The comparative example was a source tracing analysis of the same sample as above using fatty acid analysis. The specific method comprises the following steps: 0.2000g of the lyophilized sample was placed in a digestion tube. 4mL of chloroacetyl in methanol (1:10), 1mL of n-hexane, 1mL of methyl undecanoate internal standard solution (1mg/mL, diluted with methanol to constant volume) were added, and the mixture was subjected to 80 ℃ water bath for 2.5 hours. After cooling to room temperature, 5mL of 7% K was added2CO3An aqueous solution. Centrifuging at 1000 rpm for 1 min. Taking the supernatant into a sample introduction bottle. The determination of fatty acids was carried out by gas chromatography with FID detector (GC,7890A, Agilent, America). The separation of the individual fatty acids was carried out using a BD-23(60 m.times.0.25 mm,0.25 μm, Agilent) column. Fatty acid methyl ester standards (n-hexane diluted to volume) were used to determine the retention time of each fatty acid. The 22 fatty acids measured were C10:0, C12:0, C14:0, C14:1, C15:0, C15:1, C16:0, C16:1, C17:0, C17:1, C18:0, C18:1n9C, C18:2n6t, C18:2n6C, C18:3n3, C20:0, C20:1, C20:2, C20:3n6, C20:4n6, C20:5n3, C24:0, respectively. The carrier gas was nitrogen and the flow rate was 2 mL/min. The injection volume was 1. mu.L. The split ratio was 1: 29.5. The temperatures of the injection port and the detector were 270 ℃ and 280 ℃. The initial column temperature was 100 ℃ and held for 3 min. The temperature was then raised to 170 ℃ at 20 ℃/min and held for 10 min. The temperature was then increased to 200 ℃ at 4 ℃/min and held for 5 min. Then, the temperature was increased to 230 ℃ at 2 ℃/min and maintained for 8 min. It takes 52min altogether. Qualitative analysis was performed by comparing the retention time of fatty acid methyl esters to each standard sample. The fatty acid content was calculated from an internal standard of methyl undecanoate, finally converted to mass percent (w/w,%) of total fatty acids, and taking into account appropriate correction factors. Table 3 is the results of fatty acid analysis in the four-produced mutton samples. The data of the fatty acid analysis method are linearly judged, and the result shows that the original validation rate is 93.3%, and the cross validation rate is 83.3%.
TABLE 3 average mass percent and standard deviation of fatty acids in four-produced mutton samples
Note: data represent mean and standard deviation of fatty acids; the difference was significant (p < 0.05). SFA: saturated fatty acids; MUFA: a monounsaturated fatty acid; PUFA: a polyunsaturated fatty acid.
The original validation rate of linear discrimination based on differential marker metabolites is 95.8 percent, the cross validation rate is 86.7 percent, and the original validation rate and the cross validation rate are 93.3 percent and 83.3 percent respectively higher than those of the linear discrimination based on the traditional fatty acid analysis method. The method does not need complex pretreatment steps, and is simple and quick. In addition, compared with a gas chromatograph in a fatty acid analysis method which needs 52min to analyze a sample, the real-time direct analysis ion source and the quadrupole-time-of-flight mass spectrometer can analyze a sample only in 12s, so that the time is greatly saved, the efficiency is improved, and the method is very suitable for analyzing a large number of samples. Therefore, compared with the traditional fatty acid analysis method, the method disclosed by the invention has the advantages of simplicity, rapidness, high efficiency, accuracy and the like, and provides a new idea for identifying the authenticity of mutton.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A metabolic marker combination for tracing mutton origins, which is characterized by comprising at least 1 of the following compounds:
l-citrulline, 3-aminoisobutyric acid, 1, 5-diaminopentane, 3-hydroxyaspartic acid, spermine, palmitic acid, glyceric acid, nervonic acid, tetracosanic acid, alpha-linolenic acid, (9Z,11E) -13-oxo-9, 11-octadecadienoic acid, behenic acid, glycerol monolinoleate, glycerol monooleate, cholesterol, dihydrothymine, 2-deoxyguanosine, 2 '-deoxycytidine-5' -monophosphate, 2 '-deoxyadenosine-5' -monophosphate, glycolipids, L-carnosine, anserine, L-tyrosyl-tyrosine, nicotinamide or nicotinic acid, or vitamin D3.
2. The method for screening the metabolic marker combination according to claim 1, wherein the screening method comprises the steps of collecting metabonomic data of mutton samples from different origins by using a real-time direct analysis ion source and a quadrupole-time-of-flight mass spectrometer, carrying out chemometric analysis on the metabonomic data, establishing a discrimination model, extracting characteristic information to obtain a difference factor after significant difference analysis, and comparing the difference factor with a metabonomic database to obtain the metabolic marker combination with difference.
3. The screening method according to claim 2, wherein the mutton samples of different origins comprise Ningxia Tan mutton samples, Gansu black goat meat samples, New Zealand lamb meat samples or Hu sheep meat samples of Anhui province.
4. The screening method according to claim 2, wherein the mutton samples from different producing areas are crushed and extracted by extracting solution to obtain metabonomics samples, and the metabonomics samples are subjected to data acquisition by using a real-time direct analysis ion source and quadrupole-time-of-flight mass spectrometer combined instrument;
preferably, the extraction liquid comprises a polar extraction liquid and a non-polar extraction liquid;
preferably, the crushed mutton sample is extracted by using a polar extracting solution and a non-polar extracting solution in sequence;
preferably, the polar extraction solution contains at least one of water, methanol or acetonitrile;
preferably, the nonpolar extracting solution contains at least one of n-hexane, cyclohexane, petroleum ether or ethyl acetate;
preferably, the polar extracting solution is water, and the nonpolar extracting solution is n-hexane;
preferably, the volume ratio of the water to the n-hexane is 1-1.2: 1.
5. The screening method of claim 2, wherein the detection parameters of the real-time direct analysis ion source are: the gas source is selected from helium or nitrogen, the gas temperature is 400-500 ℃, the carrier gas pressure is 0.50-0.55 Mpa, and the sample injection speed is 1-2 mm/s.
6. The screening method according to claim 2, wherein the detection parameters of the quadrupole-time-of-flight mass spectrometer are: the temperature of a transmission line is 150 ℃, the cluster removing voltage is 60V, the collision energy is 10V, a metabonomics sample is analyzed by adopting a positive ion mode and a negative ion mode, the primary collection quality range is 50-1000, the secondary collection quality range is 50-1000 by adopting an IDA mode.
7. The screening method of claim 2, wherein the direct analysis ion source is combined with a quadrupole-time-of-flight mass spectrometer, and the mass spectrometer is calibrated by air mass calibration using a reference ion of [ M + H [ + ] in]+Peak locations include 149.02333, 279.15909, 391.28429, 462.14659, 536.16538, and 610.18417.
8. The metabolic marker combination as claimed in claim 1 or the screening method as claimed in any one of claims 2 to 7, is applied to tracing the source of mutton producing areas.
9. A mutton origin tracing detection method is characterized in that a discrimination model is established by applying the metabolic marker combination of claim 1, metabonomics detection is carried out on a mutton sample to be detected, and the metabonomics detection result is substituted into the discrimination model to trace the source of the mutton origin;
the discriminant model comprises at least one of a PCA model, an LDA model, a PLS-DA model or an OPLS-DA model.
10. The detection method of claim 9, wherein the discriminant models are LDA and OPLS-DA models, the OPLS-DA models satisfying the condition R2Y > Q2> 0.50;
preferably, a quality control sample is used in the detection process, wherein the quality control sample is a mixed solution of all samples to be detected in equal volume.
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| CN116933160A (en) * | 2023-09-19 | 2023-10-24 | 中国农业科学院农产品加工研究所 | Meat variety and part identification method based on lipid characteristic-machine learning |
| CN116933160B (en) * | 2023-09-19 | 2024-01-09 | 中国农业科学院农产品加工研究所 | Meat variety and part identification method based on lipid characteristic-machine learning |
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