Cold-chain aquatic product risk factor detection method

Abstract

The invention discloses a cold-chain aquatic product risk factor detection method, and relates to the technical field of risk detection, the risk factor detection method comprises the following steps: 1, traceability information binding: scanning a traceability identification code of an outer package of a frozen aquatic product to be detected, and obtaining and binding traceability information of a batch of products to a current detection task; step 2, low-temperature anti-freezing homogenization: in a non-unfreezing state, placing the sample in a homogenization bag preloaded with a low-temperature anti-freezing lysate, carrying out high-speed homogenization, fully lysing tissues and releasing intracellular and extracellular risk factors to obtain a homogenate; the anti-freezing lysis solution is a buffer system containing an anti-freezing agent, and the freezing point is lower than-25 DEG C; according to the risk factor detection method, the problems of complex sample treatment, time consumption in multi-factor detection and low target object enrichment efficiency in traditional detection are solved, and the detection efficiency and accuracy are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of risk detection technology, specifically a method for detecting risk factors in cold chain aquatic products. Background Technology

[0002] Cold chain aquatic products, rich in nutrients such as protein and water, are prone to microbial growth (such as Salmonella) during storage, transportation, and sales. Furthermore, improper low-temperature storage or spoilage can lead to the production of harmful small molecules such as histamine and formaldehyde. These risk factors not only cause deterioration in the quality of aquatic products but can also trigger food poisoning and other food safety incidents, posing a serious threat to consumer health. Therefore, establishing efficient and accurate detection technologies for risk factors in cold chain aquatic products is a crucial requirement for ensuring food safety and the healthy development of the industry.

[0003] Traditional testing requires thawing frozen aquatic products before homogenization. The thawing process is not only time-consuming (usually several hours) but can also lead to microbial growth and degradation of small molecules such as histamine, compromising the authenticity of the target analytes and affecting the accuracy of the test results. Furthermore, conventional lysis buffers are prone to freezing at low temperatures, making them unsuitable for direct processing of frozen samples, further limiting the efficiency of the testing process. Moreover, most testing technologies are designed for single-type risk factors, failing to meet the timeliness requirements of rapid customs clearance and market supervision for cold chain aquatic products. They also cannot fully eliminate the influence of matrix interference and environmental fluctuations, resulting in poor repeatability and difficulty in meeting the requirements of precise supervision. Therefore, this paper designs a risk factor detection method for cold chain aquatic products that enables direct processing of frozen samples, simultaneous multi-factor detection, strong anti-interference capabilities, and full-chain traceability. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for detecting risk factors in cold chain aquatic products, thus solving the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for detecting risk factors in cold chain aquatic products, comprising the following steps: Step 1: Traceability Information Binding: Scan the traceability identification code on the outer packaging of the frozen aquatic products to be tested, obtain and bind the traceability information of this batch of products to this testing task; Step 2, Low-temperature antifreeze homogenization: Without thawing, the sample is placed in a homogenization bag pre-filled with low-temperature antifreeze lysis buffer and homogenized at high speed to fully lyse the tissue and release intracellular and extracellular risk factors, resulting in a homogenized solution; the antifreeze lysis buffer is a buffer system containing antifreeze and has a freezing point below -25°C. Step 3, target enrichment: The supernatant after centrifugation of the homogenized liquid is loaded onto a temperature-sensitive molecularly imprinted column at a low temperature of 0-4℃ to specifically capture and enrich the target risk factor; Step 4, Elution and Internal Standard Addition: The molecularly imprinted column is brought to room temperature, the enriched target analytes are eluted and collected using elution buffer, and a certain amount of multifunctional internal standard solution is precisely added to the elution buffer; Step 5, Chip Injection and Reaction: Inject the eluent with added internal standard into the injection port of the multifunctional CRISPR microfluidic chip, which integrates: A Salmonella nucleic acid detection channel based on pre-embedded lyophilized RPA reagent, Cas12a / sgRNA complex and fluorescent reporter group; Histamine optical detection channel based on upconversion nanoparticle-labeled aptamers; Formaldehyde colorimetric detection channel based on Nash reagent colorimetric reaction; Step 6: Multispectral signal acquisition: Using a multispectral sensor, the optical signals of each reaction unit on the chip are acquired synchronously, including fluorescence signals, upconversion emission signals and visible absorption spectral signals; Step 7, AI Intelligent Analysis: The collected multidimensional spectral signals are transmitted to a processor with a built-in AI algorithm. The algorithm first performs data normalization and compensation correction based on the internal standard unit signal, and then analyzes the precise concentration of each risk factor through a convolutional neural network model. Step 8, Risk Decision and Evidence Preservation: Compare the test results with the risk threshold to generate a report, and encrypt all information and upload it to the blockchain to generate an immutable digital evidence certificate.

[0006] According to the above technical solution, the multifunctional internal standard solution mentioned in step four includes: The nucleic acid detection channel uses a fluorescently labeled DNA internal standard with a similar sequence but a base mismatch; Deuterium-labeled histamine and Carbon-13-labeled formaldehyde internal standards for small molecule detection channels.

[0007] According to the above technical solution, the AI ​​interference compensation algorithm in step seven is a multivariate correction model based on convolutional neural networks, which is used to identify and compensate for signal interference from the sample matrix, inter-channel crosstalk and non-specific adsorption.

[0008] According to the above technical solution, the method further includes: uploading the anonymized signal spectrum and detection results to a cloud server for continuous training and iterative optimization of the AI ​​model.

[0009] According to the above technical solution, the filler of the thermosensitive molecularly imprinted column is a polymer with N-isopropylacrylamide as the thermosensitive monomer. It adsorbs the target substance through hydrogen bonding at low temperature, and undergoes a hydrophilic-hydrophobic phase transition and releases the target substance after being heated to room temperature.

[0010] According to the above technical solution, in step six, the multispectral sensor integrates a miniature temperature and humidity sensor to monitor the temperature of the reaction microenvironment in real time, and inputs this parameter into the AI ​​algorithm for dynamic compensation of the reaction kinetics.

[0011] According to the above technical solution, the antifreeze lysis solution in step two is a Tris-HCl buffer system containing ethylene glycol or dimethyl sulfoxide.

[0012] This invention provides a method for detecting risk factors in cold chain aquatic products. It has the following beneficial effects: (1) This detection method achieves direct sample processing without thawing through low-temperature antifreeze homogenization technology, combined with the specific enrichment of temperature-sensitive molecular imprinting column, and simultaneously detects multiple risk factors such as Salmonella, histamine, and formaldehyde using a multifunctional CRISPR microfluidic chip. This solves the problems of complex sample processing, time-consuming multi-factor detection, and low target enrichment efficiency in traditional detection methods, and greatly improves detection efficiency and accuracy.

[0013] (2) This detection method relies on multispectral sensing technology with integrated temperature and humidity monitoring and AI algorithm based on convolutional neural network to achieve dynamic compensation for multiple factors such as sample matrix interference and environmental temperature fluctuation. Combined with the precise correction of the multifunctional internal standard system, it effectively eliminates non-specific interference in the detection process and ensures the stability and reliability of the detection results under complex cold chain aquatic product matrix.

[0014] (3) This detection method, through cloud data upload and AI model iteration optimization mechanism, enables the detection method to continuously adapt to new types of aquatic products, changes in risk factors and detection scenarios, solves the problems of rigidity and poor universality of traditional detection technology, and significantly improves the universality of the method for different cold chain aquatic products (such as fish, shrimp, shellfish, etc.).

[0015] (4) This testing method combines the binding of traceability information, the parsing of test data and the storage of evidence on the blockchain to achieve full traceability from the source of the sample to the test results. Furthermore, the blockchain technology ensures that the test report is tamper-proof, which solves the problems of easy falsification of results and broken traceability chains in traditional testing, and enhances the credibility and judicial effectiveness of the test results. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the detection process of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 One embodiment of the present invention is: a method for detecting risk factors in cold chain aquatic products, comprising the following steps: Step 1: Traceability Information Binding: Scan the traceability identification code on the outer packaging of the frozen aquatic products to be tested, obtain and bind the traceability information of this batch of products to this testing task; Step 2, Low-temperature antifreeze homogenization: Without thawing, the sample is placed in a homogenization bag pre-filled with low-temperature antifreeze lysis buffer and homogenized at high speed to fully lyse the tissue and release intracellular and extracellular risk factors, resulting in a homogenized solution; the antifreeze lysis buffer is a buffer system containing antifreeze and has a freezing point below -25°C. Step 3, target enrichment: The supernatant after centrifugation of the homogenized liquid is loaded onto a temperature-sensitive molecularly imprinted column at a low temperature of 0-4℃ to specifically capture and enrich the target risk factor; Step 4, Elution and Internal Standard Addition: The molecularly imprinted column is brought to room temperature, the enriched target analytes are eluted and collected using elution buffer, and a certain amount of multifunctional internal standard solution is precisely added to the elution buffer; Step 5, Chip Injection and Reaction: Inject the eluent with added internal standard into the injection port of the multifunctional CRISPR microfluidic chip, which integrates: A Salmonella nucleic acid detection channel based on pre-embedded lyophilized RPA reagent, Cas12a / sgRNA complex and fluorescent reporter group; Histamine optical detection channel based on upconversion nanoparticle-labeled aptamers; Formaldehyde colorimetric detection channel based on Nash reagent colorimetric reaction; Step 6: Multispectral signal acquisition: Using a multispectral sensor, the optical signals of each reaction unit on the chip are acquired synchronously, including fluorescence signals, upconversion emission signals and visible absorption spectral signals; Step 7, AI Intelligent Analysis: The collected multidimensional spectral signals are transmitted to a processor with a built-in AI algorithm. The algorithm first performs data normalization and compensation correction based on the internal standard unit signal, and then analyzes the precise concentration of each risk factor through a convolutional neural network model. Step 8, Risk Decision and Evidence Preservation: Compare the test results with the risk threshold to generate a report, and encrypt all information and upload it to the blockchain to generate an immutable digital evidence certificate.

[0019] The system defines the complete technical process for detecting risk factors in cold chain aquatic products, covering all aspects from traceability to evidence storage. It clarifies the combination of core technologies, solves the problems of fragmented traditional testing processes, difficulty in simultaneous detection of multiple factors, and easy tampering of results, and achieves one-stop, full-chain traceable testing, improving efficiency and credibility.

[0020] The multifunctional internal standard solution described in step four comprises: The nucleic acid detection channel uses a fluorescently labeled DNA internal standard with a similar sequence but a base mismatch; The deuterium-labeled histamine and Carbon-13-labeled formaldehyde internal standards used in the small molecule detection channel solve the problems of poor compatibility of internal standards and inaccurate data correction between different channels, ensuring accurate nucleic acid and small molecule detection results and avoiding cross-interference errors.

[0021] The AI ​​interference compensation algorithm described in step seven is a multivariate correction model based on convolutional neural networks. It is used to identify and compensate for signal interference from the sample matrix, inter-channel crosstalk, and non-specific adsorption, thereby solving the signal distortion problem caused by sample matrix interference and inter-channel crosstalk, improving the stability of AI analysis results, and eliminating the impact of complex samples on detection accuracy.

[0022] The method further includes uploading anonymized signal spectra and detection results to a cloud server for continuous training and iterative optimization of the AI ​​model, solving the problems of traditional AI models being rigid and difficult to adapt to new samples / factors, realizing continuous model evolution, and enhancing the universality of the detection method for different cold chain aquatic products.

[0023] The packing material of the thermosensitive molecularly imprinted column is a polymer with N-isopropylacrylamide as the thermosensitive monomer. It adsorbs the target substance through hydrogen bonding at low temperature, and releases the target substance after heating to room temperature. This solves the problems of low adsorption efficiency, difficult elution, and need for additional reagents in traditional target substance enrichment. The thermosensitive phase change simplifies the operation and reduces reagent consumption.

[0024] In step six, the multispectral sensor integrates a miniature temperature and humidity sensor to monitor the temperature of the reaction microenvironment in real time and inputs this parameter into the AI ​​algorithm for dynamic compensation of reaction kinetics, solving the problem of the influence of temperature fluctuations in the reaction microenvironment on detection kinetics, and ensuring the repeatability of results under different environments through real-time temperature correction.

[0025] The antifreeze lysis buffer described in step two is a Tris-HCl buffer system containing ethylene glycol or dimethyl sulfoxide. This solves the problems of easy tissue damage and insufficient release of risk factors after thawing of frozen samples, achieving efficient lysis without thawing, preserving the activity of risk factors, and avoiding the impact of low-temperature freezing of the lysis buffer on homogenization.

[0026] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for detecting risk factors in cold chain aquatic products, characterized in that, Includes the following steps: Step 1: Traceability Information Binding: Scan the traceability identification code on the outer packaging of the frozen aquatic products to be tested, obtain and bind the traceability information of this batch of products to this testing task; Step 2, Low-temperature antifreeze homogenization: Without thawing, the sample is placed in a homogenization bag pre-filled with low-temperature antifreeze lysis buffer and homogenized at high speed to fully lyse the tissue and release intracellular and extracellular risk factors, resulting in a homogenized solution; the antifreeze lysis buffer is a buffer system containing antifreeze and has a freezing point below -25°C. Step 3, target enrichment: The supernatant after centrifugation of the homogenized liquid is loaded onto a temperature-sensitive molecularly imprinted column at a low temperature of 0-4℃ to specifically capture and enrich the target risk factor; Step 4, Elution and Internal Standard Addition: The molecularly imprinted column is brought to room temperature, the enriched target analytes are eluted and collected using elution buffer, and a certain amount of multifunctional internal standard solution is precisely added to the elution buffer; Step 5, Chip Sample Introduction and Reaction: Inject the eluent with added internal standard into the sample inlet of the multifunctional CRISPR microfluidic chip, which integrates: A Salmonella nucleic acid detection channel based on pre-embedded lyophilized RPA reagent, Cas12a / sgRNA complex and fluorescent reporter group; Histamine optical detection channel based on upconversion nanoparticle-labeled aptamers; Formaldehyde colorimetric detection channel based on Nash reagent colorimetric reaction; Step 6: Multispectral signal acquisition: Using a multispectral sensor, the optical signals of each reaction unit on the chip are acquired synchronously, including fluorescence signals, upconversion emission signals and visible absorption spectral signals; Step 7, AI Intelligent Analysis: The collected multidimensional spectral signals are transmitted to a processor with a built-in AI algorithm. The algorithm first performs data normalization and compensation correction based on the internal standard unit signal, and then analyzes the precise concentration of each risk factor through a convolutional neural network model. Step 8, Risk Decision and Evidence Preservation: Compare the test results with the risk threshold to generate a report, and encrypt all information and upload it to the blockchain to generate an immutable digital evidence certificate.

2. The method for detecting risk factors in cold chain aquatic products according to claim 1, characterized in that: The multifunctional internal standard solution described in step four comprises: The nucleic acid detection channel uses a fluorescently labeled DNA internal standard with a similar sequence but a base mismatch; Deuterium-labeled histamine and Carbon-13-labeled formaldehyde internal standards are used in the small molecule detection channel.

3. The method for detecting risk factors in cold chain aquatic products according to claim 2, characterized in that: The AI ​​interference compensation algorithm described in step seven is a multivariate correction model based on convolutional neural networks, used to identify and compensate for signal interference from the sample matrix, inter-channel crosstalk, and non-specific adsorption.

4. The method for detecting risk factors in cold chain aquatic products according to claim 3, characterized in that: The method further includes uploading anonymized signal spectra and detection results to a cloud server for continuous training and iterative optimization of the AI ​​model.

5. The method for detecting risk factors in cold chain aquatic products according to claim 4, characterized in that: The filler of the thermosensitive molecularly imprinted column is a polymer with N-isopropylacrylamide as the thermosensitive monomer. It adsorbs the target substance through hydrogen bonding at low temperature, and undergoes a hydrophilic-hydrophobic phase transition and releases the target substance after being heated to room temperature.

6. The method for detecting risk factors in cold chain aquatic products according to claim 5, characterized in that: In step six, the multispectral sensor integrates a miniature temperature and humidity sensor to monitor the temperature of the reaction microenvironment in real time, and inputs this parameter into the AI ​​algorithm for dynamic compensation of the reaction kinetics.

7. The method for detecting risk factors in cold chain aquatic products according to claim 6, characterized in that: The antifreeze lysis buffer mentioned in step two is a Tris-HCl buffer system containing ethylene glycol or dimethyl sulfoxide.

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