A system and method for monitoring a concentration of microorganisms

Abstract

The application discloses a kind of microbial concentration monitoring system and method, including light source drive module, photoelectric conversion module, wireless transmission module, A / D module conversion module, MCU chip, host computer, LED light source, light source wavelength measurement module, the LED light source, lens, solution to be measured, photoelectric conversion module constitute the monitoring channel of monitoring microbial concentration using photoelectric conversion, the solution to be measured is the microbial culture solution for monitoring microbial concentration.The monitoring system constructed by the application converts traditional light absorption as quantitative index into light scattering, adopts optimal wavelength LED light source as light source determination system optical density OD sys , OD 600nm Is obtained by establishing partial least squares linear model and four-order polynomial nonlinear model for fitting calibration, improve the ability of anti-pigment interference, and improve the determination accuracy of high concentration (OD 600nm >2.5).

Description

Technical Field

[0001] This invention relates to the field of microbial concentration detection technology, and specifically to a microbial concentration monitoring system and method. Background Technology

[0002] Microbial concentration reflects microbial activity. By monitoring changes in microbial concentration during growth in real time, we can determine enzyme activity and the optimal time for inducing recombinant proteins, thereby monitoring enzymatic reactions and calculating their rates. Common methods for measuring microbial concentration include: dry weight method, hemocytometer method, PCT plate count method, biosensor method, metabolite method, and optical method.

[0003] Compared to the methods mentioned above, optical measurement has significant advantages. This method characterizes microbial concentration using turbidity and can achieve highly accurate measurements within 1 minute (R0). 2 >0.9), and automatically measured in a non-contact, non-destructive manner. Generally, microorganisms can be considered as particles with a diameter of approximately 1 μm, which will exhibit reflection, scattering, absorption, or transmission phenomena after being irradiated by light. According to Beer-Lambert's law, the higher the concentration of microorganisms, the more light is absorbed, reflected, and scattered by the solution, and the more significant the attenuation of light intensity. Therefore, by monitoring the transmitted light intensity in real time, changes in the concentration of microorganisms can be indirectly measured.

[0004] Due to the different spectral characteristics of various bacterial species, the wavelengths used to measure OD values ​​also vary. Generally, 400-700 nm is the range for microbial measurement, with 505 nm for mycelia and mycelia, 560 nm for yeast, and 600 nm for bacteria. When measuring OD values ​​at a wavelength of 600 nm, the following problems exist: (1) 600 nm is in the visible light range, and some microorganisms have pigment molecules or produce pigments that absorb visible light, affecting the accuracy. In addition, different batches and colors of culture media will cause the correction model to deviate to varying degrees; (2) Microbial culture and OD measurement are separated, and sampling destroys the original environment, increases the risk of contamination, and consumes samples; (3) The complete growth cycle of microorganisms is long, and the sampling workload is large. For example, Escherichia coli enters the plateau phase after 10 hours, and OD needs to be measured every half hour; (4) Judging the growth status of microorganisms depends on the experimental experience of the experimenters; (5) OD 600 The linear range is small (0.2-0.8), and the OD in microbial culture is low. 600 When the value exceeds 0.8, there is an error in the actual measurement, and it needs to be serially diluted before measurement.

[0005] It is evident that while conventional optical measurement methods can accurately, automatically, and non-destructively monitor the concentration of multiple microbial species, two major challenges remain to be addressed: poor resistance to color interference and high concentration (OD) levels. 600nm>2.5) Poor measurement accuracy. Summary of the Invention

[0006] The purpose of this invention is to provide a microbial concentration monitoring system to solve the technical problems of poor resistance to color interference and poor accuracy of high concentration measurement in the prior art.

[0007] To solve the above-mentioned technical problems, the present invention specifically provides the following technical solution:

[0008] A microbial concentration monitoring system includes a light source driving module, a photoelectric conversion module, a wireless transmission module, an A / D analog-to-digital conversion module, an MCU chip, a host computer, an LED light source, and a light source wavelength measurement module. The light source driving module is electrically connected to the MCU chip and the LED light source. The photoelectric conversion module, the A / D analog-to-digital conversion module, and the MCU chip are sequentially electrically connected. The MCU chip and the host computer are connected via a wired communication path using the UART protocol. The MCU chip and the wireless transmission module are also connected via a wired communication path using the UART protocol. The wireless transmission module and the host computer are connected via a wireless communication path using the SOCKET protocol. The LED light source, lens, test solution, and photoelectric conversion module constitute a monitoring path for monitoring microbial concentration using photoelectric conversion. The test solution is a microbial culture solution used to monitor microbial concentration.

[0009] The light source driving module is used to convert the PWM wave provided by the MCU chip into a regulated DC signal to drive the LED light source to emit stable light.

[0010] The photoelectric conversion module is used to convert the transmitted light intensity of the parallel light generated by the LED light source through the lens onto the solution to be tested into a stable current for output, so as to realize photoelectric conversion.

[0011] The A / D analog-to-digital conversion module is used to convert the current output by the photoelectric conversion module from an analog signal to a digital signal to obtain the digital signal of the output current of the photoelectric conversion module, and feeds back the digital signal of the output current to the MCU chip via the SPI protocol;

[0012] In the wireless communication path, after the MCU chip preprocesses the digital signal of the output current, it buffers the effective digital signal of the output current into the wireless transmission module using the UART protocol, and then the wireless transmission module transmits the sampled data to the host computer in real time through wireless communication based on the SOCKET protocol.

[0013] In the priority communication path, the host computer operates on the configuration of the MCU chip and the cached data in the FLASH via wired communication based on the UART protocol;

[0014] The light source wavelength calculation module is used to calculate the optimal wavelength for monitoring microbial concentration, use the optimal wavelength as the light source wavelength, and select the LED light source based on the light source wavelength.

[0015] The host computer has built-in linear and nonlinear calibration models for microbial concentration. These models are based on the system optical density OD obtained from the digital current signal within the host computer. sys Standard microbial concentrations (OD) were obtained by performing linear and nonlinear calibrations. 600nm This allows for the direct quantitative quantification of microbial concentration via current signals, with the system's optical density OD... sys The standard microbial concentration is obtained by converting the digital current signal using the Lambert-Beer law by the host computer. The standard microbial concentration is the microbial concentration when the wavelength of the light source is 600nm.

[0016] The light source driving module includes a resistor R1, a capacitor C1, a resistor R2, a driver chip MAX1916, an analog power supply AVCC, and an analog ground. One end of the resistor R1 is connected to the output terminal of the PWM wave of the MCU chip, and the other end of the resistor R1 is connected to one end of the capacitor C1 and one end of the resistor R2. The other end of the capacitor C1 is connected to the analog ground. The other end of the resistor R2 is connected to the SET terminal of the driver chip MAX1916. The GND terminal of the driver chip MAX1916 is connected to the analog ground. The LED terminal of the driver chip MAX1916 is connected to the negative terminal of the LED light source, and the analog power supply AVCC is connected to the positive terminal of the LED light source.

[0017] In a preferred embodiment of the present invention, the photoelectric conversion module includes a photovoltaic cell (PHDIODE), a capacitor (C2), a resistor (R3), a signal amplifier, a digital power supply (DVCC), a digital ground, and an analog ground. The positive terminal of the photovoltaic cell (PHDIODE) is connected to one end of the capacitor (C2), one end of the resistor (R3), and the inverting input terminal of the signal amplifier. The other ends of the capacitor (C2) and the resistor (R3) are both connected to the output terminal of the signal amplifier. The negative terminal of the photovoltaic cell (PHDIODE) is connected to the analog ground and the non-inverting input terminal of the signal amplifier. The positive power supply terminal of the signal amplifier is connected to the digital power supply (DVCC), the negative power supply terminal of the signal amplifier is connected to the digital ground, and the output terminal of the signal amplifier is connected to the A / D analog-to-digital conversion module.

[0018] In a preferred embodiment of the present invention, the LED light source, lens, and photoelectric conversion module in the monitoring path are all placed in the solution to be tested, and the contact points between the LED light source and the photoelectric conversion module and the solution to be tested are waterproofed. The light emitted by the LED light source in the monitoring path passes through the lens and the solution to be tested in sequence to reach the photocell in the photoelectric conversion module, and the optical path is 1 cm. The lens adjusts the light emitted by the LED light source into parallel light.

[0019] As a preferred embodiment of the present invention, the present invention provides a monitoring method for the microbial concentration monitoring system, comprising the following steps:

[0020] Calculate the wavelength of the light source in the monitoring system and select an LED light source based on the wavelength;

[0021] The monitoring system is based on an LED light source. The LED light source, lens, and photoelectric conversion module in the monitoring path are all placed in the solution to be tested for photoelectric conversion. The acquired digital current signal is then converted into the system optical density OD in the solution using Lambert-Beer's law. sys ;

[0022] Construct the linear calibration model and the nonlinear calibration model for microbial concentration, respectively, to adjust the system optical density OD. sys Perform linear and nonlinear calibrations for microbial concentration OD. 600nm .

[0023] As a preferred embodiment of the present invention, the wavelength of the light source in the measurement and monitoring system includes:

[0024] The absorption spectra of various microbial culture solutions of different concentrations were scanned in the range of 350–1100 nm using an ultraviolet spectrophotometer, where 400–780 nm is the visible light band and 780–1100 nm is the near-infrared band. The microorganisms included yeast, Bacillus, Arthrobacter and Escherichia coli.

[0025] Analyze the full spectrum after scanning, and mark the wavelength range in the full spectrum where the absorbance of each microbial culture solution is positively correlated with its concentration as the range for selecting the light source wavelength;

[0026] The light absorption uniformity, transmittance, stability, and color influence of LB culture solution of each microorganism under light source irradiation were used as evaluation indicators of the light source wavelength. Multiple light wavelengths were selected in the light source wavelength selection range using a gradient change method, and multiple light wavelengths were evaluated using the evaluation indicators to obtain multiple sets of evaluation indicator data.

[0027] A backpropagation (BP) neural network was used to train the network on the light wavelength and the corresponding evaluation index data of each microorganism to obtain a light source wavelength evaluation model for each microorganism that characterizes the mapping relationship between light wavelength and evaluation index. The model expression for the light source wavelength evaluation index of each microorganism is as follows:

[0028] [A,B,C,D] X =BP X (l);

[0029] In the formula, X∈[yeast; Bacillus; Arthrobacter; Escherichia coli], A, B, C, D are light absorption uniformity, transmittance, stability, and color influence of LB culture solution, respectively, l is the wavelength of light, and BP is the BP neural network;

[0030] All light wavelengths within the selected wavelength range are input into the light wavelength evaluation model of each microorganism to obtain the evaluation index data of each microorganism at all light wavelengths within the selected wavelength range.

[0031] The evaluation index data corresponding to each microorganism are averaged at the same light wavelength, and the light wavelength corresponding to the optimal evaluation index data after averaging is taken as the light source wavelength.

[0032] As a preferred embodiment of the present invention, the method for calculating the system optical density ODsys includes:

[0033] Based on the exponential decay of transmitted light intensity with increasing microbial concentration as described in Beer-Lambert's law, and the direct proportionality between the digital signal of the photoelectric conversion module's output current and the transmitted light intensity, a linear relationship between the logarithm of the digital signal of the photoelectric conversion module's output current and the microbial concentration is obtained. This linear relationship is as follows:

[0034]

[0035] In the formula, S out The digital signal representing the output current of the photoelectric conversion module, j represents the system measurement circuit parameter, and I... out I represents the transmitted light intensity; g represents the geometric parameters of the measuring instrument; I represents the transmitted light intensity. in Where is the incident light intensity, K is the microbial absorption coefficient, L is the optical path length (cm), and t is the microbial concentration (mol·L⁻¹). -1 ;

[0036] Based on the transmittance, which is represented by the ratio of transmitted light intensity to incident light intensity in the optical density calculation formula, the system optical density OD is obtained. sys The system's optical density OD has a linear relationship with microbial concentration. sys The linear relationship with microbial concentration is as follows:

[0037]

[0038] In the formula, OD sys Let T be the system optical density, C be the transmittance, and S be a constant. in The digital signal that inputs current to the photoelectric conversion module.

[0039] As a preferred embodiment of the present invention, the construction of the linear calibration model for microbial concentration includes:

[0040] Based on system optical density OD sys The linear relationship analysis with microbial concentration showed that the standard microbial concentration can be obtained by converting the system optical density for different wavelengths using a correction factor δ. The system optical density conversion formula for the standard microbial concentration is as follows:

[0041]

[0042] In the formula, OD600 nm The standard microbial concentration is δ, and the correction factor is S. out S is the digital signal of the output current of the photoelectric conversion module. in A digital signal that inputs current to the photoelectric conversion module;

[0043] Based on the system optical density conversion formula of standard microbial concentration, it is found that the digital current signal and microbial concentration have an approximately linear relationship. According to the approximately linear relationship, the partial least squares model is introduced into the system optical density conversion formula of standard microbial concentration for linear fitting to obtain the linear calibration model of microbial concentration.

[0044] The model expression for the linear calibration model of microbial concentration is:

[0045]

[0046] In the formula, OD 600nm Where A is the standard microbial concentration, D is the linear fitting coefficient, and S is the constant term. out S is the digital signal of the output current of the photoelectric conversion module. in The digital signal that inputs current to the photoelectric conversion module.

[0047] As a preferred embodiment of the present invention, the construction of the nonlinear calibration model for microbial concentration includes:

[0048] Based on the approximate linear relationship, the fourth-order polynomial model is introduced into the system optical density conversion formula of standard microbial concentration for linear fitting to obtain the nonlinear calibration model of microbial concentration.

[0049] The model expression for the nonlinear calibration model of microbial concentration is:

[0050]

[0051] In the formula, OD 600nm The standard microbial concentration is given, B1, B2, B3, and B4 are the fitting coefficients of the fourth-order polynomial, E is the constant term, and S is the standard microbial concentration. out S is the digital signal of the output current of the photoelectric conversion module. in The digital signal that inputs current to the photoelectric conversion module.

[0052] As a preferred embodiment of the present invention, the OD in the linear calibration model and the nonlinear calibration model of microbial concentration 600nm All measurements were obtained by using an ultraviolet spectrophotometer in the microbial test solution.

[0053] Compared with the prior art, the present invention has the following advantages:

[0054] The monitoring system constructed in this invention transforms the traditional quantitative indicator of light absorption into light scattering, and uses an LED light source with the optimal wavelength as the light source to measure the system's optical density OD. sys The OD was obtained by fitting and calibrating a partial least squares linear model and a fourth-order polynomial nonlinear model. 600nm It enhances the ability to resist pigment interference and increases the concentration of high-concentration (OD600) pigments. nm >2.5) Measurement accuracy. Attached Figure Description

[0055] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0056] Figure 1 This is a block diagram of the monitoring system structure provided in an embodiment of the present invention;

[0057] Figure 2 Circuit diagrams of the light source driving module and photoelectric conversion module provided in embodiments of the present invention;

[0058] Figure 3 This is a schematic diagram of the monitoring path provided in an embodiment of the present invention;

[0059] Figure 4 A flowchart of the monitoring method provided in an embodiment of the present invention;

[0060] Figure 5 Full-spectral images of various bacterial species provided in the embodiments of the present invention;

[0061] Figure 5 (a) is a full-spectrum image of yeast provided in an embodiment of the present invention;

[0062] Figure 5 (b) is a full-spectrum image of Bacillus provided in an embodiment of the present invention;

[0063] Figure 5 (c) is a full-spectrum image of Arthrobacterium provided in an embodiment of the present invention;

[0064] Figure 5 (d) is a full-spectrum image of Escherichia coli provided in an embodiment of the present invention. Detailed Implementation

[0065] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0066] Among methods for detecting microbial concentration, optical measurement methods have significant advantages. This method characterizes microbial concentration using turbidity and can achieve highly accurate measurements within 1 minute (R0). 2 The system measures microbial concentrations (>0.9) automatically in a non-contact, non-destructive manner. Generally, microorganisms can be considered as particles with a diameter of approximately 1 μm, which exhibit reflection, scattering, absorption, or transmission upon exposure to light. According to Beer-Lambert's law, the higher the concentration of microorganisms, the more light is absorbed, reflected, and scattered by the solution, and the more significant the attenuation of light intensity. Therefore, by monitoring the transmitted light intensity, the concentration of microorganisms can be indirectly measured. Thus, this invention proposes a microbial concentration monitoring system based on the principle of optical measurement. This system converts transmitted light intensity into an electrical signal through photoelectric conversion, and indirectly measures the concentration of microorganisms by monitoring the electrical signal, achieving accurate, automatic, and non-destructive monitoring of the concentration of multiple microbial species.

[0067] like Figure 1As shown, this invention provides a microbial concentration monitoring system, including a light source driving module, a photoelectric conversion module, a wireless transmission module, an A / D analog-to-digital conversion module, an MCU chip, a host computer, an LED light source, and a light source wavelength measurement module. The light source driving module is electrically connected to the MCU chip and the LED light source. The photoelectric conversion module, the A / D analog-to-digital conversion module, and the MCU chip are sequentially electrically connected. The MCU chip and the host computer are connected via a wired communication path through the UART protocol. The MCU chip and the wireless transmission module are also connected via a wired communication path through the UART protocol. The wireless transmission module and the host computer are connected via a wireless communication path through the SOCKET protocol. The LED light source, lens, test solution, and photoelectric conversion module constitute a monitoring path for monitoring microbial concentration using photoelectric conversion. The test solution is a microbial culture solution used to monitor microbial concentration.

[0068] The light source driver module is used to convert the PWM wave provided by the MCU chip into a regulated DC signal to drive the LED light source to emit a stable signal.

[0069] The photoelectric conversion module is used to convert the transmitted light intensity of the parallel light generated by the LED light source through the lens onto the solution to be tested into a stable current for output, thereby realizing photoelectric conversion;

[0070] The A / D analog-to-digital converter module is used to convert the current output by the photoelectric conversion module from an analog signal to a digital signal to obtain the digital signal of the output current of the photoelectric conversion module, and feeds back the digital signal of the output current to the MCU chip via the SPI protocol;

[0071] In the wireless communication path, after the MCU chip preprocesses the digital signal of the output current, it buffers the effective digital signal of the output current into the wireless transmission module using the UART protocol. Then, the wireless transmission module transmits the sampled data to the host computer in real time through wireless communication based on the SOCKET protocol.

[0072] In the priority communication path, the host computer operates on the configuration of the MCU chip and the cached data in FLASH via wired communication based on the UART protocol;

[0073] The monitoring system provided by this invention includes a light source wavelength calculation module, which is used to calculate the optimal wavelength for monitoring microbial concentration. This avoids the influence of microbial pigments and culture medium color. The higher the degree of light absorption in the absorption mechanism, the more obvious the Rayleigh scattering phenomenon. When the external environment changes, the production and precipitation of pigments can lead to a large deviation in the monitoring of microbial concentration. Moreover, the optimal wavelength can also improve the generalization ability of measuring the concentration of various types of microorganisms, that is, it can maintain accurate measurement results in the monitoring of various types of microbial concentrations.

[0074] The light source wavelength calculation module is used to calculate the optimal wavelength for monitoring microbial concentration, use the optimal wavelength as the light source wavelength, and select the LED light source based on the light source wavelength.

[0075] Specifically, the steps for calculating the wavelength of the light source in the monitoring system are as follows:

[0076] The absorption spectra of various microbial culture solutions of different concentrations were scanned in the range of 350–1100 nm using an ultraviolet spectrophotometer, where 400–780 nm is the visible light band and 780–1100 nm is the near-infrared band. The microorganisms included yeast, Bacillus, Arthrobacter and Escherichia coli.

[0077] Analyze the scanned full spectrum and mark the wavelength ranges in the full spectrum where the absorbance of each microbial culture solution is positively correlated with its concentration. These ranges will be used as the selection ranges for the light source wavelength. Figure 5 The scanned full spectrum shows that, under different dilution conditions, the absorbance of yeast and arthrobacterium fluctuates greatly in the 350–450 nm band, while the curves of the four microorganisms fluctuate relatively little in the 450–1050 nm band. In the 550–1000 nm range, the absorbance of each strain of diluted culture is positively correlated with the concentration, making it suitable for the concentration measurement of various types of microorganisms. Therefore, 550–1000 nm was selected as the wavelength range of the light source.

[0078] The light absorption uniformity, transmittance, stability, and color influence of LB culture solution of each microorganism under light source irradiation were used as evaluation indicators of the light source wavelength. Multiple light wavelengths were selected in the light source wavelength selection range using a gradient change method, and multiple light wavelengths were evaluated using the evaluation indicators to obtain multiple sets of evaluation indicator data.

[0079] A backpropagation (BP) neural network was used to train the network on the light wavelength and the corresponding evaluation index data of each microorganism to obtain a light source wavelength evaluation model for each microorganism that represents the mapping relationship between light wavelength and evaluation index. The model expression for the light source wavelength evaluation index of each microorganism is as follows:

[0080] [A,B,C,D] X =BP X (l);

[0081] In the formula, X∈[yeast; Bacillus; Arthrobacter; Escherichia coli], A, B, C, D are light absorption uniformity, transmittance, stability, and color influence of LB culture solution, respectively, l is the wavelength of light, and BP is the BP neural network;

[0082] All light wavelengths within the selected wavelength range are input into the light wavelength evaluation model of each microorganism to obtain the evaluation index data of each microorganism at all light wavelengths within the selected wavelength range.

[0083] The evaluation index data corresponding to each microorganism are averaged at the same light wavelength, and the light wavelength corresponding to the best evaluation index data after averaging is taken as the light source wavelength.

[0084] In the light source wavelength selection range of 550–1000 nm, evaluation index data for each microorganism at all light wavelengths within this range were obtained using the light source wavelength evaluation model. The evaluation index data for each microorganism were then averaged at the same light wavelength. The optimal light wavelength corresponding to the averaged evaluation index data was determined to be 890 nm, and this median value of 890 nm was ultimately selected as the light source wavelength. Therefore, selecting an LED light source with a wavelength of 890 nm can, on the one hand, improve the generalization ability of measurements for each strain when monitoring microbial concentration, and on the other hand, avoid interference caused by Rayleigh scattering due to high concentrations.

[0085] The monitoring path can only calculate the system optical density (OD) by monitoring the digital current signal. sys However, since the microbial concentration cannot be obtained directly, the host computer has built-in linear and nonlinear calibration models for microbial concentration, which can be used to measure the system's optical density (OD). sys The standard microbial concentration OD was obtained through calibration. 600nm The system optical density OD sys It is related to the digital current signal, and thus the accurate microbial concentration can be obtained directly by calibration of the digital current signal.

[0086] Specifically, the host computer has built-in linear and nonlinear calibration models for microbial concentration. These models are based on the system optical density OD obtained from the digital current signal within the host computer. sys Standard microbial concentrations (OD) were obtained by performing linear and nonlinear calibrations. 600nm This allows for the direct quantitative quantification of microbial concentration via current signals, and the system's optical density OD... sys The standard microbial concentration is obtained by converting the digital current signal using the Lambert-Beer law by the host computer. The standard microbial concentration is the microbial concentration when the light source wavelength is 600nm.

[0087] During photoelectric conversion, it is necessary to maintain the stability of the LED light source in order to ensure stable conversion of transmitted light intensity, thereby stabilizing the digital current signal and ultimately ensuring the stability of microbial concentration monitoring.

[0088] like Figure 2As shown, specifically, the light source driving module includes resistor R1, capacitor C1, resistor R2, driver chip MAX1916, analog power supply AVCC, and analog ground. One end of resistor R1 is connected to the output terminal of the PWM wave of the MCU chip, and the other end of resistor R1 is connected to one end of capacitor C1 and one end of resistor R2. The other end of capacitor C1 is connected to the analog ground. The other end of resistor R2 is connected to the SET terminal of driver chip MAX1916. The GND terminal of driver chip MAX1916 is connected to the analog ground. The LED terminal of driver chip MAX1916 is connected to the negative terminal of the LED light source, and the analog power supply AVCC is connected to the positive terminal of the LED light source.

[0089] The light source driver module uses the MAX1916 driver chip to provide a constant current to the LED. The RC low-pass filter in the light source driver module consists of a resistor R1 (1kΩ) and a capacitor C1 (0.1μF), with a cutoff frequency of... The frequency is much lower than the input PWM frequency (1kHz), thus filtering the PWM and converting it from a rectangular pulse wave to a stable level to stabilize the control voltage at the SET port of the MAX1916 driver chip. Bias current I Input The current flows in from the SET terminal, and the relationship between the magnitude of the current and the current-carrying resistor R2 is I. Input =(V Lowpass -V Set ) / R2, where V Lowpass This is the stable voltage after low-pass filtering. EN is the enable pin; when the port is set to high, the chip operates, and the LED pin outputs a drive current. The magnitude of this current is equal to the input current I at the SET port. Input The relationship between them is: I LED =230I Input =230(V) Lowpass -V Set Therefore, the PWM wave is converted to a stable level, stabilizing the input voltage and current of the LED driver chip MAX1916, thereby ensuring stable light source emission.

[0090] The photoelectric conversion module includes a photovoltaic cell (PHDIODE), a capacitor (C2), a resistor (R3), a signal amplifier, a digital power supply (DVCC), a digital ground, and an analog ground. The positive terminal of the photovoltaic cell (PHDIODE) is connected to one end of the capacitor (C2), one end of the resistor (R3), and the inverting input terminal of the signal amplifier. The other ends of the capacitor (C2) and the resistor (R3) are both connected to the output terminal of the signal amplifier. The negative terminal of the photovoltaic cell (PHDIODE) is connected to the analog ground and the non-inverting input terminal of the signal amplifier. The positive power supply terminal of the signal amplifier is connected to the digital power supply (DVCC), the negative power supply terminal of the signal amplifier is connected to the digital ground, and the output terminal of the signal amplifier is connected to the A / D analog-to-digital conversion module.

[0091] The photoelectric conversion module mainly includes filtering and amplification functions. After the photovoltaic cell current is filtered and noise-reduced by capacitor C2, it is amplified by a signal amplifier circuit. 4 The photoelectric conversion is completed by multiplying the analog signal by 10 times. Subsequently, the signal is sampled, quantized, and encoded by an A / D converter, ultimately converting the analog signal into a digital signal for experimental analysis.

[0092] In the monitoring path, the LED light source, lens, and photoelectric conversion module are all placed in the solution to be tested. The contact points between the LED light source and the photoelectric conversion module and the solution to be tested are waterproofed. The light emitted by the LED light source in the monitoring path passes through the lens and the solution to be tested in sequence to reach the photocell in the photoelectric conversion module, forming an optical path of 1 cm. The lens adjusts the light emitted by the LED light source into parallel light.

[0093] The monitoring system provided by this invention can convert transmitted light intensity into an electrical signal through photoelectric conversion, and indirectly measure the concentration of microorganisms by monitoring the electrical signal. By determining the optimal wavelength, the monitoring system can improve the generalization ability of measuring various strains when monitoring microbial concentration, and avoid the interference caused by Rayleigh scattering phenomenon caused by high concentration. It can achieve accurate, automatic and non-destructive monitoring of the concentration of multiple microorganisms. Furthermore, by combining linear and nonlinear calibration models for microbial concentration, it is applicable to different scenarios and microbial species, and can cover a wider range of microbial concentration monitoring situations, making it more comprehensive.

[0094] This invention provides an example of the placement of a monitoring system and a solution to be tested. To achieve in-situ measurement using the transmission method and avoid noise caused by refraction from irrelevant media, specifically, as follows... Figure 3 As shown, the monitoring path is placed within the original environment of the Erlenmeyer flask, extending into the 250ml flask via a metal tube, with a standard optical path length of 1cm. After placing 200ml of LB culture medium into the flask, ensure the liquid level covers the light path. An 890nm LED is used as the light source, offering advantages such as good stability, narrow spectral width, long lifespan, high luminous efficiency, and low power consumption. The incident light is adjusted to be parallel by a lens and directly illuminates the test solution, then the transmitted light intensity is collected by a photocell. When the microorganisms need to enter the culture state, the Erlenmeyer flask is placed in a constant-temperature shaking incubator, rotating and shaking at a constant temperature and speed. Under this environment, the test solution is in a uniformly mixed state, effectively reducing measurement errors. All contact points between the LED, photocell, and the test solution in the light path are waterproofed to prevent leakage.

[0095] This invention provides a monitoring method for a microbial concentration monitoring system, comprising the following steps:

[0096] Calculate the wavelength of the light source in the monitoring system and select an LED light source based on the wavelength;

[0097] A monitoring system based on an LED light source is constructed. The LED light source, lens, and photoelectric conversion module in the monitoring path are all placed in the solution to be tested for photoelectric conversion. The acquired digital current signal is then converted into the system optical density OD in the solution using Lambert-Beer's law. sys ;

[0098] Construct linear and nonlinear calibration models for microbial concentration, respectively, to adjust the system optical density OD. sys Perform linear and nonlinear calibrations for microbial concentration OD. 600nm .

[0099] The optimal wavelength for monitoring microbial concentration is calculated to avoid the influence of microbial pigments and culture medium color. Higher light absorption in the absorption mechanism leads to more pronounced Rayleigh scattering. When the external environment changes, pigment production and precipitation can cause significant deviations in microbial concentration monitoring. Furthermore, the optimal wavelength improves the generalization ability for measuring the concentration of various types of microorganisms, ensuring accurate measurement results across different microbial concentrations. Therefore, this invention provides a method for calculating the light source wavelength of a monitoring system, with the following steps:

[0100] The absorption spectra of various microbial culture solutions of different concentrations were scanned in the range of 350–1100 nm using an ultraviolet spectrophotometer, where 400–780 nm is the visible light band and 780–1100 nm is the near-infrared band. The microorganisms included yeast, Bacillus, Arthrobacter and Escherichia coli.

[0101] Analyze the full spectrum after scanning, and mark the wavelength range in the full spectrum where the absorbance of each microbial culture solution is positively correlated with its concentration as the range for selecting the light source wavelength;

[0102] The light absorption uniformity, transmittance, stability, and color influence of LB culture solution of each microorganism under light source irradiation were used as evaluation indicators of the light source wavelength. Multiple light wavelengths were selected in the light source wavelength selection range using a gradient change method, and multiple light wavelengths were evaluated using the evaluation indicators to obtain multiple sets of evaluation indicator data.

[0103] A backpropagation (BP) neural network was used to train the network on the light wavelength and the corresponding evaluation index data of each microorganism to obtain a light source wavelength evaluation model for each microorganism that represents the mapping relationship between light wavelength and evaluation index. The model expression for the light source wavelength evaluation index of each microorganism is as follows:

[0104] [A,B,C,D] X =BP X (l);

[0105] In the formula, X∈[yeast; Bacillus; Arthrobacter; Escherichia coli], A, B, C, D are light absorption uniformity, transmittance, stability, and color influence of LB culture solution, respectively, l is the wavelength of light, and BP is the BP neural network;

[0106] All light wavelengths within the selected wavelength range are input into the light wavelength evaluation model of each microorganism to obtain the evaluation index data of each microorganism at all light wavelengths within the selected wavelength range.

[0107] The evaluation index data corresponding to each microorganism were averaged at the same light wavelength, and the light wavelength corresponding to the optimal evaluation index data after averaging was taken as the light source wavelength.

[0108] This invention provides an example of calculating the wavelength of a light source in a monitoring system, with the following specific steps:

[0109] The absorption spectra of various microbial culture solutions of different concentrations were scanned in the range of 350–1100 nm using an ultraviolet spectrophotometer, where 400–780 nm is the visible light band and 780–1100 nm is the near-infrared band. The microorganisms included yeast, Bacillus, Arthrobacter and Escherichia coli.

[0110] Analyze the scanned full spectrum and mark the wavelength ranges in the full spectrum where the absorbance of each microbial culture solution is positively correlated with its concentration. These ranges will be used as the selection ranges for the light source wavelength. Figure 5 The scanned full spectrum shows that, under different dilution conditions, the absorbance of yeast and arthrobacterium fluctuates greatly in the 350–450 nm band, while the curves of the four microorganisms fluctuate relatively little in the 450–1050 nm band. In the 550–1000 nm range, the absorbance of each strain of diluted culture is positively correlated with the concentration, making it suitable for the concentration measurement of various types of microorganisms. Therefore, 550–1000 nm was selected as the wavelength range of the light source.

[0111] Within the wavelength range of 550–1000 nm, all light wavelengths were evaluated using the light source wavelength evaluation model, yielding evaluation index data for each microorganism at all light wavelengths within this range. The evaluation index data for each microorganism were then averaged at the same light wavelength. The optimal evaluation index data after averaging was determined to correspond to a light wavelength of 890 nm, and this median value of 890 nm was ultimately selected as the light source wavelength. Therefore, with an LED light source having a wavelength of 890 nm, the system optical density ODsys is OD0. 890nm This method can improve the generalization ability of measuring various strains when monitoring microbial concentrations, and avoid interference caused by Rayleigh scattering due to high concentrations.

[0112] The calculation methods for system optical density ODsys include:

[0113] Based on the exponential decay of transmitted light intensity with increasing microbial concentration according to Beer-Lambert's law and the proportional relationship between the digital signal of the photoelectric conversion module's output current and the transmitted light intensity, a linear relationship between the logarithm of the digital signal of the photoelectric conversion module's output current and the microbial concentration is obtained. The linear relationship between the logarithm of the digital signal of the photoelectric conversion module's output current and the microbial concentration is as follows:

[0114]

[0115] In the formula, S out The digital signal representing the output current of the photoelectric conversion module, j represents the system measurement circuit parameter, and I... out I represents the transmitted light intensity; g represents the geometric parameters of the measuring instrument; I represents the transmitted light intensity. in Where is the incident light intensity, K is the microbial absorption coefficient, L is the optical path length (cm), and t is the microbial concentration (mol·L⁻¹). -1 ;

[0116] Based on the transmittance, which is represented by the ratio of transmitted light intensity to incident light intensity in the optical density calculation formula, the system optical density OD is obtained. sys Linear relationship with microbial concentration, system optical density OD sys The linear relationship with microbial concentration is as follows:

[0117]

[0118] In the formula, OD sys Let T be the system optical density, C be the transmittance, and S be a constant. in The digital signal that inputs current to the photoelectric conversion module.

[0119] Monitoring microbial concentration involves different microbial species and monitoring scenarios, including static and dynamic monitoring. Therefore, the methods for obtaining microbial concentration through correction of digital current signals also differ. This invention provides two calibration models: a linear calibration model and a nonlinear calibration model for microbial concentration.

[0120] The construction of the linear calibration model for microbial concentration includes:

[0121] Based on system optical density OD sys The linear relationship analysis with microbial concentration showed that the standard microbial concentration can be obtained by converting the system optical density for different wavelengths using a correction factor δ. The system optical density conversion formula for the standard microbial concentration is as follows:

[0122]

[0123] In the formula, OD600 nm The standard microbial concentration is δ, and the correction factor is S. out S is the digital signal of the output current of the photoelectric conversion module. in A digital signal that inputs current to the photoelectric conversion module;

[0124] Based on the system optical density conversion formula of standard microbial concentration, it is found that the digital current signal and microbial concentration have an approximately linear relationship. According to the approximately linear relationship, the partial least squares model is introduced into the system optical density conversion formula of standard microbial concentration for linear fitting to obtain a linear calibration model of microbial concentration.

[0125] The model expression for the linear calibration model of microbial concentration is:

[0126]

[0127] In the formula, OD 600nm Where A is the standard microbial concentration, D is the linear fitting coefficient, and S is the constant term. out S is the digital signal of the output current of the photoelectric conversion module. in The digital signal that inputs current to the photoelectric conversion module.

[0128] The construction of the nonlinear calibration model for microbial concentration includes:

[0129] Based on the approximate linear relationship, the fourth-order polynomial model is introduced into the system optical density conversion formula of standard microbial concentration for linear fitting to obtain the nonlinear calibration model of microbial concentration.

[0130] The model expression for the nonlinear calibration model of microbial concentration is:

[0131]

[0132] In the formula, OD 600nm The standard microbial concentration is given, B1, B2, B3, and B4 are the fitting coefficients of the fourth-order polynomial, E is the constant term, and S is the standard microbial concentration. out S is the digital signal of the output current of the photoelectric conversion module. in The digital signal that inputs current to the photoelectric conversion module.

[0133] OD in linear and nonlinear calibration models of microbial concentration 600nm All measurements were obtained by using an ultraviolet spectrophotometer in the microbial test solution.

[0134] Through multi-species validation experiments, in static calibration, the fourth-order polynomial model showed better accuracy than the partial least squares (PLS) model, with the mean error rate for the four bacterial species ranging from 4.11% to 4.53%, and OD... 600nm Actual measurement and OD600nm The average measurement error was 0.061. Due to microbial characteristics, yeast exhibited greater growth at the plateau phase than the other three strains, while the PLS model showed higher growth at high concentrations (OD). 600nm >2.1) Better predictive performance; for example, the average error rate for yeast was 5.1%, while the average error rates for the other three species were >30%, OD 600nm Error value > 0.34. During dynamic monitoring, under uniform shaking and OD... 600nm Under the condition <1.5, the conversion factor has an approximately linear relationship, and the PLS model R 2 =0.9972. Therefore, the linear and nonlinear calibration models for microbial concentration are applicable to different scenarios and species. The two calibration models provided by this invention can cover a wider range of microbial concentration monitoring situations and are more comprehensive.

[0135] This invention also provides an example of static and dynamic monitoring. Different types of microorganisms are cultured, and their growth curves are measured using the system to analyze their responses to Gram-positive, Gram-negative bacteria, rod-shaped, cocci, and fungi. After preheating the monitoring system and culture medium for 3 hours, in a sterile environment under a laminar flow hood, seed culture of each strain is taken and transferred at a 1% inoculation rate of 200 μL to a 250 mL Erlenmeyer flask (containing 200 mL of LB liquid medium). Three groups of each strain are inoculated as parallel experiments, with one group left uninoculated for temperature compensation. After inoculation, the Erlenmeyer flasks containing the system are placed in a shaker for culture. Once the culture reaches the plateau phase, 1 mL of the solution is sampled and the OD value is measured using a UV spectrophotometer. 600nm The actual values ​​are compared with the predicted values ​​obtained from the fitting formula. Table 1 shows the measurement error ratio, sampling time, and growth environment of each bacterial species under static and dynamic predictions for the two models. QP represents the linear calibration model, and PLS represents the nonlinear calibration model.

[0136] Table 1. Measured results of multiple bacterial strains

[0137]

[0138] Table 1 lists the OD values ​​of four microbial species calculated using both PLS and fourth-order polynomial models, and through static and dynamic monitoring methods. 600nm Predicted values ​​and OD 600nm The deviation of the measured values ​​was evaluated using the mean error rate of parallel experiments as the evaluation index. Analysis shows that in static prediction, the fourth-order polynomial model has better accuracy than the PLS model, with the mean error rate for the four bacterial species ranging from 4.11% to 4.53%, and OD... 600nm Actual measurement and OD 600nmThe average prediction error was 0.061. Due to microbial characteristics, yeast exhibited greater growth at the plateau phase than the other three species, while the PLS model showed lower growth at high concentrations (OD). 600nm >2.1) Better predictive performance; for example, the average error rate for yeast was 5.1%, while the average error rates for the other three species were >30%, OD 600nm Error value > 0.34. In dynamic prediction, the PLS model and the fourth-order nonlinear model differ in predicting high concentrations of yeast OD. 600nm The numerical results were not ideal. The average error rates of PLS ​​and the fourth-order model were 94.88% and 917.53%, respectively. The error of the PLS linear model was smaller than that of the nonlinear model. There are two reasons for the error: (1) When fitting the formula, Bacillus was used as the experimental object, and its concentration was relatively small during the plateau phase (OD). 600nm <2), the model failed to accurately reflect OD at high concentrations. sys With OD 600nm The numerical relationship between them. (2) When OD 600nm When the OD value was >0.8, the sampled microbial solution was undiluted, leading to a significant error in the UV spectrophotometer's measurement of the reference value, with the error increasing progressively with increasing OD value. Except for yeast, the mean error rates of the PLS and fourth-order polynomial models for predicting Bacillus, Arthrobacter, and Escherichia coli in dynamic prediction showed little difference. Among the two models, Arthrobacter had the smallest error (1.14% and 1.84%, respectively), while Escherichia coli had the largest error (7.28% and 5.52%, respectively). The prediction effects of the two models were similar. The results indicate that in static prediction, the fourth-order polynomial nonlinear model outperforms the PLS linear model, with the mean prediction error rate for all four bacterial species less than 4.6%. In dynamic prediction, the PLS linear model showed better performance in predicting high concentrations of yeast (OD... 600nm When the value is >2), the performance is better than the fourth-order polynomial model. When predicting Bacillus, Arthrobacter, and Escherichia coli, the prediction bias between the two models is small, indicating that OD... sys With OD 600nm The correspondence is approximately linear.

[0139] The monitoring system constructed in this invention transforms the traditional quantitative indicator of light absorption into light scattering. It uses an LED light source with the optimal wavelength to measure the system's optical density (ODsys). The OD is obtained by fitting and calibrating a partial least squares linear model and a fourth-order polynomial nonlinear model. 600nm It enhances the ability to resist pigment interference and increases the concentration of high-dose pigments (OD). 600nm >2.5) Measurement accuracy.

[0140] The above embodiments are merely exemplary embodiments of this application and are not intended to limit this application. The scope of protection of this application is defined by the claims. Those skilled in the art can make various modifications or equivalent substitutions to this application within its substance and scope of protection, and such modifications or equivalent substitutions should also be considered to fall within the scope of protection of this application.

Claims

1. A microbial concentration monitoring system, characterized in that, The system includes a light source driving module, a photoelectric conversion module, a wireless transmission module, an A / D analog-to-digital conversion module, an MCU chip, a host computer, an LED light source, and a light source wavelength measurement module. The light source driving module is electrically connected to the MCU chip and the LED light source. The photoelectric conversion module, the A / D analog-to-digital conversion module, and the MCU chip are sequentially electrically connected. The MCU chip and the host computer are connected via a wired communication path using the UART protocol. The MCU chip and the wireless transmission module are also connected via a wired communication path using the UART protocol. The wireless transmission module and the host computer are connected via a wireless communication path using the SOCKET protocol. The LED light source, lens, test solution, and photoelectric conversion module constitute a monitoring path for monitoring microbial concentration using photoelectric conversion. The test solution is a microbial culture solution used to monitor microbial concentration. The light source driving module is used to convert the PWM wave provided by the MCU chip into a regulated DC signal to drive the LED light source to emit stable light. The photoelectric conversion module is used to convert the transmitted light intensity of the parallel light generated by the LED light source through the lens onto the solution to be tested into a stable current for output, so as to realize photoelectric conversion. The A / D analog-to-digital conversion module is used to convert the current output by the photoelectric conversion module from an analog signal to a digital signal to obtain the digital signal of the output current of the photoelectric conversion module, and feeds back the digital signal of the output current to the MCU chip via the SPI protocol; In the wireless communication path, after the MCU chip preprocesses the digital signal of the output current, it buffers the effective digital signal of the output current into the wireless transmission module using the UART protocol, and then the wireless transmission module transmits the sampled data to the host computer in real time through wireless communication based on the SOCKET protocol. In the priority communication path, the host computer operates on the configuration of the MCU chip and the cached data in the FLASH via wired communication based on the UART protocol; The light source wavelength calculation module is used to calculate the optimal wavelength for monitoring microbial concentration, use the optimal wavelength as the light source wavelength, and select the LED light source based on the light source wavelength. The host computer has built-in linear and nonlinear calibration models for microbial concentration. These models are based on the system optical density OD obtained from the digital current signal within the host computer. sys Standard microbial concentrations (OD) were obtained by performing linear and nonlinear calibrations. 600nm This allows for the direct quantitative quantification of microbial concentration using current signals. The system optical density OD sys The standard microbial concentration is obtained by converting the digital current signal using the Lambert-Beer law by the host computer. The standard microbial concentration is the microbial concentration when the wavelength of the light source is 600nm. The construction of the nonlinear calibration model for microbial concentration includes: Based on the approximate linear relationship, the fourth-order polynomial model is introduced into the system optical density conversion formula of standard microbial concentration for linear fitting to obtain the nonlinear calibration model of microbial concentration. The model expression for the nonlinear calibration model of microbial concentration is: ； In the formula, OD 600nm The standard microbial concentration is given, B1, B2, B3, and B4 are the fitting coefficients of the fourth-order polynomial, and E is the constant term. S is the digital signal of the output current of the photoelectric conversion module. in This is the digital signal that outputs the current from the photoelectric conversion module.

2. The microbial concentration monitoring system according to claim 1, characterized in that: The light source driving module includes a resistor R1, a capacitor C1, a resistor R2, a driver chip MAX1916, an analog power supply AVCC, and an analog ground. One end of the resistor R1 is connected to the output terminal of the PWM wave of the MCU chip, and the other end of the resistor R1 is connected to one end of the capacitor C1 and one end of the resistor R2. The other end of the capacitor C1 is connected to the analog ground. The other end of the resistor R2 is connected to the SET terminal of the driver chip MAX1916. The GND terminal of the driver chip MAX1916 is connected to the analog ground. The LED terminal of the driver chip MAX1916 is connected to the negative terminal of the LED light source, and the analog power supply AVCC is connected to the positive terminal of the LED light source.

3. The microbial concentration monitoring system according to claim 1, characterized in that: The photoelectric conversion module includes a photovoltaic cell (PHDIODE), a capacitor (C2), a resistor (R3), a signal amplifier, a digital power supply (DVCC), a digital ground, and an analog ground. The positive terminal of the photovoltaic cell (PHDIODE) is connected to one end of the capacitor (C2), one end of the resistor (R3), and the inverting input terminal of the signal amplifier. The other ends of the capacitor (C2) and the resistor (R3) are both connected to the output terminal of the signal amplifier. The negative terminal of the photovoltaic cell (PHDIODE) is connected to the analog ground and the non-inverting input terminal of the signal amplifier. The positive power supply terminal of the signal amplifier is connected to the digital power supply (DVCC), and the negative power supply terminal of the signal amplifier is connected to the digital ground. The output terminal of the signal amplifier is connected to the A / D analog-to-digital conversion module.

4. The microbial concentration monitoring system according to claim 3, characterized in that: In the monitoring path, the LED light source, lens, and photoelectric conversion module are all placed in the solution to be tested, and the contact points between the LED light source and the photoelectric conversion module and the solution to be tested are waterproofed. The light emitted by the LED light source in the monitoring path passes through the lens and the solution to be tested in sequence to reach the photocell in the photoelectric conversion module, forming an optical path of 1 cm. The lens adjusts the light emitted by the LED light source into parallel light.

5. A monitoring method for a microbial concentration monitoring system according to any one of claims 1-4, characterized in that, Includes the following steps: Calculate the wavelength of the light source in the monitoring system and select an LED light source based on the wavelength; The monitoring system is based on an LED light source. The LED light source, lens, and photoelectric conversion module in the monitoring path are all placed in the solution to be tested for photoelectric conversion. The acquired digital current signal is then converted into the system optical density OD in the solution using Lambert-Beer's law. sys ; Construct the linear calibration model and the nonlinear calibration model for microbial concentration, respectively, to adjust the system optical density OD. sys Perform linear and nonlinear calibrations for microbial concentration OD. 600nm ; The construction of the nonlinear calibration model for microbial concentration includes: Based on the approximate linear relationship, the fourth-order polynomial model is introduced into the system optical density conversion formula of standard microbial concentration for linear fitting to obtain the nonlinear calibration model of microbial concentration. The model expression for the nonlinear calibration model of microbial concentration is: ； In the formula, OD 600nm The standard microbial concentration is given, B1, B2, B3, and B4 are the fitting coefficients of the fourth-order polynomial, and E is the constant term. S is the digital signal of the output current of the photoelectric conversion module. in This is the digital signal that outputs the current from the photoelectric conversion module.

6. The monitoring method according to claim 5, characterized in that: The wavelength of the light source in the measurement and monitoring system includes: The absorption spectra of various microbial culture solutions of different concentrations were scanned in the range of 350-1100 nm using an ultraviolet spectrophotometer, where 400-780 nm is the visible light band and 780-1100 nm is the near-infrared band. The microorganisms included yeast, Bacillus, Arthrobacter and Escherichia coli. Analyze the full spectrum after scanning, and mark the wavelength range in the full spectrum where the absorbance of each microbial culture solution is positively correlated with its concentration as the range for selecting the light source wavelength; The light absorption uniformity, transmittance, stability, and color influence of LB culture solution of each microorganism under light source irradiation were used as evaluation indicators of the light source wavelength. Multiple light wavelengths were selected in the light source wavelength selection range using a gradient change method, and multiple light wavelengths were evaluated using the evaluation indicators to obtain multiple sets of evaluation indicator data. A backpropagation (BP) neural network was used to train the network on the light wavelength and the corresponding evaluation index data of each microorganism to obtain a light source wavelength evaluation model for each microorganism that characterizes the mapping relationship between light wavelength and evaluation index. The model expression for the light source wavelength evaluation index of each microorganism is as follows: [A,B,C,D] X =BP X (l); In the formula, X∈[yeast; Bacillus; Arthrobacter; Escherichia coli], A, B, C, D are light absorption uniformity, transmittance, stability, and color influence of LB culture solution, respectively, l is the wavelength of light, and BP is the BP neural network; All light wavelengths within the selected wavelength range are input into the light wavelength evaluation model of each microorganism to obtain the evaluation index data of each microorganism at all light wavelengths within the selected wavelength range. The evaluation index data corresponding to each microorganism are averaged at the same light wavelength, and the light wavelength corresponding to the optimal evaluation index data after averaging is taken as the light source wavelength.

7. The monitoring method according to claim 6, characterized in that, The method for calculating the system optical density ODsys includes: Based on the exponential decay of transmitted light intensity with increasing microbial concentration as described in Beer-Lambert's law, and the direct proportionality between the digital signal of the photoelectric conversion module's output current and the transmitted light intensity, a linear relationship between the logarithm of the digital signal of the photoelectric conversion module's output current and the microbial concentration is obtained. This linear relationship is as follows: ； In the formula, Here, j represents the digital signal of the output current of the photoelectric conversion module, and j represents the system measurement circuit parameter. denoted as , where is the transmitted light intensity; g represents the geometric parameters of the measuring instrument. Where is the incident light intensity, K is the microbial absorption coefficient, L is the optical path length (cm), and t is the microbial concentration (mol·L⁻¹). -1 ; The system optical density is obtained based on the transmittance, which is the ratio of transmitted light intensity to incident light intensity, as represented in the optical density calculation formula. The system optical density has a linear relationship with microbial concentration. The linear relationship with microbial concentration is as follows: ； In the formula, OD sys Let T be the system optical density, C be the transmittance, and S be a constant. in The digital signal that inputs current to the photoelectric conversion module.

8. The monitoring method according to claim 7, characterized in that, The construction of the linear calibration model for microbial concentration includes: Based on system optical density The linear relationship analysis with microbial concentration showed that the standard microbial concentration can be obtained by converting the system optical density for different wavelengths using a correction factor δ. The system optical density conversion formula for the standard microbial concentration is as follows: ； In the formula, OD 600nm The standard microbial concentration is given, and δ is the correction factor. The digital signal of the output current of the photoelectric conversion module, S in The digital signal that outputs the current of the photoelectric conversion module; Based on the system optical density conversion formula of standard microbial concentration, it is found that the digital current signal and microbial concentration have an approximately linear relationship. According to the approximately linear relationship, the partial least squares model is introduced into the system optical density conversion formula of standard microbial concentration for linear fitting to obtain the linear calibration model of microbial concentration. The model expression for the linear calibration model of microbial concentration is: ； In the formula, OD 600nm Where A is the standard microbial concentration, D is the linear fitting coefficient, and A is the constant term. The digital signal of the output current of the photoelectric conversion module, S in This is the digital signal that outputs the current from the photoelectric conversion module.

9. The monitoring method according to claim 5, characterized in that, The OD in the linear and nonlinear calibration models of microbial concentration 600nm All measurements were obtained by using an ultraviolet spectrophotometer in the microbial test solution.

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