Method and device for screening strains with high matrix utilization efficiency by utilizing CO2 release rate

Abstract

The invention discloses a method and a device for screening strains with high matrix utilization efficiency by utilizing a CO2 release rate, and belongs to the field of microbial screening. The method comprises the following steps: standardized culture of strains: inoculating to-be-screened strains into a solid culture medium, controlling environmental parameters, and culturing in a dark place; cO2 dynamic monitoring: collecting CO2 concentration by using a CO2 sensor integrated with temperature, humidity and pressure compensation; metabolic characteristic extraction: calculating a CO2 release rate per unit time, a release rate slope and a hypha normalized accumulative release amount; hypha growth monitoring and data synchronization: calculating a bacterial colony specific growth rate, and generating a synchronous data set with CER data; hypha growth and metabolism correlation analysis: establishing a coupling kinetic model of hypha growth and CO2 release; efficiency prediction and strain screening: predicting matrix utilization efficiency by using a multivariable regression model, and screening high-efficiency strains according to grading standards. The method has the advantages of being non-destructive, high in real-time performance and the like, and the screening period of strains with high matrix utilization efficiency is remarkably shortened.

Description

Technical Field

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

[0002] In the current production process of the edible mushroom industry, solid media (such as agricultural and forestry waste, corncobs, wood chips, sawdust, etc.) are used as the main raw materials, and their utilization efficiency is directly related to the mycelial growth rate, yield, and product quality. Therefore, screening strains with high substrate utilization efficiency is of great significance for reducing production costs, shortening the fermentation cycle, and improving the overall process stability. In addition, efficient strains can fully decompose and utilize agricultural waste, which can not only improve economic benefits but also meet the requirements of resource recycling and environmentally friendly production.

[0003] Currently, the commonly used methods for screening strains with high substrate utilization efficiency in the industry mainly include the following categories: First, the mycelial growth rate evaluation method on a plate. This method is used for preliminary screening by observing the mycelial extension speed on a solid medium plate. Its advantage is that it is simple and intuitive to operate and is suitable for preliminarily judging the activity of strains. However, the mycelial growth rate is significantly affected by culture conditions, substrate composition, and environmental factors, and cannot directly reflect the actual efficiency of the strain in degrading and transforming organic matter in the substrate, and there is a certain subjective judgment component. Second, the biological efficiency evaluation method. The so-called biological efficiency usually refers to the ratio of the yield produced by the strain during a certain solid-state fermentation cycle to the consumed substrate, which is an important indicator for measuring the overall production performance of the strain. This method can comprehensively reflect the conversion effect of the strain under actual production conditions. However, since the yield data needs to be obtained after the end of the entire fermentation cycle, its feedback cycle is long, which is not conducive to rapid screening and process adjustment, and is greatly affected by fluctuations in fermentation conditions. Third, the enzymatic activity evaluation method. Some studies use the detection of the activity of key degrading enzymes as an index, and quantitatively analyze the activity of enzymes secreted by cells or intracellular enzymes with degradation functions to predict the ability of the strain to decompose and utilize organic matter in the solid substrate. This method can reveal the internal mechanism of the strain decomposing the substrate to a certain extent, but it requires high instrumentation, has a complex experimental process, a large operation cost, and is not easy to achieve large-scale rapid screening.

[0004] In view of the problems existing in the above-mentioned methods, the present invention proposes a new technical idea for screening strains by using the CO2 release rate. During the fermentation process, when edible fungi decompose solid substrates and absorb nutrients, energy is released. As the main metabolite generated during the mycelial respiration and organic matter decomposition processes, the release rate of CO2 (Carbon-dioxide Escape Rate, CER) can directly reflect the overall metabolic activity of the strain. Strains with high substrate utilization efficiency usually have a more active metabolic process and will exhibit a higher CO2 release rate under solid-state fermentation conditions. Therefore, by using a high-precision CO2 sensor to conduct real-time online monitoring of a closed culture system, the change in CO2 concentration can be continuously recorded, and the slope, peak value, and cumulative release amount of the release rate curve can be analyzed through mathematical modeling, so as to quantitatively evaluate the substrate utilization efficiency of the strain.

[0005] In view of this, the present invention is proposed based on an in-depth analysis of the advantages and disadvantages of existing screening methods, and is precisely to solve the problems existing in traditional methods such as insufficient real-time performance, complex operation, and long feedback cycle. By using the intuitive, vivid, and non-destructive index of the CO2 release rate, an innovative and practical technical means is provided 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 rapidly and accurately screening the substrate utilization efficiency of edible fungi strains by using the CO2 release rate, so as to overcome the technical problems such as insufficient real-time performance, cumbersome operation, long feedback cycle, and data deviation existing in traditional methods for evaluating mycelial growth on plates, biological efficiency, and enzymatic activity.

[0007] To achieve the above object, the present invention provides a method for screening strains with high substrate utilization efficiency by using the CO2 release rate, which specifically includes the following steps: S1, standardized cultivation of strains: A solid medium with 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 mushroom strains to be screened on a sterile operating table, and then placed in a sealed culture chamber. The culture temperature is controlled at 25°C ± 1°C, the humidity is 70%RH ± 5%, and the culture is carried out in the dark; S2, dynamic monitoring of CO2: A CO2 sensor probe is embedded in the top of the culture chamber. The CO2 sensor probe integrates a temperature and humidity and pressure compensation module and is equipped with a PTFE waterproof and breathable membrane. It communicates with the data acquisition module through IIC, collects the CO2 concentration data in the culture chamber every 5 minutes, and uses moving average filtering to remove noise from the original data; S3, extraction of metabolic characteristics: Based on the continuously collected CO2 concentration data, the CO2 release rate CER per unit time is calculated using the ideal gas law, and the release rate slope k and the normalized CO2 cumulative release amount Q of the mycelium are obtained; S4, monitoring of mycelial growth and data synchronization: The mycelial expansion images are taken regularly 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 the specific growth rate μ. The specific growth rate μ and the CO2 release rate CER are matched in a time window, and through Savitzky-Golay filtering and Z-score normalization, the noise and dimensional differences are eliminated to generate a synchronized data set for model construction; S5, correlation analysis of mycelial growth and metabolism: The biomass growth is described by the Logistic equation, and the growth is correlated with the CO2 release rate CER to establish a coupled kinetic model of mycelial growth and CO2 release. The coupled kinetic model is optimized segmentally according to the growth stage, and then the coupled kinetic model is fitted to obtain the mycelial growth parameters , , , where is the maximum specific growth rate of the mycelium, is the CO2 production coefficient of the mycelium, is the CO2 production coefficient for mycelial maintenance metabolism; S6, efficiency prediction and strain screening: Based on the CO2 release rate slope k, the cumulative release amount Q, and the mycelial growth parameters , , , a multivariable regression model is established to predict the substrate utilization efficiency: ; In the formula, represents the substrate utilization efficiency; , , represent the regression coefficients, which are determined by fitting the experimental data; is the intercept; According to the prediction results, high-efficiency strains are screened according to the grading criteria.

[0008] Optionally, the calculation formula for the CO2 release rate CER per unit time in S3 is: ; In the formula, is the initial CO2 concentration; is CO2 concentration at time ; 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 the temperature; is the time interval; The slope k of the release rate is the rate of change of the slope of the CO2 release rate CER per unit time, and the calculation formula is: ; In the formula, is the difference in the CO2 release rate at adjacent time points; The formula for the normalized CO2 cumulative release amount Q of hyphae is: ; In the formula, is the starting time point of the experimental observation, is the ending time point of the experimental observation, and M is the dry weight of hyphae.

[0009] Optionally, in S4, the hyphal expansion images are taken regularly by an imaging system outside the incubator, and the linear growth rate v of the colony is obtained through image analysis and converted into the specific growth rate μ, including: taking the hyphal expansion images at 24-hour intervals by an imaging system outside 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, v = Δr / 24; converting the linear growth rate v into the specific growth rate through the formula Convert the linear growth rate v into the specific growth rate , in the formula, , are the linear growth rates at adjacent time points; , are , corresponding time points.

[0010] Optionally, in S5, the expression describing the biomass growth by the Logistic equation is as follows: ; In the formula, is the change rate of mycelial biomass over time; is the mycelial biomass at time t; is the maximum specific growth rate of the mycelium; is the maximum mycelial carrying capacity; Relate the growth to the CO2 release rate CER, and establish a coupled kinetic model of mycelial growth and CO2 release. The expression is as follows: .

[0011] Optionally, in the step S5, optimizing the coupled kinetic model according to the growth stage includes: In the lag phase, that is, , , the mycelium is in the metabolic initiation stage, and a metabolic activation factor is introduced to correct the coupled kinetic model as: ; In the formula, is the initial inoculum biomass; is the metabolic activation rate constant, which is determined by fitting experimental data; In the exponential growth phase, that is, , ignore the inhibition term, , and adopt the original coupled kinetic model; In the stationary phase, that is, , = 0, and a decay factor is introduced to correct the coupled kinetic model as: ; In the formula, is the decay rate constant, which is determined by fitting experimental data.

[0012] Optionally, the grading standard in the step S6 is set as follows: Grade A, that is, highly efficient strains: ≥ 65%, and ≤ 450 , ≤ 20 , Q ≤ 1200 ; Grade B, that is, medium-efficient strains: 50% ≤ < 65%, and ≤ 30 ; Grade C, that is, low-efficient strains: < 50% or ≥ 30 .

[0013] Optionally, in S1, the pretreatment step of the solid medium further includes crushing the medium to a particle size of 2-5 mm and then performing sterilization treatment.

[0014] Optionally, the measuring range of the CO2 sensor probe is designed to be adjustable from 0 to 100 vol%, the accuracy is ±0.2-2.0 vol%, and 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. Among them, the temperature measuring range of the temperature and humidity sensor is -40~125°C, the accuracy is 0.2°C, the humidity measuring range is 0~100%RH, the accuracy is ±1.8%RH, and the measuring range of the pressure sensor is 260 hPa - 1260 hPa absolute pressure, and the accuracy is ±0.5 hPa.

[0015] Optionally, the data acquisition module communicates with the main control system through the CAN bus to realize real-time data transmission and upload to the cloud platform data server.

[0016] According to another aspect of the present invention, there is also provided a device for screening strains with high substrate utilization efficiency by using the CO2 release rate, which is used to implement the method for screening strains with high substrate utilization efficiency by using the CO2 release rate as described above, including: a culture unit: a closed culture chamber with built-in gas circulation guide vanes to ensure that the standard deviation of CO2 distribution uniformity is ≤±3%, and the inclination angle of the gas circulation guide vanes is 45°±5°; a detection unit: a CO2 sensor probe, integrated with a temperature and humidity sensor and a pressure sensor, embedded in the top of the culture chamber through a sealing valve, 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; an analysis unit: configured with an embedded processor, automatically calculating the CO2 release rate CER, release rate slope k and cumulative release amount Q through a built-in algorithm, and outputting values and strain classification results according to the constructed multivariable regression model to realize the prediction of the substrate utilization efficiency of the strains; a communication unit: used to realize signal transmission and data remote monitoring between modules.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. By using a CO2 sensor probe and a corresponding circuit, non-invasive and real-time dynamic monitoring of the CO2 release rate is realized, avoiding data deviation caused by destructive sampling in traditional detection methods and reducing the determination cost; 2. Based on the obtained CO2 release data, a mathematical model is established, which can accurately evaluate the substrate utilization efficiency of strains within a short period, solving the disadvantages of long feedback period and cumbersome operation in existing detection methods such as mycelial growth rate, biological efficiency and enzymatic activity on plates, and providing a new technical means for the rapid screening of edible mushroom strains. Description of the Drawings

[0018] Figure 1 This is a schematic flow chart of the method for screening strains with high substrate utilization efficiency by using the CO2 release rate in the present invention; Figure 2 This is the circuit schematic diagram of the CO2 sensor probe in the present invention. Detailed implementation manners

[0019] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings. Those skilled in the art should understand that the described embodiments are only used to explain the present invention, rather than to limit the protection scope of the present invention. Operations without specific conditions noted in the embodiments are carried out according to conventional methods in the art or the instrument instructions.

[0020] Example 1: Refer to Figure 1 , the embodiment of the present invention provides a method for screening strains with high substrate utilization efficiency by using the CO2 release rate. By monitoring the CO2 release rate in real time to evaluate the decomposition and utilization ability of the strains on the cultivation substrate, efficient screening of edible mushroom strains is realized, including the following steps: S1, Standardized cultivation of strains: A solid medium with 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 mushroom strains to be screened on a sterile operating table, and then loaded into a sealed cultivation chamber. Among them, the culture medium substrate 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 (aperture about 2 mm) is preferably set in the cultivation chamber to promote the uniform growth of mycelium. The cultivation temperature in the cultivation chamber is controlled at 25 °C ± 1 °C, the humidity is 70%RH ± 5%, and it is cultured in the dark; It should be added that the edible mushroom strains used in the present invention can be obtained by the public through conventional channels. For example, they can be Pleurotus ostreatus, Lentinula edodes or Flammulina velutipes, etc.

[0021] S2, Dynamic monitoring of CO2: A high-concentration CO2 sensor probe is embedded in the top of the cultivation chamber, as Figure 2 shown, Figure 2 This is the circuit schematic diagram of the CO2 sensor probe in the present invention. The CO2 sensor probe integrates temperature, humidity and pressure compensation modules, and is equipped with a PTFE waterproof and breathable membrane (thickness 0.1 mm, porosity 85%) to adapt to the high-humidity environment. The measuring range of the high-concentration CO2 sensor probe is adjustable from 0 to 100 vol%, and the accuracy is ±0.2 - 2.0 vol%. It is integrated with a temperature and humidity sensor (measuring range: temperature ~40 - 125 °C, accuracy 0.2 °C, humidity 0 - 100%RH, accuracy ±1.8%RH) and a pressure sensor (measuring range 260 hPa - 1260 hPa absolute pressure, accuracy ±0.5 hPa) to compensate for the influence of external environmental parameters on the measurement results.

[0022] The sensor probe communicates with the data acquisition module via IIC. The data acquisition period is set to obtain the CO2 concentration data, temperature T, and air pressure P in the culture chamber every 5 minutes ( ). The data acquisition module establishes a connection with the embedded main control system via the CAN bus. The main control system obtains the sensor-related data every 5 minutes, removes the noise from the original data using moving average filtering (window width: 10 minutes), and uploads it to the cloud platform data server to achieve continuous recording of the real-time dynamic CO2 concentration. In addition, the main control system can also display the changes in the CO2 concentration and other environmental parameters in real time through the visualization interface.

[0023] S3, Metabolic feature extraction: Based on the continuously collected CO2 concentration data, the CO2 evolution rate CER per unit time is calculated using the ideal gas law: ; where, is the initial CO2 concentration (ppm); is the CO2 concentration (ppm) after hours (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 the temperature (°C); is the time interval (h); ; where, is the difference in the CO2 evolution rates at adjacent time points; is the time interval (5 minutes); Based on the collected data, the normalized calculation of the cumulative CO2 release amount of the mycelium (Q, unit: mg / g) is performed using the formula: ; where, Q is the normalized cumulative CO2 release amount of the mycelium (unit: mg / g); is the starting time point of the experimental observation (h), is the ending time point of the experimental observation (h), and M is the dry weight of the mycelium (g).

[0024] S4, Mycelium growth monitoring and data synchronization: Using non-destructive monitoring method, the hyphal expansion images were taken at 24-hour intervals by an external incubator imaging system (resolution ≥ 5 million pixels, equipped with a 50-mm fixed-focus lens). The colony radius was measured by the image analysis software ImageJ, and the daily expansion amount of the colony radius ∆r (unit: mm / day) was calculated. According to the daily expansion amount of the colony radius Δr, the linear growth rate v of the colony was calculated, v = Δr / 24 (unit: mm / h); and through the formula the linear growth rate v was converted into the specific growth rate μ (unit: h⁻¹); where , are the linear growth rates at adjacent time points, , are the corresponding time points; The specific growth rate (at 24-hour intervals) and the CO2 release rate CER monitored in real time (at 5-minute intervals) were aligned according to the timestamp. A 30-minute time window was used for data matching to generate a time-synchronized paired dataset for model construction. The CO2 data was smoothed using a Savitzky-Golay filter (window width = 5, polynomial order = 2) to eliminate sensor fluctuation interference. The CO2 release rate (CER) and the hyphal growth rate ( ) were Z-score standardized to eliminate the dimension difference.

[0025] S5, Association analysis of hyphal growth and metabolism: According to the theory of microbial growth and respiratory metabolism, a non-linear coupling kinetic model of hyphal growth rate ( ) and CO2 release rate (CER) was constructed, namely the substrate consumption-growth coupling model. The biomass growth was described by the Logistic equation and was associated with the CO2 release rate CER. The formula is expressed as: ; ; In the formula, is the change rate of hyphal biomass over time; is the biomass of hyphae at time t (g); is the maximum specific growth rate of hyphae (h -1 ); is the maximum biomass carrying capacity of hyphae (g) ( in the Logistic equation is the theoretically preset environmental carrying capacity, representing the theoretical upper limit of hyphal growth under specific culture conditions, determined by non-linear fitting of biomass-time series data using the Logistic model); is the coefficient of CO2 production by hyphae (μg CO2 / g), It is the coefficient of CO2 produced by mycelium for maintaining metabolism (μg CO2 / h).

[0026] Optimize the above coupling kinetic model in segments according to the growth stage (lag phase, exponential phase, stationary phase): In the lag phase ( , , the mycelium is in the metabolic initiation stage, and the CO2 release rate is low and fluctuates greatly), introduce the metabolic activation factor to correct the coupling kinetic model as: ; In the formula, is the initial inoculum biomass; is the metabolic activation rate constant, which is determined by fitting experimental data; Use the moving average method to extract the trend term, average and suppress noise through a time window (sampling once per hour, window taking 5 hours) to obtain the smooth trend curve of CER, and solve the trend term parameters through nonlinear regression (such as the Levenberg-Marquardt algorithm) , , ; In the exponential growth period ( , , the growth rate is proportional to the biomass), use the above original coupling kinetic model, and use the nonlinear least squares method to jointly fit the above two equations to calculate and obtain and ; In the growth stationary phase ( , = 0, growth is inhibited by the environment), introduce the attenuation factor to correct the model as: ; In the formula, is the attenuation rate constant (h -1 ), which is determined by fitting experimental data.

[0027] Precisely evaluate the metabolic efficiency of the strain and predict the stage adaptability according to the fitting and solution of the above model, and obtain the values of the mycelium growth parameters , , .

[0028] S6, Efficiency prediction and strain screening: From the essence of respiration: Edible fungi decompose the carbon source in the substrate through the tricarboxylic acid cycle (TCA) to generate ATP, and at the same time release CO2 (reaction formula: C6H 12 O6 + 6O2 → 6CO2 + 6H2O), establish a carbon balance model: Substrate utilization efficiency ( ) = (increase in mycelium carbon / substrate carbon consumption) × 100%; Among them, the increase in mycelium carbon = biomass × carbon content rate (the carbon content of edible mushroom mycelium is about 45 - 50%); substrate carbon consumption = initial substrate carbon content - residual substrate carbon content; the CO2 release amount can indirectly reflect the substrate carbon consumption (which needs to be corrected by the respiratory quotient RQ).

[0029] According to the above carbon balance model, based on the slope k of the CO2 release rate, the cumulative release amount Q, and the mycelium growth parameters , , , a multivariable regression model is established to predict the substrate carbon conversion rate (i.e., substrate utilization efficiency): ; In the formula, represents the substrate utilization efficiency (%); , , represent the regression coefficients, is the intercept; Collect the CO2 release curves of 300 different edible mushroom strains (such as Pleurotus ostreatus, Lentinula edodes, Flammulina velutipes, etc.), covering high, medium, and low efficiency strains, calculate k (the maximum slope in the logarithmic growth phase) and the Q value and the mycelium growth parameters ( , , ), fit the multivariable regression model, and obtain the optimal parameter values of , , , ; ; Substitute the obtained parameter values of , , , into the above formula, and use the multivariable regression model formula to output , predict the substrate utilization efficiency, and screen high-efficiency strains according to the prediction results according to the grading standard.

[0030] According to the exponential phase, give priority to examining and , which reflect the carbon conversion rate in the rapid mycelium growth stage; in the stationary phase, focus on and the decay factor , and evaluate the energy consumption efficiency of the strain to maintain metabolism. The strain screening grading standard is set as: Grade A (high-efficiency strain): ≥ 65%, and ≤ 450 (High carbon conversion efficiency), ≤20 (Low maintenance metabolic energy consumption), Q≤1200 (Less carbon loss); Grade B (medium efficiency strain): 50%≤ <65%, and ≤30 ; Grade C (low efficiency strain): <50% or ≥30 .

[0031] The present invention uses a high-precision CO2 sensor to conduct real-time online monitoring on a closed culture system, which can continuously record the change of CO2 concentration. By analyzing the slope, peak value and cumulative release amount of the release rate curve through mathematical modeling, the utilization efficiency of the strain on the substrate can be quantitatively evaluated, providing a reliable technical means for the rapid and efficient screening of edible mushroom strains, and having significant industrial application prospects.

[0032] Example 2: The embodiment of the present invention further provides a device for screening strains with high substrate utilization efficiency using the CO2 release rate, which is used to implement the above embodiments and preferred embodiments, and those that have been described will not be repeated. As used hereinafter, the terms "module" and "unit" can be a combination of software and / or hardware that can achieve a predetermined function. Although the systems described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0033] The device for screening strains with high substrate utilization efficiency using the CO2 release rate includes a culture unit, a detection unit, an analysis unit and a communication unit, wherein, Culture unit: A plurality of closed culture chambers are adopted, each chamber has a volume of about 5L, and is internally provided with a stainless steel mycelium support, and is internally provided with a gas circulation system (flow rate about 0.5 L / min, and provided with a spiral guide vane, inclination angle 45°±5°) to ensure uniform gas distribution in the chamber, with a standard deviation ≤±3%;

[0034] Detection unit: A CO2 sensor probe, integrated with a temperature and humidity sensor and a pressure sensor, is embedded in the top of the culture chamber through a sealing valve, with data compensation function and PTFE membrane protection, and is connected to the data acquisition module through an IIC interface. The collected data is transmitted to the embedded processor via a CAN bus, and the latter is built-in with data filtering and mathematical modeling algorithms; Analysis unit: An embedded processor is configured to automatically calculate the CO2 release rate CER, the release rate slope k and the cumulative release amount Q through built-in algorithms, and output according to the multivariate regression model Predict the utilization efficiency of the strain substrate based on the value and strain classification results; the prediction results are displayed in real time through a visualization interface, and at the same time support data export and cloud platform storage, which is convenient for subsequent process optimization and historical data statistics; Communication unit: used to realize signal transmission and remote data monitoring between modules.

[0035] Optionally, the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation manners, and will not be repeated here.

[0036] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0037] In the above embodiments of the present invention, the descriptions of each embodiment have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0038] The above is only the preferred implementation manner of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A method for screening strains with high substrate utilization efficiency by using the CO2 release rate, characterized in that It includes the following steps: S1, Standardized cultivation of strains: A solid medium with 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. After sterilization, it is cooled to below 28 °C. After inoculating the edible mushroom strains to be screened on a sterile operating table, it is loaded into a closed culture chamber. The culture temperature is controlled at 25 °C ± 1 °C, the humidity is 70%RH ± 5%, and it is cultured in the dark. S2, Dynamic monitoring of CO2: A CO2 sensor probe is embedded in the top of the culture chamber. The CO2 sensor probe integrates a temperature and humidity and pressure compensation module and is equipped with a PTFE waterproof and breathable membrane. It communicates with the data acquisition module through IIC, collects the CO2 concentration data in the culture chamber every 5 minutes, and uses moving average filtering to remove noise from the original data. S3, Extraction of metabolic characteristics: Based on the continuously collected CO2 concentration data, the CO2 evolution rate CER per unit time is calculated using the ideal gas law, and the evolution rate slope k and the normalized CO2 cumulative evolution amount Q of the mycelium are obtained. S4, Mycelial growth monitoring and data synchronization: The mycelial expansion images are taken regularly by an external imaging system of 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 and the CO2 evolution rate CER are matched in a time window. Through Savitzky-Golay filtering and Z-score normalization, noise and dimensional differences are eliminated, and a synchronized data set is generated for model construction. S5, Mycelial growth and metabolism correlation analysis: Describe the biomass growth through the Logistic equation, correlate the growth with the CO2 release rate CER, establish a coupled kinetic model of mycelial growth and CO2 release, and optimize the coupled kinetic model in stages according to the growth stage. Then, obtain the mycelial growth parameters by fitting the coupled kinetic model , , The values of, where is the maximum specific growth rate of mycelium, is the CO2 production coefficient of mycelium, is the CO2 production coefficient for mycelial maintenance metabolism; S6, Efficiency prediction and strain screening: Based on the slope k of the CO2 release rate, the cumulative release amount Q, and the mycelial growth parameters , , , establish a multivariate regression model to predict the substrate utilization efficiency: ； In the formula, represents the substrate utilization efficiency; , , represent regression coefficients, which are determined by fitting experimental data; is the intercept; Screen high-efficiency strains according to the prediction results according to the grading standard.

2. The method for screening strains with high substrate utilization efficiency by using the CO2 release rate according to claim 1, characterized in that, The calculation formula for the CO2 evolution rate CER per unit time in S3 is: ； In the formula, is the initial CO2 concentration; is the CO2 concentration at time ; 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 the temperature; is the time interval; The evolution rate slope k is the slope change rate of the CO2 evolution rate CER per unit time, and the calculation formula is: ； In the formula, is the difference in the CO2 release rate at adjacent time points; The formula for the normalized CO2 cumulative evolution amount Q of the mycelium is: ； wherein, is the starting time point of experimental observation, is the ending time point of experimental observation, and M is the dry weight of mycelium.

3. The method for screening strains with high substrate utilization efficiency by using the CO2 release rate according to claim 1, characterized in that, In S4, taking the mycelial expansion images regularly by an external imaging system of the incubator, obtaining the linear growth rate v of the colony through image analysis and converting it into the specific growth rate μ includes: Taking the mycelial expansion images at 24-hour intervals 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, v = Δr / 24. Convert the linear growth rate \(v\) to the specific growth rate through the formula , where , \(v_1\) and \(v_2\) are the linear growth rates at adjacent time points; , \(t_1\) and \(t_2\) are the time points corresponding to \(v_1\) and \(v_2\). , corresponding to , ​ 4. The method for screening strains with high substrate utilization efficiency using the CO2 release rate according to claim 1, characterized in that, In S5, the expression describing the biomass growth by the Logistic equation is as follows: ； In the formula, is the change rate of hyphal biomass over time; is the biomass of hyphae at time t; is the maximum specific growth rate of hyphae; is the maximum biological carrying capacity of hyphae; Relating the growth to the CO2 evolution rate CER, and establishing a coupled kinetic model of mycelial growth and CO2 release, the expression is as follows: 。 5. The method for screening strains with high substrate utilization efficiency by using the CO2 release rate according to claim 4, characterized in that, In S5, segmentally optimizing the coupled kinetic model according to the growth stage includes: During the lag phase, that is , , the hyphae are in the metabolic initiation stage, and a metabolic activation factor is introduced to modify the coupled kinetic model to: ； In the formula, is the initial inoculation biomass; is the metabolic activation rate constant, which is determined by fitting experimental data; During the exponential growth phase, i.e., , the inhibitory term is ignored, , and the original coupled kinetic model is adopted; During the steady state, that is , = 0, introduce the attenuation factor to modify the coupled dynamics model as: ； In the formula, is the decay rate constant, which is determined by fitting the experimental data.

6. The method for screening strains with high substrate utilization efficiency by using the CO2 release rate according to claim 1, characterized in that In S6, the grading standard is set as: Grade A, i.e., highly efficient strains: ≥ 65%, and ≤ 450 , ≤ 20 , Q ≤ 1200 ; Level B, i.e., medium-effective strain: 50% ≤ < 65%, and ≤ 30 ; Grade C, i.e., low-efficiency strains: <50% or ≥30 .

7. The method for screening strains with high substrate utilization efficiency by using the CO2 release rate according to claim 1, characterized in that In S1, the pretreatment step of the solid medium further includes crushing the medium to a particle size of 2 - 5 mm and then performing sterilization treatment.

8. The method for screening strains with high substrate utilization efficiency by using the CO2 release rate according to claim 1, characterized in that The measuring range of the CO2 sensor probe is adjustable from 0 to 100 vol%, with 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. Among them, the temperature measuring range of the temperature and humidity sensor is -40~125°C, with an accuracy of 0.2°C, the humidity measuring range is 0~100%RH, with an accuracy of ±1.8%RH, and the pressure sensor measuring range is 260hPa - 1260hPa absolute pressure, with an accuracy of ±0.5hPa.

9. The method for screening strains with high substrate utilization efficiency by using the 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.

10. An apparatus for screening strains with high substrate utilization efficiency using the CO2 release rate, which is used to implement the method for screening strains with high substrate utilization efficiency using the CO2 release rate according to any one of claims 1 to 9, characterized in that, It includes: Cultivation unit: A sealed cultivation chamber with built-in gas circulation guide vanes to ensure that the standard deviation of CO2 distribution uniformity is ≤±3%, and the inclination angle of the gas circulation guide vanes is 45°±5°; Detection unit: A CO2 sensor probe, integrated with a temperature and humidity sensor and a pressure sensor, is embedded in the top of the cultivation chamber through a sealing valve, has a data compensation function and PTFE membrane protection, and is connected to the data acquisition module through the IIC interface. The collected data is transmitted to the embedded processor via the CAN bus; Analysis unit: Configure an embedded processor to automatically calculate the CO2 release rate CER, release rate slope k, and cumulative release amount Q through built-in algorithms, and output values and strain classification results based on the constructed multivariate regression model to achieve the prediction of the substrate utilization efficiency of the strain; Communication unit: Used to achieve signal transmission and remote data monitoring between modules.

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