CN113484305A - Seawater and freshwater aquaculture salmonidae fish tracing method based on multi-element analysis - Google Patents
Seawater and freshwater aquaculture salmonidae fish tracing method based on multi-element analysis Download PDFInfo
- Publication number
- CN113484305A CN113484305A CN202110751723.5A CN202110751723A CN113484305A CN 113484305 A CN113484305 A CN 113484305A CN 202110751723 A CN202110751723 A CN 202110751723A CN 113484305 A CN113484305 A CN 113484305A
- Authority
- CN
- China
- Prior art keywords
- seawater
- discrimination
- salmonidae
- fresh water
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/71—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light thermally excited
- G01N21/73—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light thermally excited using plasma burners or torches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
- G06F18/24147—Distances to closest patterns, e.g. nearest neighbour classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/2431—Multiple classes
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Biology (AREA)
- Health & Medical Sciences (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Plasma & Fusion (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Farming Of Fish And Shellfish (AREA)
Abstract
The invention provides a seawater and fresh water cultured salmonidae fish traceability method based on multi-element analysis, so that salmonidae fishes from seawater and fresh water sources can be effectively distinguished, the content of Ag, Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, Ga, K, Mg, Mn, Na, Ni, Sr and Zn elements of salmonidae fishes to be detected is used as an index, and a Linear Discriminant Analysis (LDA) method, a K Nearest Neighbor (KNN) method and a Random Forest (RF) method are used for traceability analysis. The source tracing method for the salmonidae fishes cultivated in the seawater and the freshwater is established by comparing the difference of the element compositions in the seawater and the freshwater in the salmonidae fishes, and can effectively trace the sources of the salmonidae fishes from the seawater and the freshwater.
Description
Technical Field
The invention belongs to the technical field of source detection of cultured fishes, and particularly relates to a source tracing method for seawater and freshwater cultured salmonidae fishes based on multi-element analysis.
Background
Aquatic products are the foods with the highest degree of trade in the world, and in recent years, along with the increasing demand of aquatic products year by year, the trade is continuously expanded, and the fraud phenomenon is more and more serious (Liangsen, 2015; Addisu and Takele, 2015; Shen et al, 2016). And fraudulent behaviors such as species substitution of aquatic products, disorder of origin information, intentional tampering of breeding environment information and the like cause the concern of consumers on food safety (Anderson et al, 2010; Cline, 2012; Khaksar et al, 2015; Molkentin et al, 2015; Hu et al, 2018; Pardo et al, 2018; Zhang et al, 2019). Therefore, the development of the tracing technology is very important for guaranteeing the safety of aquatic products and maintaining the consumer confidence (Addisu and Takele, 2015; Li et al, 2016; Sheikha and Xu, 2017).
Salmonidae are important economically farmed species in the world, and fresh or processed salmonidae are widely traded worldwide. According to the data statistics of Food and Agricultural Organization (FAO) of the United Nations, the yield of the salmonidae fishes in 2017 is over 300 ten thousand tons (FAO, 2020). Of these, rainbow trout (Oncorhynchus mykiss) and Atlantic salmon (Salmo salar) are the two most productive Salmonidae species, and are farmed in both fresh and sea water (Shoji et al, 1996). In general, the flavor of fishes cultivated in fresh water or low salinity water is susceptible to odorous substances such as geosmin and 2-methylisoborneol produced by cyanobacteria and actinomycetes (Burr et al, 2012). Therefore, in recent years, consumers have paid special attention to the culture environment of salmonidae fish products, which has also induced illegal businesses to intentionally tamper with the information on the culture environment of salmonidae fish products. In order to maintain the rights of consumers, the establishment of a method for identifying seawater and freshwater-cultured salmonidae fishes is urgently needed.
Disclosure of Invention
The invention aims to provide a method for tracing salmonidae fishes cultivated in seawater and fresh water based on multi-element analysis, so that the salmonidae fishes from seawater and fresh water can be effectively distinguished.
The invention firstly provides a method for tracing salmonidae fishes from seawater and fresh water, which uses the contents of Ag, Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, Ga, K, Mg, Mn, Na, Ni, Sr and Zn elements of salmonidae fishes to be detected as indexes, and uses a Linear Discriminant Analysis (LDA) method, a K Nearest Neighbor (KNN) method and a Random Forest (RF) method to carry out tracing analysis;
one method is that the contents of Ag, Ba, K, Mg, Na and Sr elements of the salmonidae fishes to be detected are used as indexes, and LDA, KNN and RF are used for carrying out traceability analysis;
the method comprises the steps of LDA, KNN and RF, wherein the used index is the content of Ca, Cr, Fe, Ga, K, Na, Sr and Zn elements in the salmonidae fishes to be detected;
the method is LDA, KNN and RF method, wherein the used indexes are K, Na and Zn content in the dorsum muscle of the salmonidae fishes to be detected;
the invention also provides a discrimination model of salmonidae fishes from seawater and fresh water through LDA analysis, and a linear discrimination function using the contents of Ag, Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, Ga, K, Mg, Mn, Na, Ni, Sr and Zn as indexes is as follows:
fresh water composition is 0.08Ag +0.10 Al-0.50 Ba-0.04 Ca +2.18 Cd-1.29 Co +0.09Cr +0.31Cu +0.22Fe +0.03Ga +0.00K +0.01 Mg-1.53 Mn +0.01 Na-1.17 Ni +2.57Sr +0.58 Zn-34.61;
the seawater composition is 0.36Ag +0.12 Al-1.93 Ba-0.05Ca +4.10 Cd-0.62 Co-0.15Cr +0.43Cu +0.22Fe +0.00Ga-0.00K +0.03 Mg-2.15 Mn +0.03 Na-1.19 Ni +0.51Sr +0.78 Zn-52.81.
The traceability model taking Ag, Ba, K, Mg, Na and Sr elements as indexes has the following linear discriminant functions:
fresh water group is 0.16 Ag-0.35 Ba +0.00K +0.01Mg +0.01Na +2.24 Sr-21.20;
0.50 Ag-1.82 Ba-0.00K +0.02Mg +0.02Na +0.34 Sr-35.61;
the linear discriminant function established by LDA with Ca, Cr, Fe, Ga, K, Na, Sr and Zn as indexes is as follows:
fresh water content is-0.03 Ca +0.17Cr +0.18Fe +0.04Ga +0.00K +0.01Na +2.86Sr +0.51 Zn-25.04;
seawater is-0.04 Ca +0.05Cr +0.21Fe +0.02Ga +0.00K +0.02Na +1.54Sr +0.74 Zn-35.70;
in another traceability model, K, Na and the content of Zn element are used as indexes, and the linear discriminant function established by LDA is as follows:
fresh water group is 0.00K +0.01Na +0.15 Zn-18.48;
the seawater group is-0.00K +0.02Na +0.28 Zn-29.08.
The source tracing method for the salmonidae fishes cultivated in the seawater and the freshwater is established by comparing the difference of the element compositions in the seawater and the freshwater in the salmonidae fishes, and can effectively trace the sources of the salmonidae fishes from the seawater and the freshwater.
Detailed Description
The invention compares the difference of the element composition in the bodies of the seawater and the freshwater aquaculture salmonidae fishes, thereby establishing a source tracing method for the seawater and the freshwater aquaculture salmonidae fishes.
The present invention will be described in detail with reference to examples.
Example 1: screening test indexes
1. Sample collection
Salmonid samples (N ═ 131) were collected from china and chile from 2018, 4 to 2019, 1, where fresh water samples were taken from liu gorge region of china (FLJX, N ═ 57) and beijing region (FBJ, N ═ 12), seawater samples were taken from china tobacco pipe tower region (SYT, N ═ 45), chile monte port (SPM, N ═ 11) and natta rice port (SPN, N ═ 6) (SPM and SPN samples were purchased from american co. And drying the collected sample by using a freeze dryer, and then putting the dried sample into a dryer for storage.
2. Microwave digestion of sample
Taking 0.2g of solid fish sample, putting the solid fish sample into a digestion tank, and adding 6mL of HNO3(65%) and 2mL H2O2(30%) Pre-digestion for 31 min. After completion of the pre-digestion, ultrapure water was added to 10 mL. The digestion tank was placed in a microwave digestion apparatus (MWD-650, METASH instruments Co., Ltd., Shanghai, China) for digestion. The optimized digestion program is set as follows: in the first stage, 1600W, the climbing time is 5min, the temperature is 120 ℃, and the temperature is kept for 10 min; in the second stage, 1600W, the climbing time is 5min, the temperature is 160 ℃, and the temperature is kept for 5 min; and in the third stage, 1600W, the climbing time is 5min, the temperature is 200 ℃, and the temperature is kept for 20 min. After the digestion was completed, the digestion tank was placed in an acid expeller (SPH-2, Metash instruments, Inc., Shanghai, China), acid was expelled at 170 ℃ until the remaining liquid was less than 1mL, and the digestion tank was taken out. And after the residual liquid is cooled to room temperature, repeatedly washing with ultrapure water and metering to 25 mL. Then, the mixture was filtered through a 0.45-. mu.m filter membrane and stored at 4 ℃.
3. Inductively coupled plasma atomic emission spectrometer (ICP-AES) detection
The samples treated in step 2 were analyzed for Ag, Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, Ga, K, Mg, Mn, Na, Ni, Sr and Zn contents using ICP-AES (ICAP-6300, Thermo, USA). The working conditions of the ICP-AES instrument are as follows: power 1150W, cooling gas flow 12L min-1The flow rate of the atomizer is 1.0 L.min-1Auxiliary air flow of 0.5 L.min-1The pressure of the atomizer air was 0.2MPa, and the sample introduction flow rate was 1.0 mL/min-1. The analytical wavelength of each element is shown in Table 1. And (3) diluting the multi-element mixed standard solution step by step to obtain 6 gradients, and repeating the gradients for 3 times to establish an element standard curve. Wherein the linear range of the elements Ca, Fe, K and Na is 0.1-50 mg.L-1The linear range of other elements is 0.01-5 mg.L-1. The calibration curves for all elements have good linearity and the correlation coefficient is above 0.99 (table 1). The recovery rate of the added standard is 91.37-104.62%. The concentrations of the elements in the 10 reagent blanks were determined and the standard deviation for each element was calculated. The limit of detection (LOD) and the effective concentration (LOQ) of each element were 3.3 times and 10 times the standard deviation (table 1).
Table 1: validity data table of analysis program
4. Statistical analysis
Statistical analysis of the data was performed using SPSS 19.0 software (SPSS, inc. Data were tested for normal distribution and homogeneity of variance using the Kolmogorov-Smirnov test and the Leven test. If the data does not fit a normal distribution or a homogeneity of variance assumption, it is log transformed. Elemental differences in seawater and freshwater salmonids were compared using independent sample t-tests. Multivariate statistical analysis (SDA, KDA, KNN and RF) SAS 9.4(SAS Institute, inc. cary, North Carolina, USA) and R language (R3.6.3, "randomfort" and "caret" package) analysis software was used.
5. Content of elements in body of salmonids cultured in seawater and fresh water
The content of 17 elements (Ag, Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, Ga, K, Mg, Mn, Na, Ni, Sr and Zn) in the dorsal muscle of salmonidae was determined in total and expressed as Mean. + -. standard deviation (Mean. + -. S.D). Wherein the content of K, Mg and Na element in seawater and fresh water Salmonidae is more than 1000 μ g-1Ca and Fe contents exceeding 100. mu.g.g-1The content of Al, Cr, Ga and Zn elements exceeds 10 mu g-1The contents of Ag, Ba, Co, Cu, Mn and Ni elements exceed 1 mu g-1The content of Cd and Sr is less than 1 mug-1。
Independent sample t-tests were used to compare 17 elements (Ag, Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, Ga, K, Mg, Mn, Na, Ni, Sr, and Zn) in seawater and freshwater farmed salmonidae bodies. The results show that there are significant differences (P <0.05) between the 8 elements (Ca, Cr, Fe, Ga, K, Na, Sr and Zn), where Ga and Na are higher in seawater salmonids than in freshwater salmonids, and Ca, Cr, Fe, K, Sr and Zn are higher in freshwater fish (table 2).
Table 2: mean and standard deviation (mug. g) of element content in bodies of salmonids cultured in seawater and fresh water-1Dry weight)
Note: different lower case letters indicate significant differences between groups (P <0.05) according to independent sample t-test analysis.
Example 2: establishing discrimination model for salmonidae fishes cultured in seawater and fresh water
Stepwise Discriminant Analysis (SDA) can be used to screen out the index that best distinguishes different groups of samples. Establishing 4 data sets according to the detection data in the embodiment; where dataset 1 contains all 17 elements; after 17 elements are subjected to SDA screening, Ag, Ba, K, Mg, Na and Sr elements are screened out to form a data set 2; data set 3 consisted of elements (Ca, Cr, Fe, Ga, K, Na, Sr and Zn) that were significantly different in seawater and freshwater farmed salmonidae fish by t-test; data set 4 consisted of Na, K and Zn elements from data set 3 screened for SDA. By using the 4 data sets, the accuracy of the discrimination models of the seawater and freshwater aquaculture salmonidae fishes based on different variables is compared to establish an optimal discrimination model.
And (3) combining the 4 data sets, and establishing a discrimination model of the seawater and freshwater cultured salmonidae fishes by adopting a Linear Discrimination Analysis (LDA) method, a K Nearest Neighbor (KNN) analysis method and a Random Forest (RF) method.
The samples are randomly divided into two parts, namely a training set (containing 80% of samples, N is 104) and a prediction set (containing 20% of samples, N is 27), and the training set and the prediction set are respectively used for establishing a discriminant model (initial discriminant rate) and testing the discriminant model prediction capability (predicted discriminant rate), and the cross-testing discriminant rate is obtained after the model is subjected to cross-testing.
First, using dataset 1 (containing Ag, Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, Ga, K, Mg, Mn, Na, Ni, Sr, and Zn), the initial discrimination, cross-check discrimination, and predicted discrimination of the LDA method model were 97.12%, 95.19%, and 92.59%, respectively (table 3). The linear discriminant function is as follows: fresh water composition is 0.08Ag +0.10 Al-0.50 Ba-0.04 Ca +2.18 Cd-1.29 Co +0.09Cr +0.31Cu +0.22Fe +0.03Ga +0.00K +0.01 Mg-1.53 Mn +0.01 Na-1.17 Ni +2.57Sr +0.58 Zn-34.61; the seawater composition is 0.36Ag +0.12 Al-1.93 Ba-0.05Ca +4.10 Cd-0.62 Co-0.15Cr +0.43Cu +0.22Fe +0.00Ga-0.00K +0.03 Mg-2.15 Mn +0.03 Na-1.19 Ni +0.51Sr +0.78 Zn-52.81.
In the K Nearest Neighbor (KNN) analysis method, the K value is 5, and in the initial discrimination, 1 fresh water sample and 2 seawater samples are wrongly discriminated; after cross-checking, 2 fresh water samples and 2 seawater samples are wrongly judged; the prediction is concentrated, and 1 fresh water sample and 1 seawater sample are wrongly judged. The overall initial, cross-test and predicted discrimination rates were 97.12%, 96.15% and 92.59%, respectively.
The initial discrimination, cross-check discrimination and predicted discrimination obtained by the RF model were 96.15%, 95.19% and 96.30%, respectively (table 3). The discrimination accuracy of the 3 models is over 92 percent, and the RF prediction discrimination rate is the highest and reaches 96.30 percent.
Table 3: fresh water (FS) and seawater (SS) cultured salmonidae fish discrimination result based on 17 elements and multivariate statistical analysis
Using dataset 2 (containing Ag, Ba, K, Mg, Na and Sr), the LDA model was constructed with the following linear discriminant functions: fresh water group is 0.16 Ag-0.35 Ba +0.00K +0.01Mg +0.01Na +2.24 Sr-21.20; the seawater group is 0.50 Ag-1.82 Ba-0.00K +0.02Mg +0.02Na +0.34 Sr-35.61. The initial discrimination rate, the cross-check discrimination rate and the prediction discrimination rate of the LDA model are respectively 98.08%, 97.12% and 96.30%. The initial discrimination rate, the cross-check discrimination rate and the prediction discrimination rate of the KNN model are 97.12%, 96.15% and 92.59%, respectively. The initial discrimination, cross-check discrimination and predicted discrimination obtained by the RF model were 97.12%, 93.88% and 96.30%, respectively (table 4). The discrimination precision of 3 models is compared, so that the LDA discrimination rate is the highest and is more than 96%.
Table 4: discrimination results of salmonidae fishes cultivated in fresh water (FS) and seawater (SS) based on Ag, Ba, K, Mg, Na and Sr elements and multivariate statistical analysis
Using dataset 3 (containing Ca, Cr, Fe, Ga, K, Na, Sr, and Zn), the LDA model establishes a linear discriminant function as follows: fresh water content is-0.03 Ca +0.17Cr +0.18Fe +0.04Ga +0.00K +0.01Na +2.86Sr +0.51 Zn-25.04; the seawater is-0.04 Ca +0.05Cr +0.21Fe +0.02Ga +0.00K +0.02Na +1.54Sr +0.74 Zn-35.70. The LDA discrimination results showed that the initial discrimination, cross-check discrimination and prediction discrimination of the fresh water samples were 100%, 100% and 92.86%, respectively, and the initial discrimination, cross-check discrimination and prediction discrimination of the seawater samples were 91.84%, 91.84% and 92.31%, respectively. The overall initial discrimination rate, cross-check discrimination rate and prediction discrimination rate were 96.15%, 96.15% and 92.59%, respectively. The KNN (k is 5) discrimination results show that the initial discrimination rate, the cross-check discrimination rate and the prediction discrimination rate of the fresh water sample are 100%, 98.18% and 92.86%, respectively, and the initial discrimination rate, the cross-check discrimination rate and the prediction discrimination rate of the seawater sample reach 100%. The overall initial discrimination rate, cross-check discrimination rate and prediction discrimination rate of the KNN model were 100%, 99.04% and 96.30%, respectively. The RF discrimination results showed that the initial discrimination, cross-check discrimination and predicted discrimination of the fresh water samples were 96.36%, 96.36% and 92.86%, respectively, and the initial discrimination, cross-check discrimination and predicted discrimination of the seawater samples were 93.88%, 93.88% and 100%, respectively. The overall initial discrimination, cross-check discrimination and predicted discrimination for the RF model were 95.19%, 95.19% and 96.30%, respectively (table 5). And comparing the discrimination accuracy of the 3 models, and finding that the initial discrimination rate and the cross-checking discrimination rate of the KNN are the highest, and the models with the highest prediction discrimination rate are the KNN and the RF.
Table 5: discrimination results of salmonidae fishes cultivated in fresh water (FS) and seawater (SS) based on Ca, Cr, Fe, Ga, K, Na and Sr elements, Zn and multivariate statistical analysis
Data set 4 contains only K, Na and Zn, and the LDA model discriminant function based on data set 4 is as follows: fresh water group is 0.00K +0.01Na +0.15 Zn-18.48; the seawater group is-0.00K +0.02Na +0.28 Zn-29.08. The initial discrimination rate, the cross-check discrimination rate and the prediction discrimination rate obtained by the LDA model are respectively 96.15%, 96.15% and 100%. The initial discrimination rate, the cross-check discrimination rate, and the predicted discrimination rate of the KNN (k ═ 5) model were 99.04%, and 100%, respectively. The initial discrimination, cross-check discrimination and predicted discrimination obtained by the RF model were 97.12%, 96.15% and 96.30%, respectively (table 6). And 3 models are compared to find that the KNN discrimination rate is the highest and is more than 99%.
Table 6: discrimination results of salmonidae fishes cultivated in fresh water (FS) and seawater (SS) based on Na, K and Zn and multivariate statistical analysis
By comparing the discrimination rates of different data sets and different discrimination models, the discrimination rates of 3 models based on different data sets are higher than 92 percent, which shows that the multielement analysis combined with multivariate statistical analysis is an effective method for identifying the salmonidae fishes cultured in seawater and freshwater. The KNN model based on the data set 4 has the highest discrimination rate and the discrimination precision is more than 99%.
Claims (8)
1. A method for tracing salmonidae fishes from seawater and fresh water is characterized in that the content of Ag, Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, Ga, K, Mg, Mn, Na, Ni, Sr and Zn elements of salmonidae fishes to be detected is used as an index, and a linear discriminant analysis LDA method, a K nearest neighbor KNN method and a random forest RF method are used for tracing analysis.
2. The method according to claim 1, wherein the content of Ag, Ba, K, Mg, Na and Sr elements in the Salmonidae fishes to be detected is used as an index for the analysis of the source by LDA, KNN and RF methods.
3. The method of claim 1, wherein the LDA, KNN and RF methods are used, and wherein the indices used are the contents of Ca, Cr, Fe, Ga, K, Na, Sr and Zn elements in the salmonids to be detected.
4. The method of claim 1, wherein said method is LDA, KNN and RF method, and wherein the indicators used are K, Na and Zn element content in salmonids to be detected.
5. A traceability model of salmonidae fish of sea water and fresh water sources is characterized in that the model is built by a Linear Discriminant Analysis (LDA) method, wherein a linear discriminant function is as follows:
fresh water composition is 0.08Ag +0.10 Al-0.50 Ba-0.04 Ca +2.18 Cd-1.29 Co +0.09Cr +0.31Cu +0.22Fe +0.03Ga +0.00K +0.01 Mg-1.53 Mn +0.01 Na-1.17 Ni +2.57Sr +0.58 Zn-34.61;
the seawater composition is 0.36Ag +0.12 Al-1.93 Ba-0.05Ca +4.10 Cd-0.62 Co-0.15Cr +0.43Cu +0.22Fe +0.00Ga-0.00K +0.03 Mg-2.15 Mn +0.03 Na-1.19 Ni +0.51Sr +0.78 Zn-52.81.
6. The source model of claim 5, wherein the linear discriminant function of the model is as follows:
fresh water group is 0.16 Ag-0.35 Ba +0.00K +0.01Mg +0.01Na +2.24 Sr-21.20;
the seawater group is 0.50 Ag-1.82 Ba-0.00K +0.02Mg +0.02Na +0.34 Sr-35.61.
7. The source model of claim 5, wherein the linear discriminant function of the model is as follows:
fresh water content is-0.03 Ca +0.17Cr +0.18Fe +0.04Ga +0.00K +0.01Na +2.86Sr +0.51 Zn-25.04;
the seawater is-0.04 Ca +0.05Cr +0.21Fe +0.02Ga +0.00K +0.02Na +1.54Sr +0.74 Zn-35.70.
8. The source model of claim 5, wherein the linear discriminant function of the model is as follows:
fresh water group is 0.00K +0.01Na +0.15 Zn-18.48;
the seawater group is-0.00K +0.02Na +0.28 Zn-29.08.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110751723.5A CN113484305B (en) | 2021-07-03 | 2021-07-03 | Seawater and freshwater aquaculture salmonidae fish tracing method based on multi-element analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110751723.5A CN113484305B (en) | 2021-07-03 | 2021-07-03 | Seawater and freshwater aquaculture salmonidae fish tracing method based on multi-element analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113484305A true CN113484305A (en) | 2021-10-08 |
| CN113484305B CN113484305B (en) | 2022-09-30 |
Family
ID=77940484
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110751723.5A Active CN113484305B (en) | 2021-07-03 | 2021-07-03 | Seawater and freshwater aquaculture salmonidae fish tracing method based on multi-element analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113484305B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106383162A (en) * | 2016-09-09 | 2017-02-08 | 南京财经大学 | Method for distinguishing geographical indications of rice |
| CN110596079A (en) * | 2019-08-14 | 2019-12-20 | 暨南大学 | Method for effectively distinguishing sturgeons in different producing areas |
| CN110596078A (en) * | 2019-08-14 | 2019-12-20 | 暨南大学 | Method for tracing and identifying origin of geographical marked mandarin fish |
-
2021
- 2021-07-03 CN CN202110751723.5A patent/CN113484305B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106383162A (en) * | 2016-09-09 | 2017-02-08 | 南京财经大学 | Method for distinguishing geographical indications of rice |
| CN110596079A (en) * | 2019-08-14 | 2019-12-20 | 暨南大学 | Method for effectively distinguishing sturgeons in different producing areas |
| CN110596078A (en) * | 2019-08-14 | 2019-12-20 | 暨南大学 | Method for tracing and identifying origin of geographical marked mandarin fish |
Non-Patent Citations (6)
| Title |
|---|
| CUI HAN 等: "The effect of the seasons on geographical traceability of salmonid based on multi-element analysis", 《FOOD CONTROL》 * |
| CUI HAN 等: "The ffect of the seasons on geographical traceability of salmonid based on multi-element analysis", 《FOOD CONTROL》 * |
| LI LI 等: "Use of elemental profiling and isotopic signatures to dfferentiate Pacific white shrimp (Litopenaeus vannamei) from freshwater and seawater culture", 《FOOD CONTROL》 * |
| 宋伟 等: "不同地区大西洋鲑鱼无机元素含量的对比分析", 《食品工业科技》 * |
| 汪学英 等: "海产和淡水养殖南美白对虾肌肉中无机元素的含量比较", 《常熟理工学院学报》 * |
| 陈锦 等: "湛江海水养殖鱼和淡水养殖鱼肌肉中的矿物质元素分析", 《微量元素与健康研究》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113484305B (en) | 2022-09-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Gopi et al. | Isotopic and elemental profiling to trace the geographic origins of farmed and wild-caught Asian seabass (Lates calcarifer) | |
| Gao et al. | The different ways microplastics from the water column and sediment accumulate in fish in Haizhou Bay | |
| Varrà et al. | Multi-element signature of cuttlefish and its potential for the discrimination of different geographical provenances and traceability | |
| Liu et al. | Antioxidant capacities and main reducing substance contents in 110 fruits and vegetables eaten in China | |
| Han et al. | The effect of the seasons on geographical traceability of salmonid based on multi-element analysis | |
| Li et al. | Assessment of elemental profiling for distinguishing geographic origin of aquacultured shrimp from India, Thailand and Vietnam | |
| Koutrotsios et al. | Rare earth elements concentration in mushroom cultivation substrates affects the production process and fruit‐bodies content of P leurotus ostreatus and C yclocybe cylindracea | |
| CN109916990A (en) | A method of based on the mineral element fingerprint verification sheep known for its fine thick wool meat place of production | |
| Cao et al. | Copper-induced metabolic variation of oysters overwhelmed by salinity effects | |
| Varrà et al. | Classification of transformed anchovy products based on the use of element patterns and decision trees to assess traceability and country of origin labelling | |
| Zhao et al. | Distribution and seasonal variation of picoplankton in Sanggou Bay, China | |
| CN113484305B (en) | Seawater and freshwater aquaculture salmonidae fish tracing method based on multi-element analysis | |
| del Rio-Lavín et al. | Stable isotope and trace element analysis for tracing the geographical origin of the Mediterranean mussel (Mytilus galloprovincialis) in food authentication | |
| Yan et al. | Plant genotypic diversity effects on soil nematodes vary with trophic level | |
| Daryanani et al. | Statolith chemistry: a new tool to understand the ecology and provenance of octopus | |
| KR101532870B1 (en) | Method of determining the origin of marine species using stable isotope | |
| Hirschberger | Global rattan trade: pressure on forest resources, analysis and challenges | |
| Xu et al. | Geographical discrimination of swimming crabs (Portunus trituberculatus) using stable isotope and multi-element analyses | |
| Mercante et al. | Potential toxic effect of ammonia in reservoirs with Tilapia culture in cages | |
| Patten et al. | Spatial variability in picophytoplankton, bacteria and viruses in waters of the Great Australian Bight (southern Australia) | |
| Zarei et al. | Histamine and heavy metals content of canned tuna fish | |
| Fang et al. | First evidence of meso-and microplastics on the mangrove leaves ingested by herbivorous snails and induced transcriptional responses | |
| Bai et al. | Elemental analysis of wild Eriocheir sinensis: Determining the geographic origin and human health risk assessment | |
| Han et al. | Evaluation of elemental analysis assisted by chemometrics for authenticating production methods and geographical origins of salmonids | |
| Zhang et al. | The research progress of detection method of formaldehyde in food |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |