Optical fiber fluorescence sensing detection system for food germs

Abstract

The invention relates to an optical fiber fluorescence sensing detection system for food germs, which comprises a sensing module and a detection module, the sensing module comprises an optical fiber fluorescence sensing module (an optical assembly and a fluorescence reaction module) and an environment sensing module, and the optical assembly comprises an optical fiber array, an optical filter, a collimating lens and an image sensor; the fluorescence reaction module comprises an excitation light source and a multi-channel fluorescence reaction tank; the excitation light source irradiates a solution in the centrifugal tube in the multi-channel fluorescence reaction tank to excite a fluorescence signal; optical fiber array fluorescence signals are transmitted; the optical filter is used for gating the fluorescence signal; the collimating lens amplifies and focuses the fluorescence signal; the image sensor is used for imaging a fluorescence signal; the environment sensing module comprises a plurality of sensors for collecting environment data; the detection module obtains a fluorescence intensity image obtained by the image sensor and environment data collected by the environment sensing module, and carries out germ detection on the solution of the food sample. The method can be used for effectively detecting food germs.

Description

Technical Field

[0001] The invention relates to the technical field of food safety detection, and in particular to an optical fiber fluorescence sensing detection system for food pathogens. Background Art

[0002] As an important cornerstone for protecting human health and social stability, food safety has always been the focus of scientific research and practical application. However, traditional food safety testing methods are difficult to meet the needs of on-site real-time monitoring due to problems such as complex operation and high cost.

[0003] A variety of advanced detection methods have been developed for many risk factors of food safety, such as viruses, pathogenic bacteria, toxins and fungi, including electrochemical detection, colorimetric analysis, mass spectrometry liquid phase technology and fluorescence detection. Among them, the fluorescence method has received extensive attention and application due to its outstanding performance in high sensitivity and high selectivity detection. By utilizing the fluorescence characteristics of substances under excitation light, the fluorescence method can quickly and sensitively detect trace harmful substances in food, and has broad application prospects.

[0004] For high-precision detection of pathogenic microorganisms, viruses, etc., environmental factors such as food temperature and humidity also have an important impact on the accuracy of detection. In addition, like food processing and production, food transportation is also one of the important links that cause food contamination. Real-time monitoring of different geographical locations during food transportation can provide important help for the study of the development and changes of pathogenic microorganism concentrations in food and the improvement of detection accuracy.

[0005] Machine learning provides a strong guarantee for the accurate identification of pathogenic microorganisms in food safety testing. Changes in the concentration of pathogenic microorganisms will cause changes in signals such as current, fluorescence or images. By extracting data-related features through machine learning algorithms and training the model, rapid classification and concentration identification of pathogens can be achieved.

[0006] In summary, environmental parameters such as temperature, humidity, and geographic location are key factors in the accurate detection of food safety and the analysis of its regular changes. However, the current detection methods based on fluorescence technology have not effectively integrated these environmental parameter information, and the detection accuracy is not high enough.

[0007] Therefore, there is an urgent need for a new type of high-performance, low-cost and integrated portable food safety testing system to achieve comprehensive integration and intelligent analysis of test data and provide more advanced and convenient technical means for on-site food safety monitoring. Summary of the invention

[0008] To this end, the technical problem to be solved by the present invention is to overcome the problem that the food safety detection method based on fluorescence technology in the prior art has not effectively integrated environmental parameters such as temperature, humidity, geographical location, and the detection accuracy is not high enough.

[0009] In order to solve the above technical problems, the present invention provides a fiber optic fluorescence sensing detection system for food pathogens, comprising a sensing module and a detection module, wherein the sensing module comprises a fiber optic fluorescence sensing module and an environmental sensing module, wherein:

[0010] The optical fiber fluorescence sensing module includes an optical component and a fluorescence reaction module, wherein the optical component includes an optical fiber array, a filter, a collimating lens and an image sensor arranged in sequence; the fluorescence reaction module includes an excitation light source and a multi-channel fluorescence reaction tank; the excitation light source is used to irradiate the solution in the centrifuge tube in the multi-channel fluorescence reaction tank to excite the fluorescence signal, and the solution in the centrifuge tube is the solution of the food sample to be detected; the optical fiber array is connected to the multi-channel fluorescence reaction tank for the transmission of the fluorescence signal; the filter is used to select the fluorescence signal in the optical fiber array; the collimating lens is used to amplify and focus the fluorescence signal selected by the filter; the image sensor is used to image the amplified and focused fluorescence signal of the fluorescence signal to obtain a fluorescence intensity image;

[0011] The environmental sensing module includes a geographic location sensor, a temperature sensor and a humidity sensor, which are used to collect geographic location data, temperature data and humidity data respectively;

[0012] The detection module is used to obtain the fluorescence intensity image obtained by the image sensor, and the geographical location, temperature data and humidity data collected by the geographical location sensor, temperature sensor and humidity sensor respectively, and perform pathogen concentration detection on the solution of the food sample according to the acquired data.

[0013] In one embodiment of the present invention, in order to eliminate the influence of background light, before collecting the fluorescence intensity image, the detection module first collects the background image obtained by the image sensor when the excitation light source is not excited, and performs differential processing on the fluorescence intensity image collected by the detection module after the excitation light source is excited and the background image to remove the interference of ambient background light.

[0014] In one embodiment of the present invention, it is assumed that there is a linear relationship between the fluorescence intensity I corresponding to the fluorescence intensity image and the sample concentration c, which is expressed as:

[0015] I=k·c+I 0

[0016] Where k is the linear conversion coefficient from c to I; I 0 is the initial fluorescence intensity when the sample concentration is 0.

[0017] In one embodiment of the present invention, in order to eliminate the influence of noise on the output of the image sensor, the real-time output value of the image sensor is subjected to mean filtering, and the formula is:

[0018]

[0019] Among them, I=[I 1 ,I 2 ,…,I n ], n∈[1,N], N is the number of fiber array channels, and ΔT is the sampling period.

[0020] In one embodiment of the present invention, the detection module uses the collected fluorescence image and temperature data, humidity data, and geographic location data as inputs of the THPIRBFNN model;

[0021] The activation function of the hidden layer of the THPIRBFNN model is a Gaussian radial basis function of the environmental parameter matrix, wherein the environmental parameters include temperature data, humidity data, and geographic location data, and the expression is as follows:

[0022]

[0023] Among them, Y THPI is the Gaussian radial basis function of the environment parameter matrix, represents the mass center of the jth neuron, σ represents the width of the Gaussian kernel; the input data is represented by the environmental parameter matrix [T,H,P,I 1 ,I 2 ,I 3 …I N ] indicates that T is the ambient temperature, H is the ambient humidity, P is the geographic location information, and I 1 ,I 2 ,I 3 …I N Represents the fluorescence intensity of multi-channel detection.

[0024] In one embodiment of the present invention, the detection module further includes an optimization unit, wherein the optimization unit is used to optimize the linear weights of the output layer neurons and the center point parameters of the hidden layer neurons of the THPIRBFNN model;

[0025] The linear weights of the output layer neurons of the THPIRBFNN model are updated according to the following formula:

[0026] |G k+1 |=|G k |+k G ×|η·ΔG|

[0027]

[0028] Among them, |G k+1 | and |G k | are the weights before and after the update, kG is the influence coefficient of the environmental parameter matrix on weight optimization, η is the learning rate, and E is the minimization error function; the output state represents different concentration levels or classification results, and m is the total number of these possible output states; It represents the predicted output value for a certain input sample when the model output state is i, Z i Indicates the actual output value or true concentration corresponding to the sample;

[0029] The center points of the hidden layer neurons of the THPIRBFNN model are updated according to the following formula:

[0030] |c k+1 |=|c k |+k c ×|η·Δc|

[0031]

[0032] Among them, k c is the influence coefficient of the environmental parameter matrix on the optimization of the hidden layer neuron center point, and the width of the Gaussian kernel is updated according to the following formula:

[0033] |σ k+1 |=|σ k |+k σ ×|η·Δσ|

[0034]

[0035] Among them, k σ is the influence coefficient of the environmental parameter matrix on the Gaussian kernel width optimization.

[0036] In one embodiment of the present invention, the THPIRBFNN model uses a preset loss function LOSS during training, which is expressed as follows:

[0037]

[0038] Where N is the total number of samples in the test set, is the concentration prediction state, and Z is the actual concentration of the sample.

[0039] In one embodiment of the present invention, the detection module further includes an evaluation unit, which is used to perform an accuracy ACC evaluation on the identification result of the sample concentration, and the formula is:

[0040]

[0041] Among them, TP represents the number of true positive examples, FN represents the number of false negative examples, TN represents the number of true negative examples, and FP represents the number of false positive examples.

[0042] In one embodiment of the present invention, the detection module further includes a temperature control module, and the temperature control module is in contact with the multi-channel fluorescence reaction tank to achieve temperature control of the solution in the centrifuge tube in the multi-channel fluorescence reaction tank.

[0043] The above technical solution of the present invention has the following advantages compared with the prior art:

[0044] The optical fiber fluorescence sensing detection system for food pathogens described in the present invention can be effectively applied in food safety detection, and by integrating the sensing module and the detection module, rapid and accurate detection of target substances in food samples can be achieved.

[0045] The present invention improves the sensitivity and stability of the system by optimizing image sensor (camera) parameters and calibrating multiple channels; at the same time, combined with a machine learning model, it achieves accurate identification of sample concentrations under different environments;

[0046] The invention has the advantages of small size, light weight, and easy operation, and is suitable for the needs of on-site rapid detection, providing an efficient and reliable solution for food safety detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments of the present invention in conjunction with the accompanying drawings.

[0048] Figure 1 is a schematic diagram of the structure of an optical fiber fluorescence sensing module in an embodiment of the present invention;

[0049] Figure 2 is a schematic diagram of the structure of a detection module in an embodiment of the present invention;

[0050] Figure 3 is a schematic diagram of a human-computer interaction interface of an optical fiber fluorescence sensing detection system in an embodiment of the present invention;

[0051] Figure 4 This is a temperature control performance test analysis diagram of the optical fiber fluorescence sensing detection system in an embodiment of the present invention;

[0052] Figure 5 It is a schematic diagram of multi-channel fluorescence detection signal calibration of the optical fiber fluorescence sensing detection system in an embodiment of the present invention;

[0053] Figure 6 This is a comparison chart of the fluorescence performance test between the optical fiber fluorescence sensing detection system in the embodiment of the present invention and the commercial microplate reader;

[0054] Figure 7 This is a stability test diagram of the optical fiber fluorescence sensing detection system in an embodiment of the present invention;

[0055] Figure 8 This is a diagram showing the dynamic monitoring results of African swine fever virus under different environments in an embodiment of the present invention;

[0056] Fig. 9 This is a result diagram of linearity detection and concentration identification of African swine fever virus in an embodiment of the present invention;

[0057] Fig.10 This is a diagram showing the dynamic monitoring results of Salmonella in an embodiment of the present invention;

[0058] Fig.11 It is a graph showing the linearity detection and concentration identification results of Salmonella in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0060] Reference Figure 1 As shown, the present invention relates to an optical fiber fluorescence sensing detection system for food pathogens, comprising: a sensing module and a detection module, wherein the sensing module comprises an optical fiber fluorescence sensing module and an environmental sensing module, wherein:

[0061] The optical fiber fluorescence sensing module includes an optical component and a fluorescence reaction module, wherein the optical component includes an optical fiber array, a filter, a collimating lens and an image sensor arranged in sequence; the fluorescence reaction module includes an excitation light source and a multi-channel fluorescence reaction tank; the excitation light source is used to irradiate the solution in the centrifuge tube in the multi-channel fluorescence reaction tank to excite the fluorescence signal, and the solution in the centrifuge tube is the solution of the food sample to be detected; the optical fiber array is connected to the multi-channel fluorescence reaction tank for the transmission of the fluorescence signal; the filter is used to select the fluorescence signal in the optical fiber array; the collimating lens is used to amplify and focus the fluorescence signal selected by the filter; the image sensor is used to image the amplified and focused fluorescence signal of the fluorescence signal to obtain a fluorescence intensity image;

[0062] The environmental sensing module includes a geographic location sensor, a temperature sensor and a humidity sensor, which are used to collect geographic location data, temperature data and humidity data respectively;

[0063] The detection module is used to obtain the fluorescence intensity image obtained by the image sensor, and the geographical location, temperature data and humidity data collected by the geographical location sensor, temperature sensor and humidity sensor respectively, and perform pathogen concentration detection on the solution of the food sample according to the acquired data.

[0064] Furthermore, the image sensor includes but is not limited to a CMOS micro camera, a CCD camera, a photodetector, etc., and is used for detecting multi-channel fluorescence signals.

[0065] Furthermore, the end of the optical fiber array, the filter, and the collimating lens are arranged in the optical component slot.

[0066] Further, the detection module of the present embodiment also includes a touch display screen, a signal processing system, and a power supply module. The signal processing system includes a wireless signal transmission module (such as a WIFI signal transmission module) and a main controller (such as a Raspberry Pi development board, an STM32 series development board). The signal processing system is connected to an image sensor and an environmental sensing module, respectively, for collecting the fluorescence intensity image and environmental data of the optical fiber, and the main controller can transmit the data in real time to a remote terminal or a mobile device through the wireless signal transmission module. The power supply module is used to provide electrical energy for the detection module.

[0067] Furthermore, the main controller of this embodiment also has a built-in machine learning model for performing pathogen detection on solutions of food samples.

[0068] Furthermore, the detection module of this embodiment also includes a temperature control module, which includes a PID controller and a heating refrigerator, and the heating refrigerator is in contact with the multi-channel fluorescence reaction tank. The temperature control module realizes temperature control of the heating refrigerator through the PID controller, thereby realizing temperature control of the multi-channel fluorescence reaction tank.

[0069] The following is a detailed introduction to this embodiment:

[0070] like Figure 1 As shown, the optical fiber fluorescence sensing detection system for food pathogens is portable in this embodiment. The optical fiber fluorescence sensing detection system designed in this example realizes rapid and accurate detection of foodborne pathogens by integrating multiple functional modules. When the sample is detected, the centrifuge tube containing the sample solution is placed in a multi-channel fluorescence reaction tank for detection. The fluorescence signal is finally accurately captured in the form of an image through the optical fiber array and transmitted to the main controller for in-depth analysis. The system realizes full-automatic detection and analysis of multi-channel and multiple targets through image segmentation and RGB three-channel pixel intensity extraction. The intensity of the fluorescence signal is closely related to the concentration of the target. In addition, the system also integrates the global positioning system GPS positioning and temperature and humidity sensing functions, which can record and display relevant information of the detection environment in real time. With the help of the wireless signal transmission module, the temperature, humidity, location information and test results can be transmitted to a mobile phone or a personal server in real time, providing users with fast portable reading and remote control functions of the test data. In order to achieve fast and intelligent analysis of the fluorescence detection signal, the system combines digital signal processing technology and advanced machine learning models.

[0071] In the actual product, the multi-channel fluorescence reaction tank is placed at the core position of the fiber optic fluorescence sensing detection system (it can be placed in the center of the system), and a heating refrigerator is installed on its back. The heating refrigerator is closely connected to the proportional-integral-derivative (PID) controller, and the temperature of the multi-channel fluorescence reaction tank is controlled bidirectionally by precisely controlling the heating refrigerator (TEC). The LED light source (i.e., the excitation light source) is cleverly embedded in the multi-channel fluorescence reaction tank, and the generation of fluorescence signals is stimulated by the precise control of the main controller. The optical fiber array connected to the bottom of the multi-channel fluorescence reaction tank is responsible for transmitting the excited fluorescence signal to the image sensor (micro camera) through the filter and the collimating lens. The image data captured by the image sensor will be transmitted to the main controller for more in-depth data processing and analysis. At the same time, the main controller is also connected to the geographic location sensor (GPS sensor) and the temperature and humidity sensor for real-time collection of environmental data. The test results and monitoring data are intuitively displayed on the touch screen. This design enables the fiber optic fluorescence sensing detection system to provide strong technical support for rapid detection in the field of food safety.

[0072] In a possible implementation, in order to eliminate the influence of background light, the optical fiber fluorescence sensing detection system first collects the optical fiber array image (i.e., background image) when the LED light source is not excited before collecting the fluorescence excitation image, and then differentiates the fluorescence image collected after the LED light source is excited from the background image to remove the interference of the ambient background light. The image acquired by the micro camera contains multi-channel fluorescence data, and the fluorescence intensity corresponding to each channel can be obtained by the position of different optical fibers in the fluorescence image. The RGB pixel values ​​in the circular area corresponding to the optical fiber end face in the fluorescence image are extracted to obtain the fluorescence intensity of each optical fiber end face, so as to determine the concentration of the detected object in different channels.

[0073] This embodiment assumes that there is a linear relationship between the fluorescence intensity I corresponding to the fluorescence intensity image and the sample concentration x, which can be expressed as:

[0074] I=k·c+I 0 (1)

[0075] Where k is the linear conversion coefficient from c to I. When the sample concentration is 0, the initial fluorescence intensity is I 0 .

[0076] At the same time, in order to eliminate the influence of hardware circuit or environmental noise on the image sensor output, the real-time output of the image sensor is processed by mean filtering, and the formula is as follows:

[0077]

[0078] Among them, I=[I 1 ,I 2 ,…,I n ], n∈[1,N], N is the number of fiber channels, and ΔT is the sampling period.

[0079] The detection module uses the collected fluorescence image and temperature data, humidity data, and geographic location data as training inputs for the THPIRBFNN model. The THPIRBFNN model is a radial basis function neural network under the influence of the environmental parameter matrix.

[0080] The activation function of the hidden layer of the THPIRBFNN model is the Gaussian radial basis function of the environment parameter matrix, which is expressed as follows:

[0081]

[0082] Among them, Y THPI is the Gaussian radial basis function of the environment parameter matrix, represents the centroid of the jth neuron, σ represents the width of the Gaussian kernel; the input data is expressed as [T,H,P,I 1 ,I 2 ,I 3 …I N ] represents, where T is the ambient temperature, H is the ambient humidity, P is the geographic location information, I 1 ,I 2 ,I 3 …I N Represents the fluorescence intensity of multi-channel detection. By adjusting the center and width of the basis function, the THPIRBFNN model can learn and approximate complex nonlinear mapping relationships, which can be used for accurate detection and identification of output states.

[0083] The main controller in the detection module of this embodiment further includes an optimization unit. In order to optimize the parameters of the THPIRBFNN model, the linear weights of the output layer neurons of the THPIRBFNN model are updated according to equations (6) and (7):

[0084] |G k+1 |=|G k |+k G ×|η·ΔG|(6)

[0085]

[0086] Among them, |G k+1 | and |G k | are the weights before and after the update, k Gis the influence coefficient of the environmental parameter matrix on the weight optimization, η is the learning rate, and E is the minimization error function. By continuously adjusting the weights, the model can better fit the actual data, thereby improving the accuracy of the prediction of the concentration of pathogens. By adjusting the learning rate η, a faster convergence speed and higher prediction accuracy can be achieved while ensuring the stability of the model. By minimizing the error function, the parameters of the model can be continuously optimized, which helps to more accurately identify and predict the concentration of pathogens. In this embodiment, the output state represents different concentration levels or classification results, and m is the total number of these possible output states. It represents the predicted output value for a certain input sample when the model output state is i, and Z i represents the actual output value or true concentration corresponding to the sample. In this embodiment, the goal of the model is to predict the concentration of pathogenic bacteria based on the input fluorescence image and environmental data. Therefore, and Z i The gap between the two reflects the accuracy of the model prediction. By continuously optimizing the model parameters and structure, this gap can be reduced and the prediction accuracy of the model can be improved.

[0087] Similarly, the center point of the hidden layer neuron of the THPIRBFNN model is updated according to equations (8) and (9):

[0088] |c k+1 |=|c k |+k c ×|η·Δc|(8)

[0089]

[0090] Among them, k c is the influence coefficient of the environmental parameter matrix on the optimization of the hidden layer neuron center point, and the width of the Gaussian kernel is updated according to equations (10) and (11):

[0091] |σ k+1 |=|σ k |+k σ ×|η·Δσ| (10)

[0092]

[0093] k σ is the influence coefficient of the environmental parameter matrix on the Gaussian kernel width optimization.

[0094] The THPIRBFNN model of this embodiment adopts a preset loss function LOSS during training, and its expression is as follows:

[0095]

[0096] Where N is the total number of samples in the test set, is the concentration prediction state, and Z is the actual concentration of the sample.

[0097] The main controller in the detection module of this embodiment further includes an evaluation unit, which is used to evaluate the recognition result of the sample concentration by using the accuracy (ACC), and the calculation method is as follows:

[0098]

[0099] Among them, TP represents the number of true positive examples, FN represents the number of false negative examples, TN represents the number of true negative examples, and FP represents the number of false positive examples. In this embodiment, TP represents the number of samples where pathogens are actually present and the model predicts correctly; FN represents the number of samples where pathogens are actually present but the model predicts incorrectly; TN represents the number of samples where pathogens are actually not present and the model predicts correctly; FP represents the number of samples where pathogens are actually not present but the model predicts incorrectly. By calculating these indicators, the classification performance of the model can be comprehensively evaluated.

[0100] In this embodiment, if Figure 3 As shown in the figure, in order to realize the control and data visualization of the portable optical fiber fluorescence sensing detection system, a data acquisition program with a human-computer interaction interface is designed using Python. After the program starts running and the system is initialized, a human-computer interaction interface with data display function is created, and the event loop is entered to wait for user operation. The designed human-computer interaction interface is shown in the figure. Figure 3 As shown, the touch screen simultaneously displays multiple information such as real-time fluorescence signals from the sample, ambient temperature and humidity, GPS coordinates, power, date and time in digital and graphical form. These data can be transmitted to the mobile phone along with the wireless signal transmission module. The touch screen has "Start", "Pause", "End", "Save", "Refresh" buttons and a "Category" drop-down menu. By clicking the "Start" button, the fluorometer starts the real-time detection function, performs fluorescence detection every 15 seconds, and refreshes the data display. By clicking the "Save" button, the fiber optic fluorescence sensing detection system will save the current and collected data information. By clicking the "Pause" button, the fiber optic fluorescence sensing detection system will pause data collection and data refresh. By clicking the "Stop" button, the fiber optic fluorescence sensing detection system will end data collection and data display. By clicking the "Refresh" button, the fiber optic fluorescence sensing detection system will obtain the current GPS data (geolocation data). By clicking the "Category" drop-down menu, select the type of target object, and the collected fluorescence intensity will be automatically converted into the concentration of the object to be measured according to the trained model.

[0101] In this embodiment, if Figure 4As shown, in order to achieve precise control of the temperature of the multi-channel fluorescence reaction tank of the portable fiber optic fluorescence sensing detection system, the PID parameters of the temperature control module are adjusted. The temperature T is set to 39°C, P is 0.056, I is 103.7, D ​​is 25.92, the sampling period is 259ms, and position PID control is adopted. In the simulation experiment, a centrifuge tube containing 50ul sample solution was added to the multi-channel fluorescence reaction tank. After 4 minutes of reaction, it was taken out and centrifuged and shaken, and then put into the multi-channel fluorescence reaction tank again. This process was repeated three times for a total of 100 minutes. Figure 4 As shown in (a), the experimental results show that the actual temperature is maintained between 38.9-39.1℃ when in standby mode. When adding or taking samples, the temperature fluctuation does not exceed 0.2℃ and can quickly return to stability. Error analysis is shown in Figure 4 As shown in (b), during the measurement process, the time when the error was within the range of ±0.05℃ accounted for 94.4%, the time when the error was within the range of ±0.1℃ accounted for as high as 98.3%, and the time when the error was within the range of ±0.15℃ accounted for 99.3%. This shows that the temperature control module has excellent stability and accuracy after the PID parameters are adjusted. At the same time, the battery life of the device was tested. The results show that when the temperature control is turned on, the battery life is 4.7 hours; when the temperature control is not turned on, the battery life reaches 7 hours.

[0102] In this embodiment, if Figure 5 As shown in the figure, in order to eliminate process errors and ensure the accuracy and consistency of multi-channel fluorescence detection signals, a set of 13 FITC solutions with different fluorescence intensities were selected for calibration. The reagents numbered 1-13 were tested in different channels in turn, and the concentration of FITC was gradually increased from 1 to 13. The obtained fluorescence image is shown in the figure. Figure 5 (a). It can be seen that when the FITC concentration is low, the excitation light is stronger than the emission light, and the fiber image is darker (actually dark blue). As the FITC concentration gradually increases, the emission light intensity gradually becomes stronger than the excitation light intensity, and the fiber image is brighter (actually bright green). The fluorescence intensity value corresponding to the fiber fluorescence image is shown in Figure 5 As shown in (b), channel 8 is selected as the standard output channel. It can be seen that the remaining 7 channels are positively correlated with the standard output channel. Therefore, the g channel light intensity of channels 1-7 is used as the independent variable, and the g value of channel 8 is used as the dependent variable for linear fitting calibration. The results show that the linear correlation is good and the fitting effect is good.

[0103] In this embodiment, if Figure 6As shown, in order to evaluate the sensitivity of the portable fluorescence detector, FITC was used as the standard fluorophore and tested at different concentrations (0-10 μM) and compared with a commercial device microplate reader (Synergy H1, Xinling Bio). The fluorescence photos taken by the optical fiber fluorescence sensing detection system proposed in this embodiment are shown in FIG. Figure 6 As shown in (a), as the FITC concentration decreases, the fluorescence image gradually changes from a brighter color (green in reality) to a darker color (blue in reality), and the fluorescence signal gradually weakens. Figure 6 As shown in (b), the horizontal axis is the concentration of FITC solution, and the vertical axis is the fluorescence intensity. Curve FL2 represents the test result of the fiber optic fluorescence sensing detection system, and curve FL1 is the test result of the commercial microplate reader. It can be seen from the figure that the trends of the test results of the two are consistent. In the low concentration range of FITC of 0-1μM, both devices have good linearity. 2 Indicates the degree of linear relationship between the dependent variable and the independent variable, R 2 The value range of is [0,1], and the closer to 1, the better the model fit. The sensitivity of the optical fiber fluorescence sensing detection system proposed in this embodiment is 19459μM -1 , R 2 =0.9902, the sensitivity of the microplate reader is 1774.8μM -1 , R 2 =0.9626. This shows that in the low fluorescence meter intensity range, the sensitivity of the portable fiber optic fluorescence sensing detection system is 11 times higher than that of the commercial microplate reader, and the linearity is better, which reflects the excellent performance of the fiber optic fluorescence sensing detection system.

[0104] In this embodiment, if Figure 7 As shown in the figure, the stability of the portable optical fiber fluorescence sensing detection system was tested. Two FITC solutions with different concentrations (high concentration of 10μM and low concentration of 1nM) were tested continuously in real time at intervals of 15s for 1 hour. The optical fiber fluorescence images collected during the test are shown in the figure below. Figure 7 (a) is shown. The test results are shown in Figure 7 (b) shows good stability.

[0105] In one possible implementation, Figure 8 As shown in the figure, the portable optical fiber fluorescence sensing detection system designed in this example is used for 10 1 -10 4 The African swine fever virus at 100 copies / μl was dynamically monitored and tested for linearity. Considering the interference of environmental factors in actual use, the experiment was conducted at two locations with different temperatures and humidity. Figure 8 .like Figure 8As shown in (a), different concentrations of African swine fever virus were monitored in real time at point A, with a temperature of 19°C and a humidity of 37.5%. The fluorescence intensity gradually increased with the increase of reaction time and reached stability within 20 minutes. Similarly, Figure 8 As shown in (b), different concentrations of African swine fever were monitored in real time at point B, with a temperature of 21.8°C and a humidity of 31.2%. The reaction rates of different concentrations were different, with strong distinguishability.

[0106] In one possible implementation, see Fig. 9 ,like Fig. 9 As shown in (a), nucleic acid fluorescence detection was performed on African swine fever virus in two different environments and 5 different concentrations, and data were collected 30 times. The horizontal axis represents the nucleic acid concentration of African swine fever virus, and the vertical axis represents the fluorescence intensity. The test results show good linearity, R 2 The value is 0.9815. In addition, Fig. 9 As shown in (b), the proposed machine learning algorithm was used to train the detection data containing different concentrations and different environments, achieving a 100% concentration recognition accuracy. The experimental results show that the system can effectively identify African swine fever virus under different conditions and successfully avoid the interference of environmental factors.

[0107] In one possible implementation, Fig.10 As shown in the figure, the portable optical fiber fluorescence sensing detection system designed in this example can detect the concentration of 10 1 -10 5 The dynamic monitoring and linearity test of CFU / ml of Salmonella was carried out. CFU / ml indicates the number of single cells contained in each milliliter of bacterial solution. The real-time monitoring of different concentrations of Salmonella was carried out to verify the dynamic test function of the designed detection system. The fluorescence intensity gradually increased with the increase of reaction time, and the reaction rates between different concentrations were different, which had strong distinguishability. Six different concentrations of Salmonella were tested 30 times. For details, please see Fig.11 , data such as Fig.11 As shown in (a), the horizontal axis is the nucleic acid concentration of Salmonella, and the vertical axis is the fluorescence intensity. The fluorescence intensity images of Salmonella at different concentrations are obviously different, and the test results have good linearity, R 2 =0.9808. Fig.11 As shown in (b), the proposed machine learning algorithm was used to train data of Salmonella of different concentrations, achieving concentration recognition with an accuracy of 100%.

[0108] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0109] Obviously, the above embodiments are merely examples for clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from these are still within the protection scope of the invention.

Claims

1. An optical fiber fluorescence sensing detection system for food pathogens, characterized in that: It includes a sensing module and a detection module, wherein the sensing module includes an optical fiber fluorescence sensing module and an environmental sensing module, wherein: The optical fiber fluorescence sensing module includes an optical component and a fluorescence reaction module, wherein the optical component includes an optical fiber array, a filter, a collimating lens and an image sensor arranged in sequence; the fluorescence reaction module includes an excitation light source and a multi-channel fluorescence reaction tank; the excitation light source is used to irradiate the solution in the centrifuge tube in the multi-channel fluorescence reaction tank to excite the fluorescence signal, and the solution in the centrifuge tube is the solution of the food sample to be detected; the optical fiber array is connected to the multi-channel fluorescence reaction tank for the transmission of the fluorescence signal; the filter is used to select the fluorescence signal in the optical fiber array; the collimating lens is used to amplify and focus the fluorescence signal selected by the filter; the image sensor is used to image the amplified and focused fluorescence signal of the fluorescence signal to obtain a fluorescence intensity image; The environmental sensing module includes a geographic location sensor, a temperature sensor and a humidity sensor, which are used to collect geographic location data, temperature data and humidity data respectively; The detection module is used to obtain the fluorescence intensity image obtained by the image sensor, and the geographical location, temperature data and humidity data collected by the geographical location sensor, temperature sensor and humidity sensor respectively, and perform pathogen concentration detection on the solution of the food sample according to the acquired data.

2. The optical fiber fluorescence sensing detection system for food pathogens according to claim 1, characterized in that: In order to eliminate the influence of background light, before collecting the fluorescence intensity image, the detection module first collects the background image obtained by the image sensor when the excitation light source is not excited, and then performs differential processing on the fluorescence intensity image collected by the detection module after the excitation light source is excited and the background image to remove the interference of ambient background light.

3. The optical fiber fluorescence sensing detection system for food pathogens according to claim 1, characterized in that: Assume that there is a linear relationship between the fluorescence intensity I corresponding to the fluorescence intensity image and the sample concentration c, which can be expressed as: I=k·c+I0 Where k is the linear conversion coefficient from c to I; I0 is the initial fluorescence intensity when the sample concentration is 0.

4. The optical fiber fluorescence sensing detection system for food pathogens according to claim 1, characterized in that: In order to eliminate the influence of noise on the output of the image sensor, the real-time output value of the image sensor is subjected to mean filtering, and the formula is: Where I=[I1,I2,…,I n ], n∈[1,N], N is the number of fiber array channels, and ΔT is the sampling period.

5. The optical fiber fluorescence sensing detection system for food pathogens according to claim 1, characterized in that: The detection module uses the collected fluorescence image and temperature data, humidity data, and geographic location data as inputs of the THPIRBFNN model; The activation function of the hidden layer of the THPIRBFNN model is a Gaussian radial basis function of the environmental parameter matrix, wherein the environmental parameters include temperature data, humidity data, and geographic location data, and the expression is as follows: Among them, Y THPI is the Gaussian radial basis function of the environment parameter matrix, represents the mass center of the jth neuron, σ represents the width of the Gaussian kernel; the input data is represented by the environmental parameter matrix [T,H,P,I1,I2,I3…I N ] indicates that T is the ambient temperature, H is the ambient humidity, P is the geographical location information, I1, I2, I3…I N Represents the fluorescence intensity of multi-channel detection.

6. The optical fiber fluorescence sensing detection system for food pathogens according to claim 5, characterized in that: The detection module also includes an optimization unit, wherein the optimization unit is used to optimize the linear weights of the output layer neurons and the center point parameters of the hidden layer neurons of the THPIRBFNN model; The linear weights of the output layer neurons of the THPIRBFNN model are updated according to the following formula: |G k+1 |=|G k |+k G ×|η·ΔG| Among them, |G k+1 | and |G k | are the weights before and after the update, k G is the influence coefficient of the environmental parameter matrix on weight optimization, η is the learning rate, and E is the minimization error function; the output state represents different concentration levels or classification results, and m is the total number of these possible output states; It represents the predicted output value for a certain input sample when the model output state is i, Z i Indicates the actual output value or true concentration corresponding to the sample; The center points of the hidden layer neurons of the THPIRBFNN model are updated according to the following formula: |c k+1 |=|c k |+k c ×|η·Δc| Among them, k c is the influence coefficient of the environmental parameter matrix on the optimization of the hidden layer neuron center point, and the width of the Gaussian kernel is updated according to the following formula: |s k+1 |=|s k |+k σ ×|η·Δσ| Among them, k σ is the influence coefficient of the environmental parameter matrix on the Gaussian kernel width optimization.

7. The optical fiber fluorescence sensing detection system for food pathogens according to claim 6, characterized in that: The THPIRBFNN model uses a preset loss function LOSS during training, which is expressed as follows: Where N is the total number of samples in the test set, is the concentration prediction state, and Z is the actual concentration of the sample.

8. The optical fiber fluorescence sensing detection system for food pathogens according to claim 1, characterized in that: The detection module also includes an evaluation unit, which is used to perform an accuracy ACC evaluation on the identification result of the sample concentration, and the formula is: Among them, TP represents the number of true positive examples, FN represents the number of false negative examples, TN represents the number of true negative examples, and FP represents the number of false positive examples.

9. The optical fiber fluorescence sensing detection system for food pathogens according to claim 1, characterized in that: The detection module also includes a temperature control module, which is in contact with the multi-channel fluorescence reaction tank to achieve temperature control of the solution in the centrifuge tube in the multi-channel fluorescence reaction tank.

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