Rapid microorganism analysis system based on photon sensing and fluorescent probe

Abstract

A rapid microbial analysis (RMA) system that can detect pathogenic microorganisms such as E. coli with high accuracy, sensitivity, and specificity in food samples. The RMA combines fluorescent probes with photon induction technology to monitor microbial metabolism, providing early warning of pathogen contamination. The RMA was tested on coconut water samples with a detection limit of 1 CFU / mL, outperforming traditional culture medium methods. The RMA exhibits excellent consistency and repeatability across different channels and instruments, making it a promising tool for rapid, one-step pathogen detection in resource-limited environments.

Description

Technical fields:

[0001] This invention relates to the technical field of rapid microbial analysis systems, with the primary objective of achieving rapid detection and early warning of pathogenic microorganisms in food safety. The technology incorporates microbial detection and photon sensing analysis, enabling more precise formulation of food safety strategies. This technology belongs to the fields of microbiology and photon sensing analysis, while also connecting with experimental biology and medical analysis, with the aim of large-scale application in food safety supervision and industry. Background technology:

[0002] Microbiological testing is a crucial step in food safety, and traditional methods include microbial culture, immunological methods, and molecular biological methods. Microbial culture is considered the gold standard for microbial testing; it selects appropriate culture media based on microbial characteristics to achieve their extraction and proliferation. However, this method has low efficiency, requires several days of incubation, and is very energy- and labor-intensive.

[0003] Immunological assays, based on the mechanism of antigen-antibody recognition, can rapidly diagnose microorganisms. However, they often suffer from high cross-reactivity, meaning that false positives are prone to occur during the testing process. Molecular biological assays (such as PCR technology) are characterized by high efficiency, high effectiveness, and strong characterization, making them particularly suitable for the detection of new species or potential pathogens. However, they require complex experimental designs or rely on expensive equipment.

[0004] To address the shortcomings of traditional detection methods, this invention presents a rapid microbial analysis system. This system combines a benzene light probe with photon sensing technology to monitor microbial metabolic processes. Compared to microbial culture methods, this system eliminates the need for microbial culture, significantly reducing detection time. Compared to immunoassay, this invention avoids crosstalk issues, enabling more efficient analysis of microbial entities. Compared to molecular biology detection, this system utilizes a benzene light source and photon sensing technology, reducing reliance on expensive equipment and enabling rapid detection of pathogenic microorganisms in food even in resource-constrained environments. Summary of the Invention:

[0005] In view of this, the present invention discloses a rapid microbial analysis system. This device combines fluorescent probes and photon detection technology, and can rapidly detect pathogenic microorganisms, such as Escherichia coli, in food samples by monitoring fluorescence and color changes produced by microbial metabolism. Specifically, the RMA comprises four modules: a photoelectric detection module, a temperature control module, a main control module, and a light source module. This invention can detect E. coli at a detection limit of 1 CFU / mL, which is superior to traditional culture methods. It helps improve the efficiency and accuracy of food safety monitoring and provides a simple and easy-to-use tool for pathogen monitoring worldwide.

[0006] 1. Photoelectric detection module:

[0007] The photoelectric detection module utilizes specific fluorescence signals generated during microbial metabolism. These signals are acquired by a high-sensitivity photodetector and amplified and converted using analog circuitry for high-precision signal acquisition. The microbial sample to be tested is placed in the detection area, and a fluorescent matrix (such as 4-methylumbelliferone-β-D-glucuronide, MUG) is added. During microbial metabolism, MUG is decomposed, releasing fluorescence signals. A photodetector (photodiode / photomultiplier tube) receives these photon signals. The photodetector receives the fluorescence signal and converts it into a current signal. A current-to-voltage conversion circuit converts the weak current signal output by the photodetector into a voltage signal. The signal is then converted to a voltage signal and processed by an amplification and filtering circuit to remove noise, optimize signal quality, and ensure data accuracy. Finally, the main control module processes the data to generate the final measurement result.

[0008] 2. Temperature control module

[0009] The temperature control module maintains a suitable microbial culture environment, ensuring the stability and accuracy of the detection process. The system uses a DS18B20 temperature sensor to monitor the culture environment temperature in real time and employs a thermoelectric cooler (TEC) to provide precise heating or cooling. Heating / cooling power is precisely adjusted via a PWM control circuit. When the temperature is below the set value, the control circuit drives the heating element (resistance wire / quartz heating tube); when the temperature is above the set value, the TEC cooler starts operating to lower the temperature. The system continuously adjusts the power to maintain the temperature at the target value.

[0010] 3. Main control module

[0011] The main control module is responsible for data processing, system control, and communication with the host computer. The system core uses an STM32F103VET6 microcontroller and is equipped with a 16-channel 12-bit AD converter to convert the analog signals from the photoelectric detection module into digital signals. Data is optimized using filtering algorithms and data fitting methods, and microbial concentration is calculated based on a standard curve. Data is transmitted to the host computer via UART / USB, and historical measurement records are stored locally.

[0012] 4. Light source module

[0013] The light source module provides illumination of specific wavelengths to excite fluorescence produced by microbial metabolism. The system employs a multi-track LED light source (365nm, 590nm, and UV LEDs), and the light source driving circuit uses a common anode driving method to achieve precise control. Based on the metabolic characteristics of the microorganisms being tested, an appropriate wavelength of LED light source is selected, and the brightness is controlled via PWM to reduce power consumption and optimize the signal. The LED illuminates the sample, triggering a fluorescence reaction, and finally, background noise is removed through a filter to improve detection accuracy. Attached image description:

[0014] Figure 1 Overall design block diagram of rapid microbial analysis system

[0015] Figure 2 A schematic diagram illustrating the detection process and principle of a rapid microbial analysis system.

[0016] Figure 3 To establish the standard curve equation, A shows the standard curve equation obtained by detecting E. coli in coconut water using a rapid microbial analysis system; B shows the consistency between different instrument channels; C shows the differences between different instruments.

[0017] Figure 4 For two sets of test results using a rapid microbial analysis system and a national standard method, A and B are correlation analyses comparing the results of the two sets of test results using the national standard method and the rapid microbial analysis system, respectively; C and D are consistency analyses of different rapid microbial analysis system instruments using the diluted solutions of group 1 and group 2, respectively; E and F are repeatability analyses of the two sets of results using the rapid microbial analysis system (E for group 1 and F for group 2, respectively).

[0018] Figure 5 To validate the specificity and sensitivity of the rapid microbial analysis system, A is a study on the specificity of the rapid microbial analysis system for Escherichia coli in coconut water; B is a study on the sensitivity of the rapid microbial analysis system in detecting Escherichia coli in coconut water; C is a study on bacterial interference of the rapid microbial analysis system; and D is the test results of using the rapid microbial analysis system to detect actual coconut water samples from different sources.

Claims

1. A rapid microbial analysis system that combines photon sensing technology with a benzene light probe to monitor microbial metabolic processes for rapid detection of *Pseudomonas aeruginosa*. This system utilizes the specific reaction between a fluorescent probe and microbial metabolites, achieving real-time monitoring through changes in photon signals, thus improving detection speed and accuracy. The system's detection principle is based on 4-methylumbelliferone-β-D-glucuronide (MUG) as a fluorescent substrate, which undergoes hydrolysis under the action of glucuronidase produced by *Pseudomonas aeruginosa*, releasing a fluorescent signal, thereby achieving highly selective detection.

2. According to the method of claim 1, the photoelectric detection module converts the light signal generated by microbial metabolites into an electrical signal, uses a Hamamatsu S1227-66BR photodetector (response wavelength range 300-700nm) to receive the fluorescence signal and detect the change in photon flux, converts it into a voltage signal by an OPA129 high-impedance operational amplifier, further amplifies it by an INA333 precision instrumentation amplifier, and performs data transmission and digital processing by a 12-bit ADC.

3. According to the method described in claim 1, the main control module adopts an STM32F103VET6 microcontroller, receives photoelectric detection data through a UART serial port, processes the data using mean filtering and exponential smoothing algorithms, calculates the colony concentration based on the standard curve lg C = -0.8460t + 8.6711, and transmits the data to the host computer via a USB serial port.

4. According to the method of claim 1, the light source module uses a 365nm ultraviolet LED (LZ1-00UV00) as the main excitation light source to irradiate the MUG substrate, a 590nm yellow LED (XLamp XP-G3) as the background light source for contrast optical correction, and a UV LED (Nichia NVSU233A) to provide additional ultraviolet light enhancement signal acquisition. All LEDs are controlled by a constant current drive circuit and adopt a common anode drive mode to ensure uniform illumination intensity.

5. According to the method of claim 1, the temperature control module uses a DS18B20 temperature sensor for temperature monitoring and transmits data through the 1-Wire communication protocol. The temperature control circuit uses PWM regulation to control the TEC1-12706 and combines it with a PID control algorithm to adjust the temperature feedback. The temperature control range is 5-50℃, and the temperature is adjusted by PWM regulation and PID control algorithm.

6. According to the method described in claims 1-5, the sample preparation and detection process includes taking 1 mL of sample with a sterile syringe, adding 4 mL of MUG reagent, mixing well, and then placing it into the analysis system for detection, setting parameters, and starting the system to monitor fluorescence intensity.

7. According to the method described in claims 1-6, the results are compared with those obtained by plate counting and a rapid detection system. The same samples are diluted and inoculated into culture medium for 24 hours before colony counting. When detecting E. coli samples at a concentration of 10⁶ CFU / mL, the correlation coefficient between the data obtained by the rapid microbial analysis system and the data obtained by the standard plate counting method is as high as R⁻¹. 2 =0.999.

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