Tracing method of selenium-rich agricultural products and application thereof

Abstract

The invention relates to the technical field of agricultural product safety and detection, and discloses a traceability method of selenium-rich agricultural products. The method comprises the following steps: acquiring an original near-infrared spectral image by using a near-infrared spectrometer, and carrying out preprocessing operation to obtain preprocessed spectral data; performing feature extraction on the preprocessed spectral data by using a machine learning algorithm, identifying significant spectral features related to the selenium content, training a prediction model based on data in a sample set, and testing and correcting the trained model by using data in a verification set to obtain a prediction model; all key information about the selenium-rich agricultural products is encrypted and uploaded to a block chain network, an intelligent contract is designed and deployed, and a standard process of selenium-rich agricultural product authentication is defined; detecting a to-be-detected sample; the invention further discloses application of the method to identification of the origin of selenium-rich agricultural products. According to the method, the product quality is improved, the trust of consumers is enhanced, and more commercial opportunities are brought to enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural product safety and detection, and particularly relates to a traceability method and application of selenium-rich agricultural products. Background Art

[0002] With the increasing attention of consumers to food safety and nutritional health, selenium-rich agricultural products, as a high-quality food rich in the trace element selenium, have gradually gained market favor. However, traditional traceability methods have many deficiencies in the quality control and certification process of selenium-rich agricultural products. Existing traceability systems usually rely on paper labels and centralized database records. These methods are not only easily tampered with, but also have low information transparency, poor data coherence and reliability. In addition, traditional detection means such as chemical analysis can accurately determine the selenium content, but are complex to operate, costly and time-consuming, making it difficult to achieve large-scale rapid detection. In terms of agricultural production management, the lack of real-time monitoring and data analysis tools makes it difficult for farmers to optimize planting plans according to actual situations, affecting the quality and yield of selenium-rich agricultural products. At the same time, regulatory authorities also face the problems of low efficiency of manual review and high regulatory costs when dealing with a large number of agricultural products. Therefore, there is an urgent need for an efficient, transparent and tamper-proof traceability method to ensure that every link of selenium-rich agricultural products from the field to the table can be effectively monitored and managed, improving the market competitiveness of products and the trust of consumers. Therefore, we have proposed a traceability method and application of selenium-rich agricultural products. Summary of the Invention

[0003] The main purpose of the present invention is to provide a traceability method and application of selenium-rich agricultural products, which can effectively solve the problems in the background art.

[0004] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0005] A traceability method of selenium-rich agricultural products, comprising the following steps:

[0006] S1. Pretreatment of raw materials: Uniformly pulverize and sieve the selenium-rich agricultural products to be tested to obtain a pretreated sample, and divide the pretreated sample into a sample set and a validation set;

[0007] S2. Acquisition and pretreatment of spectral images: Use a near-infrared spectrometer to collect the original near-infrared spectral images of the pretreated samples, and perform pretreatment operations on the collected original near-infrared spectral images to obtain pretreated spectral data;

[0008] S3, Data Analysis and Model Establishment: Use machine learning algorithms to extract features from the preprocessed spectral data, identify significant spectral features related to selenium content, train a prediction model based on the data in the sample set, and use the data in the validation set to test and correct the trained model to obtain the prediction model;

[0009] S4, Tamper - proof Record Supported by Blockchain Technology: Upload all key information about selenium - rich agricultural products to the blockchain network in an encrypted manner, design and deploy a smart contract, and define the standard process for selenium - rich agricultural product certification;

[0010] S5, Detection of Samples to be Tested: Develop a user - friendly front - end interface so that consumers can access complete product traceability information by scanning the QR code on the product or using a mobile application.

[0011] Preferably, in the S2, the scanning wavelength range of the near - infrared spectrometer is 800 - 2500 nm, it is scanned 3 - 5 times, and the resolution is 5 nm.

[0012] Preferably, in the S2, preprocessing operations are performed on the collected original near - infrared spectral images, including but not limited to:

[0013] Baseline correction: Eliminate the influence of background noise and instrument drift;

[0014] Smoothing filtering: Reduce random noise and improve the signal - to - noise ratio;

[0015] Standardization: Make data from different batches comparable;

[0016] Normalization: Convert spectral data to the same numerical range for subsequent analysis.

[0017] Preferably, in the S2, after obtaining the preprocessed spectral data, it further includes: adding detailed label information to the spectral data of each sample, where the label information includes: sample number, collection date, geographical location, and agronomic measures.

[0018] Preferably, in the S3, the machine learning algorithms include: principal component analysis PCA, partial least squares regression PLSR, and support vector machine SVM.

[0019] Preferably, in the S3, when training a prediction model based on the data in the sample set, it further includes using cross - validation or external validation methods to optimize model parameters and improve the generalization ability of the model.

[0020] Preferably, in the S4, the key information of the selenium - rich agricultural products includes but not limited to production process, environmental monitoring data, spectral analysis results, and selenium content prediction values.

[0021] Preferably, in the step S4, the encryption method can be any one of asymmetric encryption and hash function.

[0022] Preferably, in the step S4, a smart contract is designed and deployed, and the standard process for the certification of selenium-rich agricultural products is defined, which specifically includes the following steps:

[0023] S401: Determine the goals and participating party roles of the smart contract, clarify the selenium content threshold and other production specifications of selenium-rich agricultural products, and define the permissions and responsibilities of farmers, processing plants, testing institutions, regulatory departments and consumers;

[0024] S402: Define contract events, including data upload, certification application, approval and certificate revocation, and write smart contract code using Solidity or Chaincode to implement data verification, automatic certification, certificate management and permission control;

[0025] S403: Conduct unit tests and integration tests, and reduce Gas consumption through performance optimization to improve execution efficiency;

[0026] S404: Select the Ethereum blockchain platform, configure multiple nodes, and assign different permissions to each node to ensure that only authorized nodes can participate in the consensus mechanism;

[0027] S405: Compile the written smart contract into bytecode, upload it to the network through a blockchain client tool, and set initial parameters such as certification standards and participating party addresses;

[0028] S406: Use a blockchain browser or a custom monitoring tool to view the contract status and transaction records in real time, and record important operation logs for subsequent auditing and troubleshooting;

[0029] S407: The production enterprise submits a certification application through the front-end interface, uploads necessary production data and test reports, and the smart contract automatically verifies whether the submitted data conforms to the preset format and standards;

[0030] S408: The designated testing institution conducts a preliminary review of the submitted data, performs spectral analysis using a near-infrared spectrometer to obtain the test results of the selenium content, and uploads the results to the blockchain;

[0031] S409: The smart contract automatically determines whether the product meets the standards according to the preset selenium content threshold. If it meets the standards, an electronic certification certificate is generated and stored on the blockchain, and at the same time, the production enterprise and consumers are notified; if it is found that the product has quality problems or violations, the regulatory department can submit a revocation application through the front-end interface, and the smart contract automatically verifies the legality of the revocation application. If it is correct, the certification certificate of the product is revoked, and the relevant parties are notified, informing the reasons for the certificate revocation and subsequent handling measures.

[0032] The present invention also discloses an application of a traceability method for selenium-rich agricultural products, which is applied to identify the origin of selenium-rich agricultural products.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. By introducing near-infrared spectroscopy analysis technology and advanced machine learning algorithms, the present invention can accurately identify significant spectral features related to selenium content and establish a reliable prediction model. This analysis method based on scientific data ensures the accurate assessment of the quality of selenium-rich agricultural products, guaranteeing the quality and safety of products from the source. Consumers can access complete product traceability information by scanning the QR code on the product or using a mobile application, enhancing their trust in the quality of the purchased products and promoting the healthy development of the market.

[0035] 2. Using blockchain technology, the present invention uploads all key information about selenium-rich agricultural products, including production processes, environmental monitoring data, spectral analysis results, and selenium content prediction values, to the blockchain network in an encrypted manner. The distributed ledger feature of the blockchain ensures that these data cannot be tampered with once entered, guaranteeing the authenticity and integrity of the traceability information. The deployment of smart contracts further defines the standard process for the certification of selenium-rich agricultural products, realizing automated and transparent management, improving the transparency and credibility of the entire supply chain, and effectively preventing counterfeit and shoddy products from entering the market.

[0036] 3. The present invention not only provides a complete set of quality monitoring tools for farmers and agricultural enterprises but also helps them understand the growth status and quality changes of selenium-rich agricultural products in real time, enabling them to take optimization measures in a timely manner to improve product quality. Government regulatory departments can use this system to strengthen the supervision and management of the agricultural product market, ensure compliance with relevant regulations and standards, and protect the rights and interests of consumers. Through the certification and revocation procedures automatically triggered by smart contracts, the traditional manual review process is simplified, greatly improving the supervision efficiency, reducing the management cost, and promoting the intelligent and standardized development of the agricultural industry.

[0037] 4. By providing detailed traceability information, the present invention helps selenium-rich agricultural products gain higher recognition and competitiveness in the market. Consumers can easily obtain detailed product information, understand its origin and quality, and enhance their purchasing confidence. In addition, the transparent traceability system also contributes to brand building, enhancing the brand image and market reputation of enterprises. For export enterprises, a perfect traceability system is also more likely to meet the strict requirements of the international market and promote the development of international trade. In summary, the method of the present invention not only improves product quality but also enhances consumer trust, bringing more business opportunities to enterprises. Description of the Drawings

[0038] Figure 1This is a flowchart of a traceability method for selenium-rich agricultural products of the present invention. Detailed implementation manners

[0039] To make the technical means, creative features, achieved purposes and effects of the present invention easy to understand, the present invention will be further described below in conjunction with specific implementation manners.

[0040] As Figure 1 shown, a traceability method for selenium-rich agricultural products includes the following steps:

[0041] S1. Pretreatment of raw materials: Uniformly pulverize and sieve the selenium-rich agricultural products to be tested to obtain a pretreated sample, and divide the pretreated sample into a sample set and a validation set;

[0042] S2. Acquisition and pretreatment of spectral images: Use a near-infrared spectrometer to acquire the original near-infrared spectral images of the pretreated sample, and perform pretreatment operations on the acquired original near-infrared spectral images to obtain pretreated spectral data;

[0043] Among them, the pretreatment operations performed on the acquired original near-infrared spectral images include, but are not limited to:

[0044] Baseline correction: Eliminate the influence of background noise and instrument drift;

[0045] Smoothing filter: Reduce random noise and improve the signal-to-noise ratio;

[0046] Standardization: Make the data of different batches comparable;

[0047] Normalization: Convert the spectral data to the same numerical range for subsequent analysis.

[0048] Among them, after obtaining the pretreated spectral data, it also includes: adding detailed tag information to the spectral data of each sample, where the tag information includes: sample number, collection date, geographical location, and agronomic measures.

[0049] S3. Data analysis and model establishment: Use machine learning algorithms to extract features from the pretreated spectral data, identify significant spectral features related to selenium content, train a prediction model based on the data in the sample set, and use the data in the validation set to test and correct the trained model to obtain a prediction model;

[0050] Among them, the machine learning algorithms include: principal component analysis PCA, partial least squares regression PLSR, and support vector machine SVM.

[0051] Among them, based on the data in the sample set, training a prediction model also includes using methods such as cross-validation or external validation to optimize model parameters and improve the generalization ability of the model.

[0052] S4. Tamper - proof Records Supported by Blockchain Technology: Upload all key information about selenium - rich agricultural products to the blockchain network in an encrypted manner, design and deploy smart contracts, and define the standard process for the certification of selenium - rich agricultural products;

[0053] Among them, the key information of the selenium - rich agricultural products includes but is not limited to the production process, environmental monitoring data, spectral analysis results, and selenium content prediction values.

[0054] Among them, designing and deploying smart contracts and defining the standard process for the certification of selenium - rich agricultural products specifically include the following steps:

[0055] S401: Determine the goals and participant roles of the smart contract, clarify the selenium content threshold and other production specifications of selenium - rich agricultural products, and define the permissions and responsibilities of farmers, processing plants, testing institutions, regulatory departments, and consumers;

[0056] S402: Define contract events, including data upload, certification application, approval, and certificate revocation, and write smart contract code using Solidity or Chaincode to implement data verification, automatic certification, certificate management, and permission control;

[0057] S403: Conduct unit tests and integration tests, and reduce Gas consumption through performance optimization to improve execution efficiency;

[0058] S404: Select the Ethereum blockchain platform, configure multiple nodes, and assign different permissions to each node to ensure that only authorized nodes can participate in the consensus mechanism;

[0059] S405: Compile the written smart contract into bytecode, upload it to the network through the blockchain client tool, and set initial parameters such as certification standards and participant addresses;

[0060] S406: Use a blockchain browser or a custom monitoring tool to view the contract status and transaction records in real - time, record important operation logs for subsequent auditing and troubleshooting;

[0061] S407: Production enterprises submit certification applications through the front - end interface, upload necessary production data and test reports, and the smart contract automatically verifies whether the submitted data meets the preset format and standards;

[0062] S408: The designated testing institution conducts a preliminary review of the submitted data, performs spectral analysis using a near - infrared spectrometer to obtain the test results of selenium content, and uploads the results to the blockchain;

[0063] S409: The smart contract automatically determines whether the product meets the standard according to the preset selenium content threshold. If it meets the standard, it generates an electronic certification certificate and stores it on the blockchain, and notifies the production enterprise and consumers at the same time. If quality problems or violations are found in the product, the regulatory department can submit a revocation application through the front-end interface. The smart contract automatically verifies the legality of the revocation application. If it is correct, it revokes the certification certificate of the product and notifies the relevant parties, informing the reasons for the revocation of the certificate and the subsequent handling measures.

[0064] S5. Detection of the sample to be tested: Develop a user-friendly front-end interface so that consumers can access the complete product traceability information by scanning the QR code on the product or using a mobile application.

[0065] The present invention also discloses an application of a traceability method for selenium-rich agricultural products, which is applied to identify the origin of selenium-rich agricultural products.

[0066] By introducing near-infrared spectroscopy analysis technology and advanced machine learning algorithms, the present invention can accurately identify significant spectral features related to selenium-rich content and establish a reliable prediction model. This analysis method based on scientific data ensures the accurate assessment of the quality of selenium-rich agricultural products, guaranteeing the quality and safety of products from the source. Consumers can access complete product traceability information by scanning the QR code on the product or using a mobile application, enhancing their trust in the quality of the purchased products and promoting the healthy development of the market. Using blockchain technology, all key information about selenium-rich agricultural products, including production processes, environmental monitoring data, spectral analysis results, and selenium content prediction values, is uploaded to the blockchain network in an encrypted manner. The distributed ledger feature of the blockchain ensures that these data cannot be tampered with once entered, ensuring the authenticity and integrity of the traceability information. The deployment of smart contracts further defines the standard process for the certification of selenium-rich agricultural products, achieving automated and transparent management, improving the transparency and credibility of the entire supply chain, and effectively preventing counterfeit and shoddy products from entering the market. It not only provides farmers and agricultural enterprises with a complete set of quality monitoring tools but also helps them understand the growth status and quality changes of selenium-rich agricultural products in real time, enabling them to take optimization measures in a timely manner to improve product quality. Government regulatory departments can use this system to strengthen the supervision and management of the agricultural product market, ensure compliance with relevant regulations and standards, and protect the rights and interests of consumers. Through the certification and revocation procedures automatically triggered by smart contracts, the traditional manual review process is simplified, greatly improving the supervision efficiency, reducing the management cost, and promoting the intelligent and standardized development of the agricultural industry. By providing detailed traceability information, it helps selenium-rich agricultural products gain higher recognition and competitiveness in the market. Consumers can easily obtain detailed product information, understand its origin and quality, enhancing their purchasing confidence. In addition, the transparent traceability system also contributes to brand building, enhancing the brand image and market reputation of enterprises. For export enterprises, a complete traceability system is also more likely to meet the strict requirements of the international market and promote the development of international trade. In summary, the method of the present invention not only improves product quality but also enhances consumer trust, bringing more business opportunities to enterprises.

[0067] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only used to illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and all these changes and improvements fall within the scope of the present invention claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A traceability method for selenium-rich agricultural products, characterized in that: Including the following steps: S1. Pretreatment of raw materials: Uniformly pulverize the selenium-rich agricultural products to be tested, sieve them to obtain a pretreated sample, and divide the pretreated sample into a sample set and a validation set; S2. Collection and pretreatment of spectral images: Use a near-infrared spectrometer to collect the original near-infrared spectral images of the pretreated samples, and perform pretreatment operations on the collected original near-infrared spectral images to obtain the pretreated spectral data; S3. Data analysis and model establishment: Use machine learning algorithms to extract features from the pretreated spectral data, identify the significant spectral features related to the selenium content, train a prediction model based on the data in the sample set, and use the data in the validation set to test and correct the trained model to obtain a prediction model; S4. Tamper-proof record supported by blockchain technology: Upload all the key information about the selenium-rich agricultural products to the blockchain network in an encrypted manner, design and deploy a smart contract, and define the standard process for the certification of selenium-rich agricultural products; S5. Detection of samples to be tested: Develop a user-friendly front-end interface so that consumers can access the complete product traceability information by scanning the QR code on the product or using a mobile application.

2. The traceability method of a selenium-rich agricultural product according to claim 1, characterized in that: In the S2, the scanning wavelength range of the near-infrared spectrometer is 800 - 2500 nm, scan 3 - 5 times, and the resolution is 5 nm.

3. The traceability method of a selenium-rich agricultural product according to claim 1, characterized in that: In the S2, performing pretreatment operations on the collected original near-infrared spectral images includes, but is not limited to: Baseline correction: Eliminate the influence of background noise and instrument drift; Smoothing filtering: Reduce random noise and improve the signal-to-noise ratio; Standardization: Make the data of different batches comparable; Normalization: Convert the spectral data into the same numerical range for subsequent analysis.

4. The traceability method of a selenium-rich agricultural product according to claim 1, wherein: In the S2, after obtaining the pretreated spectral data, it also includes: adding detailed label information to the spectral data of each sample, where the label information includes: sample number, collection date, geographical location, and agronomic measures.

5. The traceability method of a selenium-rich agricultural product according to claim 1, wherein: In the S3, the machine learning algorithms include: principal component analysis PCA, partial least squares regression PLSR, and support vector machine SVM.

6. The traceability method of a selenium-rich agricultural product according to claim 1, characterized in that: In the S3, when training a prediction model based on the data in the sample set, it also includes using methods such as cross-validation or external validation to optimize the model parameters and improve the generalization ability of the model.

7. The traceability method of a selenium-rich agricultural product according to claim 1, characterized in that: In the S4, the key information of the selenium-rich agricultural products includes, but is not limited to, the production process, environmental monitoring data, spectral analysis results, and selenium content prediction values.

8. The traceability method of a selenium-rich agricultural product according to claim 1, characterized in that: In the S4, the encryption method can adopt any one of asymmetric encryption and hash functions.

9. The traceability method of a selenium-rich agricultural product according to claim 1, characterized in that: In the S4, designing and deploying a smart contract and defining the standard process for the certification of selenium-rich agricultural products specifically includes the following steps: S401: Determine the goals and participant roles of the smart contract, clarify the selenium content threshold and other production specifications of the selenium-rich agricultural products, and define the permissions and responsibilities of farmers, processing plants, testing institutions, regulatory departments, and consumers; S402: Define contract events, including data upload, authentication application, approval of review, and certificate revocation, and write smart contract code using Solidity or Chaincode to implement data verification, automatic authentication, certificate management, and permission control; S403: Conduct unit tests and integration tests, and reduce Gas consumption through performance optimization to improve execution efficiency; S404: Select the Ethereum blockchain platform, configure multiple nodes, and assign different permissions to each node to ensure that only authorized nodes can participate in the consensus mechanism; S405: Compile the written smart contract into bytecode, upload it to the network through blockchain client tools, and set initial parameters such as authentication criteria and participant addresses; S406: Use a blockchain browser or custom monitoring tool to view the contract status and transaction records in real time, and record important operation logs for subsequent auditing and troubleshooting; S407: The production enterprise submits an authentication application through the front-end interface, uploads the necessary production data and test reports, and the smart contract automatically verifies whether the submitted data conforms to the preset format and standards; S408: The designated inspection agency conducts a preliminary review of the submitted data, performs spectral analysis using a near-infrared spectrometer to obtain the test results of selenium content, and uploads the results to the blockchain; S409: The smart contract automatically determines whether the product meets the standards according to the preset selenium content threshold. If it meets the standards, an electronic authentication certificate is generated and stored on the blockchain, and the production enterprise and consumers are notified at the same time. If quality problems or violations are found in the product, the regulatory department can submit a revocation application through the front-end interface. The smart contract automatically verifies the legality of the revocation application. If it is correct, the authentication certificate of the product is revoked, and the relevant parties are notified, informing the reasons for the certificate revocation and subsequent handling measures.

10. Use of a traceability method for selenium-rich agricultural products according to any one of claims 1-9, characterized in that: It is applied to identify the origin of selenium-rich agricultural products.

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