Method and device for screening strains with high substrate utilization efficiency using CO2 release rate

Abstract

The present invention discloses a method and apparatus for screening strains with high substrate utilization efficiency using CO2 release rate, belonging to the field of microbial screening. The method comprises: standardized strain culture: inoculating the strain to be screened onto a solid culture medium, controlling environmental parameters and culturing in the dark; dynamic CO2 monitoring: collecting CO2 concentration using a CO2 sensor with integrated temperature, humidity, and pressure compensation; metabolic signature extraction: calculating the CO2 release rate per unit time, the release rate slope, and the normalized cumulative release of hyphae; hyphae growth monitoring and data synchronization: calculating the colony specific growth rate and generating a synchronized data set with CER data; hyphae growth metabolic correlation analysis: establishing a coupled kinetic model of hyphae growth and CO2 release; efficiency prediction and strain screening: using a multivariate regression model to predict substrate utilization efficiency and screening efficient strains according to grading criteria. The present invention has the advantages of being non-destructive and highly real-time, significantly shortening the screening cycle for strains with high substrate utilization efficiency.

Description

Technical Field

[0001] The present invention relates to the fields of biological fermentation and microbial screening, and in particular to a method and device for screening strains with high substrate utilization efficiency by utilizing CO2 release rate. Background Art

[0002] In the current edible fungi production process, solid culture media (such as agricultural and forestry waste, corncobs, wood chips, sawdust, etc.) serve as primary raw materials. Their utilization efficiency directly impacts mycelial growth rate, yield, and product quality. Therefore, identifying strains with high substrate utilization efficiency is crucial for reducing production costs, shortening fermentation cycles, and improving overall process stability. Furthermore, highly efficient strains can fully decompose and utilize agricultural waste, improving economic efficiency while also adhering to resource recycling and environmentally friendly production requirements.

[0003] Currently, the commonly used methods for screening strains with high substrate utilization efficiency mainly include the following categories: 1. This method uses plate-based mycelial growth rate evaluation to conduct preliminary screening by observing mycelial extension speed on solid culture plates. Its advantages are simplicity, intuitiveness, and suitability for preliminary assessment of strain activity. However, mycelial growth rate is significantly affected by culture conditions, substrate composition, and environmental factors. It cannot directly reflect the strain's actual efficiency in degrading and converting organic matter in the substrate, and it is also subject to subjective judgment. 2. Biological efficiency evaluation. Biological efficiency generally refers to the ratio of yield produced by a strain to substrate consumed during a given solid-state fermentation cycle and is an important indicator of a strain's overall production performance. This method can comprehensively reflect the conversion efficiency of a strain under actual production conditions. However, since yield data must be obtained after the entire fermentation cycle, the feedback cycle is long, making it difficult to conduct rapid screening and process adjustments. Furthermore, it is significantly affected by fluctuations in fermentation conditions. 3. Enzyme activity evaluation. Some studies have used the activity of key degradative enzymes as indicators, quantitatively analyzing the activity of secreted or intracellular enzymes with degradative functions to predict the strain's ability to degrade and utilize organic matter in solid matrices. This method can reveal the intrinsic mechanism of the strain's matrix decomposition to a certain extent, but it has high requirements for instrumentation and equipment, complex experimental procedures, high operating costs, and is not easy to achieve large-scale rapid screening.

[0004] In response to the problems existing in the above-mentioned methods, the present invention proposes a new technical approach to strain screening using CO2 release rate. During the fermentation process, edible fungi release energy when decomposing solid substrates and absorbing nutrients. CO2 is the main metabolite produced during mycelial respiration and organic matter decomposition. Its release rate (Carbon-dioxide Escape Rate, CER) can intuitively reflect the overall metabolic activity of the strain. Strains with high substrate utilization efficiency usually have more active metabolic processes and exhibit higher CO2 release rates under solid-state fermentation conditions. Therefore, a high-precision CO2 sensor is used to perform real-time online monitoring of the closed culture system, which can continuously record changes in CO2 concentration. The slope, peak value and cumulative release amount of the release rate curve are analyzed through mathematical modeling, thereby quantitatively evaluating the strain's utilization efficiency of the substrate.

[0005] In light of this, the present invention, based on an in-depth analysis of the strengths and weaknesses of existing screening methods, aims to address the limitations of traditional methods, including limited real-time performance, complex operations, and long feedback cycles. By utilizing the intuitive, vivid, and non-destructive indicator of CO2 release rate, this invention provides an innovative and practical technical approach for the rapid and accurate screening of high-efficiency edible fungi strains. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for quickly and accurately screening the substrate utilization efficiency of edible fungi strains by using the CO2 release rate, so as to overcome the technical problems of traditional plate mycelial growth, biological efficiency and enzymatic activity evaluation methods, such as insufficient real-time performance, cumbersome operation, long feedback cycle and data deviation.

[0007] To achieve the above object, the present invention provides a method for screening strains with high substrate utilization efficiency by utilizing CO2 release rate, which specifically comprises the following steps: S1, standardized strain culture: a solid culture medium with a moisture content of 62%±2% and a sawdust and wheat bran ratio of 7:3 is sterilized at 121°C for 2 hours, cooled to below 28°C after sterilization, inoculated with the edible fungus strain to be screened on a sterile operating table and placed in a closed culture chamber, the culture temperature is controlled to be 25°C±1°C, the humidity is controlled to be 70%RH±5%, and culture is carried out in the dark; S2, dynamic CO2 monitoring: a CO2 sensor probe is embedded in the top of the culture chamber, the CO2 sensor probe integrates a temperature, humidity and pressure compensation module, and is provided with a PTFE waterproof and breathable membrane, communicates with a data acquisition module through IIC, collects CO2 concentration data in the culture chamber every 5 minutes, and removes noise from the original data by using a sliding average filter; S3, metabolic feature extraction: based on the continuously collected CO2 concentration data, The CO2 release rate per unit time CER was calculated using the ideal gas law, and the release rate slope k and the normalized CO2 cumulative release amount Q of the hyphae were obtained; S4, hyphae growth monitoring and data synchronization: hyphae expansion images were taken at regular intervals by the incubator external imaging system, and the colony linear growth rate v was obtained through image analysis and converted into the specific growth rate μ. The specific growth rate μ was matched with the CO2 release rate CER in the time window, and the noise and dimensional differences were eliminated through Savitzky-Golay filtering and Z-score normalization processing to generate a synchronized data set for model construction; S5, hyphae growth metabolism correlation analysis: biomass growth was described by the Logistic equation, and the growth was correlated with the CO2 release rate CER. A coupled kinetic model of hyphae growth and CO2 release was established, and the coupled kinetic model was optimized in segments according to the growth stage. Then, the hyphae growth parameters were obtained by fitting the coupled kinetic model. 、 、 The value of , where is the maximum specific growth rate of mycelium, is the CO2 production coefficient of mycelium, Maintain the metabolic CO2 production coefficient for mycelium;

[0008] S6, Efficiency prediction and strain screening: based on CO2 release rate slope k, cumulative release amount Q and mycelial growth parameters 、 、 , a multivariate regression model was established to predict substrate utilization efficiency:

[0009] ;

[0010] Where, represents substrate utilization efficiency; 、 、 represents the regression coefficient, which is determined by fitting the experimental data; is the intercept;

[0011] According to the prediction results, efficient strains were screened according to the grading standards.

[0012] Optionally, the calculation formula for the CO2 release rate per unit time CER in S3 is:

[0013] ;

[0014] Where, is the initial CO2 concentration; for CO2 concentration at the moment; V is the effective volume of the culture chamber; is the molar mass of CO2, = 44 g / mol; R is the gas constant, R = 8.314 L kPa / (mol·K); T represents temperature; is the time interval;

[0015] The release rate slope k is the slope change rate of the CO2 release rate CER per unit time, and the calculation formula is:

[0016] ;

[0017] Where, is the difference in CO2 release rate between adjacent time points;

[0018] The formula for the normalized cumulative CO2 release Q of mycelium is:

[0019] ;

[0020] Where, is the starting time point of the experimental observation, is the termination time point of the experimental observation, and M is the dry weight of mycelium.

[0021] Optionally, in S4, the image of hyphae expansion is taken at regular intervals by an external imaging system of the incubator, and the linear growth rate v of the colony is obtained through image analysis, and converted into a specific growth rate μ, which includes: taking images of hyphae expansion at intervals of 24 hours by an external imaging system of the incubator, measuring the colony radius through image analysis, and calculating the daily expansion amount Δr of the colony radius; calculating the linear growth rate v of the colony according to the daily expansion amount Δr of the colony radius, where v=Δr / 24; and converting the specific growth rate μ into the specific growth rate μ by the formula: Convert linear growth rate v to specific growth rate , where 、 is the linear growth rate at adjacent time points; 、 For 、 The corresponding time point.

[0022] Optionally, in S5, the expression for describing biomass growth by the Logistic equation is as follows:

[0023] ;

[0024] Where, is the rate of change of mycelial biomass over time; is the biomass of mycelium at time t; is the maximum specific growth rate of mycelium; is the maximum bio-carrying capacity of mycelium;

[0025] By correlating growth with CO2 release rate (CER), a coupled kinetic model of mycelial growth and CO2 release was established, which is expressed as follows:

[0026] .

[0027] Optionally, in S5, optimizing the coupled kinetic model in sections according to the growth stages includes:

[0028] During the hysteresis period, , , the mycelium is in the metabolic startup stage, and the metabolic activation factor is introduced The coupled dynamic model is modified as follows:

[0029] ;

[0030] Where, is the initial inoculum biomass; is the metabolic activation rate constant, which is determined by fitting the experimental data;

[0031] During the exponential growth phase, , ignoring the suppression terms, , the original coupled dynamics model was adopted;

[0032] In the stable period, , =0, introduce attenuation factor The coupled dynamic model is modified as follows:

[0033] ;

[0034] Where, is the decay rate constant, which is determined by fitting the experimental data.

[0035] Optionally, the grading standard in S6 is set as: Grade A, i.e., high-efficiency strains: ≥65%, and ≤450 , ≤20 , Q≤1200 ; Grade B, namely medium-efficiency strains: 50% ≤ <65%, and ≤30 ; Grade C, i.e. low efficiency strains: <50% or ≥30 .

[0036] Optionally, in S1, the pretreatment step of the solid culture medium further comprises crushing the culture medium to a particle size of 2 to 5 mm and then sterilizing the culture medium.

[0037] Optionally, the CO2 sensor probe has an adjustable range of 0 to 100 vol% with an accuracy of ±0.2 to 2.0 vol%, and is integrated with a temperature and humidity sensor and a pressure sensor to compensate for the influence of external environmental parameters on the measurement results. The temperature range of the temperature and humidity sensor is -40 to 125°C with an accuracy of 0.2°C, the humidity range is 0 to 100%RH with an accuracy of ±1.8%RH, and the pressure sensor range is 260hPa-1260hPa absolute pressure with an accuracy of ±0.5hPa.

[0038] Optionally, the data acquisition module communicates with the main control system via the CAN bus to achieve real-time data transmission and upload to the cloud platform data server.

[0039] According to another aspect of the present invention, a device for screening strains with high substrate utilization efficiency using CO2 release rate is also provided, which is used to implement the method for screening strains with high substrate utilization efficiency using CO2 release rate as described above, including: a culture unit: a closed culture chamber with a built-in gas circulation guide plate, ensuring that the standard deviation of CO2 distribution uniformity is ≤±3%, and the inclination angle of the gas circulation guide plate is 45°±5°; a detection unit: a CO2 sensor probe, which integrates a temperature and humidity sensor and a pressure sensor, is embedded in the top of the culture chamber through a sealing valve, has a data compensation function and PTFE membrane protection, is connected to a data acquisition module through an IIC interface, and the collected data is transmitted to an embedded processor via a CAN bus; an analysis unit: configured with an embedded processor, automatically calculates the CO2 release rate CER, the release rate slope k, and the cumulative release amount Q through a built-in algorithm, and outputs the CO2 release rate CER, the release rate slope k, and the cumulative release amount Q according to the constructed multivariate regression model. Values ​​and strain grading results are used to predict the utilization efficiency of the strain matrix; Communication unit: used to realize signal transmission and data remote monitoring between modules.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. By using CO2 sensor probes and corresponding circuits, non-invasive, real-time dynamic monitoring of CO2 release rate is achieved, avoiding data deviation caused by destructive sampling in traditional detection methods and reducing measurement costs;

[0042] 2. Based on the obtained CO2 release data, a mathematical model was established, which can accurately evaluate the substrate utilization efficiency of the strain in a relatively short period of time. This model solves the shortcomings of the existing plate mycelium growth rate, biological efficiency and enzymatic activity detection methods, such as long feedback cycle and cumbersome operation, and provides a new technical means for the rapid screening of edible fungi strains. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Schematic diagram of the process of the present invention for screening strains with high substrate utilization efficiency by utilizing CO2 release rate;

[0044] Figure 2 This is a circuit diagram of the CO2 sensor probe in the present invention. DETAILED DESCRIPTION

[0045] The technical solution of the present invention is described in detail below with reference to the accompanying drawings. It should be understood by those skilled in the art that the described embodiments are only used to explain the present invention and are not intended to limit the scope of protection of the present invention. The operations not specifying specific conditions in the examples are all carried out in accordance with conventional methods in the art or instrument specifications.

[0046] Example 1: Reference Figure 1 The embodiment of the present invention provides a method for screening strains with high substrate utilization efficiency by using CO2 release rate. By real-time monitoring of the CO2 release rate, the ability of the strain to decompose and utilize the cultivation substrate is evaluated, thereby achieving efficient screening of edible fungi strains. The method includes the following steps:

[0047] S1, standardized culture of strains:

[0048] A solid culture medium containing sawdust and wheat bran in a ratio of 7:3 and a moisture content of 62%±2% is sterilized at 121°C for 2 hours, cooled to below 28°C after sterilization, inoculated with the edible fungus strain to be screened on a sterile operating table, and then placed in a closed culture chamber, wherein the culture medium matrix is ​​pretreated and crushed to a particle size of 2-5 mm to ensure the uniformity of the culture medium. A stainless steel mycelium support (pore diameter of about 2 mm) is preferably provided in the culture chamber to promote uniform growth of mycelium. The culture chamber is controlled to have a culture temperature of 25°C±1°C and a humidity of 70%RH±5%, and culture is carried out in the dark.

[0049] It should be noted that the edible fungi strains used in the present invention are available to the public through conventional channels, for example, they may be oyster mushrooms, shiitake mushrooms or enoki mushrooms.

[0050] S2, CO2 dynamic monitoring:

[0051] A high concentration CO2 sensor probe is embedded in the top of the culture chamber, such as Figure 2 As shown, Figure 2 This is the circuit schematic of the CO2 sensor probe in the present invention. The CO2 sensor probe integrates temperature, humidity, and pressure compensation modules and features a PTFE waterproof and breathable membrane (0.1 mm thick, 85% porosity) to accommodate high-humidity environments. The high-concentration CO2 sensor probe features an adjustable range of 0 to 100 vol% with an accuracy of ±0.2 to 2.0 vol%. It integrates with a temperature and humidity sensor (range: ~40-125°C, accuracy of 0.2°C; humidity: 0 to 100% RH, accuracy of ±1.8% RH) and a pressure sensor (range: 260 hPa to 1260 hPa absolute, accuracy of ±0.5 hPa) to compensate for the effects of external environmental parameters on measurement results.

[0052] The sensor probe communicates with the data acquisition module through IIC, and the data acquisition cycle is set to obtain the CO2 concentration data in the culture chamber every 5 minutes ( ), temperature T, and air pressure P. The data acquisition module is connected to the embedded main control system via the CAN bus. The main control system acquires sensor data every 5 minutes, removes noise from the raw data using a sliding average filter (with a 10-minute window), and then uploads it to the cloud platform data server, enabling continuous and real-time recording of CO2 concentration dynamics. Furthermore, the main control system can display real-time changes in CO2 concentration and other environmental parameters on a visual interface.

[0053] S3, metabolic feature extraction:

[0054] Based on the continuously collected CO2 concentration data, the CO2 release rate per unit time CER is calculated using the ideal gas law:

[0055] ;

[0056] Where, is the initial CO2 concentration (ppm); for CO2 concentration (ppm) after one hour (i.e., at time t); V is the effective volume of the culture chamber (L); is the molar mass of CO2, = 44 g / mol; R is the gas constant, R = 8.314 L kPa / (mol·K); T represents temperature (℃); is the time interval (h);

[0057] Based on the data and the CER formula, a CER curve was drawn over time, and the release rate slope k (unit: ppm / min) was calculated:

[0058] ;

[0059] Where, is the difference in CO2 release rate between adjacent time points; is the time interval (5 minutes);

[0060] Based on the collected data, the cumulative CO2 release of mycelium was normalized and calculated (Q, unit: mg / g) using the formula:

[0061] ;

[0062] Where Q is the normalized cumulative release of CO2 by mycelium (unit: mg / g); is the starting time point of experimental observation (h), is the end time point of the experimental observation (h), and M is the dry weight of mycelium (g).

[0063] S4, mycelium growth monitoring and data synchronization:

[0064] A non-destructive monitoring method was used. An external imaging system (resolution ≥ 5 million pixels, equipped with a 50 mm fixed-focus lens) was installed in the incubator to capture hyphal expansion images at 24-hour intervals. The colony radius was measured using the image analysis software ImageJ, and the daily expansion of the colony radius ∆r (unit: mm / day) was calculated. The linear growth rate v of the colony was calculated based on the daily expansion of the colony radius Δr, v = Δr / 24 (unit: mm / h). The formula Convert the linear growth rate v to the specific growth rate μ (unit: h⁻¹); where 、 is the linear growth rate at adjacent time points, 、 is the corresponding time point;

[0065] Specific growth rate The CO2 release rate (CER) and mycelial growth rate (CER) were aligned by time stamp (24 h interval) and matched with the real-time CO2 release rate (CER) (5 min interval) using a 30 min time window to generate a time-synchronized paired dataset for model construction. The CO2 data were smoothed using a Savitzky-Golay filter (window width = 5, polynomial order = 2) to eliminate sensor fluctuation interference. ) to perform Z-score standardization to eliminate dimensional differences.

[0066] S5, mycelial growth and metabolic association analysis:

[0067] According to the theory of microbial growth and respiratory metabolism, the mycelial growth rate ( ) and CO2 release rate (CER), namely the substrate consumption-growth coupling model, which describes biomass growth through the Logistic equation and associates the CO2 release rate CER. The formula is expressed as:

[0068] ;

[0069] ;

[0070] Where, is the rate of change of mycelial biomass over time; is the biomass of mycelium at time t (g); is the maximum specific growth rate of mycelium (h -1 ); is the maximum biomass carrying capacity of mycelium (g) (in the Logistic equation is the theoretically preset environmental carrying capacity, which represents the theoretical upper limit of mycelial growth under specific culture conditions and is determined by nonlinear fitting of biomass-time series data using a logistic model); is the CO2 production coefficient of mycelium (μg CO2 / g), The CO2 production coefficient of mycelium maintenance metabolism (μg CO2 / h).

[0071] The above coupled kinetic model is optimized in sections according to the growth stages (hysteresis phase, exponential phase, and stable phase):

[0072] During the hysteresis period ( , , the mycelium is in the metabolic startup stage, the CO2 release rate is low and fluctuates greatly), the introduction of metabolic activation factors The coupled dynamic model is modified as follows:

[0073] ;

[0074] Where, is the initial inoculum biomass; is the metabolic activation rate constant, which is determined by fitting the experimental data;

[0075] The trend term is extracted using the sliding average method. The noise is suppressed by averaging the time window (sampling once per hour, with a window of 5 hours) to obtain a smooth trend curve of the CER. The trend term parameters are solved using nonlinear regression (such as the Levenberg-Marquardt algorithm). 、 、 ;

[0076] During the exponential growth period ( , , the growth rate is proportional to the biomass), using the original coupled kinetic model and the nonlinear least squares method to jointly fit the above two equations, the calculation results are and ;

[0077] During the growth stabilization period ( , = 0, growth is inhibited by the environment), introducing the attenuation factor The revised model is:

[0078] ;

[0079] Where, is the decay rate constant (h -1 ), determined by fitting the experimental data.

[0080] By fitting and solving the above model, accurate evaluation of strain metabolic efficiency and stage adaptability prediction can be achieved to obtain hyphae growth parameters. 、 、 The value of .

[0081] S6, Efficiency prediction and strain screening:

[0082] The essence of respiration: Edible fungi decompose the carbon source in the matrix through the tricarboxylic acid cycle (TCA) to generate ATP, while releasing CO2 (reaction formula: C6H 12 O6+6O2→6CO2+6H2O), establish a carbon balance model:

[0083] Substrate utilization efficiency ( ) = (bacteria carbon increment / substrate carbon consumption) 100%;

[0084] Among them, the carbon increment of the fungus body = biomass × carbon content (the carbon content of edible fungi mycelium is about 45~50%); the substrate carbon consumption = initial substrate carbon content - residual substrate carbon content; CO2 release can indirectly reflect the substrate carbon consumption (needs to be corrected by respiratory quotient RQ).

[0085] According to the above carbon balance model, based on the CO2 release rate slope k, cumulative release amount Q and mycelial growth parameters 、 、 , establish a multivariate regression model to predict substrate carbon conversion rate (i.e. substrate utilization efficiency):

[0086] ;

[0087] Where, represents substrate utilization efficiency (%); 、 、 represents the regression coefficient, is the intercept;

[0088] 300 groups of different edible fungi strains (such as oyster mushrooms, shiitake mushrooms, and enoki mushrooms) were collected, covering the CO2 release curves of high, medium, and low efficiency strains, and k (maximum slope during the logarithmic growth period) and Q value and mycelial growth parameters ( 、 、 ), fitting the multivariate regression model, and obtaining the optimal parameters 、 、 、 The value of ;

[0089] The parameters obtained 、 、 、 Substitute the value of into the above formula, and use the multivariate regression model formula to output , predict the substrate utilization efficiency, and screen the efficient strains according to the grading standards based on the prediction results.

[0090] According to the index period, priority inspection and , which reflects the carbon conversion rate during the rapid growth stage of mycelium; in the stable period, the focus is on and attenuation factor , the principle of evaluating the energy efficiency of strains in maintaining metabolism, the strain screening and grading standards are set as follows:

[0091] Grade A (highly effective strains): ≥65%, and ≤450 (high carbon conversion efficiency), ≤20 (Maintain low metabolic energy consumption), Q≤1200 (less carbon loss);

[0092] Grade B (intermediate-effect strains): 50% ≤ <65%, and ≤30 ;

[0093] Grade C (low-efficiency strains): <50% or ≥30 .

[0094] The present invention uses a high-precision CO2 sensor to perform real-time online monitoring of the closed culture system, which can continuously record the changes in CO2 concentration. Through mathematical modeling, the slope, peak value and cumulative release amount of the release rate curve are analyzed, thereby quantitatively evaluating the strain's utilization efficiency of the substrate. This provides a reliable technical means for the rapid and efficient screening of edible fungi strains and has significant industrial application prospects.

[0095] Example 2: This embodiment of the present invention further provides an apparatus for screening strains with high substrate utilization efficiency using CO2 release rate, which is used to implement the above-mentioned embodiments and preferred embodiments. Details already described will not be repeated here. As used below, the terms "module" and "unit" may refer to a combination of software and / or hardware that implements a predetermined function. While the systems described in the following embodiments are preferably implemented using software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0096] The device for screening strains with high substrate utilization efficiency by using CO2 release rate includes a culture unit, a detection unit, an analysis unit and a communication unit, wherein:

[0097] Culture unit: Multiple sealed culture chambers are used, each with a volume of approximately 5L, and a built-in stainless steel mycelium support. A built-in gas circulation system (flow rate of approximately 0.5L / min, with spiral guide vanes and an inclination angle of 45°±5°) is also included to ensure uniform gas distribution within the chamber, with a standard deviation of ≤±3%;

[0098] Detection unit: CO2 sensor probe, integrated with temperature, humidity and pressure sensors, embedded in the top of the culture chamber through a sealing valve. It has data compensation function and PTFE membrane protection. It is connected to the data acquisition module through the IIC interface. The collected data is transmitted to the embedded processor via the CAN bus. The embedded processor has built-in data filtering and mathematical modeling algorithms.

[0099] Analysis unit: Equipped with an embedded processor, it automatically calculates the CO2 release rate CER, release rate slope k and cumulative release amount Q through the built-in algorithm, and outputs the CO2 release rate CER, release rate slope k and cumulative release amount Q according to the multivariate regression model. The matrix utilization efficiency of the strains can be predicted based on the values ​​and strain classification results. The prediction results are displayed in real time through a visual interface, and data export and cloud platform storage are supported to facilitate subsequent process optimization and historical data statistics.

[0100] Communication unit: used to realize signal transmission and data remote monitoring between modules.

[0101] Optionally, specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0102] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0103] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0104] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for screening strains with high substrate utilization efficiency by using CO2 release rate, characterized in that: The following steps are involved: S1, standardized culture of strains: a solid culture medium containing sawdust and wheat bran in a ratio of 7:3 and a moisture content of 62% ± 2% was sterilized at 121°C for 2 hours, cooled to below 28°C after sterilization, inoculated with the edible fungus strain to be screened on a sterile operating table, and placed in a sealed culture chamber. The culture temperature was controlled at 25°C ± 1°C and the humidity was 70% RH ± 5%, and cultured in the dark; S2, CO2 dynamic monitoring: A CO2 sensor probe is embedded in the top of the culture chamber. The CO2 sensor probe integrates temperature, humidity and pressure compensation modules and has a PTFE waterproof and breathable membrane. It communicates with the data acquisition module through IIC. The CO2 concentration data in the culture chamber is collected every 5 minutes, and the raw data is de-noised using a sliding average filter. S3, metabolic feature extraction: Based on the continuously collected CO2 concentration data, the ideal gas law is used to calculate the CO2 release rate per unit time CER, and the release rate slope k and the mycelium-normalized CO2 cumulative release amount Q are obtained; S4, hyphae growth monitoring and data synchronization: hyphae expansion images are captured at regular intervals using an external imaging system installed in the incubator. The linear growth rate v of the colony is obtained through image analysis and converted into the specific growth rate μ. The specific growth rate μ is then time-window matched with the CO2 release rate CER. Savitzky-Golay filtering and Z-score normalization are then performed to eliminate noise and dimensional differences, generating a synchronized dataset for model construction. S5, mycelial growth and metabolic correlation analysis: The biomass growth was described by the Logistic equation, and the growth was correlated with the CO2 release rate (CER). A coupled kinetic model of mycelial growth and CO2 release was established, and the coupled kinetic model was optimized in sections according to the growth stage. The mycelial growth parameters were then obtained by fitting the coupled kinetic model. 、 、 The value of , where is the maximum specific growth rate of mycelium, is the CO2 production coefficient of mycelium, Maintain the metabolic CO2 production coefficient for mycelium; S6, Efficiency prediction and strain screening: based on cumulative release Q and mycelial growth parameters 、 、 , a multivariate regression model was established to predict substrate utilization efficiency: ； Where, represents substrate utilization efficiency; 、 、 represents the regression coefficient, which is determined by fitting the experimental data; is the intercept; According to the prediction results, efficient strains were screened according to the grading standards; The calculation formula of the CO2 release rate per unit time CER in S3 is: ； Where, is the initial CO2 concentration; is the CO2 concentration at time t; is the effective volume of the culture chamber; is the molar mass of CO2, = 44 g / mol; R is the gas constant, R = 8.314 ; T represents temperature; is the time interval; The release rate slope k is the slope change rate of the CO2 release rate CER per unit time, and the calculation formula is: ； Where, is the difference in CO2 release rate between adjacent time points; The formula for the normalized cumulative CO2 release Q of mycelium is: ； Where, is the starting time point of the experimental observation, is the termination time point of experimental observation, M is the dry weight of mycelium; In S4, the hyphae expansion images are captured at regular intervals by an external imaging system of the incubator, and the linear growth rate v of the colony is obtained by image analysis and converted into a specific growth rate μ, which includes: The hyphae expansion images were taken at 24-hour intervals using an external imaging system in the incubator. The colony radius was measured by image analysis, and the daily expansion of the colony radius Δr was calculated. The linear growth rate v of the colony is calculated based on the daily expansion of the colony radius Δr, v=Δr / 24; By formula The linear growth rate v is converted into the specific growth rate μ, where 、 is the linear growth rate at adjacent time points; 、 For 、 The corresponding time point; In S5, the expression describing biomass growth by the Logistic equation is as follows: ； Where, is the rate of change of mycelial biomass over time; is the biomass of mycelium at time t; is the maximum specific growth rate of mycelium; is the maximum bio-carrying capacity of mycelium; By correlating growth with CO2 release rate (CER), a coupled kinetic model of mycelial growth and CO2 release was established, which is expressed as follows: ； Optimizing the coupled kinetic model in sections according to the growth stage includes: During the hysteresis period, , , the mycelium is in the metabolic startup stage, and the metabolic activation factor is introduced The coupled dynamic model is modified as follows: ； Where, is the initial inoculum biomass; is the metabolic activation rate constant, which is determined by fitting the experimental data; During the exponential growth phase, , ignoring the suppression terms, , the original coupled dynamics model was adopted; In the stable period, , , introducing the attenuation factor The coupled dynamic model is modified as follows: ； Where, is the decay rate constant, which is determined by fitting the experimental data; The grading standards in S6 are set as follows: Grade A, i.e. highly effective strains: ≥65%, and ≤450 , ≤20 , Q≤1200 ; Grade B, namely medium-efficiency strains: 50% ≤ <65%, and ≤30 ; Grade C, i.e. low-efficiency strains: <50% or ≥30 .

2. The method for screening strains with high substrate utilization efficiency by utilizing CO2 release rate according to claim 1, characterized in that: In the above S1, the pretreatment step of the solid culture medium further includes crushing the culture medium to a particle size of 2 to 5 mm and then sterilizing it.

3. The method for screening strains with high substrate utilization efficiency by utilizing CO2 release rate according to claim 1, characterized in that: The CO2 sensor probe has an adjustable range of 0 to 100 vol% and an accuracy of ±0.2 to 2.0 vol%. It is integrated with a temperature and humidity sensor and a pressure sensor to compensate for the influence of external environmental parameters on the measurement results. The temperature and humidity sensor has a temperature range of -40 to 125°C with an accuracy of 0.2°C, a humidity range of 0 to 100%RH with an accuracy of ±1.8%RH, and a pressure sensor with an absolute pressure range of 260hPa to 1260hPa with an accuracy of ±0.5hPa.

4. The method for screening strains with high substrate utilization efficiency by utilizing CO2 release rate according to claim 1, characterized in that: The data acquisition module communicates with the main control system through the CAN bus to achieve real-time data transmission and upload to the cloud platform data server.

5. A device for screening strains with high substrate utilization efficiency by using CO2 release rate, used to implement the method for screening strains with high substrate utilization efficiency by using CO2 release rate according to any one of claims 1 to 4, characterized in that: include: Culture unit: A closed culture chamber with a built-in gas circulation guide vane to ensure the standard deviation of CO2 distribution uniformity ≤±3%, and the inclination angle of the gas circulation guide vane is 45°±5°; Detection unit: CO2 sensor probe, integrated with temperature and humidity sensor and pressure sensor, embedded in the top of the culture chamber through a sealing valve, equipped with data compensation function and PTFE membrane protection, connected to the data acquisition module through the IIC interface, and the collected data is transmitted to the embedded processor via the CAN bus; Analysis unit: Equipped with an embedded processor, it automatically calculates the CO2 release rate CER, release rate slope k and cumulative release amount Q through the built-in algorithm, and outputs it based on the constructed multivariate regression model The value and strain classification results are used to predict the strain substrate utilization efficiency; Communication unit: used to realize signal transmission and data remote monitoring between modules.

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