Method for identifying producing area of Basa fish based on hydrogen and oxygen isotope

Abstract

The invention provides a method for identifying the producing area of Basa fish based on hydrogen and oxygen isotopes. The method comprises the following steps: S1, acquiring abundance values a and b of delta < 18 > O (permillage) and delta < D (permillage) in water of the meat part of a to-be-detected Basa fish sample; s2, obtaining theoretical abundance values f (a) and f (b) of delta < 18 > O (per mill) and delta < D (per mill) in water for culturing Basa fish according to the values a and b; s3, obtaining a Basa fish meat part 18O fractionation coefficient xi1 and a Basa fish meat part D fractionation coefficient xi2 through f (a), f (b), a and b values; and S4, if the xi 1 is less than 42% and / or the xi 2 is less than 23%, judging that the to-be-detected sample is true. The identification method is high in accuracy, low in cost, convenient to implement and high in 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, comprising the following steps: S1, obtaining δ18O(‰) and δD(‰) abundance values ​​a and b in the flesh of the basa fish sample to be tested; S2, obtaining theoretical δ18O(‰) and δD(‰) abundance values ​​f(a) and f(b) in the water of farmed basa fish through the a and b values; S3, obtaining the 18O fractionation coefficient ξ1 of the flesh of basa fish and the D fractionation coefficient ξ2 of the flesh of basa fish through the f(a), f(b), a and b values; S4, if ξ1 is less than 42% and / or ξ2 is less than 23%, the sample to be tested is judged to be true.

[0006] In one embodiment, 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)|.

[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 of identifying the origin of Basa fish using the ξ1 as an indicator is based on January to April and August to December.

[0009] In one embodiment, the method of identifying the origin of Pangasius using the ξ2 as an indicator is based on January to April and November to December.

[0010] In one embodiment, in step S1, the method for obtaining water from the fleshy part of the Basa fish sample to be tested is as follows: taking the fleshy part of the Basa fish sample;

[0011] Remove moisture from the surface of the basa fish; mash the meat portion after the moisture is removed into mud; put the muddy meat portion into a centrifuge tube for centrifugation; take the supernatant after the sample is centrifuged as the water sample to be tested.

[0012] In one embodiment, 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.

[0013] In one embodiment, the origin of the Basa fish is Mekong River 106E, 013N. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The realization of the purpose, functional features and advantages of the present invention will be further described in conjunction with the embodiments and with reference to the accompanying drawings. In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on the structures shown in these accompanying drawings without creative work.

[0015] Figure 1-Figure 3 This is the linear relationship diagram between δ18O(‰), δD(‰) in the water for basa fish farming in A1 basa fish farming area in Yunnan, and δ18O(‰), δD(‰) in the water of basa fish body;

[0016] Figure 4-Figure 6 This is the linear relationship diagram between δ18O(‰), δD(‰) in the water for basa fish farming in A2 basa fish farming area in Yunnan, and δ18O(‰), δD(‰) in the water of basa fish body;

[0017] Figure 4-Figure 9 This is the linear relationship diagram between δ18O(‰), δD(‰) in the water for basa fish farming in A3 basa fish farming area in Yunnan, and δ18O(‰), δD(‰) in the water of basa fish body;

[0018] Figure 10-12 This is the linear relationship diagram between δ18O(‰), δD(‰) in the water for basa fish farming in Yunnan B1 basa fish farming area, and δ18O(‰), δD(‰) in the water of basa fish body;

[0019] Figure 13-Figure 15 This is the linear relationship diagram between δ18O(‰), δD(‰) in the water for basa fish farming in Yunnan B2 basa fish farming area, and δ18O(‰), δD(‰) in the water of basa fish body;

[0020] Figure 16-Figure 18 This is the linear relationship diagram between δ18O(‰), δD(‰) in the water for basa fish farming in Yunnan B3 basa fish farming area, and δ18O(‰), δD(‰) in the water of basa fish body;

[0021] Figure 19-21 This is the linear relationship diagram between δ18O(‰), δD(‰) in the water used for basa fish farming in D1 basa fish farming area in Hainan, and δ18O(‰), δD(‰) in the water of basa fish;

[0022] Figure 22-Figure 24 This is the linear relationship diagram between δ18O(‰), δD(‰) in the water used for basa fish farming in D2 basa fish farming area in Hainan, and δ18O(‰), δD(‰) in the water of basa fish;

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

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

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

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

[0027] Figure 37-Figure 39 Linear relationship between δ18O(‰) and δD(‰) in basa fish farming water in the N1-N3 basa fish farming areas of the Mekong River, and δ18O(‰) and δD(‰) in the water of basa fish.

[0028] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection 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 ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0031] Basa fish is a scaleless fish with no scales on its body. It has two body colors, one with a blue-black back and a white belly, and the other with a pink-white back and a white belly. Basa fish is an important freshwater aquaculture species in Southeast Asian countries, especially Vietnam.

[0032] As Basa fish is white and tender, rich in nutrients, rich in protein, vitamin A, calcium and other elements, as well as unique abdominal fat, it has a high development and utilization value. Therefore, Basa fish is gradually welcomed by the world market, especially in China, where the market prospect of Basa fish is very great.

[0033] In recent years, large-scale basa fish breeding bases have emerged in China, such as Hunan, Jiangxi, Guangxi, Guangdong, Yunnan and other regions. The annual production of basa fish even exceeds that of Vietnam.

[0034] Compared with other types of fish, the breeding conditions of Basa fish are relatively harsh. For example, the water temperature suitable for the growth of Basa fish is 22-32℃. When it is below 20℃, as the water temperature decreases, its growth rate slows down. When the water temperature is below 13℃, Basa fish will be frostbitten or die directly. In other words, when the water temperature is low, the production of Basa fish is seriously affected. Therefore, in Hunan, Jiangxi, Guangxi and other places, Basa fish can only be farmed in a few seasons when the temperature is relatively high. The Mekong River is located in the tropical and subtropical regions, and its temperature remains at a high level all year round, which is particularly suitable for breeding Basa fish. Moreover, it can be farmed all year round. In addition, the Basa fish in the Mekong River is farmed in live water, and no hormones are required during breeding. The quality of the farmed Basa fish is higher than that in other regions. Combining the above factors, the price of Basa fish farmed in the Mekong River is higher than that of Basa fish farmed in other regions.

[0035] Since prices vary depending on the place of origin, some unscrupulous merchants use some means to falsify the origin of Basa fish in order to gain unfair profits, which has seriously impacted the Basa fish market.

[0036] Based on this, this application uses hydrogen and oxygen isotope analysis to derive a set of methods that can distinguish the origin of Basa fish, thereby protecting the Basa fish market, safeguarding the interests of regular merchants, and protecting the rights and interests of consumers.

[0037] Through three years of sampling, this method obtained multiple Basa fish samples and multiple water samples (a total of 135 Basa fish samples and 135 water samples were sampled in the Mekong River in three years), and the 18O, D abundance of these samples was tested to form a relatively large database. The rules were summarized from the database and a method was found to identify false origin labeling on the market.

[0038] Please refer to the table below for sample information

[0039] Table 1 Sampling information of thirteen basa fish breeding areas

[0040]

[0041]

[0042] The seven domestic provinces and cities in Table 1 are the main basa fish farming areas in recent years. More than 44% of the basa fish on 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 strict requirements on the water temperature for breeding, it is generally not suitable for breeding Basa fish in the northern part of China, and it is also not suitable for breeding Basa fish in most areas of the central region. For example, Hubei, Jiangxi, Hunan and other places have large seasonal temperature differences, so the water temperature needs to be monitored all year round. When the water temperature is low, the water needs to be heated, and when the water temperature is high, the water needs to be cooled, which increases the breeding cost of Basa fish to a certain extent. In addition, due to the influence of temperature, these areas are not suitable for breeding Basa fish all year round. It is often more appropriate to breed in summer or autumn, which can also reduce breeding costs.

[0044] Guangxi, Guangdong, Hainan and other places are more suitable for the breeding of Basa fish because of their small seasonal temperature differences and relatively high general temperatures.

[0045] By testing samples from the above 7 provinces and cities in China, the isotope data in Table 2 below were obtained:

[0046] The method for obtaining water from the flesh of the Basa fish sample to be tested is as follows: taking the flesh of the Basa fish sample; removing moisture from the surface of the Basa fish; mashing the flesh after the moisture is removed into mud; placing the muddy flesh into a centrifuge tube for centrifugation; and taking the supernatant after the sample is centrifuged as the water sample to be tested.

[0047] During the transportation process, the surface of Basa fish samples will be contaminated with various moisture, which is different from the water in its breeding. Therefore, it will cause great interference in the determination of 18O and D Fengdu of Basa fish samples.

[0048] Based on this, in order to avoid the interference of exogenous moisture to the greatest extent, the moisture on the surface of Basa fish can be wiped with absorbent paper, or it can be treated with desiccant, but the moisture cannot be removed by drying, because the drying process will cause the loss of moisture in the fleshy part of the sample, and indirectly lead to isotope diversion, thus affecting the test results.

[0049] In the present application, moisture on the surface of the basa fish sample is removed by air drying. Specifically, a high-speed fan is used to blow on the surface of the sample, and the air drying time does not exceed 30 seconds. The air drying temperature is room temperature, for example, 15°C to 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 the δ18O(‰) of aquaculture water and the δ18O(‰) of basa fish flesh, and comparing the δD(‰) of aquaculture water and the δD(‰) of basa fish flesh, it can be found that in the same water quality, the δ18O(‰) in the basa fish flesh is significantly higher than that in aquaculture water, which means that an enrichment effect has occurred.

[0054] The reason is that after taking in food or water, Basa fish will give priority to 18O and D (relative to 16O and 1H) in its body, which leads to this enrichment effect.

[0055] In order to study the isotope diversion law, the isotope test results of all Mekong River samples are displayed by month, as shown in Tables 3-5.

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

[0057]

[0058]

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

[0060]

[0061]

[0062]

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

[0064]

[0065]

[0066]

[0067] From Tables 3 to 5, we can see that in the Mekong River, the δ18O(‰) in aquaculture water suddenly dropped from May and lasted until the end of September (lasting until October in the N1 database, until the end of September in the N2 database, and until October in the N3 database). It can be seen that the δ18O(‰) did not drop smoothly, but dropped precipitously, and then gradually became stable.

[0068] After local investigation, it was found that the rainy season in the sampling area (106E, 013N) lasts from May to October. This shows that local rainfall has a great impact on δ18O(‰).

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

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

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

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

[0073] Table 6 shows the data of 12 samples collected from the A1 breeding area in Yunnan for 12 consecutive months.

[0074]

[0075]

[0076] Table 7 shows the data of 12 samples collected from the A2 breeding area in Yunnan for 12 consecutive months.

[0077]

[0078] Table 8 is a table of 9 samples collected from the A3 breeding area in Yunnan for 9 consecutive months.

[0079]

[0080]

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

[0082]

[0083] Table 10 is a table of 10 samples collected from the B2 breeding area in Yunnan for 10 consecutive months.

[0084]

[0085]

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

[0087]

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

[0089]

[0090]

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

[0092]

[0093]

[0094]

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

[0096]

[0097]

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

[0099]

[0100]

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

[0102]

[0103]

[0104] Table 17 is a table of 28 samples collected from the E3 breeding area in Yunnan for 8 consecutive months.

[0105]

[0106]

[0107]

[0108] For the A1-A3 aquaculture areas, the data in Table 6 show that there is an excellent linear correlation between δ18O(‰) and δD(‰) in aquaculture water (refer to Figure 1 ), f(a) is the value of δ18O(‰) in aquaculture water, f(b) is the value of δD(‰) in aquaculture water, a is the value of δ18O(‰) in water in the fleshy part, b is the value of δD(‰) in water in the fleshy part: 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 fleshy part (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 aquaculture water and the δD(‰) of water in the fleshy part (refer to Figure 3 ): f(b)=0.4679*b-13.258, R2=0.941;

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

[0112] Fractionation coefficient of D: ξ2 = |[bf(b)] / f(b)|;

[0113]

[0114] The above ξ1 ranges from 42% to 48%, and the range of ξ2 ranges from 26% to 34%.

[0115] From the data in Table 7, it can be concluded that there is an excellent linear correlation between δ18O(‰) and δD(‰) in 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 fleshy part (refer to Figure 5 ): f(a)=0.7371*a+1.8605, R2=0.963;

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

[0118] The fractionation coefficient of 18O and D in this breeding area is:

[0119]

[0120] The above ξ1 ranges from 45% to 51%, and the range of ξ2 ranges from 25% to 32%.

[0121] From the data in Table 8, it can be concluded that there is an excellent linear correlation between δ18O(‰) and δD(‰) in aquaculture water (refer to 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 fleshy part (refer to Figure 8 ): f(a)=0.8887*a+3.282, R2=0.9717;

[0123] There is a certain linear relationship between the δD(‰) of aquaculture water and the δD(‰) of water in the fleshy part (refer to Fig. 9 ): f(b)=0.4493*b-15.121, R2=0.9781;

[0124] The fractionation coefficient of 18O and D in this breeding area is:

[0125]

[0126] The above ξ1 ranges from 45% to 55%, and the range of ξ2 ranges from 26% to 32%.

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

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

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

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

[0131] The fractionation coefficient of 18O and D in this breeding area is:

[0132]

[0133] The above ξ1 ranges from 49% to 52%, and the range of ξ2 ranges from 23% to 32%.

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

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

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

[0137] The fractionation coefficient of 18O and D in this breeding area is:

[0138]

[0139] The above ξ1 ranges from 51% to 54%, and the range of ξ2 ranges from 25% to 32%.

[0140] From the data in Table 11, it can be concluded that there is an excellent linear correlation between δ18O(‰) and δD(‰) in aquaculture water (refer to Fig.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 fleshy part (refer to Fig.17 ): f(a)=0.7934*a+2.3744, R2=0.9747;

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

[0143] The fractionation coefficient of 18O and D in this breeding area is:

[0144]

[0145] The above ξ1 ranges from 50% to 55%, and the range of ξ2 ranges from 24% to 29%.

[0146] The above is the relevant data relationship 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 very good, and the linear relationship between f(b) and b is still poor.

[0147] For the D1-D3 aquaculture areas, the data in Table 12 show that there is an excellent linear correlation between δ18O(‰) and δD(‰) in aquaculture water (refer to Fig.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 fleshy part (refer to Fig. 20 ): f(a)=0.3066*a-1.2978, R2=0.9529;

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

[0150] The fractionation coefficient of 18O and D in this breeding area is:

[0151]

[0152]

[0153] The above ξ1 ranges from 47% to 51%, and the range of ξ2 ranges from 28% to 36%.

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

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

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

[0157]

[0158]

[0159] The above ξ1 ranges from 42% to 53%, and the range of ξ2 ranges from 26% to 36%.

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

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

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

[0163]

[0164]

[0165] The above ξ1 ranges from 42% to 54%, and the range of ξ2 ranges from 29% to 37%.

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

[0167] For the E1-E3 aquaculture areas, the data in Table 15 show that there is an excellent linear correlation between δ18O(‰) and δD(‰) in aquaculture water (refer to Fig.28 ): f(a)=7.5671*f(b)+5.4579, R2=0.9972;

[0168] There is also a good linear relationship between the δ18O(‰) of the aquaculture water and the δ18O(‰) of the water in the fleshy part (refer to Fig.29 ): f(a)=0.5686*a+0.5751, R2=0.9631;

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

[0170]

[0171]

[0172] The above ξ1 ranges from 49% to 53%, and the range of ξ2 ranges from 34% to 40%.

[0173] From the data in Table 16, it can be concluded that there is an excellent linear correlation between δ18O(‰) and δD(‰) in aquaculture water (refer to Fig.31 ): f(a)=7.5836*f(b)+5.5902, R2=0.998;

[0174] There is also a good linear relationship between the δ18O(‰) of the aquaculture water and the δ18O(‰) of the water in the fleshy part (refer to Fig.32 ): f(a)=0.7084*a+1.6872, R2=0.9678;

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

[0176]

[0177]

[0178] The above ξ1 ranges from 48% to 56%, and the range of ξ2 ranges from 26% to 38%.

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

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

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

[0182]

[0183]

[0184]

[0185] The above ξ1 ranges from 43% to 47%, and the range of ξ2 ranges from 35% to 41%.

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

[0187] For the basa fish in the market, only the δ18O(‰) and δD(‰) in the flesh of the basa fish can be tested. The water used to farm basa fish is not available, but in order to effectively test its authenticity, the δ18O(‰) and δD(‰) in the water used to farm basa fish are indispensable. From the above linear relationship between f(a), f(b), a and b, it can be seen that only the values ​​of a and b need to be tested to calculate the values ​​of f(a) and f(b). Although there is a linear relationship between f(b) and b, in general, the linear relationship between f(a) and f(b) is the most stable, and R2 is maintained above 0.99 (the average value is 0.9958). However, in the 12 sets of data, the correlation coefficient between f(a) and a is equivalent to the correlation coefficient between f(b) and b, and their linear correlation is not as good as the correlation between f(a) and f(b).

[0188] In order to further study the δ18O and δD isotope data of Pangasius in the Mekong River aquaculture area, the fractionation coefficients of the N1, N2 and N3 aquaculture areas of the Mekong River can be obtained through Table 3.

[0189]

[0190]

[0191]

[0192] Since rain has a greater impact on δ18O and δD in aquaculture areas, the first three months of the six months from May to October have almost no reference value.

[0193] From the data of N1-N3, we can see that:

[0194] The ξ1 data range of N1 breeding area in January-April and August-December is: 20%-29%;

[0195] The ξ2 data range of N1 breeding area in January-April and November-December is: 4%-18%;

[0196] The ξ1 data range of N2 breeding area in January-April and August-December is: 20%-29%;

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

[0198] The ξ1 data range of N3 breeding area in January-April and August-December is: 15%-26%;

[0199] The ξ2 data range of N3 breeding area in January-April and November-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 Figure 37-Figure 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, we can calculate f(a) by measuring the a value, and then calculate f(b) by f(a). Therefore, when facing samples on the market, we can directly measure the a value and b value, so as to obtain the values ​​of f(a) and f(b), and then we can obtain ξ1 and ξ2.

[0212] The details are as follows:

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

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

[0215] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are 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 abundance values ​​of δ18O(‰) and δD(‰) in the water of farmed pangasius are obtained through a and b values, f(a) and f(b); S3, obtaining the 18O fractionation coefficient ξ1 of the fleshy part of basa fish and the D fractionation coefficient ξ2 of the fleshy part of 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.

2. The method for identifying the origin of pangasius based on hydrogen and oxygen isotopes as claimed in claim 1, characterized in that: 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)|.

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

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 of identifying the origin of Basa fish using the ξ1 as an indicator is based on January to April and August to December.

5. The method for identifying the origin of pangasius based on hydrogen and oxygen isotopes as claimed in claim 3, characterized in that: The method of identifying the origin of Basa fish using the ξ2 as an indicator is based on January to April and November to December.

6. 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.

7. The method for identifying the origin of pangasius based on hydrogen and oxygen isotopes as claimed in claim 6, 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.

8. The method for identifying the origin of pangasius based on hydrogen and oxygen isotopes according to claim 1, characterized in that: The origin of the Basa fish is Mekong River 106E and 013N.

Images