CN110687189A - Method for distinguishing milk producing area tracing and identifying based on multiple elements and multiple indexes - Google Patents

Method for distinguishing milk producing area tracing and identifying based on multiple elements and multiple indexes Download PDF

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CN110687189A
CN110687189A CN201910952388.8A CN201910952388A CN110687189A CN 110687189 A CN110687189 A CN 110687189A CN 201910952388 A CN201910952388 A CN 201910952388A CN 110687189 A CN110687189 A CN 110687189A
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milk
sample
delta
distinguishing
stable isotope
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侯建波
张晓峰
谢文
洪灯
张明哲
史颖珠
陆顺
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ZHEJIANG ENTRY-EXIT INSPECTION AND QUARANTINE BUREAU
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Abstract

The application relates to the field of food traceability identification, in particular to a milk producing area traceability identification method based on multi-element and multi-index distinguishing. A retrospective identification method for distinguishing milk producing areas based on multiple elements and multiple indexes is characterized in that an inductively coupled plasma mass spectrometer (ICP-MS) and an Isotope Ratio Mass Spectrometer (IRMS) are used for measuring the content of 40 elements such as Li and the like in milk and the ratio of stable isotopes of carbon, nitrogen, hydrogen and oxygen and carrying out multivariate statistical analysis, and delta is used for carrying out delta statistical analysis13C,δ15N,δ2The combined indexes of H, As, Se, Rb, Sr, Mo, Cs, Ba and Tl are used for testing and statistically comparing the milk samples in the milk region, the feasibility of the milk sample for judging the origin tracing of the milk producing area is analyzed, a judgment model is provided, and the integral judgment rate is 92.4%.

Description

Method for distinguishing milk producing area tracing and identifying based on multiple elements and multiple indexes
Technical Field
The application relates to the field of food traceability identification, in particular to a milk producing area traceability identification method based on multi-element and multi-index distinguishing.
Background
The milk has rich nutritive value and is convenient to eat, so that the milk is popular with consumers. With the improvement of the living standard of people, the consumption is continuously increased, and the requirement on the milk quality is higher and higher. With the development of global economy integration and trade facilitation, imported milk continues to become the subject of daily choice for domestic consumers. However, the same type and specification of milk may bring several times or even dozens of times of price difference due to different producing areas, so the attribute of the producing area of the milk has very important significance for determining the value of the milk.
With respect to the requirements of food production places, countries and regions such as European Union, United states, Japan and the like have established traceability systems one after another. The food safety law of China also makes a clear explanation on establishment of a food safety tracing system according to law and guarantee of traceability of food. Because the isotope fingerprint and mineral element content of the organism are closely related to the growth mode and environment thereof, the stable isotope traceability technology and the element analysis technology become effective tools for food traceability and production place attribute protection.
The origin of milk has been analyzed by measuring the stable isotope ratio of C, N, H, O, S, Sr in milk samples, proteins thereof, and amino acids. Researchers also carry out research on milk producing areas by methods such as infrared spectroscopy, fingerprint spectroscopy, radio frequency identification technology, microbial community analysis and the like.
The Chinese invention patent application (publication number: CN108593829A, published: 2018-09-28) discloses a stable isotope analysis method for milk producing area tracing influence factors, which comprises the following steps: collecting milk samples in different lactation periods or different sampling times, and measuring the stable isotope ratio of the milk samples after freeze drying; if the stable isotope ratios of the milk samples in different lactation periods are different, determining that the lactation periods are factors influencing the tracing of the milk producing areas; and if the stable isotope ratios of the milk samples at different sampling times are different, judging that the sampling time is a factor influencing the tracing of the milk producing area. This patent discloses factors for tracing the origin of milk, but does not further disclose a discriminant model.
The applicant applies for Chinese patent application (application No. 2019108407413, application date: 20190906) and determines the carbon-nitrogen stable isotope ratio (delta) of milk by an elemental analysis-isotope mass spectrometer (EA-IRMS)13C and delta15N), multipurpose gas preparation-continuous flow stable isotope mass spectrometer (GasBench-IRMS) for determining hydrogen-oxygen stable isotope ratio (delta) of water in milk2H and delta18O). In delta of milk13C,δ15N,δ2H and delta18And the four indexes are analysis objects, milk samples of Australia, Austria, Germany, Spain, New Zealand, Italy and China are tested and statistically compared, the feasibility of judging the milk origin tracing is analyzed, a judgment model is provided, and the integral judgment accuracy reaches 84.3%.
Disclosure of Invention
In order to further improve the overall judgment accuracy, the application aims to provide a method for retrospectively identifying milk producing areas based on multi-element and multi-index distinction, which measures and performs multivariate statistical analysis on the content of 40 elements such as Li and the like and the ratio of stable isotopes of carbon, nitrogen, hydrogen and oxygen in milk by an inductively coupled plasma mass spectrometer (ICP-MS) and an Isotope Ratio Mass Spectrometer (IRMS), and simultaneously uses delta to judge the accuracy of the overall judgment13C,δ15N,δ2The combined indexes of H, As, Se, Rb, Sr, Mo, Cs, Ba and Tl are used for testing and statistically comparing the milk samples in the milk region, the feasibility of the milk sample for judging the origin tracing of the milk producing area is analyzed, a judgment model is provided, and the integral judgment rate is 92.4%.
In order to achieve the above object, the present application adopts the following technical solutions:
method for distinguishing milk producing area tracing and identifying based on multiple elements and multiple indexesThe milk sample is directly or freeze-dried into powder, and after being uniformly ground, the carbon stable isotope ratio delta is measured by an elemental analysis-isotope mass spectrometer EA-IRMS13C and nitrogen stable isotope ratio delta15N; determination of hydrogen stable isotope ratio delta in milk water by directly passing milk sample through multipurpose gas preparation-continuous flow stable isotope mass spectrometer GasBench-IRMS2H; freeze drying milk into powder, and measuring As, Se, Rb, Sr, Mo, Cs, Ba and Tl by ICP-MS;
will delta13C,δ15N,δ2Introducing 11 index parameters of H, As, Se, Rb, Sr, Mo, Cs, Ba and Tl into the model, and establishing a discriminant function:
Y(Germany)=–329.08–17.26δ3C+30.86δ15N–0.20δ2H+0.19As+155.26Se+1.86Rb–0.36Sr+64.34Mo–150.08Cs–2.42Ba+5.53Tl;
Y(Australia)=–424.06–20.37δ3C+34.48δ15N+0.49δ2H+0.02As+186.47Se+1.91Rb+3.21Sr+29.67Mo–159.45Cs–3.42Ba+8.82Tl;
Y(Austria)=–429.81–19.09δ3C+33.14δ15N–0.70δ2H+0.52As+148.57Se+2.47Rb–2.33Sr+100.68Mo–173.93Cs–4.14Ba+6.61Tl;
Y(New Zealand)=–479.08–22.17δ3C+33.58δ15N+0.29δ2H+0.04As+219.31Se+2.30Rb+2.46Sr+10.05Mo–153.03Cs–1.26Ba+8.72Tl;
Y(Spain)=–306.70–16.33δ3C+27.78δ15N+0.11δ2H+0.59As+178.04Se+1.80Rb+0.43Sr+87.86Mo–138.96Cs–2.51Ba+6.82Tl;
Y(Italy)=–285.46–14.89δ3C+28.71δ15N–0.37δ2H+0.48As+151.38Se+1.65Rb–1.34Sr+103.47Mo–123.78Cs+0.97Ba+5.32Tl;
Y(China)=–227.04–13.88δ3C+20.16δ15N–0.59δ2H+0.41As+152.27Se+1.69Rb+0.54Sr+57.05Mo–87.98Cs–5.23Ba+3.59Tl;
When blind samples from different regions are identified and classified, the measurement results of the samples are respectively substituted into the discrimination model, the Y values of the regions are compared, and the blind samples belong to the regions with the maximum Y values.
Preferably, milk is freeze-dried into powder, the powder is uniformly ground, 0.50g of dry powder is taken to be put into a microwave digestion tank, 8mL of nitric acid is added for microwave digestion, acid is removed to 2mL of nitric acid, water is added for constant volume to 25mL, the mixture is uniformly mixed, and ICP-MS is used for measuring As, Se, Rb, Sr, Mo, Cs, Ba and Tl.
Preferably, the mineral element determination: inductively coupled plasma mass spectrometer parameters: radio frequency power: 1550 w; sampling depth: 8.0 mm; argon carrier gas flow: 0.8L/min; temperature of the atomization chamber: 4 ℃; flow rate of argon diluted gas: 0.3L/min; extraction of lens 1 voltage: 0V; extraction of lens 2 voltage: -160V; omega deflection voltage: -85V; omega lens voltage: 8.7V; collision cell entrance voltage: -40V; collision cell exit voltage: -60V.
Preferably, a 2. mu.L milk sample is placed in a tin cup, sealed, passed through an autosampler into an elemental analyzer, and the carbon stable isotope ratio, δ, is determined13C。
Preferably, the milk is freeze-dried into powder, the powder is uniformly ground, 0.8mg of the dry powder is taken and placed in a tin cup, the tin cup is sealed, the dry powder enters an element analyzer through an automatic sample injector, and the nitrogen stable isotope ratio delta is measured15N。
Preferably, the parameters of the elemental analyzer for measuring the ratio of stable isotopes of carbon to nitrogen are as follows: flow rate of He carrier gas 200mL/min, CO2Reference gas flow rate of 90mL/min, oxygen flow rate of 180mL/min, oxidation oven temperature: 960 ℃, 640 ℃ in the reduction furnace and 50 ℃ in the water trap.
Preferably, the mixed milk is taken from 500 mu L to 12mL, the platinum catalyst is added into the mixed milk, the mixed milk is sealed, the sample bottle is placed into an automatic sample injector, and H is added2Filling the sample bottle with the mixed gas of the/He gas in a blowing mode, and expelling air in the bottle; sealing and static balancing the sample bottle for 60min to ensure that the mixed gas in the sample bottle2H/1H and in milk2H/1H reaches isotope equilibrium; headspace sample introduction, delta test by GasBench-IRMS2H。
Preferably, determination of hydrogen stable isotope ratio GasBench parameter: h2/He or CO2100mL/min for the mixed gas flow rate/He, 5min for each sample purge, 0.5mL/min for the carrier gas flow rate He, constant temperature for the GC column: 50 ℃ and the temperature of a sample injection plate is 25 ℃.
Preferably, the isotope ratio mass spectrometer parameters: an ionization mode: EI ion source, ion source voltage: 3.06kV, vacuum degree: 1.2X 10-6mBar, current: 1.5 mA; test instrument delta13C、δ15The continuous measurement precision of N is less than 0.06 thousandth, and N is 10; delta2The continuous measurement precision of H is less than 0.4 per mill, and n is 10.
Preferably, parallel testing of each sample is averaged during the analysis, with a standard sample interspersed with every 5 samples.
The content of 40 elements such as Li and the like in milk and the ratio of stable isotopes of carbon, nitrogen, hydrogen and oxygen are measured by an inductively coupled plasma mass spectrometer (ICP-MS) and an Isotope Ratio Mass Spectrometer (IRMS). The variance analysis, the principal component analysis, the cluster analysis and the discriminant analysis are respectively carried out on the carbon, nitrogen, hydrogen and oxygen stable isotopes and the multi-element detection indexes of 79 milk in seven areas of Australia, Austria, Germany, Spain, New Zealand, Italy and China through the multivariate statistical analysis. The results of the study show that13C,δ15N,δ2The overall discrimination rate of the combined indexes of H, As, Se, Rb, Sr, Mo, Cs, Ba and Tl to the milk region is 92.4 percent. The method can provide some necessary information for the traceability analysis of the milk producing area, and can be used as an effective reference index for the traceability of the milk producing area.
Drawings
FIG. 1 is a scatter plot of scores for PC1 and PC 2.
FIG. 2 is a tree diagram of a system clustering of milk from different origins; a: germany, B: spain, C: italy, D: australia, E: new zealand, F: china, G: austria.
Detailed Description
1 test part
1.1 instruments and reagents
Model 7900 inductively coupled plasma mass spectrometer (Agilent); CEM MARS 6 type microwave digestion apparatus (CEM Corp.). Flash model 2000 elemental analyzer (Thermo Fisher corporation); Delta/Mat 252GasBench II multipurpose gas preparation apparatus (Thermo Fisher Co.); delta V Advantage type stable isotope mass spectrometer (Thermo Fisher Corp.); 2.5Triad type lyophilizer (LabConco Corp.); analytical balance model CPA225D (accurate to 0.01mg, sartorius scientific instruments (beijing) ltd.). Tin cups (9 mm. times.5 mm, Thermo Fisher Co.); platinum (Pt) catalyst (Thermo Fisher Co.).
Sample preparation: germany 9 samples (3 brands), spain 4 samples (1 brand), italy 7 samples (2 brands), australian 35 samples (12 brands), new zealand 12 samples (4 brands), china 7 samples (6 brands), austria 5 samples (1 brand).
1.2 working conditions of the apparatus
And (3) mineral element determination: inductively coupled plasma mass spectrometer parameters: radio frequency power: 1550 w; sampling depth: 8.0 mm; argon carrier gas flow: 0.8L/min; temperature of the atomization chamber: 4 ℃; flow rate of argon diluted gas: 0.3L/min; extraction of lens 1 voltage: 0V; extraction of lens 2 voltage: -160V; omega deflection voltage: -85V; omega lens voltage: 8.7V; collision cell entrance voltage: -40V; collision cell exit voltage: -60V.
Determination of stable isotope ratio: elemental analyzer parameters (determination of carbon to nitrogen stable isotope ratio): flow rate of He carrier gas 200mL/min, CO2Reference gas flow rate of 90mL/min, oxygen flow rate of 180mL/min, oxidation oven temperature: 960 ℃, 640 ℃ in the reduction furnace and 50 ℃ in the water trap. GasBench parameter (determination of hydrogen, oxygen stable isotope ratio): h2/He or CO2100mL/min for the mixed gas flow rate/He, 5min for each sample purge, 0.5mL/min for the carrier gas flow rate He, constant temperature for the GC column: 50 ℃ and the temperature of a sample injection plate is 25 ℃. Isotope ratio mass spectrometer parameters: an ionization mode: EI ion source, ion source voltage: 3.06kV, vacuum degree: 1.2X 10- 6mBar, current: 1.5 mA. Test instrument delta13C、δ15N and delta18The continuous measurement precision of O is less than 0.06 thousandth (n is 10); delta2The accuracy of continuous measurement of H is < 0.4 ‰ (n ═ 10). During the analysis, each sample is subjected to parallel testing to obtain an average value, and every 5 samples are alternated with one standard sample.
SPSS 20.0 is adopted for data processing to carry out one-factor variance analysis, Duncan multiple comparison analysis, principal component analysis, clustering analysis and discriminant analysis on the data.
1.3 test methods
1.3.1 determination of elemental content
Freeze-drying milk into powder, grinding uniformly, taking 0.50g of dry powder to a microwave digestion tank, adding 8mL of nitric acid, performing microwave digestion, dispelling acid to about 2mL of nitric acid, adding water to a constant volume of 25mL, mixing uniformly, and measuring by ICP-MS.
1.3.2 determination of carbon-to-nitrogen stable isotope ratio
A2-microliter milk sample is placed in a tin cup, sealed and enters an element analyzer through an automatic sample injector to determine the carbon stable isotope ratio. Freeze-drying milk into powder, grinding uniformly, taking 0.8mg of dry powder, placing the dry powder in a tin cup, sealing, entering an element analyzer through an automatic sample injector, and determining the ratio of the nitrogen stable isotope.
1.3.3 determination of Hydrogen stable isotope ratio
Adding platinum (Pt) catalyst into a mixed milk sample bottle of 500 mu L to 12mL, sealing, placing the sample bottle into an automatic sample injector, and adding H2the/He mixed gas is filled in the sample bottle through a blowing mode, and air in the bottle is expelled. Sealing and static balancing the sample bottle for 60min to ensure that the mixed gas in the sample bottle2H/1H and in milk2H/1H reaches isotopic equilibrium. And (4) introducing a headspace sample, and testing by GasBench-IRMS.
1.3.4 determination of oxygen stable isotope ratio
Taking 500 mu L to 12mL of mixed milk, sealing, placing the sample bottle in an automatic sample injector, and adding CO2the/He mixed gas is filled into the sample bottle through a purging mode to remove the air in the bottle. Sealing and standing the sample bottle for 18h to ensure that the mixed gas CO is balanced2Is/are as follows18O/16O and milkIn (1)18O/16O reaches an isotopic equilibrium state. And (4) introducing a headspace sample, and testing by GasBench-IRMS.
2 discussion and analysis
2.1 analysis of the elemental content and the Stable isotope ratio differences in milk
Experiment on 4 stable isotope ratios (delta)13C、δ15N、δ2H and delta18O) and 40 mineral elements (Li, Na, Mg, Al, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Se, Rb, Sr, Mo, Ru, Ag, Cd, In, Sn, Sb, Cs, Ba, La, Pr, Nd, Sm, Re, Tl, Pb, Bi, Th, U). 13 elements (Li, Sc, Ga, Ru, Sn, In, La, Pr, Nd, Re, Pb, Th, U) were not detected In all the samples tested. 27 elements (Na, Mg, Al, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, Cd, Sb, Cs, Ba, Sm, Tl, Bi) with detection results. The results of the analysis of variance and Duncan multiple comparison analysis of 27 elements are shown in Table 1, and significant differences (p) exist<0.05) with 4 stable isotope ratios delta13C,δ15N,δ2H,δ18O and 10 elements: as, Se, Rb, Sr, Mo, Cd, Cs, Ba, Tl and Bi.
Figure BDA0002224983650000061
2.2 principal Components analysis of elemental content and Stable isotope ratio in milk
The principal component analysis (R type: centralization and standardization processing are carried out on the 14 parameters with significant differences in milk among different countries), wherein the cumulative variance contribution rate of the first 5 principal components reaches 73.60%, and as can be seen from the feature vectors of the principal components, the 1 st principal component PC1 mainly integrates the content information of Rb, Cs and Tl 3 elements, and the 2 nd principal component PC2 mainly integrates the content information of delta13C,δ15N,δ2H and delta18O4 information, the 3 rd principal component PC3 mainly integrates the content information of Se and Cd 2 elements, the 4 th principal component PC4 mainly integrates the content information of Ba and Bi 2 elements,the 5 th main component PC5 mainly integrates the content information of As, Sr and Mo 3 elements. The information of a plurality of parameters in the sample can be comprehensively embodied by the principal component analysis, the standardized scores of PC1 and PC2 are used for drawing, as shown in figure 1, the 1 st and the 2 nd principal components can be used for distinguishing Australia, Austria, Germany, New Zealand, Italy and China to a certain extent.
2.3 Cluster analysis of elemental content and Stable isotope ratio in milk
The standardized scores of the first 5 principal components were used for cluster analysis (systematic clustering, euclidean distance, Ward method), and the results are shown in fig. 2, where the tree graph was cut at 5.50 cluster distance, and the milk samples were divided into 8 categories, of which category 1 was mainly germany and italian samples, and of which 1 was chinese. Class 2 is primarily a chinese sample, which includes 2 spanish and 2 italian samples. The 3 rd group is mainly austria samples, including 1 german sample and 1 italian sample. Categories 4, 5 and 6 are primarily australian samples, including 2 spain and 1 new zealand samples, and categories 7 and 8 are primarily new zealand samples, including 1 australian and 1 germany sample. Therefore, milk from different production areas can be classified by multi-parameter principal component analysis using stable isotope ratio and multi-element combination.
2.4 Cluster analysis of elemental content and Stable isotope ratio in milk
The 4 stable isotope ratios, the 10 mineral element indexes, the stable isotope ratio and the element combination index are subjected to discriminant analysis comparison (discriminant analysis: stepwise discriminant method, Fisher linear discriminant analysis), the results are shown in table 2, the 10 mineral element stepwise discriminant analysis screening parameters are As, Se, Rb, Sr, Mo and Ba, and the cross-checking accuracy is 67.1%. And (4) carrying out discriminant analysis by adopting the stable isotope ratio, wherein the cross checking accuracy is more than 80%. Due to delta2H and delta18All O is derived from H in milk2O has a certain correlation, and delta is introduced18The O finishing discrimination is not significantly improved, so delta is adopted13C,δ15N,δ2H and mineral element combination index for distinguishing milk producing areaAnd (6) analyzing.
TABLE 2 Distinguishing analysis of milk from different origins
At a significance level of 0.05, δ will be13C,δ15N,δ2Introducing 11 index parameters of H, As, Se, Rb, Sr, Mo, Cs, Ba and Tl into the model, wherein the established discriminant function is As follows:
Y(Germany)=–329.08–17.26δ3C+30.86δ15N–0.20δ2H+0.19As+155.26Se+1.86Rb–0.36Sr+64.34Mo–150.08Cs–2.42Ba+5.53Tl
Y(Australia)=–424.06–20.37δ3C+34.48δ15N+0.49δ2H+0.02As+186.47Se+1.91Rb+3.21Sr+29.67Mo–159.45Cs–3.42Ba+8.82Tl;
Y(Austria)=–429.81–19.09δ3C+33.14δ15N–0.70δ2H+0.52As+148.57Se+2.47Rb–2.33Sr+100.68Mo–173.93Cs–4.14Ba+6.61Tl;
Y(New Zealand)=–479.08–22.17δ3C+33.58δ15N+0.29δ2H+0.04As+219.31Se+2.30Rb+2.46Sr+10.05Mo–153.03Cs–1.26Ba+8.72Tl;
Y(Spain)=–306.70–16.33δ3C+27.78δ15N+0.11δ2H+0.59As+178.04Se+1.80Rb+0.43Sr+87.86Mo–138.96Cs–2.51Ba+6.82Tl;
Y(Italy)=–285.46–14.89δ3C+28.71δ15N–0.37δ2H+0.48As+151.38Se+1.65Rb–1.34Sr+103.47Mo–123.78Cs+0.97Ba+5.32Tl;
Y(China)=–227.04–13.88δ3C+20.16δ15N–0.59δ2H+0.41As+152.27Se+1.69Rb+0.54Sr+57.05Mo–87.98Cs–5.23Ba+3.59Tl;
When blind samples from different regions are identified and classified, the measurement results of the samples can be respectively substituted into the discrimination model, the Y values of the regions are compared, and the blind samples belong to the regions with the maximum Y values.
In the experiment, the contents of 40 elements such as Li and the like in milk and the ratios of stable isotopes of carbon, nitrogen, hydrogen and oxygen are measured by an inductively coupled plasma mass spectrometer (ICP-MS) and an Isotope Ratio Mass Spectrometer (IRMS). The variance analysis, the principal component analysis, the cluster analysis and the discriminant analysis are respectively carried out on the carbon, nitrogen, hydrogen and oxygen stable isotopes and the multi-element detection indexes of 79 milk in seven areas of Australia, Austria, Germany, Spain, New Zealand, Italy and China through the multivariate statistical analysis. The results of the study show that13C,δ15N,δ2The overall discrimination rate of the combined indexes of H, As, Se, Rb, Sr, Mo, Cs, Ba and Tl to the milk region is 92.4 percent. The method can provide some necessary information for the traceability analysis of the milk producing area, and can be used as an effective reference index for the traceability of the milk producing area.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present disclosure, including any person skilled in the art, having the benefit of the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art. The general principles defined in this application may be implemented in other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. A retrospective identification method for distinguishing milk producing areas based on multiple elements and indexes is characterized in that milk samples are directly or freeze-dried into powder, the powder is uniformly ground, and a carbon stable isotope ratio delta is measured by an elemental analysis-isotope mass spectrometer EA-IRMS13C and nitrogen stable isotope ratio delta15N; the milk sample directly passes through the multipurpose gasMethod for determining hydrogen stable isotope ratio delta in milk water by using bulk preparation-continuous flow stable isotope mass spectrometer GasBench-IRMS2H; freeze drying milk into powder, and measuring As, Se, Rb, Sr, Mo, Cs, Ba and Tl by ICP-MS;
will delta13C,δ15N,δ2Introducing 11 index parameters of H, As, Se, Rb, Sr, Mo, Cs, Ba and Tl into the model, and establishing a discriminant function:
Y(Germany)=–329.08–17.26δ3C+30.86δ15N–0.20δ2H+0.19As+155.26Se+1.86Rb–0.36Sr+64.34Mo–150.08Cs–2.42Ba+5.53Tl;
Y(Australia)=–424.06–20.37δ3C+34.48δ15N+0.49δ2H+0.02As+186.47Se+1.91Rb+3.21Sr+29.67Mo–159.45Cs–3.42Ba+8.82Tl;
Y(Austria)=–429.81–19.09δ3C+33.14δ15N–0.70δ2H+0.52As+148.57Se+2.47Rb–2.33Sr+100.68Mo–173.93Cs–4.14Ba+6.61Tl;
Y(New Zealand)=–479.08–22.17δ3C+33.58δ15N+0.29δ2H+0.04As+219.31Se+2.30Rb+2.46Sr+10.05Mo–153.03Cs–1.26Ba+8.72Tl;
Y(Spain)=–306.70–16.33δ3C+27.78δ15N+0.11δ2H+0.59As+178.04Se+1.80Rb+0.43Sr+87.86Mo–138.96Cs–2.51Ba+6.82Tl;
Y(Italy)=–285.46–14.89δ3C+28.71δ15N–0.37δ2H+0.48As+151.38Se+1.65Rb–1.34Sr+103.47Mo–123.78Cs+0.97Ba+5.32Tl;
Y(China)=–227.04–13.88δ3C+20.16δ15N–0.59δ2H+0.41As+152.27Se+1.69Rb+0.54Sr+57.05Mo–87.98Cs–5.23Ba+3.59Tl;
When blind samples from different regions are identified and classified, the measurement results of the samples are respectively substituted into the discrimination model, the Y values of the regions are compared, and the blind samples belong to the regions with the maximum Y values.
2. The retrospective identification method for distinguishing the milk producing areas based on multiple elements and multiple indexes according to claim 1 is characterized in that milk is freeze-dried into powder, the powder is uniformly ground, 0.50g of dry powder is taken to be put into a microwave digestion tank, 8mL of nitric acid is added to carry out microwave digestion, the acid is removed to 2mL of nitric acid, water is added to the tank to be constant volume to 25mL, the mixture is mixed evenly, and ICP-MS is used for measuring As, Se, Rb, Sr, Mo, Cs, Ba and Tl.
3. The retrospective identification method for distinguishing the milk producing areas based on multiple elements and multiple indexes according to claim 2 is characterized in that mineral element determination: inductively coupled plasma mass spectrometer parameters: radio frequency power: 1550 w; sampling depth: 8.0 mm; argon carrier gas flow: 0.8L/min; temperature of the atomization chamber: 4 ℃; flow rate of argon diluted gas: 0.3L/min; extraction of lens 1 voltage: 0V; extraction of lens 2 voltage: -160V; omega deflection voltage: -85V; omega lens voltage: 8.7V; collision cell entrance voltage: -40V; collision cell exit voltage: -60V.
4. The method for retrospective identification of milk producing areas based on multi-element and multi-index differentiation as claimed in claim 1, wherein 2 μ L of milk sample is placed in a tin cup, sealed, put into an element analyzer through an auto-sampler, and the ratio δ of carbon stable isotope is measured13C。
5. The method for retrospective identification of milk producing areas based on multi-element and multi-index differentiation as claimed in claim 1, wherein the milk is freeze-dried into powder, ground uniformly, 0.8mg of the powder is put in a tin cup, sealed, put into an elemental analyzer by an auto sampler, and the nitrogen stable isotope ratio δ is measured15N。
6. The retrospective identification method for distinguishing the milk producing areas based on multiple elements and multiple indexes according to claim 4 or 5, characterized in that the parameters of an element analyzer for measuring the stable isotope ratio of carbon and nitrogen are as follows: he carrier gas flow rate of 200mL/min,CO2Reference gas flow rate of 90mL/min, oxygen flow rate of 180mL/min, oxidation oven temperature: 960 ℃, 640 ℃ in the reduction furnace and 50 ℃ in the water trap.
7. The multi-element and multi-index based milk producing area distinguishing tracing and identifying method as claimed in claim 1, wherein a sample bottle of 500 μ L to 12mL of blended milk is taken, a platinum catalyst is added, the sample bottle is sealed, an automatic sample injector is placed in the sample bottle, and H is added2Filling the sample bottle with the mixed gas of the/He gas in a blowing mode, and expelling air in the bottle; sealing and static balancing the sample bottle for 60min to ensure that the mixed gas in the sample bottle2H/1H and in milk2H/1H reaches isotope equilibrium; headspace sample introduction, delta test by GasBench-IRMS2H。
8. The retrospective identification method for distinguishing the milk producing areas based on multiple elements and multiple indexes according to claim 7 is characterized in that the GasBench parameter of the hydrogen stable isotope ratio is determined as follows: h2/He or CO2100mL/min for the mixed gas flow rate/He, 5min for each sample purge, 0.5mL/min for the carrier gas flow rate He, constant temperature for the GC column: 50 ℃ and the temperature of a sample injection plate is 25 ℃.
9. The retrospective identification method for distinguishing the milk producing areas based on multiple elements and multiple indexes according to claim 1 is characterized in that the isotope ratio mass spectrometer parameters are as follows: an ionization mode: EI ion source, ion source voltage: 3.06kV, vacuum degree: 1.2X 10- 6mBar, current: 1.5 mA; test instrument delta13C、δ15The continuous measurement precision of N is less than 0.06 thousandth, and N is 10; delta2The continuous measurement precision of H is less than 0.4 per mill, and n is 10.
10. The retrospective identification method of milk producing areas based on multi-element and multi-index differentiation according to claim 9, wherein the samples are tested in parallel during the analysis process to obtain an average value, and every 5 samples are interspersed with one standard sample.
CN201910952388.8A 2019-10-08 2019-10-08 Method for distinguishing milk producing area tracing and identifying based on multiple elements and multiple indexes Pending CN110687189A (en)

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111505101A (en) * 2020-04-29 2020-08-07 中国工程物理研究院核物理与化学研究所 Uranium ore producing area classification method based on principal component analysis
CN112362827A (en) * 2020-11-25 2021-02-12 上海海关动植物与食品检验检疫技术中心 Method for tracing milk producing area
CN113671013A (en) * 2021-08-17 2021-11-19 山东省海洋资源与环境研究院(山东省海洋环境监测中心、山东省水产品质量检验中心) Construction method and identification method of portunus trituberculatus origin tracing model
CN114324676A (en) * 2022-03-09 2022-04-12 中国农业科学院蜜蜂研究所 Method for identifying royal jelly produced by different food grains
CN114414653A (en) * 2022-01-24 2022-04-29 杭州海关技术中心 Cherry producing area tracing method based on fruit stones

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2001324427A (en) * 2000-05-16 2001-11-22 Nippon Steel Corp Method for high accuracy boron analysis in iron and steel
CN107424003A (en) * 2017-05-05 2017-12-01 浙江省农业科学院 A kind of red bayberry place of production source tracing method based on isotope ratio rate and multielement
CN108982692A (en) * 2018-07-27 2018-12-11 深圳出入境检验检疫局食品检验检疫技术中心 The method that elemental analysis-stable isotope mass spectrum differentiates the milk powder place of production
CN109187130A (en) * 2018-09-14 2019-01-11 青海省畜牧兽医科学院 A kind of yak meat source tracing method based on mineral element discrimination model
CN109239176A (en) * 2018-09-19 2019-01-18 青海省畜牧兽医科学院 A kind of yak meat place of production source tracing method based on multielement and stable isotope

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2001324427A (en) * 2000-05-16 2001-11-22 Nippon Steel Corp Method for high accuracy boron analysis in iron and steel
CN107424003A (en) * 2017-05-05 2017-12-01 浙江省农业科学院 A kind of red bayberry place of production source tracing method based on isotope ratio rate and multielement
CN108982692A (en) * 2018-07-27 2018-12-11 深圳出入境检验检疫局食品检验检疫技术中心 The method that elemental analysis-stable isotope mass spectrum differentiates the milk powder place of production
CN109187130A (en) * 2018-09-14 2019-01-11 青海省畜牧兽医科学院 A kind of yak meat source tracing method based on mineral element discrimination model
CN109239176A (en) * 2018-09-19 2019-01-18 青海省畜牧兽医科学院 A kind of yak meat place of production source tracing method based on multielement and stable isotope

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111505101A (en) * 2020-04-29 2020-08-07 中国工程物理研究院核物理与化学研究所 Uranium ore producing area classification method based on principal component analysis
CN111505101B (en) * 2020-04-29 2023-04-18 中国工程物理研究院核物理与化学研究所 Uranium ore producing area classification method based on principal component analysis
CN112362827A (en) * 2020-11-25 2021-02-12 上海海关动植物与食品检验检疫技术中心 Method for tracing milk producing area
CN113671013A (en) * 2021-08-17 2021-11-19 山东省海洋资源与环境研究院(山东省海洋环境监测中心、山东省水产品质量检验中心) Construction method and identification method of portunus trituberculatus origin tracing model
CN114414653A (en) * 2022-01-24 2022-04-29 杭州海关技术中心 Cherry producing area tracing method based on fruit stones
CN114414653B (en) * 2022-01-24 2023-12-01 杭州海关技术中心 Cherry origin tracing method based on kernel
CN114324676A (en) * 2022-03-09 2022-04-12 中国农业科学院蜜蜂研究所 Method for identifying royal jelly produced by different food grains

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