A method for tracing the origin of chinese hong kong oysters based on metabolites and mineral elements

Abstract

The present application belongs to the technical field of origin traceability of Hong Kong oysters in China, and discloses a method for origin traceability of Hong Kong oysters in China based on metabolites and mineral elements. The method uses a combination of eight hepatopancreas tissue metabolic markers and two mineral elements, extracts metabolites in the hepatopancreas tissue of Hong Kong oysters in China by using a mixture of methanol and water, detects by using nuclear magnetic resonance technology, and establishes a database containing metabolic fingerprint maps of Hong Kong oysters in China with different geographical origins. The method also includes using inductively coupled plasma tandem mass spectrometry to determine the content of trace mineral elements in the hepatopancreas tissue of Hong Kong oysters in China with different geographical origins. Finally, by combining metabolite and mineral element data and applying machine learning analysis methods, a highly efficient identification model of Hong Kong oysters in China with different geographical origins is successfully constructed. The model has strong operability, simple construction method, high sensitivity and good specificity, and can effectively realize the geographical origin identification of Hong Kong oyster samples in China.

Description

Technical Field

[0001] This invention relates to the field of oyster origin traceability technology in Hong Kong, China, and more specifically, to a method for oyster origin traceability in Hong Kong, China based on metabolites and mineral elements. Background Technology

[0002] With increasing emphasis on food safety and consumer rights protection, accurate traceability of food origins has become an important issue. This is especially true in the seafood sector, where shellfish such as oysters present unique challenges to traceability due to their distinctive biological characteristics and ecological environment.

[0003] Traditional methods for food traceability, such as stable isotope and mineral content analysis, have been widely used. However, these methods have limited effectiveness in complex marine ecosystems, particularly for species with segmented farming models like the Hong Kong oyster. Furthermore, oysters, as a species highly susceptible to environmental factors, exhibit significant differences in their biogeographical characteristics across different production areas. Research from the Chinese Academy of Sciences indicates that systematic biogeographical studies of oysters using molecular biology and biogeography methods can provide theoretical support for geographical traceability of oysters.

[0004] In recent years, advancements in analytical techniques, particularly the application of inductively coupled plasma mass spectrometry (ICP-MS) and nuclear magnetic resonance (NMR), have made it possible to perform more precise analysis of trace elements and metabolites in oysters. These technological developments have provided new perspectives and methods for tracing the origin of oysters and other seafood products.

[0005] Current technology does not yet have a method to trace the origin of oysters from Hong Kong, China, using modern intelligent analysis techniques. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to overcome the above-mentioned problems existing in the prior art and provide a combination of markers for tracing the origin of oysters in Hong Kong, China.

[0007] The second objective of this invention is to provide a method for tracing the origin of oysters from Hong Kong, China, based on metabolites and mineral elements.

[0008] This invention combines metabolite and mineral element analysis, utilizing advanced NMR and ICP-MS techniques to provide a novel solution for the geographical origin tracing of oysters. By establishing a detailed metabolic fingerprint database of Hong Kong oysters, incorporating mineral element analysis, and combining it with machine learning analysis methods, this invention can accurately identify Hong Kong oysters from different geographical origins, effectively overcoming the limitations of traditional methods and improving the accuracy and reliability of origin tracing.

[0009] The objective of this invention is achieved through the following technical solution:

[0010] The marker combination used for tracing the origin of oysters from Hong Kong, China, consists of eight metabolites and two mineral elements. The eight metabolites are proline, theophylline, alanine, ornithine, nicotinamide adenine dinucleotide, valine, malonic acid, and ethanolamine. The two mineral elements are zinc and selenium.

[0011] The present invention also provides the application of combination A as a traceability marker for oysters originating in Hong Kong, China. Combination A consists of 8 metabolites and 2 mineral elements. The 8 metabolites are proline, theophylline, alanine, ornithine, nicotinamide adenine dinucleotide, valine, malonic acid, and ethanolamine. The 2 mineral elements are zinc and selenium.

[0012] This invention also provides a method for tracing the origin of Hong Kong oysters based on metabolites and mineral elements, comprising the following steps:

[0013] S1. Metabolite extraction: Metabolites were extracted from the hepatopancreatic tissue of oysters from Hong Kong, China, using a mixture of methanol and water as the extraction solvent.

[0014] S2. Metabolomics analysis: Metabolomics analysis of each sample was performed using nuclear magnetic resonance technology to obtain the original metabolic fingerprint of each liver and pancreas sample.

[0015] S3. Spectrum Processing: The raw metabolic fingerprint spectrum is processed using the R software package Rnmr1D to generate a two-dimensional matrix representing metabolite information. Metabolite peak identification, peak area integration, and absolute quantification are then performed on the matrix.

[0016] S4. Trace element analysis: The contents of 16 trace elements in the hepatopancreatic tissue of oysters from Hong Kong, China were determined by ICP-MS technology, and the data were integrated with the metabolite matrix for subsequent machine learning analysis.

[0017] S5. Machine Learning Classification Analysis: Use the Random Forest algorithm in the R package tidymodels to perform multi-group classification analysis on the data obtained in S4; randomly select 3 / 4 of the data of Hong Kong oysters as the training set and 1 / 4 as the test set.

[0018] S6. Model Performance Comparison: Based on 8 metabolites and 2 mineral elements, the confusion matrix analysis of the test set clearly shows that oyster samples from different geographical sources in Hong Kong, China can be effectively classified. The 8 metabolites are proline, theophylline, alanine, ornithine, nicotinamide adenine dinucleotide, valine, malonic acid, and ethanolamine; the 2 mineral elements are zinc and selenium.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] This invention proposes an innovative method for tracing the origin of Hong Kong oysters, based on diagnostic biomarkers of metabolites and mineral elements. Specifically, this method uses a combination of eight hepatopancreatic tissue metabolic biomarkers and two mineral elements, belonging to the field of food origin traceability technology. This invention extracts metabolites from the hepatopancreatic tissue of Hong Kong oysters using a mixture of methanol and water, and then detects them using nuclear magnetic resonance (NMR) technology, thereby establishing a database containing metabolic fingerprints of Hong Kong oysters from different geographical origins. Furthermore, this method includes using inductively coupled plasma tandem mass spectrometry (ICP-MS) to determine the content of trace mineral elements in the hepatopancreatic tissue of Hong Kong oysters from different geographical origins. Finally, by combining metabolite and mineral element data and applying machine learning analysis methods, this invention successfully constructs an efficient model for identifying Hong Kong oysters from different geographical origins. This model is highly operable, simple to construct, and possesses high sensitivity and good specificity, effectively enabling the geographical origin identification of Hong Kong oyster samples. The diagnostic biomarker screening method and the resulting diagnostic model of this invention provide a novel solution for the fields of food safety and origin traceability. Attached Figure Description

[0021] Figure 1 The bar chart represents the evaluation metrics of the test set when all variables of the 2021 sample dataset are used as input; the chart shows the performance of each evaluation metric (including accuracy, F-score, Kappa score, precision, recall, and ROC_AUC) in different machine learning models;

[0022] Figure 2 AUC heatmaps for different groups of an independent test set with all variables of the 2020 samples as input; the graph reveals the discriminative power of different classification groups in various machine learning models, with the color intensity reflecting the differences in AUC values;

[0023] Figure 3 A bar chart is presented to show the performance of various evaluation indicators of the test set when using eight differentially expressed metabolites and two mineral elements from the 2021 samples as input data; the chart details the performance of the model evaluation indicators based on these specific variables.

[0024] Figure 4 This is a heatmap showing the AUC of different groups on an independent test set using eight differentially expressed metabolites and two mineral elements from 2020 samples as input data; the graph visually demonstrates the classification performance based on these specific variables.

[0025] Figure 5This figure illustrates the confusion matrix of a test set using eight differential metabolites and two mineral elements as input; it visually demonstrates the accuracy and error of classification of Hong Kong oysters from different geographical origins.

[0026] Figure 6 The figure shows the differential analysis of eight metabolites and two mineral elements. It illustrates the concentration differences of these variables in oyster samples from different geographical sources in Hong Kong, China, and intuitively reveals the importance of each variable in distinguishing different geographical sources. Detailed Implementation

[0027] The specific embodiments of the present invention will be further described below. It should be noted that these descriptions are for the purpose of aiding understanding the present invention, but do not constitute a limitation thereof. Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0028] Example 1

[0029] A method for tracing the origin of Hong Kong oysters based on metabolites and mineral elements includes the following steps:

[0030] (1) Sample collection:

[0031] 2021: Oyster hepatopancreas samples were collected from representative aquaculture areas in Guangdong and Guangxi, China, as the basis for analysis: Qinzhou (QZ), Beihai (BH), Zhanjiang (ZJ), Yangjiang Haizhuzi Oyster Industry Co., Ltd. (HZ), Yashao Town, Yangdong District, Yangjiang (YJ), Yangjiang Tianbo Aquatic Technology Co., Ltd. (TB), Haiyan Town, Taishan City, Jiangmen (TS), Jiangmen Fengxuan Agricultural Technology Co., Ltd. (FX), Zhuhai (ZH), and Shanwei (SW).

[0032] 2020: Samples collected from Zhanjiang City (ZJ), Yashao Town, Yangdong District, Yangjiang City (YJ), Shanwei City (SW), and Haiyan Town, Taishan City, Jiangmen City (TS) will serve as independent test sets.

[0033] (2) Metabolite extraction: A mixture of methanol and water (volume ratio of 2:1) was used as the extraction solvent to extract metabolites from the hepatopancreatic tissue of oysters from Hong Kong, China. The procedure was as follows: approximately 100 mg of oyster hepatopancreatic tissue was accurately weighed and placed into a 2 mL centrifuge tube. 1 mL of methanol / water (volume ratio of 2:1) solution was added and mixed well. Then, a grinding steel ball was added and the mixture was shaken and pulverized in a cryogenic grinder. The mixture was then centrifuged (10 min, 12,000 g, 4 °C). The supernatant was extracted and the above operation was repeated. The two supernatants were mixed and dried in a centrifugal concentrator for later use.

[0034] (3) Metabolomics analysis: Metabolomics analysis was performed on each sample using nuclear magnetic resonance technology to obtain the original metabolic fingerprint of each liver and pancreas sample.

[0035] (4) Spectrum processing: The raw metabolic fingerprint spectrum was processed using the R software package Rnmr1D to generate a two-dimensional matrix representing metabolite information, and the matrix was processed for metabolite peak identification, peak area integration and absolute quantification.

[0036] (5) Trace element analysis: The contents of 16 trace elements in the hepatopancreas tissue of oysters from Hong Kong, China were determined by ICP-MS technology, and the data were integrated with the metabolite matrix for subsequent machine learning analysis.

[0037] (6) Machine Learning Classification Analysis: Seven machine learning algorithms from the R package tidymodels (including Decision Tree (DT), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), Random Forest (RF), Multiple Linear Regression (MLR), and Least Absolute Shrinkage and Selection Operator (LASSO)) were used to perform multi-group classification analysis on the data obtained in step (5). Three-quarters of the data from 2021 were randomly selected as the training set, and one-quarter as the test set. The process was iterated 20 times, and the average values ​​of the accuracy, F-score, Kappa score, Precision score, Recall score, and ROC_AUC score of different models on the test set were statistically compared. Figure 1 In addition, samples from 2020 were used as an independent test set. Figure 2 After comprehensive analysis, it was found that the model based on the Random Forest (RF) algorithm had the best performance.

[0038] (7) Feature Screening: Based on the RF model, the mean accuracy reduction (MDA) of out-of-bag errors was used for feature screening, ultimately identifying an effective combination of 8 differential metabolites and 2 mineral elements. The 8 metabolites included proline, theophylline, alanine, ornithine, nicotinamide adenine dinucleotide (NAD+), valine, malonate, and ethanolamine, while the 2 mineral elements were zinc (Zn) and selenium (Se).

[0039] (8) Dataset learning: The selected dataset is used again to learn with 7 machine learning algorithms. The same method as in step 6 is used to evaluate the performance of different models on the test set and independent test set.

[0040] (9) Model performance comparison: A comprehensive comparison of the performance of different models based on 8 differential metabolites and 2 mineral element combinations. Figure 3 and Figure 4 The study found that the random forest (RF)-based model performed optimally. Confusion matrix analysis of the test set clearly demonstrated that oyster samples from different geographical origins in Hong Kong, China, could be effectively classified. Figure 5 ).

[0041] (10) Differential Analysis: Differential analysis was performed on the 8 differentially expressed metabolites and 2 mineral elements. For example... Figure 4 As shown, the concentrations of these 10 substances varied significantly among oyster samples from Hong Kong, China, from different geographical sources.

[0042] (11) Multi-classification model construction: Based on a dataset of 8 different metabolites and 2 mineral element combinations, a multi-classification model was constructed using the RF algorithm to accurately identify Hong Kong oysters from different geographical origins.

[0043] The embodiments of the present invention have been described in detail above, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and these variations still fall within the protection scope of the present invention.

Claims

1. The application of combination A in the preparation of products containing traceability markers for oysters originating in Hong Kong, China, characterized in that, Combination A consists of 8 metabolites and 2 mineral elements. The 8 metabolites are proline, theophylline, alanine, ornithine, nicotinamide adenine dinucleotide, valine, malonic acid, and ethanolamine. The 2 mineral elements are zinc and selenium.

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