阅读说明：本技术 利用天然丰度稳定同位素和dna基因分型鉴定生物制品 (Identification of biological products using natural abundance stable isotopes and DNA genotyping ) 是由 约翰·P·贾斯帕 于 2019-11-15 设计创作，主要内容包括：大量材料的天然丰度稳定同位素分析和生物材料的DNA基因分型的组合应用,是在供应链中识别此类产品的高度特异性(约1：1×10～(17))的指纹识别方法。(The combination of natural abundance stable isotope analysis of bulk material and DNA genotyping of biological material shouldIt is used to identify the high specificity of such products in the supply chain (about 1: 1X 10) 17 ) The fingerprint identification method of (1).)

1. A method for objectively characterizing a biological sample comprising a genome, a proteome, a catabolite, or a metabolic component, comprising:

(a) obtaining isotopic data from elements present in the sample; providing a mathematical array comprising isotope data, the mathematical array being fixed in a readable form, the readable form on which the mathematical array is fixed being an identification of the sample,

(b) obtaining genomic, proteomic, catabolic or metabolomic data of said sample,

(c) constructing an integrated identification data array from the isotopic data obtained in step (a) and the genomic, proteomic, catabolic or metabolomic data obtained in step (b),

(d) provides an objective characterization of the biological sample.

2. The method of claim 1, wherein the isotope data does not include data obtained from a labeling agent.

3. The method of claim 1 or 2, wherein the element is selected from elements having two or more isotopes.

4. A process according to any one of claims 1 to 3, wherein the element is selected from hydrogen, carbon, nitrogen, oxygen, sulphur, chlorine and bromine and combinations thereof.

5. The method of claim 3, wherein the isotope is a stable isotope.

6. The method of claim 5, wherein the stable isotope is selected from¹H、²H、¹²C、¹³C、¹⁴N、¹⁵N、¹⁶O、¹⁸O、³²S、³⁴S、³⁵Cl、³⁷Cl、⁷⁹Br and⁸¹br and combinations thereof.

7. The method of claim 6, wherein the isotope is selected from the following paired isotopes:¹h and²H、¹²c and¹³C、¹⁴n and¹⁵N、¹⁶o and¹⁸O、³²s and³⁴S、³⁵cl and³⁷Cl、⁷⁹br and⁸¹Br。

8. the method of claim 6, wherein the isotopes are selected from the following isotope ratios:²H/¹H、¹³C/¹²C、¹⁵N/¹⁴N、¹⁸O/¹⁶O、³⁴S/³²S、³⁷Cl/³⁵cl and⁸¹Br/⁷⁹Br。

9. the method of any one of claims 1-8, wherein the isotopic data and the genomic, proteomic, catabolic or metabolomic data are intrinsic data of the sample.

10. The method of any one of claims 1 to 9, wherein the integration data (c) is fixed in a computer or machine readable form.

11. The method of claim 1, wherein the biological sample comprises a genetic component and the genomic data is obtained by genotyping.

12. The method of claim 11, wherein the genetic component is selected from the group consisting of DNA, RNA, nucleotide fragments, and nucleic acids.

13. The method of claim 1, wherein the isotope data is given in accordance with a reference standard.

14. A data array for objectively characterizing a biological sample comprising a genome, a proteome, a catabolite, or a metabolic component, comprising:

(a) isotopic data from elements present in the sample; providing a mathematical array comprising said isotope data, said mathematical array being fixed in a readable form, said readable form on which said mathematical array is fixed being an identification of said sample, and

(b) genomic, proteomic, catabolic or metabolomic data of a sample,

wherein the isotopic data of (a) and the genomic, proteomic, catabolic or metabolomic data of (b) are integrated into an identification data array for objectively characterizing a biological sample.

15. The data array of claim 14, wherein the element is selected from elements having two or more isotopes.

16. The data array of claim 15 or 16, wherein the element is selected from the group consisting of hydrogen, carbon, nitrogen, oxygen, sulfur, chlorine, and bromine, and combinations thereof.

17. The data array of claim 15, wherein the isotope is a stable isotope.

18. The data array of claim 17, wherein the stable isotope is selected from the group consisting of¹H、²H、¹²C、¹³C、¹⁴N、¹⁵N、¹⁶O、¹⁸O、³²S、³⁴S、³⁵Cl、³⁷Cl、⁷⁹Br and⁸¹br and combinations thereof.

19. The data array of claim 18, wherein the isotope is selected from the following paired isotopes:¹h and²H、¹²c and¹³C、¹⁴n and¹⁵N、¹⁶o and¹⁸O、³²s and³⁴S、³⁵cl and³⁷Cl、⁷⁹br and⁸¹Br。

20. the data array of claim 18, wherein the isotopes are selected from the following isotope ratios:²H/¹H、¹³C/¹²C、¹⁵N/¹⁴N、¹⁸O/¹⁶O、³⁴S/³²S、³⁷Cl/³⁵cl and⁸¹Br/⁷⁹Br。

21. the data array of any of claims 14-20, wherein the isotopic data and the genomic, proteomic, catabolic, or metabolomic data are intrinsic data of the sample.

22. A data array according to any of claims 14 to 20, wherein the integrated data is fixed in a computer or machine readable form.

23. The data array of claim 14, wherein the biological sample comprises a genetic component and the genomic data is obtained by genotyping.

24. The data array of claim 14, wherein the genetic component is selected from the group consisting of DNA, RNA, nucleotide fragments, and nucleic acids.

25. The data array of claim 14, wherein the isotope data is given in accordance with a reference standard.

Technical Field

A method of combining stable isotopes and DNA genotyping, comprising a mathematical array of concentration ratios of isotopes found in a biological sample and coexisting genetic information from DNA or RNA, said mathematical array being presented in computer readable form and commensurate with the results of the analysis, whereby the sample can be distinguished from other similar samples, said computer readable form also being indexed by stored sample information. The stored sample information may be displayed when desired. By the combined stable isotope and DNA identification of the present invention, samples can be safely tracked through the supply chain for the manufacture, sale, and use of the samples.

Background

In ecology and genetics, the reaction norm, also known as the norm of the reaction (norm of the reaction), describes the phenotypic expression pattern of the same genotype in a range of environments. One use of reaction specification is to describe how different species (especially related species) react to different environments. However, different genotypes within the same species may also exhibit different response profiles with respect to specific phenotypic traits and environmental variables. For each genotype, phenotypic trait, and environmental variable, there may be different response criteria; in other words, there may be considerable complexity in the interrelationship between the genetic and environmental factors that determine a trait.

Gene-environment interaction (or genotype-environment interaction or GxE) means that two different genotypes respond differently to environmental changes. The response specification is a graph showing the relationship between genes and environmental factors when phenotypic differences persist. They may help illustrate the interaction of gxe. When the reaction specification is not parallel, genes that interact with the environment are present. This indicates that each genotype reacts differently to environmental changes. The environmental change may be a physical, chemical, biological, behavioral pattern, or life event.

Thus, there is a need to provide methods for assessing gene-environment interactions, but current methods do not achieve the desired degree of specificity. The present invention overcomes the disadvantages of the current methods.

Disclosure of Invention

The present invention relates to a method for the objective characterization of a biological sample comprising a genome, a proteome, a catabolite or a metabolic component, comprising:

(a) obtaining isotopic data from elements present in the sample; providing a mathematical array comprising said isotope data, said mathematical array being fixed in a readable form, said readable form having said mathematical array fixed thereon being an identification of said sample,

(b) obtaining genomic, proteomic, catabolic or metabolomic data of a sample,

(c) constructing an integrated identification data array from the isotopic data obtained in step (a) and the genomic, proteomic, catabolic or metabolomic data obtained in step (b),

(d) provides an objective characterization of the biological sample.

In other embodiments, the invention relates to a method wherein the isotope data does not include data obtained from a labeling agent.

In other embodiments, the invention relates to a method wherein the element is selected from elements having two or more isotopes.

In other embodiments, the present invention relates to a process wherein the element is selected from the group consisting of hydrogen, carbon, nitrogen, oxygen, sulfur, chlorine and bromine and combinations thereof.

In other embodiments, the invention relates to a method wherein the isotope is a stable isotope.

In other embodiments, the invention relates to a method wherein the stable isotope is selected from¹H、²H、¹²C、¹³C、¹⁴N、¹⁵N、¹⁶O、¹⁸O、³²S、³⁴S、³⁵Cl、³⁷Cl、⁷⁹Br and⁸¹br and combinations thereof.

In other embodiments, the invention relates to a method wherein the isotope is selected from the following paired isotopes:¹h and²H、¹²c and¹³C、¹⁴n and¹⁵N、¹⁶o and¹⁸O、³²s and³⁴S、³⁵cl and³⁷Cl、⁷⁹br and⁸¹Br。

in other embodiments, the invention relates to a method wherein the isotopes are selected from the following isotope ratios:²H/¹H、¹³C/¹²C、¹⁵N/¹⁴N、¹⁸O/¹⁶O、³⁴S/³²S、³⁷Cl/³⁵cl and⁸¹Br/⁷⁹Br。

in other embodiments, the invention relates to a method wherein the isotopic data and the genomic, proteomic, catabolic or metabolomic data are intrinsic to the product.

In other embodiments, the invention relates to a method wherein the integration data (c) is fixed in a computer or machine readable form.

In other embodiments, the invention relates to a method wherein the biological product comprises a genetic component and the genomic data is obtained by genotyping.

In other embodiments, the invention relates to a method wherein the genetic component is selected from the group consisting of DNA, RNA, nucleotide fragments and nucleic acids.

In other embodiments, the invention relates to a method wherein the isotope data is given according to a reference standard.

In other embodiments, the invention relates to a data array for objective characterization of a biological sample comprising a genetic, proteomic, catabolic or metabolic component, comprising:

(a) isotopic data from elements present in the sample; providing a mathematical array comprising said isotope data, said mathematical array being fixed in a readable form, said readable form on which said mathematical array is fixed being an identification of said sample, and

(b) genomic, proteomic, catabolic or metabolomic data of a sample,

wherein the isotopic data of (a) and the genomic, proteomic, catabolic or metabolomic data of (b) are integrated into an identification data array for objectively characterizing a biological sample.

In other embodiments, the invention relates to data arrays wherein the elements are selected from elements having two or more isotopes.

In other embodiments, the invention relates to data arrays wherein the elements are selected from the group consisting of hydrogen, carbon, nitrogen, oxygen, sulfur, chlorine, and bromine, and combinations thereof.

In other embodiments, the invention relates to data arrays wherein the isotope is a stable isotope.

In other embodiments, the invention relates to data arrays wherein the stable isotope is selected from¹H、²H、¹²C、¹³C、¹⁴N、¹⁵N、¹⁶O、¹⁸O、³²S、³⁴S、³⁵Cl、³⁷Cl、⁷⁹Br and⁸¹br and combinations thereof.

In other embodiments, the invention relates to data arrays wherein the isotopes are selected from the following pairs of isotopes:¹h and²H、¹²c and¹³C、¹⁴n and¹⁵N、¹⁶o and¹⁸O、³²s and³⁴S、³⁵cl and³⁷Cl、⁷⁹br and⁸¹Br。

in other embodiments, the invention relates to data arrays wherein the isotopes are selected from the following isotope ratios:²H/¹H、¹³C/¹²C、¹⁵N/¹⁴N、¹⁸O/¹⁶O、³⁴S/³²S、³⁷Cl/³⁵cl and⁸¹Br/⁷⁹Br。

in other embodiments, the invention relates to data arrays in which isotopic data and genomic, proteomic, catabolic, or metabolomic data are intrinsic to the product.

In other embodiments, the invention relates to data arrays in which the integrated data is fixed in a computer-or machine-readable form.

In other embodiments, the invention relates to a data array, wherein the biological products comprise a genetic component and the genomic data is obtained by genotyping.

In other embodiments, the invention relates to data arrays wherein the genetic component is selected from the group consisting of DNA, RNA, nucleotide fragments, and nucleic acids.

In other embodiments, the invention relates to data arrays in which isotope data is given according to a reference standard.

Drawings

FIG. 1 shows a flow chart of the G E fingerprinting process of the present invention.

FIGS. 2A, 2B and 2C show three graphs of typical biological analytes for a sample of seed in the GxE invention of the present invention to facilitate distinguishing between different plant seeds of different varieties grown under different conditions (e.g., different regions). Fig. 2A illustrates the same G, different E: i.e., the same genetic makeup, different growth/biosynthesis environments. Fig. 2B shows a different G, the same E: i.e., different genetic makeup, the same growth/biosynthesis environment. Fig. 2C illustrates the same and different G and E: i.e., the same or different genetic makeup, and the same or different growth/biosynthesis environments.

FIG. 3 is a two-dimensional graph showing the statistical distribution of the G E sample analysis data in the form of an elliptical distribution. The ellipse is denoted as "e" and the centroid of the ellipse is denoted as "c".

Fig. 4 shows the bare axis of a three-dimensional plot for a gxe sample analysis build. In this example, the x-axis represents the isotopic composition of water, and represents the isotopic difference (δ) between hydrogen and oxygen (H, O) of water according to the International Atomic Energy Agency (IAEA) standard₁). The y-axis represents the isotopic composition of the bulk biomass of the sample (e.g., carbohydrates, proteins, lipids, nucleic acids, etc. from the sample), which is expressed as the isotopic difference (δ) of the IAEA standard for carbon, nitrogen, and sulfur (C, N, S)₂). The z-axis represents the genetic parameter (G) based on genetic sample homology or variability on a scale of 0 to 1.

Fig. 5 shows a three-dimensional graph of a G × E sample analysis using the coordinate system shown in fig. 4. The x-axis defines P for deuterium and oxygen 18C1 (principal component 1), PC1(δ D, δ)¹⁸O). The y-axis defines PC2 (principal component 2) for carbon 13, and optionally nitrogen 15 and/or sulfur 34, PC2(δ)¹³C, or alternatively δ¹⁵N and/or delta³⁴S). The z-axis represents the genetic parameter (G) based on homology or variability of genetic samples on a scale of 0 to 1. The figure shows an ellipsoid "e" with a vector "v" from the origin of the x, y, z coordinates to the center of the ellipsoid (i.e., centroid "c").

Fig. 6 shows a three-dimensional plot of the gxe sample analysis from fig. 5, showing the projection of ellipsoids on each of the (x, y), (y, z) and (x, z) planes.

Detailed Description

G × E: g (genetics). times.E (environment) is a powerful and elegant concept for tracking and identifying biological material-i.e., material containing DNA or RNA (e.g., plant seeds of corn, wheat, cotton, etc.) -versus the environment by stable isotopes.

In the present invention, we assert that a genotype has a relationship of gxe: for example, "iowa" (genotype) soybeans planted in iowa are likely to have very different (C, H, O, N, S) isotopic compositions-same DNA, different isotopic signals than "iowa" soybeans planted in china. This difference can be manifested in a number of ways, such as an X-Y plot (DNA phenotype versus large number of isotopes).

While the concept of gxe is well known, the methods and data arrays of the present invention for determining and quantifying gxe are believed to be novel. As shown herein, the present methods and data allow for new and useful applications for identifying biological samples or identifying and/or distinguishing two or more biological samples. The present methods and data provide a powerful means for performing operations that previously could not be performed. The present invention employs unique combinations and integrations of genetic fingerprint data (i.e., genomic or sequencing data) with high resolution isotope ratio mass spectrometry data to provide an integrated data array that can be used in the methods described herein.

Isotope Ratio Mass Spectrometry (IRMS) is a specialized branch of mass spectrometry that exploits the relative abundance of isotopes in a particular sampleMass spectrometry was performed. This method allows for the accurate measurement of naturally occurring isotopic mixtures. The instruments used for such precise determination of isotope ratios are mostly of the sector magnetic field type. The IRMS field is of great interest because mass differences between different isotopes can lead to isotope fractionation. The effect of this fractionation enables the isotopic composition of the samples to be measured, thereby providing a window for understanding their biological or physical history. Consider the following example. Deuterium (D or)²H) Is almost ordinary hydrogen (¹H) Twice as much. Thus, ordinary water H₂The mass of the O molecule and HDO (water molecules in which one hydrogen of the water molecule is replaced by deuterium) are very different. Processes involving the evaporation of water or the cleavage of hydrogen bonds or the dissociation of hydrogen bonds between water and/or other molecules will exhibit fractionation phenomena. Thus, water sources at different locations on the earth are likely to have different isotope ratios or "fingerprints" that distinguish between D and H.

Isotope ratios are usually given relative to a standard, and given a delta value, the relative isotopic abundance can be calculated. Reference standards can be found in Hayes, J.M., Practice and Principles of Isotropic Measurements in Organic Geochemistry, 2 nd edition, 8.2002, page tables 1 to 15, the points of Table 1 being extracted herein, which reference is incorporated by reference in its entirety.

Nucleic acid sequencing, such as DNA or RNA sequencing, can be used to determine the sequence of a single gene (DNA) or a gene encoding an RNA structure (e.g., ribosomal 16S subunit) for gene identification. This method is used to study the genome and the proteins it encodes. The advent of relatively inexpensive and rapid sequencing methods has enabled the determination of DNA and RNA sequences from biological samples for their identification. These methods have led to the field of genomics, which focuses on the structure, function, evolutionary map and editing of the genome, i.e., the genetic material of an organism. In preparing samples for sequencing, Polymerase Chain Reaction (PCR) is used to replicate specific DNA fragments. In other words, the DNA sequence is exponentially amplified to produce sufficient quantities of gene sequencing material.

FIG. 1 shows a flow chart of the G E fingerprinting process of the present invention. The biological sample is split into two distinct parallel paths. In one path, the sample is prepared for Isotope Ratio (IR) mass spectrometry. This typically involves burning the sample to small molecules, such as CO₂、N₂、SO₂Etc. and then their isotope ratios are analyzed in an isotope ratio mass spectrometer. In another path, the sample is prepared for genetic analysis. This may involve various extraction, purification and concentration protocols to isolate DNA or RNA from a sample, followed by an amplification process such as PCR. The DNA or RNA material is then sequenced using conventional sequencing methods to genetically characterize the sample to provide a gene expression profile. Data collected from two separate analytical pathways are integrated or combined to produce a single array of isotope ratio mass spectral data and genetic data, or a gxe spectrum. The combination of the two data paths may be performed in different ways, as described below.

Relative specificity and stable isotope analysis of DNA

DNA analysis stable isotope analysis

GMW: DNA, e.coli: about 3X 10⁹GMW: span about 2 to 10⁹Dalton (R)

Specificity: about 1:10⁷Specificity of light isotope

Wherein GMW is (gram molecular weight)

DNA and naturally stable isotopes (each having a specificity of about 10)⁷) The combination of (a) is a particularly strong pair, resulting in very high specificity.

We evaluated the ability of stable isotopes to complex with human DNA. We estimate specificity to be about 1/10¹⁷And is very high. From our point of view, DNA is ourOne of many compounds that are commonly subject to isotopic fingerprinting. Other biological compounds include RNA, proteins, peptides and amino acids, catabolites and metabolites.

We evaluated the complexing power of a large number of stable isotopes with bacterial DNA-DNA specificity 3X 10¹⁷. We estimated specificity to be about 3/10¹⁷This is very high for such evaluations.

In the above, δ is a measure of the difference (positive or negative) by a few thousandths (per mil or "% o") relative to an internationally recognized standard. For example, consider carbon 12 and carbon 13, δ¹³And C is determined as:

δ¹³C＝{[(¹³C/¹²C)_{sample (I)}/(¹³C/¹²C)_{Standard of merit}]-1}x1000‰。

The determination and calculation of other isotope ratios is also similar.

DNA is one of the many compounds that we can identify using isotopic fingerprinting. Thus, we can assess the overall composition of biological samples (e.g., wheat seeds, cotton) and separately assess the quantitative indicators of co-existing DNA genotypes. Stable isotope fingerprinting of DNA molecules (as a whole organic phase) and their genotypes will be an example of the key application of our method. In general, we can perform isotope fingerprinting on bulk (bulk) materials (e.g., bulk wheat seeds, etc.) and quantitative indicators of their DNA genotypes. Although genomic identification (i.e., genotyping) of genetic material (e.g., DNA, RNA, nucleic acid fragments, nucleic acids) in a sample is an important application of this method, other biological materials can be analyzed and identified for this purpose. For example, proteomics can be used to obtain identification information of proteins, peptides, and amino acids in a sample. Catabolism and metabolomics can be used to obtain identification information of catabolites and metabolites in a sample. Likewise, other "omics" technologies and information can be obtained from other biological components.

DNA is a linear sequence of four base pair nucleotides (G-guanine, T-thymine, A-adenine and C-cytosine) that encodes genetic information. Furthermore, RNA based on nucleotides (G-guanine, U-uracil, A-adenine and C-cytosine) is also disclosed.

As described above, natural abundance stable isotopes (e.g., C, H, O, N, S) record the isotopic origin of biological materials with very high specificity. In particular, the stable isotopes of water, H and O, record the environment of biosynthesis of the material (E). C. The N and S isotopes record the isotopic composition of the biological material itself to provide highly specific isotopic fingerprints.

In one embodiment, the application of gxe may be displayed on bivariate plots (x, y-plots) as shown in fig. 2A, 2B and 2C. Three examples are specifically enumerated:

FIG. 2A illustrates the same genetic origin of biological plant material grown in different environments. In other words, the same G, but a different E. A of samples grown in two different environments₁And A₂The expected data ellipse range of (a) illustrates this result.

Fig. 2B illustrates biological plant material of different genetic origin grown in the same environment. In other words, different G, but the same E. Even though the ellipses a1 and B1 are shown directly above on the E-axis, it is expected that they will show environmental independent isotopic differences, since the elemental composition of the sample differs due to genetic differences. The dashed ellipse (offset) in the figure illustrates this isotopic effect due to genetic factors, and it is expected that the offset will be offset from the center of the ellipse. However, if there is an environmental impact, it is expected to be much greater than that shown in example 2C.

Fig. 2C shows the growth of biological plant material from the same or different sources in the same or different environments. In other words, arrays based on the same or different G's and the same or different E's. This is illustrated by ellipses a1, B1, a2 and B2, where the dashed ellipses represent possible offsets.

Parameterized genetics: linear DNA molecules consist of two types of sequences: the conserved and variable regions of the DNA strand consist of four base pairs (ATCG or AUCG). These DNA sequences can then be translated into protein or expressed in the form of protein.

For these sequences, we can use the correlation coefficient (r) of DNA or protein²) As a reference area or location. The correlation coefficients of these reference parts are from 0 to 1, r²0 denotes no correlation, r²1 indicates a perfect match.

Fig. 3 represents the statistical distribution of data from a gxe sample analysis as a two-dimensional plot in the form of an elliptical distribution (ellipse "E" with centroid). The size and shape of the ellipse depends on the statistical distribution of the sample. The centroid of this set of data describes the statistical average of all data points in a given dimension.

Fig. 4 shows the bare axis of a three-dimensional plot for a gxe sample analysis build. The x-axis represents the isotopic composition of water, expressed as the isotopic difference (δ 1) between hydrogen and oxygen (H, O) of water according to the international atomic energy agency standard. This aspect is based on the water uptake of the sample and is highly dependent on geographical and geochemical parameters, i.e. location and biological conditions. The y-axis represents the isotopic composition of the entire biomass of the sample (e.g., carbohydrates, proteins, lipids, nucleic acids, etc. from the sample), and represents the isotopic difference (δ 2) for the carbon, nitrogen, sulfur (C, N, S) standards of the international atomic energy agency. This aspect describes the isotopic composition of biomass produced under a given set of environmental conditions. The z-axis represents the genetic parameter (G) based on homology or variability of genetic samples on a scale of 0 to 1. These data are obtained using standard sequencing techniques and algorithms and calculations for assessing genetic similarity or differences between biological samples.

Fig. 5 shows a three-dimensional graph of a G × E sample analysis using the coordinate system shown in fig. 4.

The x-axis defines the PC1 (principal component 1), PC1(δ D, δ) for deuterium and oxygen 18¹⁸O). The y-axis defining carbon 13

PC2 (principal component 2), and optionally nitrogen 15 and/or sulfur 34, PC2(δ¹³C、δ¹⁵N、δ³⁴S). The z-axis represents the genetic parameter based on homology or variability of genetic samples on a scale of 0 to 1. The figure shows an ellipsoid "e" from the origin of the x, y, z coordinates toThe vector at the center of the ellipsoid (i.e., centroid "c") is "v," which is useful in comparing differences between two or more samples, each having its own unique ellipsoid.

FIG. 6 shows a three-dimensional plot of the G E sample analysis from FIG. 5, projecting a three-dimensional ellipsoid as a two-dimensional ellipsoid into either (x, y), (y, z) and (x, z) planes. The purpose of fig. 6 is to split the data into two-dimensional groups for easier visualization, interpretation and analysis.

Examples

The following examples further describe and demonstrate the scope of the present invention. Many variations are possible without departing from the spirit and scope of the invention, and thus these examples are for illustrative purposes only and are not to be construed as limitations of the invention.

Example 1

Same wine grape planted in the Sonomagu and the Napagu

This is an example of the planting of genetically identical grape varieties of vitis vinifera under two similar but different geographical conditions and climatic environments.

In this example, the genetic genes (G) are identical, but the environmental conditions (E) for grape growth are different. It is expected that the genetic map will be the same, but the final product of grapes will vary due to differences in growth conditions such as soil, water, fertilizer, etc.

The expected data is shown in fig. 2A.

Example 2

Two different wine grapes planted in Sonoma valley

This is an example of planting genetically diverse varieties of vitis vinifera under the same geographical and climatic conditions.

In this example, the genetic genes (G) are different, but the environmental conditions (E) for grape growth are the same. It is expected that genetic maps will vary. The final product of grapes should be different due to isotopic differences in grape components superimposed on different growth conditions (e.g. soil, water, fertilizer, etc.). It is expected that this environmental difference will manifest as a greater isotopic difference, which is much greater than that caused by genetic factors.

The expected data is shown in fig. 2B.

Example 3

Two different wine grapes planted in Sonoma valley and Napagu

This is an example of different vitis vinifera varieties growing in two adjacent but different geographical and climatic environments.

In this example, the genetic genes (G) are different, as are the environmental conditions (E) under which the grapes are grown. It is expected that genetic maps will vary. The final grape product will vary depending on the growth conditions, such as soil, water, fertilizer, etc.

The expected data is shown in fig. 2C.

Example 4

Crops of the same seed, e.g. maize, grown with two different nitrogen fertilizers

This is an example of planting genetically identical corn varieties on the same land, partly with a synthetic nitrogen source (haber process synthetic fertilizer) and partly with an organic nitrogen source (manure).

In this example, the genetic genes (G) are identical, but the environmental conditions (E) for corn growth are different. It is expected that the genetic profile will be the same, but that the final product of corn will vary due to the difference in the nitrogen source.

The expected data is shown in fig. 2A.

Example 5

Arabica and robusta coffee grown in two different places

In this example, coffee Arabica or Arabica (Coffea Arabica) and coffee robusta (Coffea canephora, also known as Coffea robusta) (two different G) are planted in two different areas, e.g. brazil and vietnam (two different E). Compared to robusta coffee, arabica coffee is generally preferred because of its better quality, taste and aroma. Robusta coffee is considered low grade and is generally described as more harsh and bitter. The sale price of the Arabica coffee beans is generally more than 1.5 times higher than that of the Apocynum coffee beans. The arabica beans account for about 60% of the world's production, and the robusta beans account for about 40%. Therefore, it is necessary to determine the coffee samples and their origins to identify arabica coffee grown in brazil or vietnam from robusta coffee grown in these two same locations. It would be highly desirable to avoid the possibility that robusta coffee growing in vietnam is mistaken for high quality arabica coffee growing in brazil, or that robusta coffee growing in brazil is mistaken for brazilian arabica coffee.

The expected data is shown in fig. 2C.

Reference to the literature

U.S Patent No.7,323,341B1,Stable Isotopic Identification and Method For Identifying Products By Isotopic Concentration,to Jasper,issued January 29,2008.

U.S.Patent No.8,367,414B2,Tracing Processes Between Precursors and Products By Utilizing Isotopic Relationships,to Jasper,issued February 5,2013.

PCT Application Publication No.WO2015/103183,Method for Continuously Monitoring Chemical orBiological Processes,to Jasper,published July 9,2015.

PCT Application Publication No.WO2016/109631,Isotopic Identification and Tracing ofBiologic Products,to Jasper,publishedNovember 12,2015.

PCT Application Publication No.WO2015/103183,Molecular Isotopic Engineering,to Jasperpublished July 7,2016.

Hayes,J.M.,Practice and Principles of Isotopic Measurements in Organic Geochemistry,Revision 2,August 2002,pages 1-15,particularly Table 1.

Incorporation by reference

The entire disclosure of each patent document referred to herein, including certificates of correction, patent application documents, scientific literature, government reports, websites, and other references, is incorporated herein by reference in its entirety for all purposes. In case of conflict, the present specification will control.

Equivalents of

The present invention may be embodied in other specific forms without departing from its spirit or essential characteristics. The above-described embodiments are illustrative in all respects, rather than limiting, of the invention described herein. In various embodiments of the method and system of the present invention, the terminology includes is used to describe the steps or components, and it is also contemplated that the method and system consist or consist essentially of such steps or components. In addition, the order of steps or order of performing certain operations is immaterial so long as the invention remains operable. Also, two or more steps or operations may be performed simultaneously.

In the specification, the singular forms also include the plural forms unless the context clearly dictates otherwise. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. In case of conflict, the present specification will control.

Further, it is to be appreciated that in some instances, the compositions can be described as consisting of the components prior to mixing, as some components can further react or transform into other materials upon mixing.

All percentages and ratios used herein are by weight unless otherwise indicated.