Food safety detection method based on carbon dot monitoring

Abstract

The invention discloses a food safety detection method based on carbon dot monitoring, which comprises a sensor array construction step, a medicinal material and food detection step, a detection data analysis step and a tracing platform establishment step, and the detection data analysis step is used for processing and analyzing the signal data obtained in the medicinal material and food detection step and outputting a processing result. By constructing the multi-channel sensor array, the purpose of n-to-n interaction between the sensing unit and the target detection substance is achieved, a high-throughput detection method is provided for safety detection of medicinal food, and the purpose of simultaneous detection of multiple components in the medicinal food is achieved; and a prediction model suitable for the sensor array is established for the detection data analysis step, so that the purpose of qualitatively and quantitatively analyzing the antioxidant component in the medicinal food based on the fluorescence detection signal is achieved, and the freshness of the medicinal food is conveniently detected.

Description

Technical Field

[0001] The present invention relates to the technical field of food safety detection, and specifically provides a food safety detection method based on carbon dot monitoring. Background Technique

[0002] The global demand for medicinal materials food is increasing day by day, which has triggered worldwide attention to the safety issues of food homologous to medicine and food. Medicinal materials food is easily affected by various safety hazards. For example, factors such as temperature and humidity will accelerate the oxidation of medicinal materials food, thereby reducing the freshness of the food. For medicinal materials food with freshness below the specified level, it should be prevented from flowing into the market for sale. Therefore, it is necessary to detect the freshness of medicinal materials food. And the antioxidant substances in medicinal materials food are important factors to prevent the oxidation and deterioration of medicinal materials food. Therefore, it is necessary to detect the antioxidant substances in medicinal materials food. However, the existing food safety detection methods are not convenient for synchronously qualitatively and quantitatively analyzing multiple antioxidant substance components in medicinal materials food, reducing the detection efficiency, and not convenient for predicting the freshness and available time range.

[0003] The defects of the existing food safety detection methods are as follows: In patent document CN112857445A, the freshness of food is mainly detected according to the storage, transportation and environmental factor parameters of food. However, the above patent cannot achieve the purpose of qualitatively and quantitatively analyzing multiple components of antioxidant substances in medicinal materials food, and cannot achieve the purpose of detecting the freshness of medicinal materials food according to the components of medicinal materials food; In patent document CN115009579B, the detection of the inflation amount of food packaging bags is mainly realized by a pressure sensor, a first camera and a second camera. However, the above patent does not consider the problem of testing and calibrating a multi-channel sensor array; In patent document CN115452892A, the main purpose is to detect the position nodes of areas where crops with serious pesticide residues are located. However, it does not consider the purpose of detecting the freshness of medicinal materials food based on the concentrations of various antioxidant substances and amine component compounds in various medicinal materials food and predicting the available time range of medicinal materials food; In application document CN110348863A, the main consideration is how to use the Internet and mobile phone APPs to achieve the purpose of sharing open food commodity information by people, without considering how to improve the non-tamperability of data and the problem of authentic traceability of medicinal materials food. Summary of the Invention

[0004] The purpose of the present invention is to provide a food safety detection method based on carbon dot monitoring to solve the problems raised in the above background technique.

[0005] To achieve the above object, the present invention provides the following technical solutions: A food safety detection method based on carbon dot monitoring, including a sensor array construction step, a medicinal material and food detection step, a detection data analysis step, and a traceability platform establishment step. The sensor array construction step is used for the sensor array in the medicinal material and food detection step. The detection data analysis step processes and analyzes the signal data obtained in the medicinal material and food detection step and outputs the processing result; The sensor array construction step includes establishing a sensing unit, constructing a sensor array, and establishing a prediction model; Establishing a sensing unit: Based on the carbon dot detection technology, preparing a fluorescent nanoprobe, and using the fluorescent nanoprobe as the sensing material to obtain the sensing unit; Constructing a sensor array: Based on the difference in the response characteristics generated by the binding of the nanoprobe to different components in the medicinal material and food, arranging multiple groups of sensing units to obtain the sensor array; Establishing a prediction model: Reacting a variety of single-type antioxidant substances and amine component compounds with known concentrations with the sensor array to construct a prediction model for the detection data analysis step; The detection data analysis step includes the normalization processing of the fluorescence signal, the qualitative analysis of antioxidant substances, the quantitative prediction of antioxidant substances, and the prediction of the freshness of the medicinal material and food; Normalization processing of the fluorescence signal: Performing normalization processing on the fluorescence data obtained from the medicinal material and food detection step, and obtaining the unit response characteristic signal from the fluorescence signal to generate the unit spectral fingerprint; Qualitative analysis of antioxidant substances: Based on the information in the cloud database and the unit spectral fingerprint information, performing qualitative analysis on the antioxidant substances in the medicinal material and food; Quantitative prediction of antioxidant substances: Based on the prediction model generated in the sensor array step and the unit response characteristic signal, performing quantitative analysis on the antioxidant substances; Prediction of the freshness of the medicinal material and food: Based on the output data of the quantitative analysis of antioxidant substances, predicting the freshness of the detected medicinal material and food.

[0006] Preferably, the establishment of the sensing unit specifically includes selecting three precursors and preparing three different green fluorescent carbon dots by hydrothermal method or chemical reduction method or solvothermal method, respectively coupling the three different green fluorescent carbon dots with orange fluorescent rhodamine B (RhB) to generate the corresponding RhB@CDs dual-color fluorescent nanoprobe, and selecting the RhB@CDs dual-color fluorescent nanoprobe as the sensing material to obtain the corresponding sensing unit; Constructing the sensor array includes setting a multi-channel array structure based on the difference in the response characteristics of different sensing units to different antioxidant substances, and arranging the sensing units according to the multi-channel array structure to obtain the sensor array.

[0007] Preferably, the establishment of the prediction model includes the testing and calibration of the sensor array and the establishment of the prediction model; The testing and calibration of the sensor array is to detect a single type of antioxidant substance through the sensor array, respectively obtain the overall detection signal output by the sensor array and the detection signals output by each group of sensing units constituting the sensor array. For the detection signals output by the sensing units that generate characteristic responses to a single type of antioxidant substance, the abnormal detection signals are identified through clustering analysis or anomaly detection algorithms, and the corresponding sensing units are corrected according to the detection signals; For the overall detection signal Sz output by the sensor array and the detection signals Si output by each group of sensing units constituting the sensor array obtained after correction, the ideal output signal Sx is predicted based on a linear regression model. The error between the overall detection signal Sz and the ideal output signal Sx is analyzed through an error metric algorithm to predict whether there is signal interference between the sensing units, and the array structure of the sensing units is corrected based on the prediction results.

[0008] Preferably, the establishment of the prediction model includes using the corrected sensor array to detect a variety of single-type antioxidant substances with known concentrations, and constructing a first prediction model regarding the antioxidant substance concentration and the response characteristic signal according to the detection results; Use the corrected sensor array to detect amine component compounds with known concentrations, and construct a second prediction model regarding the amine component compound concentration and the response signal according to the detection results.

[0009] Preferably, the medicinal material food detection step includes the excitation of the signal that can be collected and the collection of the fluorescence signal; The excitation of the signal that can be collected is to contact the medicinal material food sample to be detected with the sensor array, use an excitation light source to output excitation light to the sensor array, and excite the sensor array to generate a fluorescence signal. The collection of the fluorescence signal is to use a fluorescence signal collection device to obtain the fluorescence signal output by the sensor array, where the fluorescence signal includes the overall fluorescence signal output by the sensor array and the unit fluorescence signal output by each sensing unit in the sensor array.

[0010] Preferably, the normalization processing of the fluorescence signal specifically includes, for the overall fluorescence signal and the unit fluorescence signal, filtering the background light and environmental noise in the fluorescence signal using a filtering technology, and performing normalization processing on the filtered fluorescence signal to obtain fluorescence signals in the same format; Obtaining the unit response characteristic signal: Extract the unit response characteristic signal from the fluorescence signal after standardization processing. The unit response characteristic signal includes fluorescence color, fluorescence intensity, peak wavelength, decay time, and full width at half maximum. For the unit response characteristic signal, use a pattern recognition algorithm to identify and classify the unit response characteristic signal to obtain the same group of response characteristic signals, where the same group of response characteristic signals are the response characteristic signals generated by a group of sensing units for a certain antioxidant substance component; Generate a unit spectral fingerprint for the same group of response characteristic signals.

[0011] Preferably, the qualitative analysis of the fluorescence signal specifically includes obtaining the antioxidant substance types and corresponding spectral fingerprint information from the cloud database to generate a database for qualitative analysis; Compare the unit spectral fingerprint with the information in the database for qualitative analysis to qualitatively analyze the antioxidant substances in the tested medicinal material food samples.

[0012] Preferably, the quantitative analysis of the antioxidant substances specifically includes, for the same group of response characteristic signals, calculating the concentration of the corresponding type of antioxidant substance through the first prediction model to quantitatively analyze the corresponding type of antioxidant substance in the tested medicinal material food, and calculating the concentration of the amine component compound in the tested medicinal material food through the second prediction model to quantitatively analyze the amine component compound in the tested medicinal material food.

[0013] Preferably, the prediction of the freshness of the medicinal material food specifically includes, for the quantitative analysis results of various antioxidant substances and the quantitative analysis results of amine component compounds in the tested medicinal material food, detecting the relationship model between the contents of various antioxidant substances and the concentration of amine component compounds through multiple linear regression analysis, denoted as the third prediction model; The quantitative analysis of the antioxidant substances further includes the following steps: Obtain the same group of response characteristic signals corresponding to at least three time points during the detection of the medicinal material food and the time intervals between adjacent two time points. For the same group of response characteristic signals corresponding to the obtained at least three time points, conduct quantitative analysis of the antioxidant substances through the first prediction model to obtain the antioxidant substance concentration corresponding to the time point, and establish a material quantity decay prediction model based on the quantitative analysis results and the time intervals between adjacent two time points, where the material quantity decay prediction model is used to predict the remaining amount of antioxidant substances in the tested medicinal material food corresponding to future time points; According to the predicted remaining amount of antioxidant substances, use the third prediction model to predict the concentration of amine component compounds in the tested medicinal material food within a future time period, and analyze the freshness of the tested medicinal material food and the time range for the medicinal material food to maintain available freshness.

[0014] Preferably, the steps of establishing the traceability platform include obtaining the origin information, processing information during the production of the medicinal material food, and transportation information during the transportation of the medicinal material food provided by the medicinal material food supplier from the cloud database, obtaining the detection result information output by the detection data analysis step, and establishing the traceability platform by using machine learning algorithms and blockchain technology.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. By including the step of constructing a sensor array, the present invention constructs a multi-channel sensor array to achieve the purpose of n-to-n interaction between the sensing unit and the target detection substance, providing a high-throughput detection method for the safety detection of medicinal material food, realizing the simultaneous detection of multiple components in the medicinal material food, facilitating the acceleration of the food safety detection efficiency of medicinal materials, expanding the scope of application of the detection method, and establishing a prediction model applicable to the sensor array for the detection data analysis step to achieve the purpose of qualitatively and quantitatively analyzing the antioxidant components in the medicinal material food based on the fluorescence detection signal, which is convenient for detecting the freshness of the medicinal material food.

[0016] 2. After the construction of the sensor array is completed, the present invention uses antioxidant substances with known concentrations to detect the sensing unit and the sensor array, and corrects the structure of the sensing unit and the sensor array according to the detection results, thereby avoiding the phenomenon of mutual interference between the sensing units, which is beneficial to improving the accuracy of the detection of medicinal material food.

[0017] 3. By establishing the first prediction model and the second prediction model, the present invention monitors and predicts the concentrations of various antioxidant substances and amine component compounds in the medicinal material food in real time, generates the third prediction model by calculating the relationship between the concentrations of various antioxidant substances and amine component compounds, and constructs a material quantity attenuation prediction model to predict the remaining content of various antioxidant substances, thereby predicting the concentration of amine component compounds and the time range for maintaining the available freshness of the medicinal material food in the future time period, which is convenient for prompting consumers and avoiding the inflow of unfresh and unusable medicinal material food into the market.

[0018] 4. By including the step of establishing the traceability platform, based on the origin information, processing information during the production of the medicinal material food, and transportation information during the transportation of the medicinal material food provided by the medicinal material food supplier, obtaining the detection result information output by the detection data analysis step, and establishing the traceability platform by using machine learning algorithms and blockchain technology, the present invention realizes the detection and traceability of genuine medicinal material food, avoids the phenomena of counterfeiting of medicinal material food and falsification of detection results, improves the security and transparency of data, prevents the forgery or tampering of relevant data, and enhances the credibility of medicinal material products and the trust of consumers. Description of the Drawings

[0019] Figure 1It is the overall method step diagram of the present invention; Figure 2 It is the flow chart of the sensor array construction steps of the present invention; Figure 3 It is the flow chart of the detection data analysis steps of the present invention. Specific embodiments

[0020] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] Please refer to Figure 1 、 Figure 2 and Figure 3 For an embodiment provided by the present invention: A food safety detection method based on carbon dot monitoring, including sensor array construction steps, medicinal material and food detection steps, detection data analysis steps, and traceability platform establishment steps. The sensor array construction steps are used for the sensor array of the medicinal material and food detection steps. The sensor array construction steps include establishing a sensing unit, constructing a sensor array, and establishing a prediction model; Specifically, establishing a sensing unit includes selecting three precursors to prepare three different green fluorescent carbon dots by hydrothermal method, chemical reduction method, or solvothermal method. The precursors include sugar precursors, amino acid precursors, and organic small molecule precursors, and the surface modification of carbon dots can be carried out to introduce surface functional groups to improve the effect of carbon dots on antioxidant substances and amine component compounds. Coupling the three different green fluorescent carbon dots with orange fluorescent rhodamine B (RhB) respectively to generate corresponding RhB@CDs dual-color fluorescent nanoprobes, and selecting the RhB@CDs dual-color fluorescent nanoprobes as the sensing material to obtain the corresponding sensing unit; Constructing a sensor array includes setting a multi-channel array structure based on the difference in the response characteristics of different sensing units to different antioxidant substances, and arranging the sensing units according to the multi-channel array structure to obtain a sensor array.

[0022] Furthermore, by setting a bicolor nanosensor as the sensing material, when the group price of amine component compounds in the detected sample increases, the detection signal changes from orange to green, which can more intuitively display the concentration of amine component compounds in medicinal materials and foods. Utilizing the mutual relationship between the decrease in the freshness of medicinal materials and foods and the increase in the concentration of amine component compounds, people can generally understand the freshness of medicinal materials and foods. By setting up a sensor array, the purpose of the n-to-n interaction between the sensing unit and the target detection substance is achieved, providing a high-throughput detection method for the safety detection of medicinal materials and foods, realizing the purpose of simultaneous detection of multiple components in medicinal materials and foods, facilitating the acceleration of the safety detection efficiency of medicinal materials and foods, expanding the applicable range of the detection method, and being applicable to the safety detection of medicinal materials and foods with complex components.

[0023] Please refer to Figure 1 and Figure 2 , an embodiment provided by the present invention: a food safety detection method based on carbon dot monitoring, including establishing a prediction model including testing and calibration of the sensor array and establishing a prediction model; The testing and calibration of the sensor array is to detect a single type of antioxidant substance through the sensor array, respectively obtain the overall detection signal output by the sensor array and the detection signals output by each group of sensing units constituting the sensor array. For the detection signals output by the sensing units that produce characteristic responses to a single type of antioxidant substance, identify the abnormal detection signals through cluster analysis or anomaly detection algorithms, and correct the corresponding sensing units according to the detection signals. For the overall detection signal Sz output by the sensor array and the detection signals Si output by each group of sensing units constituting the sensor array obtained after correction, predict the ideal output signal Sx based on a linear regression model, analyze the error between the overall detection signal Sz and the ideal output signal Sx through an error metric algorithm, predict whether there is signal interference between the sensing units, and correct the array structure of the sensing units based on the prediction results. Establishing a prediction model includes using the corrected sensor array to detect multiple single-type antioxidant substances with known concentrations respectively, and constructing a first prediction model regarding the concentration of antioxidant substances and the response characteristic signals according to the detection results; Using the corrected sensor array to detect amine component compounds with known concentrations, and constructing a second prediction model regarding the concentration of amine component compounds and the response signals according to the detection results Further, identify the abnormal detection signals in the detection signals output by the sensing units. According to the output channels corresponding to the sensing units, determine the sensing units corresponding to the abnormal detection signals, which facilitates the adjustment or replacement of the sensing units. For the detection signals Si output by multiple groups of sensing units, where i is the sensing unit number, use the least squares method to estimate and assign the corresponding weights of the detection signals Si of the corresponding sensing units, and then obtain the ideal output signal Sx for multiple groups of detection signals. By obtaining the error between the overall detection signal Sz and the ideal output signal Sx and analyzing whether the error is within the allowable error, determine whether there is signal interference between the sensing units. When the error exceeds the allowable error, correct the sensor array structure, thereby avoiding the phenomenon of mutual interference between the sensing units and being beneficial to improving the accuracy of medicinal material and food detection.

[0024] Please refer to Figure 1 and Figure 2 , an embodiment provided by the present invention: A food safety detection method based on carbon dot monitoring. Establishing a prediction model includes using the modified sensor array to detect various single-type antioxidant substances with known concentrations respectively, and constructing a first prediction model about the antioxidant substance concentration and the response characteristic signal according to the detection results, where the response characteristic signals include fluorescence intensity, peak wavelength, decay time, and full width at half maximum. It includes using the modified sensor array to detect amine component compounds with known concentrations, and constructing a second prediction model about the amine component compound concentration and the response signal according to the detection results, where the response characteristic signals include fluorescence color, fluorescence intensity, peak wavelength, decay time, and full width at half maximum.

[0025] Further, by establishing a first prediction model for predicting the antioxidant substance concentration and a second prediction model for predicting the amine component compound, it is used to predict the concentrations of various antioxidant substances and amine component compounds in the detected medicinal materials in the data analysis step of the detection, providing a basis for subsequent steps.

[0026] Please refer to Figure 1 and Figure 3 , an embodiment provided by the present invention: A food safety detection method based on carbon dot monitoring. The medicinal material and food detection step includes the excitation of signal collection and the collection of fluorescence signals; The excitation of the signal to be collected is to bring the medicinal material food sample to be detected into contact with the sensor array, and use an excitation light source to output excitation light to the sensor array. The excitation light source can be selected from an ultraviolet lamp, a blue light generator or other excitation light source generators to excite the sensor array to generate a fluorescence signal. The collection of the fluorescence signal is to use a fluorescence signal collection device to obtain the fluorescence signal output by the sensor array. The fluorescence signal includes the overall fluorescence signal output by the sensor array and the unit fluorescence signal output by each sensing unit in the sensor array. The normalization processing of the fluorescence signal specifically includes, for the overall fluorescence signal and the unit fluorescence signal, filtering the background light and environmental noise in the fluorescence signal by using a filtering technology, and performing normalization processing on the filtered fluorescence signal to obtain fluorescence signals in the same format.

[0027] Furthermore, for the obtained multiple groups of fluorescence signals, filtering processing and normalization processing are respectively performed, thereby removing the background light interference and environmental noise interference in the fluorescence signals, as well as removing the fluctuation interference caused by environmental temperature and humidity factors, which is beneficial to improving the accuracy of the detection results. By performing normalization processing on the fluorescence signals, fluorescence signals in the same format are obtained, which is convenient for subsequent processing and analysis of multiple groups of fluorescence signals, reduces the difficulty and complexity of the detection data analysis steps, and improves the detection efficiency to a certain extent.

[0028] Please refer to Figure 1 and Figure 3 , an embodiment provided by the present invention: a food safety detection method based on carbon dot monitoring. The detection data analysis step processes and analyzes the signal data obtained in the medicinal material food detection step and outputs a processing result. The detection data analysis step includes the normalization processing of the fluorescence signal, the qualitative analysis of the antioxidant substance, the quantitative prediction of the antioxidant substance, and the prediction of the freshness of the medicinal material food; Obtaining the unit response characteristic signal: Extracting the unit response characteristic signal from the normalized fluorescence signal. The unit response characteristic signal includes fluorescence color, fluorescence intensity, peak wavelength, decay time, and full width at half maximum. For the unit response characteristic signal, using a pattern recognition algorithm to identify and classify the unit response characteristic signal to obtain the same group of response characteristic signals. The same group of response characteristic signals is the response characteristic signals generated by a group of sensing units for an antioxidant substance component, and feature confirmation is performed on the obtained same group of response characteristic signals to avoid the phenomenon of confusion of the response characteristic signals; Generating a unit spectral fingerprint for the same group of response characteristic signals; The qualitative analysis of the fluorescence signal specifically includes obtaining the antioxidant substance types and corresponding spectral fingerprint information from the cloud database to generate a database for qualitative analysis; Comparing the unit spectral fingerprint with the information in the database for qualitative analysis to perform qualitative analysis on the antioxidant substances in the detected medicinal material food sample; For the overall fluorescence signal after standardization processing, the overall response characteristic signal is obtained by principal component analysis method, a comprehensive characteristic holographic fingerprint spectrum of the multi-sensor array is generated, and the correlation between the comprehensive characteristic holographic fingerprint spectrum and multiple groups of unit spectral fingerprints is analyzed, which is convenient for realizing the monitoring of various antioxidant substances through the comprehensive characteristic holographic fingerprint spectrum, and can be used to analyze the relationship between the components of various antioxidant substances.

[0029] Furthermore, a variety of features are extracted from the fluorescence signal in standard format obtained from the sensing unit, and the same-group response characteristic signal generated by the sensing unit for one substance among a variety of antioxidant substances and amine component compounds is obtained from a variety of response characteristic signals, and then the spectral fingerprint of one substance is obtained. By comparing the spectral fingerprint with the spectral fingerprint information in the database, the antioxidant substance components are identified to realize the qualitative analysis of antioxidant substances. And through the setting of the sensor array, the purpose of multi-component analysis of medicinal materials and foods is realized, which is beneficial to improving the detection efficiency of medicinal materials and foods.

[0030] Please refer to Figure 1 and Figure 3 An embodiment provided by the present invention: A food safety detection method based on carbon dot monitoring. The quantitative analysis of antioxidant substances specifically includes, for the same-group response characteristic signals, calculating the concentration of the corresponding type of antioxidant substance through the first prediction model, quantitatively analyzing the corresponding type of antioxidant substance in the detected medicinal materials and foods, calculating the concentration of amine component compounds in the detected medicinal materials and foods through the second prediction model, and quantitatively analyzing the amine component compounds in the detected medicinal materials and foods.

[0031] The prediction of the freshness of medicinal materials and foods specifically includes, for the quantitative analysis results of various antioxidant substances and the quantitative analysis results of amine component compounds in the detected medicinal materials and foods, detecting the relationship model between the contents of various antioxidant substances and the concentration of amine component compounds through multiple linear regression analysis method, denoted as the third prediction model; The quantitative analysis of antioxidant substances further includes the following steps: obtaining the same-group response characteristic signals corresponding to at least three time points during the detection process of medicinal materials and foods and the time duration between adjacent two time points, for the obtained same-group response characteristic signals corresponding to at least three time points, performing quantitative analysis of antioxidant substances through the first prediction model to obtain the concentration of antioxidant substances corresponding to the time points, and establishing a material quantity attenuation prediction model based on the quantitative analysis results and the time duration between adjacent two time points, where the material quantity attenuation prediction model is used to predict the remaining amount of antioxidant substances in the detected medicinal materials and foods corresponding to future time points; According to the predicted remaining amount of antioxidant substances, using the third prediction model to predict the concentration of amine component compounds in the detected medicinal materials and foods within a future time period, and analyzing the freshness of the detected medicinal materials and foods and the time range for the medicinal materials and foods to maintain available freshness.

[0032] Furthermore, by constructing a first prediction model and using the response characteristic signals of the same group to predict the concentration of the corresponding antioxidant substances, the purpose of quantitative analysis of various antioxidant substances in medicinal materials and foods is achieved. By constructing a second prediction model, the purpose of real-time monitoring of the concentration of amine component compounds in medicinal materials and foods is achieved, and a prediction model regarding the concentration of various antioxidant substances and the concentration of amine component compounds is constructed to study the relationship between various antioxidant substances and amine component compounds. A material quantity attenuation prediction model is established to achieve real-time prediction of the remaining quantity of various antioxidant substances, and then real-time prediction of the concentration of amine component compounds. According to the linear relationship between the concentration of amine component compounds and the freshness of the food, the freshness of the food is predicted in real time, and according to the specified minimum available freshness standard of medicinal materials and foods, the available time range of medicinal materials and foods is predicted. A feedback unit is set up to, when the detected result shows that the medicinal materials and foods exceed the predicted available time range, feedback the detected result to the supplier or the market supervision party, facilitating the timely recall of non-fresh products and being conducive to ensuring the safety of medicinal materials and foods.

[0033] Please refer to Figure 1 , an embodiment provided by the present invention: A food safety detection method based on carbon dot monitoring. The steps for establishing the traceability platform include obtaining the origin information, the processing process information of the medicinal materials and foods, and the transportation process information of the detected medicinal materials and foods provided by the supplier of the medicinal materials and foods from the cloud database, obtaining the detected result information output by the detected data analysis step, using machine learning algorithms and blockchain technology to establish a traceability platform, and each group of medicinal materials and foods is provided with a corresponding unique identifier, such as a two-dimensional code or an RFID tag. By scanning the unique identifier, the relevant information used for the corresponding medicinal materials and foods can be obtained from the traceability platform, improving the security and transparency of the data, preventing the occurrence of relevant data forgery or tampering phenomena, and enhancing the credibility of the medicinal materials products and the trust of consumers.

[0034] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be encompassed by the present invention. Any reference signs in the claims should not be regarded as limiting the claims involved.

Claims

1. A food safety detection method based on carbon dot monitoring, characterized in that, It includes a sensor array construction step, a medicinal material and food detection step, a detection data analysis step, and a traceability platform establishment step. The sensor array construction step is used to construct the sensor array for the medicinal material and food detection step. The detection data analysis step processes and analyzes the signal data obtained in the medicinal material and food detection step and outputs the processing result; The sensor array construction step includes establishing a sensing unit, constructing a sensor array, and establishing a prediction model; Establishing a sensing unit: Based on the carbon dot detection technology, preparing a fluorescent nanoprobe, and using the fluorescent nanoprobe as the sensing material to obtain the sensing unit; Constructing a sensor array: Based on the differences in the response characteristics generated by the interaction between the nanoprobe and different components in the medicinal material and food, arranging multiple groups of sensing units to obtain the sensor array; Establishing a prediction model: Reacting a variety of single-type antioxidant substances and amine component compounds with known concentrations with the sensor array to construct a prediction model for the detection data analysis step; The detection data analysis step includes the normalization of fluorescence signals, the qualitative analysis of antioxidant substances, the quantitative prediction of antioxidant substances, and the prediction of the freshness of medicinal materials and food; Normalization of fluorescence signals: Normalizing the fluorescence data obtained from the medicinal material and food detection step, and obtaining the unit response characteristic signals from the fluorescence signals to generate the unit spectral fingerprint; Qualitative analysis of antioxidant substances: Based on the information in the cloud database and the unit spectral fingerprint information, qualitatively analyze the antioxidant substances in the medicinal material and food; Quantitative prediction of antioxidant substances: Based on the prediction model generated in the sensor array step and the unit response characteristic signals, quantitatively analyze the antioxidant substances; Prediction of the freshness of medicinal materials and food: Based on the output data of the quantitative analysis of antioxidant substances, predict the freshness of the detected medicinal materials and food.

2. The food safety detection method based on carbon dot monitoring according to claim 1, characterized in that: The establishment of the sensing unit specifically includes selecting three precursors and preparing three different green fluorescent carbon dots by hydrothermal method, chemical reduction method, or solvothermal method. Respectively coupling the three different green fluorescent carbon dots with orange fluorescent rhodamine B (RhB) to generate the corresponding RhB@CDs dual-color fluorescent nanoprobe, and selecting the RhB@CDs dual-color fluorescent nanoprobe as the sensing material to obtain the corresponding sensing unit; Constructing the sensor array includes setting a multi-channel array structure based on the differences in the response characteristics of different sensing units to different antioxidant substances, and arranging the sensing units according to the multi-channel array structure to obtain the sensor array.

3. The food safety detection method based on carbon dot monitoring according to claim 2, wherein: The establishment of the prediction model includes the testing and calibration of the sensor array and the establishment of the prediction model; The testing and calibration of the sensor array is to detect a single type of antioxidant substance through the sensor array, respectively obtain the overall detection signal output by the sensor array and the detection signals output by each group of sensing units constituting the sensor array. For the detection signals output by the sensing units that generate characteristic responses to a single type of antioxidant substance, identify the abnormal detection signals through clustering analysis method or anomaly detection algorithm, and correct the corresponding sensing units for the detection signals; For the overall detection signal Sz output by the sensor array obtained after correction and the detection signals Si output by each group of sensing units constituting the sensor array, based on a linear regression model, predict the ideal output signal Sx. Analyze the error between the overall detection signal Sz and the ideal output signal Sx through an error metric algorithm to predict whether there is signal interference between the sensing units, and correct the array structure of the sensing units based on the prediction results.

4. The food safety detection method based on carbon dot monitoring according to claim 3, wherein: The establishment of the prediction model includes using the corrected sensor array to detect various single-type antioxidant substances with known concentrations respectively, and constructing a first prediction model about the antioxidant substance concentration and the response characteristic signal according to the detection results; Use the corrected sensor array to detect amine component compounds with known concentrations, and construct a second prediction model about the amine component compound concentration and the response signal according to the detection results.

5. The food safety detection method based on carbon dot monitoring according to claim 4, characterized in that: The medicinal material food detection steps include the excitation of signal acquisition and the acquisition of fluorescence signals; The excitation of signal acquisition is to bring the medicinal material food sample to be detected into contact with the sensor array, use an excitation light source to output excitation light to the sensor array, and excite the sensor array to generate fluorescence signals. The acquisition of fluorescence signals is to use a fluorescence signal acquisition device to obtain the fluorescence signals output by the sensor array, where the fluorescence signals include the overall fluorescence signal output by the sensor array and the unit fluorescence signal output by each sensing unit in the sensor array.

6. The food safety detection method based on carbon dot monitoring according to claim 5, characterized in that: The normalization processing of the fluorescence signals specifically includes, for the overall fluorescence signal and the unit fluorescence signal, using a filtering technology to filter the background light and environmental noise in the fluorescence signals, and performing normalization processing on the filtered fluorescence signals to obtain fluorescence signals in the same format; Obtaining the unit response characteristic signals: Extract the unit response characteristic signals from the normalized fluorescence signals, where the unit response characteristic signals include fluorescence color, fluorescence intensity, peak wavelength, decay time, and full width at half maximum. For the unit response characteristic signals, use a pattern recognition algorithm to identify and classify the unit response characteristic signals to obtain the same group of response characteristic signals, where the same group of response characteristic signals are the response characteristic signals generated by a group of sensing units for one antioxidant substance component; Generate a unit spectral fingerprint for the same group of response characteristic signals.

7. The food safety detection method based on carbon dot monitoring according to claim 6, wherein: The qualitative analysis of the fluorescence signals specifically includes obtaining the antioxidant substance types and the corresponding spectral fingerprint information from the cloud database to generate a database for qualitative analysis; Compare the unit spectral fingerprint with the information in the database for qualitative analysis to qualitatively analyze the antioxidant substances in the detected medicinal material food sample.

8. The food safety detection method based on carbon dot monitoring according to claim 7, characterized in that: The quantitative analysis of the antioxidant substances specifically includes, for the same group of response characteristic signals, calculating the concentration of the corresponding type of antioxidant substance through the first prediction model to quantitatively analyze the corresponding type of antioxidant substance in the detected medicinal material food, and calculating the concentration of the amine component compound in the detected medicinal material food through the second prediction model to quantitatively analyze the amine component compound in the detected medicinal material food.

9. The food safety detection method based on carbon dot monitoring according to claim 8, wherein: The prediction of the freshness of the medicinal material food specifically includes the quantitative analysis results of various antioxidant substances and the quantitative analysis results of amine component compounds in the detected medicinal material food, and a relationship model between the contents of various antioxidant substances and the concentration of amine component compounds is detected through multiple linear regression analysis, which is denoted as the third prediction model; The quantitative analysis of the antioxidant substances further includes the following steps: obtaining the same group of response characteristic signals corresponding to at least three time points during the detection process of the medicinal material food and the time duration between two adjacent time points, performing quantitative analysis of the antioxidant substances on the same group of response characteristic signals corresponding to the obtained at least three time points through the first prediction model, obtaining the concentration of the antioxidant substances corresponding to the time points, and establishing a material quantity decay prediction model based on the quantitative analysis results and the time duration between two adjacent time points, where the material quantity decay prediction model is used to predict the remaining amount of antioxidant substances in the detected medicinal material food corresponding to future time points; According to the predicted remaining amount of antioxidant substances, the third prediction model is used to predict the concentration of amine component compounds in the detected medicinal material food within a future time period, and the freshness of the detected medicinal material food and the time range for the medicinal material food to maintain available freshness are analyzed.

10. A food safety detection method based on carbon dot monitoring according to claim 1, characterized in that: The steps for establishing the traceability platform include obtaining the origin information, processing process information, and transportation process information of the detected medicinal material food provided by the medicinal material food supplier from the cloud database, obtaining the detection result information output by the detection data analysis step, and establishing a traceability platform using machine learning algorithms and blockchain technology.

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