Method for identifying the origin of Pangasius bocourti based on hydrogen and oxygen isotopes

Abstract

The present invention provides a method for identifying the origin of basa fish based on hydrogen and oxygen isotopes, comprising the following steps: S1. Obtain the abundance values a and b of δ18O (‰) and δD (‰) in the water in the meat part of the basa fish sample to be tested; S2. Obtain the theoretical abundance values f(a) and f(b) of δ18O (‰) and δD (‰) in the water for the cultured basa fish through the a and b values; S3. Obtain the 18O fractionation coefficient ξ1 of the basa fish meat part and the D fractionation coefficient ξ2 of the basa fish meat part through the f(a), f(b), a, and b values; S4. If ξ1 is less than 42% and / or ξ2 is less than 23%, then determine that the sample to be tested is genuine. The identification method of the present invention has high accuracy, low cost, convenient implementation, and strong operability.

Description

Technical Field

[0001] The present invention relates to the technical field of origin tracing, and in particular to a method for identifying the origin of basa fish based on hydrogen and oxygen isotopes. Background Art

[0002] Basa fish is an important freshwater aquaculture species in Southeast Asian countries and a unique high-quality economic fish in the Mekong River Basin. Due to the high economic benefits of Basa fish, a large number of fake Basa fish have appeared on the market in recent years. Fake Basa fish are of poor quality, the meat is not tender enough, the taste is slightly rough, and the taste is not as delicious as the real Basa fish. The nutritional value of fake Basa fish will be reduced. Hormones and other substances may be added during the breeding process, resulting in poor content. In contrast, real Basa fish is rich in protein, unsaturated fatty acids and multiple vitamins, which are beneficial to human health. Fake Basa fish is mainly geographically fake (origin fake), that is, Basa fish farmed outside the country impersonating Basa fish farmed in the Mekong River, which has a serious impact on the Basa fish market.

[0003] At present, there are many methods for geo-fake, but most of them are relatively complex, and the operability or executability overlaps; for example, the application number is 201911141295.3, a mixed identification method and system for the origin of imported salmon, which combines infrared spectrum data, isotope mass spectrum data, mineral element data, and amino acids to trace the origin of imported salmon. This method requires a lot of data to be collected and is difficult to promote and implement. Another example is the application number 202010480809.4, a method for identifying the origin of fish. This method uses otoliths as the characterization object of the origin of commercially available fish, and the principle of the ratio detection method is clearer. It can better represent the origin information than the traditional method and can more accurately and reliably determine the origin of fish. However, this method mainly requires the collection of otoliths, and rare earth elements are used as indicators, HR-ICP-MS is used as an analysis method, and statistical modeling such as artificial neural networks, discriminant analysis, and cluster analysis is used. New statistical methods can be used for execution. The entire process of this method is complicated and not suitable for promotion and implementation. Summary of the invention

[0004] The main purpose of the present invention is to provide a method for identifying the origin of Basa fish based on hydrogen and oxygen isotopes, aiming to solve the problem of fake origin of Basa fish in the market.

[0005] To solve the above problems, the present application provides a method for identifying the origin of basa fish based on hydrogen and oxygen isotopes, including the following steps: S1. Obtain the abundance values a and b of δ18O (‰) and δD (‰) in the water of the meat part of the basa fish sample to be tested; S2. Obtain the theoretical abundance values f(a) and f(b) of δ18O (‰) and δD (‰) in the water of the farmed basa fish through the a and b values; S3. Obtain the 18O fractionation coefficient ξ1 of the basa fish meat part and the D fractionation coefficient ξ2 of the basa fish meat part through the f(a), f(b), a, and b values; S4. If ξ1 is less than 42% and / or ξ2 is less than 23%, then determine that the sample to be tested is genuine.

[0006] In one embodiment, f(a) = 8.173 * f(b) + 3.7936, f(a) = 0.7981 * a + 0.2388; ξ1 = |[a - f(a)] / f(a)|; ξ2 = |[b - f(b)] / f(b)|.

[0007] In one embodiment, the range of ξ1 is 15% - 29%, and the range of ξ2 is 1% - 19%.

[0008] In one embodiment, the method for identifying the origin of basa fish using ξ1 as an index is based on January - April and August - December.

[0009] In one embodiment, the method for identifying the origin of basa fish using ξ2 as an index is based on January - April and November - December.

[0010] In one embodiment, in step S1, the method for obtaining the water in the meat part of the basa fish sample to be tested is as follows: Take the meat part of the basa fish sample;

[0011] Remove the water on the surface of the basa fish; Mash the dewatered meat part into a paste; Put the paste - like meat part into a centrifuge tube for centrifugation; Take the supernatant after centrifugation of the sample as the water sample to be tested.

[0012] In one embodiment, the method for removing the water on the surface of the basa fish is to air - dry it with a blower, and the air - drying time does not exceed 30 seconds, and the temperature is 15°C - 30°C.

[0013] In one embodiment, the origin of the basa fish is 106E, 013N of the Mekong River. Description of the Drawings

[0014] The realization, functional features and advantages of the present invention will be further described in conjunction with embodiments with reference to the accompanying drawings. To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for description in the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on the structures shown in these drawings.

[0015] Figures 1 - 3 Graph of the linear relationship between δ18O (‰) and δD (‰) in the water for basa fish farming in the Yunnan A1 basa fish farming area, and between δ18O (‰) and δD (‰) in the water in basa fish bodies;

[0016] Figures 4 - 6 Graph of the linear relationship between δ18O (‰) and δD (‰) in the water for basa fish farming in the Yunnan A2 basa fish farming area, and between δ18O (‰) and δD (‰) in the water in basa fish bodies;

[0017] Figures 4 - 9 Graph of the linear relationship between δ18O (‰) and δD (‰) in the water for basa fish farming in the Yunnan A3 basa fish farming area, and between δ18O (‰) and δD (‰) in the water in basa fish bodies;

[0018] Figures 10 - 12 Graph of the linear relationship between δ18O (‰) and δD (‰) in the water for basa fish farming in the Yunnan B1 basa fish farming area, and between δ18O (‰) and δD (‰) in the water in basa fish bodies;

[0019] Figures 13 - 15 Graph of the linear relationship between δ18O (‰) and δD (‰) in the water for basa fish farming in the Yunnan B2 basa fish farming area, and between δ18O (‰) and δD (‰) in the water in basa fish bodies;

[0020] Figures 16 - 18 Graph of the linear relationship between δ18O (‰) and δD (‰) in the water for basa fish farming in the Yunnan B3 basa fish farming area, and between δ18O (‰) and δD (‰) in the water in basa fish bodies;

[0021] Figures 19 - 21 Graph of the linear relationship between δ18O (‰) and δD (‰) in the water for basa fish farming in the Hainan D1 basa fish farming area, and between δ18O (‰) and δD (‰) in the water in basa fish bodies;

[0022] Figures 22 - 24 Graph of the linear relationship between δ18O (‰) and δD (‰) in the water for basa fish farming in the Hainan D2 basa fish farming area, and between δ18O (‰) and δD (‰) in the water in basa fish bodies;

[0023] Figures 25 - 27 The linear relationship diagram between δ18O (‰) and δD (‰) in the water used for basa fish farming in the basa fish farming area D3 in Hainan, and the δ18O (‰) and δD (‰) in the water in the basa fish body;

[0024] Figures 28 - 30 The linear relationship diagram between δ18O (‰) and δD (‰) in the water used for basa fish farming in the basa fish farming area E1 in Hainan, and the δ18O (‰) and δD (‰) in the water in the basa fish body;

[0025] Figures 31 - 33 The linear relationship diagram between δ18O (‰) and δD (‰) in the water used for basa fish farming in the basa fish farming area E2 in Hainan, and the δ18O (‰) and δD (‰) in the water in the basa fish body;

[0026] Figures 34 - 36 The linear relationship diagram between δ18O (‰) and δD (‰) in the water used for basa fish farming in the basa fish farming area E3 in Hainan, and the δ18O (‰) and δD (‰) in the water in the basa fish body;

[0027] Figures 37 - 39 The linear relationship diagram between δ18O (‰) and δD (‰) in the water used for basa fish farming in the basa fish farming areas N1 - N3 in the Mekong River, and the δ18O (‰) and δD (‰) in the water in the basa fish body.

[0028] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0029] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0030] In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement them. When the combination of the technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the protection scope required by the present invention.

[0031] Basa fish is a scaleless fish species. Its body surface has no scales, and there are two body colors. One has a blue - black back and a relatively white abdomen, and the other has a pink - white back and a white abdomen. Basa fish is an important freshwater aquaculture variety in Southeast Asian countries, especially in Vietnam.

[0032] Due to the tender and white flesh, rich nutrition of Pangasius bocourti, which contains abundant elements such as protein, vitamin A, calcium, etc., as well as the unique abdominal fat mass, it has high development and utilization value. Therefore, Pangasius bocourti has gradually gained popularity in the global market. Especially in China, the market prospect of Pangasius bocourti is extremely broad.

[0033] In recent years, large-scale Pangasius bocourti breeding bases have emerged successively in China, such as in regions like Hunan, Jiangxi, Guangxi, Guangdong, Yunnan, etc. The annual output of Pangasius bocourti in these regions even exceeds that of Vietnam.

[0034] Compared with other types of fish, the breeding conditions of Pangasius bocourti are relatively more demanding. For example, the suitable water temperature for the growth of Pangasius bocourti is 22 - 32 °C. When the water temperature is lower than 20 °C, as the water temperature decreases, its growth rate slows down. When the water temperature is lower than 13 °C, Pangasius bocourti will be frostbitten or even die directly. That is to say, when the water temperature is low, it seriously affects the output of Pangasius bocourti. Therefore, in places like Hunan, Jiangxi, and Guangxi, Pangasius bocourti can only be bred in several seasons with relatively high temperatures. The Mekong River, due to its location in the tropical and subtropical regions, has a relatively high water temperature throughout the year, which is especially suitable for breeding Pangasius bocourti. Moreover, it can be bred throughout the year. In addition, the Pangasius bocourti in the Mekong River is bred in flowing water and no hormones need to be added during breeding. The fish quality of the bred Pangasius bocourti is higher than that in other regions. Considering the above factors, the price of the Pangasius bocourti bred in the Mekong River is higher than that of the Pangasius bocourti bred in other regions.

[0035] Due to the price difference caused by the origin, some unscrupulous merchants use some means to forge the origin of Pangasius bocourti to seek improper benefits, which has seriously impacted the Pangasius bocourti market.

[0036] Based on this, through the hydrogen and oxygen isotope analysis method, this application has obtained a method to distinguish the origin of Pangasius bocourti, which can thus protect the Pangasius bocourti market, safeguard the interests of regular merchants, and protect the rights and interests of consumers.

[0037] Through three years of sampling, this method has obtained multiple Pangasius bocourti samples and multiple water samples (a total of 135 Pangasius bocourti samples and 135 water samples were taken from the Mekong River samples in three years). And the abundances of 18O and D in these samples were detected, forming a relatively large database, and summarizing the rules from the database to find a method to identify the falsely labeled origin in the market.

[0038] The sample information is shown in the following table

[0039] Table 1 Sampling information table of thirteen Pangasius bocourti breeding areas

[0040]

[0041]

[0042] The 7 domestic provinces and cities in Table 1 are the main basa fish farming areas in recent years. More than 44% of the basa fish in the market comes from the above-mentioned farming areas, and other farming areas are mainly concentrated in Yunnan, Guangxi, Hainan and other places.

[0043] Since basa fish has relatively strict requirements for the breeding water temperature, generally, it is not suitable for breeding basa fish in the northern regions of China, and it is also not very suitable for breeding basa fish in most parts of the central regions. For example, in places such as Hubei, Jiangxi, and Hunan, due to the large seasonal temperature difference, the water temperature needs to be monitored throughout the year. When the water temperature is low, the water needs to be heated, and when the water temperature is high, the water temperature needs to be cooled, which increases the breeding cost of basa fish to a certain extent. In addition, due to the influence of temperature, it is not very suitable to breed basa fish throughout the year in these areas. It is often more suitable to breed in summer or autumn, which can also reduce the breeding cost.

[0044] However, places such as Guangxi, Guangdong, and Hainan are more suitable for breeding basa fish due to their small seasonal temperature difference and relatively high general temperature.

[0045] By detecting the samples of the above 7 domestic provinces and cities, the isotope data in Table 2 below is obtained:

[0046] Among them, the method for obtaining the water in the meat part of the basa fish sample to be detected is as follows: take the meat part of the basa fish sample; remove the water on the surface of the basa fish; mash the meat part after removing the water into mud; put the mashed meat part into a centrifuge tube for centrifugation; take the supernatant after centrifugation of the sample as the water sample to be detected.

[0047] During the transportation of the basa fish sample, its surface will be contaminated with various waters, and the various waters it is contaminated with are not of the same source as the breeding water, which will cause great interference when measuring the 18O and D abundances of the basa fish sample.

[0048] Based on this, in order to avoid the interference of exogenous water to the greatest extent, the water on the surface of the basa fish can be wiped with blotting paper, or it can be treated with a desiccant, but the water cannot be removed by drying, because during the drying process, the water in the meat part of the sample will be lost, which will also indirectly lead to isotope fractionation, thus affecting the detection results.

[0049] In this application, the water on the surface of the basa fish sample is removed by the air-drying method. Specifically, the surface of the sample is blown by a high-speed blower, and the air-drying time does not exceed 30 seconds, and the air-drying temperature is suitable at room temperature, such as the temperature is 15°C - 30°C.

[0050] Table 2 7 Provinces in China and the Mekong River 18 Isotope Data Table of O and D

[0051]

[0052]

[0053] By comparing δ18O (‰) of the aquaculture water, δ18O (‰) of the flesh of Pangasius bocourti, δD (‰) of the aquaculture water, and δD (‰) of the flesh of Pangasius bocourti, it can be found that in the same water quality, δ18O (‰) in the flesh of Pangasius bocourti is significantly higher than δ18O (‰) of the aquaculture water, that is, an enrichment effect occurs.

[0054] The reason is that after Pangasius bocourti ingests food or water, it preferentially takes in 18O and D (relative to 16O and 1H) in its body, resulting in this enrichment effect.

[0055] To study the isotope fractionation law, the isotope detection results of all Mekong River samples are presented separately by month, as shown in Tables 3 - 5.

[0056] Table 3 is the data table of 38 samples collected from the Mekong River for 12 consecutive months

[0057]

[0058]

[0059] Table 4 is the data table of 42 samples collected from the Mekong River for 12 consecutive months

[0060]

[0061]

[0062]

[0063] Table 5 is the data table of 55 samples collected from the Mekong River for 12 consecutive months

[0064]

[0065]

[0066]

[0067] It can be seen from Tables 3 - 5 that in the Mekong River, since May, δ18O (‰) in the aquaculture water has suddenly decreased and continued until the end of September (until October in the N1 database; until the end of September in the N2 database; until October in the N3 database). It can be seen that δ18O (‰) does not decrease smoothly, but drops abruptly and then gradually levels off.

[0068] Through local investigation, it is found that the rainy season in the sampling area (106E, 013N) lasts from May to October. Thus, it can be seen that local rainfall has a great impact on δ18O (‰).

[0069] In terms of the sampling time, the sampling in Hubei was in November (average value of -7.5), the sampling times in Guangxi were in August, September, and November (average value of -8.2), the sampling time in Jiangxi was in October (average value of -5.5), the sampling time in Hunan was in October (average value of -5.9), and the sampling time in Guangdong was in October (average value of -6.6).

[0070] Comparing with the N1-N3 database, the δ18O (‰) value in Hubei in November is much lower than that in N1-N3; the δ18O (‰) data in Guangxi in August and September are both lower than that in N1-N3, while in November it is much higher than that in N1-N3; the δ18O (‰) in Jiangxi in October is much higher than that in N1-N3; the δ18O (‰) value in Hunan in October is much higher than that in N1-N3; the δ18O (‰) data in Guangdong in October is much higher than that in N1-N3. Therefore, it can be seen that the δ18O (‰) of the above-mentioned provinces will not interfere with the δ18O (‰) value of Pangasius bocourti in the Mekong River in the market.

[0071] The data that can interfere with the δ18O (‰) of the Mekong River tending to δ18O (‰) are A1-A3, B1-B3, D1-D3, and E1-E3.

[0072] For further analysis, please refer to the data in Table 6 - Table 17:

[0073] Table 6 is the data table of 12 samples collected continuously for 12 months in the A1 farming area in Yunnan

[0074]

[0075]

[0076] Table 7 is the data table of 12 samples collected continuously for 12 months in the A2 farming area in Yunnan

[0077]

[0078] Table 8 is the data table of 9 samples collected continuously for 9 months in the A3 farming area in Yunnan

[0079]

[0080]

[0081] Table 9 is a data table of 11 samples collected continuously for 11 months in the B1 breeding area of Yunnan

[0082]

[0083] Table 10 is a data table of 10 samples collected continuously for 10 months in the B2 breeding area of Yunnan

[0084]

[0085]

[0086] Table 11 is a data table of 8 samples collected continuously for 8 months in the B3 breeding area of Yunnan

[0087]

[0088] Table 12 is a data table of 30 samples collected continuously for 10 months in the D1 breeding area of Yunnan

[0089]

[0090]

[0091] Table 13 is a data table of 27 samples collected continuously for 9 months in the D2 breeding area of Yunnan

[0092]

[0093]

[0094]

[0095] Table 14 is a data table of 38 samples collected continuously for 12 months in the D3 breeding area of Yunnan

[0096]

[0097]

[0098] Table 15 is a data table of 33 samples collected continuously for 7 months in the E1 breeding area of Yunnan

[0099]

[0100]

[0101] Table 16 is a data table of 31 samples collected continuously for 9 months in the E2 breeding area of Yunnan

[0102]

[0103]

[0104] Table 17 is the data table of 28 samples collected continuously for 8 months in the E3 aquaculture area of Yunnan.

[0105]

[0106]

[0107]

[0108] For the A1 - A3 aquaculture areas, from the data in Table 6, it can be obtained that there is an excellent linear correlation between δ18O (‰) and δD (‰) in the aquaculture water (refer to Figure 1 ), where f(a) is the value of δ18O (‰) in the aquaculture water, f(b) is the value of δD (‰) in the aquaculture water, a is the value of δ18O (‰) in the water of the xylem, and b is the value of δD (‰) in the water of the xylem: f(a) = 7.423 * f(b) + 3.4428, R2 = 0.9902;

[0109] There is also a good linear relationship between the δ18O (‰) of the aquaculture water and the δ18O (‰) of the water in the xylem (refer to Figure 2 ): f(a) = 0.7761 * a + 1.8823, R2 = 0.9824;

[0110] There is a certain linear relationship between the δD (‰) of the aquaculture water and the δD (‰) of the water in the xylem (refer to Figure 3 ): f(b) = 0.4679 * b - 13.258, R2 = 0.941;

[0111] From this analysis, it can be obtained that in addition to the good linear relationships between f(a), f(b), a, and b, the fractionation coefficients of 18O and D in this aquaculture area can also be obtained. Here, the fractionation coefficient of 18O: ξ1 = |[a - f(a)] / f(a)|;

[0112] The fractionation coefficient of D: ξ2 = |[b - f(b)] / f(b)|;

[0113]

[0114] The range of the above ξ1 is between 42% - 48%, and the range of ξ2 is between 26% - 34%.

[0115] From the data in Table 7, it can be obtained that there is an excellent linear correlation between δ18O (‰) and δD (‰) in the aquaculture water (refer to Figure 4 ): f(a) = 7.828 * f(b) + 7.5519, R2 = 0.9965;

[0116] There is also a good linear relationship between the δ18O (‰) of the aquaculture water and the δ18O (‰) of the water in the xylem (see Figure 5 ): f(a) = 0.7371*a + 1.8605, R2 = 0.963;

[0117] There is a certain linear relationship between the δD (‰) of the aquaculture water and the δD (‰) of the water in the xylem (see Figure 6 ): f(b) = 0.479*b - 13.228, R2 = 0.9824;

[0118] The fractionation coefficients of 18O and D in this aquaculture area:

[0119]

[0120] The range of the above ξ1 is between 45% - 51%, and the range of ξ2 is between 25% - 32%.

[0121] It can be obtained from the data in Table 8 that there is an excellent linear correlation between the δ18O (‰) and δD (‰) in the aquaculture water (see Figure 7 ): f(a) = 7.6136*f(b) + 5.6505, R2 = 0.9986;

[0122] There is also a good linear relationship between the δ18O (‰) of the aquaculture water and the δ18O (‰) of the water in the xylem (see Figure 8 ): f(a) = 0.8887*a + 3.282, R2 = 0.9717;

[0123] There is a certain linear relationship between the δD (‰) of the aquaculture water and the δD (‰) of the water in the xylem (see Figure 9 ): f(b) = 0.4493*b - 15.121, R2 = 0.9781;

[0124] The fractionation coefficients of 18O and D in this aquaculture area:

[0125]

[0126] The range of the above ξ1 is between 45% - 55%, and the range of ξ2 is between 26% - 32%.

[0127] The above are the relevant data relationships of the three aquaculture areas A1 - A3 in Yunnan. From this data relationship, it can be seen that in the aquaculture areas A1 - A3, the correlation between the δ18O (‰) and δD (‰) values in the aquaculture water is excellent. That is, by detecting the value of one of them, the value of the other can be calculated through a linear relationship.

[0128] It can be concluded from the data in Table 9 that there is an excellent linear correlation between δ18O (‰) and δD (‰) in the aquaculture water (refer to Figure 10 ): f(a) = 7.5626 * f(b) + 5.3871, R2 = 0.9969;

[0129] There is also a good linear relationship between the value of δ18O (‰) in the aquaculture water and the δ18O (‰) of the water in the xylem (refer to Figure 11 ): f(a) = 0.4484 * a - 0.4145, R2 = 0.9774;

[0130] There is a certain linear relationship between δD (‰) in the aquaculture water and δD (‰) of the water in the xylem (refer to Figure 12 ): f(b) = 0.4388 * b - 15.38, R2 = 0.965;

[0131] The fractionation coefficients of 18O and D in this aquaculture area:

[0132]

[0133] The range of the above ξ1 is between 49% - 52%, and the range of ξ2 is between 23% - 32%.

[0134] It can be concluded from the data in Table 10 that there is an excellent linear correlation between δ18O (‰) and δD (‰) in the aquaculture water (refer to Figure 13 ): f(a) = 7.6729 * f(b) + 6.2142, R2 = 0.9986;

[0135] There is also a good linear relationship between the value of δ18O (‰) in the aquaculture water and the δ18O (‰) of the water in the xylem (refer to Figure 14 ): f(a) = 0.5481 * a + 0.5453, R2 = 0.9708;

[0136] There is a certain linear relationship between δD (‰) in the aquaculture water and δD (‰) of the water in the xylem (refer to Figure 15 ): f(b) = 0.4285 * b - 16.04, R2 = 0.9854;

[0137] The fractionation coefficients of 18O and D in this aquaculture area:

[0138]

[0139] The range of the above ξ1 is between 51% - 54%, and the range of ξ2 is between 25% - 32%.

[0140] It can be concluded from the data in Table 11 that there is an excellent linear correlation between δ18O (‰) and δD (‰) in the aquaculture water (refer toFigure 16 ): f(a) = 7.5554 * f(b) + 5.532, R2 = 0.9945;

[0141] There is also a good linear relationship between the δ18O (‰) of the aquaculture water and the δ18O (‰) of the water in the xylem (refer to Figure 17 ): f(a) = 0.7934 * a + 2.3744, R2 = 0.9747;

[0142] There is a certain linear relationship between the δD (‰) of the aquaculture water and the δD (‰) of the water in the xylem (refer to Figure 18 ): f(b) = 0.4338 * b - 15.828, R2 = 0.973;

[0143] The fractionation coefficients of 18O and D in this aquaculture area:

[0144]

[0145] The range of the above ξ1 is between 50% - 55%, and the range of ξ2 is between 24% - 29%.

[0146] The above are the relevant data relationships of the three aquaculture areas B1 - B3 in Yunnan. From this data relationship, it can be seen that in the B1 - B3 aquaculture areas, the correlation between the δ18O (‰) and δD (‰) values in the aquaculture water is still excellent, and the linear relationship between f(b) and b is still poor.

[0147] For the D1 - D3 aquaculture areas, through the data in Table 12, it can be obtained that there is an excellent linear correlation between the δ18O (‰) and δD (‰) in the aquaculture water (refer to Figure 19 ): f(a) = 7.9028 * f(b) + 7.6599, R2 = 0.9919;

[0148] There is also a good linear relationship between the δ18O (‰) of the aquaculture water and the δ18O (‰) of the water in the xylem (refer to Figure 20 ): f(a) = 0.3066 * a - 1.2978, R2 = 0.9529;

[0149] There is a certain linear relationship between the δD (‰) of the aquaculture water and the δD (‰) of the water in the xylem (refer to Figure 21 ): f(b) = 0.3977 * b - 12.097, R2 = 0.9927;

[0150] The fractionation coefficients of 18O and D in this aquaculture area:

[0151]

[0152]

[0153] The range of the above ξ1 is between 47% and 51%, and the range of ξ2 is between 28% and 36%.

[0154] From the data in Table 13, it can be obtained that there is an excellent linear correlation between δ18O (‰) and δD (‰) in the aquaculture water (refer to Figure 22 ): f(a) = 7.7409 * f(b) + 6.6675, R2 = 0.9967;

[0155] There is also a good linear relationship between the value of δ18O (‰) in the aquaculture water and the δ18O (‰) of the water in the xylem (refer to Figure 23 ): f(a) = 0.8046 * a + 1.6216, R2 = 0.9712;

[0156] There is a certain linear relationship between δD (‰) in the aquaculture water and δD (‰) of the water in the xylem (refer to Figure 24 ): f(b) = 0.4078 * b - 11.75, R2 = 0.9826;

[0157]

[0158]

[0159] The range of the above ξ1 is between 42% and 53%, and the range of ξ2 is between 26% and 36%.

[0160] From the data in Table 14, it can be obtained that there is an excellent linear correlation between δ18O (‰) and δD (‰) in the aquaculture water (refer to Figure 25 ): f(a) = 7.7813 * f(b) + 6.9458, R2 = 0.9956;

[0161] There is also a good linear relationship between the value of δ18O (‰) in the aquaculture water and the δ18O (‰) of the water in the xylem (refer to Figure 26 ): f(a) = 0.9163 * a + 2.5844, R2 = 0.9746;

[0162] There is a certain linear relationship between δD (‰) in the aquaculture water and δD (‰) of the water in the xylem (refer to Figure 27 ): f(b) = 0.4292 * b - 10.53, R2 = 0.9742;

[0163]

[0164]

[0165] The range of the above ξ1 is between 42% and 54%, and the range of ξ2 is between 29% and 37%.

[0166] The above are the relevant data relationships of the three breeding areas D1 - D3 in Hainan. From this data relationship, it can be seen that in the D1 - D3 breeding areas, the correlation between δ18O (‰) and δD (‰) values in the breeding water is still extremely good; the linear relationships between f(a) and a, and between f(b) and b are also relatively good.

[0167] For the E1 - E3 breeding areas, through the data in Table 15, it can be obtained that there is an extremely good linear correlation between δ18O (‰) and δD (‰) in the breeding water (refer to Figure 28 ): f(a) = 7.5671 * f(b) + 5.4579, R2 = 0.9972;

[0168] There is also a relatively good linear relationship between the value of δ18O (‰) in the breeding water and the δ18O (‰) of the water in the xylem (refer to Figure 29 ): f(a) = 0.5686 * a + 0.5751, R2 = 0.9631;

[0169] There is a certain linear relationship between δD (‰) in the breeding water and δD (‰) of the water in the xylem (refer to Figure 30 ): f(b) = 0.4087 * b - 11.536, R2 = 0.9758;

[0170]

[0171]

[0172] The range of the above ξ1 is between 49% - 53%, and the range of ξ2 is between 34% - 40%.

[0173] Through the data in Table 16, it can be obtained that there is an extremely good linear correlation between δ18O (‰) and δD (‰) in the breeding water (refer to Figure 31 ): f(a) = 7.5836 * f(b) + 5.5902, R2 = 0.998;

[0174] There is also a relatively good linear relationship between the value of δ18O (‰) in the breeding water and the δ18O (‰) of the water in the xylem (refer to Figure 32 ): f(a) = 0.7084 * a + 1.6872, R2 = 0.9678;

[0175] There is a certain linear relationship between δD (‰) in the breeding water and δD (‰) of the water in the xylem (refer to Figure 33 ): f(b) = 0.3989 * b - 12.082, R2 = 0.9721;

[0176]

[0177]

[0178] The range of the above ξ1 is between 48% and 56%, and the range of ξ2 is between 26% and 38%.

[0179] From the data in Table 17, it can be obtained that there is an excellent linear correlation between δ18O (‰) and δD (‰) in the aquaculture water (refer to Figure 34 ): f(a) = 7.7441 * f(b) + 6.8087, R2 = 0.9948;

[0180] There is also a good linear relationship between the value of δ18O (‰) in the aquaculture water and the value of δ18O (‰) in the water of the xylem (refer to Figure 35 ): f(a) = 0.6272 * a + 0.6491, R2 = 0.9631;

[0181] There is a certain linear relationship between δD (‰) in the aquaculture water and δD (‰) in the water of the xylem (refer to Figure 36 ): f(b) = 0.3984 * b - 11.929, R2 = 0.9643;

[0182]

[0183]

[0184]

[0185] The range of the above ξ1 is between 43% and 47%, and the range of ξ2 is between 35% and 41%.

[0186] The above are the relevant data relationships in the three aquaculture areas E1 - E3 in Hainan. From this data relationship, it can be seen that in the aquaculture areas E1 - E3, the correlation between the values of δ18O (‰) and δD (‰) in the aquaculture water is still excellent; the linear relationship between f(a) and a, and the linear relationship between f(b) and b are also relatively good.

[0187] For basa fish in the market, the only things that can be detected are δ18O (‰) and δD (‰) in the meat of basa fish. The water used to farm basa fish is inaccessible. However, for effective detection of its authenticity, δ18O (‰) and δD (‰) in the water for farming basa fish are indispensable. From the linear relationships of f(a), f(b), a, and b above, it can be seen that by only detecting the values of a and b, the values of f(a) and f(b) can be calculated. Although there is a linear relationship between f(b) and b, generally speaking, the linear relationship between f(a) and f(b) is the most stable, and R2 remains above 0.99 (the average value is 0.9958). However, among the 12 groups of data, the correlation coefficients between f(a) and a and between f(b) and b are comparable, and their linear correlations are not as good as the correlation between f(a) and f(b).

[0188] To further study the δ18O and δD isotope data of basa fish in the Mekong River farming area, the fractionation coefficients of the N1, N2, and N3 farming areas in the Mekong River can be obtained from Table 3.

[0189]

[0190]

[0191]

[0192] Since rainwater has a great impact on δ18O and δD in the farming area, among the 6 months from May to October, the first 3 months have little reference value.

[0193] From the data of N1 - N3, it can be seen that:

[0194] The ξ1 data range of the N1 farming area from January to April and from August to December is: 20% - 29%;

[0195] The ξ2 data range of the N1 farming area from January to April and from November to December is: 4% - 18%;

[0196] The ξ1 data range of the N2 farming area from January to April and from August to December is: 20% - 29%;

[0197] The ξ2 data range of the N2 farming area from January to April and from November to December is: 1% - 16%;

[0198] The ξ1 data range of the N3 farming area from January to April and from August to December is: 15% - 26%;

[0199] The ξ2 data range of the N3 farming area from January to April and from November to December is: 1% - 19%;

[0200] It can be seen that in the N1-N3 breeding area of ​​the Mekong River, the fractionation coefficient ξ1 of 18O is significantly smaller than the fractionation coefficients of A1-A3, B1-B3, D1-D3, and E1-E3. Therefore, ξ1 can be used to identify the authenticity of Basa fish on the market in January-April and August-December.

[0201] In addition, although the fractionation coefficient ξ2 of D is obviously smaller than the fractionation coefficients of A1-A3, B1-B3, D1-D3, and E1-E3 as a whole, ξ2 can also be used as an indicator to identify the authenticity of Basa fish on the market.

[0202] Of course, if ξ1 and ξ2 are used as dual indicators, the identification accuracy will be higher (when measuring isotope abundance, it is affected by the instrument and the operator, and there may be certain errors in the measurement).

[0203] The δ18O and δD data of Basa fish vary greatly due to regional reasons. The main reasons are the feed sources of Basa fish, as well as the latitude and longitude, altitude, humidity, and rainfall of each region. In addition, since the water in the Mekong River aquaculture area is flowing, while the aquaculture water in other aquaculture areas is generally not flowing, this may be the reason for the difference in isotope fractionation.

[0204] Integrating the data of N1-N3, we get the linear relationship between f(a) and f(b), the linear relationship between f(a) and a, and the linear relationship between f(b) and b as follows:

[0205] Please refer to Figures 37 - 39 :

[0206] f(a)=8.173*f(b)+3.7936, R2=0.995;

[0207] f(a)=0.7981*a+0.2388, R2=0.9904;

[0208] f(b)=0.9173*b+1.7505, R2=0.8369;

[0209] The mass of element D is relatively small and is affected by the environment, so its abundance fluctuates greatly. It may be that in the Mekong River, it is affected by rainfall, temperature, and water flow, which leads to a poor linear relationship between f(b) and b.

[0210] That is, when faced with samples on the market, it is impossible to accurately obtain the value of f(b) by measuring the b value and then using the linear relationship between f(b) and b. This leads to a large error in the calculation of ξ2.

[0211] However, since the linear relationship between f(a) and f(b) is excellent, and the linear relationship between f(b) and a is also excellent, the value of f(a) can be calculated by measuring the value of a, and then f(b) can be calculated through f(a). Therefore, for the samples in the market, the values of a and b can be directly measured, so that the values of f(a) and f(b) can be obtained, and further ξ1 and ξ2 can be obtained.

[0212] Specifically as follows:

[0213] ξ1 = |[a - f(a)] / f(a)| = [0.2019 * a - 0.2388] / (0.7981 * a + 0.2388);

[0214] ξ2 = |[b - f(b)] / f(b)| = |(b - 0.097a + 0.435) / (0.097a - 0.435)|.

[0215] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structural transformation made under the inventive concept of the present invention by using the content of the specification and drawings of the present invention, or any direct / indirect application in other related technical fields, is included in the patent protection scope of the present invention.

Claims

1. A method for identifying the origin of pangasius based on hydrogen and oxygen isotopes, characterized in that: The steps include: S1. Obtain the abundance values ​​a and b of δ18O (‰) and δD (‰) in the flesh of the tested Basa fish sample; S2. The theoretical δ18O (‰) and δD (‰) abundance values ​​f (a) and f (b) of the water of farmed pangasius are obtained by using the values ​​a and b; S3, obtaining the 18O fractionation coefficient ξ1 of the flesh part of the basa fish and the D fractionation coefficient ξ2 of the flesh part of the basa fish through the values ​​of f(a), f(b), a and b; S4. If ξ1 is less than 42% and / or ξ2 is less than 23%, the sample to be tested is judged to be true; f(a)=8.173*f(b)+3.7936, f(a)=0.7981*a+0.2388; ξ1=|[af(a)] / f(a)|;ξ2=|[bf(b)] / f(b)|; The method of identifying the origin of pangasius using the ξ1 as an indicator is based on January to April and August to December; The method of identifying the origin of pangasius using the ξ2 as an indicator is based on January to April and November to December; The origin of the Basa fish is Mekong River 106E and 013N.

2. The method for identifying the origin of pangasius based on hydrogen and oxygen isotopes as claimed in claim 1, characterized in that: The range of ξ1 is 15%-29%, and the range of ξ2 is 1%-19%.

3. The method for identifying the origin of pangasius based on hydrogen and oxygen isotopes as claimed in claim 1, characterized in that: In step S1, the method for obtaining water from the meat part of the pangasius fish sample to be tested is as follows: Take the fleshy part of the Pangasius fish sample; Remove moisture from the surface of pangasius; Mash the fleshy part after removing moisture into a paste; The muddy fleshy part is placed in a centrifuge tube for centrifugation; Take the supernatant after centrifugation of the sample as the water sample to be tested.

4. The method for identifying the origin of pangasius based on hydrogen and oxygen isotopes as claimed in claim 3, characterized in that: The method for removing moisture from the surface of the basa fish is to air dry it with a fan, and the air drying time does not exceed 30 seconds and the temperature is 15° C. to 30° C.

Images