KR20170053360A - Method for monitoring and control of amino acid fermentation process using Near-infrared spectrophotometer - Google Patents
Method for monitoring and control of amino acid fermentation process using Near-infrared spectrophotometer Download PDFInfo
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- C12P—FERMENTATION OR ENZYME-USING PROCESSES TO SYNTHESISE A DESIRED CHEMICAL COMPOUND OR COMPOSITION OR TO SEPARATE OPTICAL ISOMERS FROM A RACEMIC MIXTURE
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- C12M41/00—Means for regulation, monitoring, measurement or control, e.g. flow regulation
- C12M41/30—Means for regulation, monitoring, measurement or control, e.g. flow regulation of concentration
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Abstract
The present invention relates to a method for measuring an amino acid fermentation broth using a near infrared ray spectroscopic analyzer and a method for real time simultaneous multi-analysis control of a process using the signal generated at the time of fermentation process control. More particularly, Using a Near Infrared Spectrophotometer (NIRS), multiple fermentations, including the concentrations of key substrates, including amino acid concentrations, sugar concentrations, and ammonia nitrogene concentrations, and the absorbance indicating bacterial growth, It is possible to control the fermentation process in real time automatically by using each electrical analysis signal generated at the same time and monitor the process control factor in real time at the same time and to optimize process control and maximize productivity. By measurement method And real-time management of the fermentation process.
In order to accomplish the above object, there is provided a method for real-time management of an amino acid fermentation process using a near infrared ray spectroscopic analyzer according to the present invention, comprising the steps of: collecting 300 samples of fermentation broth of amino acid fermentation process at intervals of 2 hours; Measuring 250 samples of the 300 samples using the NIR spectrometer; Performing physicochemical standard analysis on the 250 samples; Pre-processing the measured source spectrum; Selecting a spectral region specific to a concentration of a component to be quantified among the pre-processed spectra and developing a calibration calibration model through spectral analysis chemistry of the spectrum of the selected region; The developed calibration calibration model was compared with the 50 samples which were not subjected to the step of measuring the raw spectrum among the samples collected at the step of collecting the sample, and the analysis results obtained by physicochemical analysis were compared with each other Wherein the calibration and calibration model obtained through the calibration and optimization is measured by a near infrared spectrometer, wherein a plurality of fermentation process control factors are simultaneously measured and monitored in real time, By utilizing each electrical analysis signal generated during the measurement, the fermentation process is automatically controlled in real time to maximize the process control and productivity.
Description
The present invention relates to a method for real-time management of an amino acid fermentation process using a near-infrared spectrometer, and more particularly, to a fermentation process for fermenting an amino acid using a near infrared spectrophotometer (NIRS) A plurality of fermentation process control factors including an amino acid and an absorbance indicating the growth of important substrates including bacillus (Sugar) and ammonia nitrogens and microorganisms are simultaneously measured and monitored in real time, and each of the resulting electrical analyzes The present invention relates to a method for real-time management of an amino acid fermentation process using a near-infrared spectroscopic analyzer capable of accurately controlling the fermentation process in real time using signals to maximize the productivity of the process.
The fermentation process is a process for efficiently producing the desired production material efficiently by inoculating the microorganism into a medium capable of growth and production, and it is essential to optimize the physical and chemical processes suitable for the physiology of the microorganism.
The physical factors involved in the optimization of the fermentation process are agitation water, temperature, and internal pressure. The chemical factors are medium composition, pH, dissolved oxygen, and osmotic pressure. The factors that are the result of cultivation are the growth of the microorganism, the target product, and the co-product that accumulates like the main product.
Since the instruments such as stirring water, temperature, internal pressure, pH and dissolved oxygen among the above fermentation control factors have already been developed as highly reliable measurement sensors, on-line real time analysis is widely used and real-time monitoring and control is possible with current technology It is a management factor that has been applied to microbial fermentation industry for a long time.
However, only the above factors that are normally applied to the current fermentation process management can be used to instantly analyze the time course changes of the major components of the medium and the products (growth of the target product and bacteria) essential for real-time management and optimization of the fermentation process Because there are no measuring instruments (sensors, etc.) or analytical methods developed, they are dependent on off-line analysis methods or experience, so that application to fermentation process optimization and process control is very low compared to chemical processes.
In other words, the fermentation process experiment and the production site management to date have been carried out by analyzing the fermentation broth as a sample and then analyzing it with a dedicated dedicated analyzer in order to know the changes of the main components over time, the growth of the bacteria, And offline analysis that analyzes and manages the fermentation phenomenon is used. This method is not a result of instantaneous analysis of biochemical reaction results by microorganisms, but it is a non - real - time analysis result, and it has a disadvantage that cost efficiency is low due to low efficiency in fermentation optimization or management and much time and manpower for analysis work.
The present inventors have recognized that it is impossible to accurately manage the fermentation process at the industrial scale as well as the process optimization when the fermentation process is managed by utilizing the results of analyzing the fermentation main components analyzed in the offline and non-real time When NIRS was used, it was confirmed that the main management factors of the fermentation process could be analyzed in real time on-line, so that the change of the fermentation factor over time was monitored in real time, and the result was converted into an electrical signal. The present invention has been accomplished by developing a new method capable of feeding back to the system and automatically controlling in real time.
Fermentation process is the most important process in the process of manufacturing industrial scale fermentation products, which has a great influence on product quality and cost. On-line real-time monitoring and control of the fermentation process can create an environment that maximizes the microbial activity compared to the existing non-real-time off-line method. Thus, efficient and effective production of the desired product Can be maximized and the change status of the main factors of the process can be monitored in real time. Therefore, it is possible to grasp the physiological change of the microorganism due to the change of the main factor in real time and cope with it automatically so that the optimization of the fermentation process, Method, that is, the process management method that can maximize the productivity by minimizing the unknown loss that can not be solved because it is not monitored in the off-line management.
In addition, it is a new fermentation process management method that can reduce the error by itself in off-line analysis method and can shorten process development and optimization time, and can be effectively utilized in research and production.
This embodiment of the present invention is based on the rapid development of chemometrics (analytical metrology), which is a metrological analysis chemical program capable of accurately and promptly processing information of spectroscopic analysis spectrum and generation of electronics, information communication, This is due to the fact that it has become possible to apply the spectroscopic analytical technology to a wider range of applications.
In the meantime, NIRS technology can continuously analyze various components simultaneously in a short time without pretreatment of samples, and it is possible to analyze by using high performance liquid chromatography (HPLC ) Method and Kjeldahl method for analyzing nitrogen component can be analyzed simultaneously without pretreatment of sample at the same time, so that it is possible to perform real-time analysis by directly attaching a sensor or a probe to the fermentation broth. Therefore, it is possible to dramatically reduce the analysis time, cost, and labor force, as well as a new real-time method which is being applied to various processes in a manner capable of rapid quantitative analysis of several seconds or tens of seconds without using harmful chemicals used for pretreatment It is a technique that is being spotlighted by quantitative analysis.
This NIRS analysis method can be analyzed without sample destruction and pretreatment as well as simultaneous analysis of many substances. It is an optimal analytical method for analyzing continuous microbial reaction in the fermentation tube on-line Thereby completing the present invention.
The application of the existing NIR technology is mainly used for analyzing the quality of agricultural products, such as analyzing the sugar content of apples and citrus fruits (non-destructive total analysis) (Korean Industrial Food Engineering Society, Vol. 11, No. 1 p38 ~ 48, Korean Chemical Society 2005, Vol.49, No. 6, Korean Society of Machine Tool Engineers, Vol. 15, No. 5) Process Control of Petrochemical Process (Korean Near Infrared Spectroscopy Society 2001. June 01, P. 1082 ~ 1082, Korean Near Infrared Spectroscopy Society 2002, p63 ~ 73). However, fermentation process through on - line simultaneous real - time multiplex analysis of amino acids such as sugars, ammonia, and microorganisms, which are the main control factors of amino acid fermentation, and amino acids such as glutamine, threonine and lysine (NIR), which can control the fermentation process in real time, is necessary for the management and development of amino acid product fermentation process at the research stage as well as industrial scale .
Disclosure of Invention Technical Problem [8] Accordingly, the present invention has been made in order to solve the problems of the prior art as described above, and it is an object of the present invention to provide a fermentation broth for fermentation of amino acids using microorganisms using an NIRS (Near Infrared Spectrophotometer) And a plurality of fermentation process control factors including the absorbance indicating the growth of important substrates and microorganisms including sugar and ammonia nitrite are simultaneously measured and monitored in real time, Time management method of an amino acid fermentation process using a near-infrared spectroscopic analyzer capable of automatically controlling the process in real time to maximize the productivity of the process control and accurate process control.
More specifically, the present invention relates to the development of an on-line real-time, simultaneous multiple analysis method that replaces the conventional offline analysis method that has been applied to the fermentation experiment or the fermentation production plant of the amino acid fermentation process using corynebacterium using NIRS. In other words, NIRS is used for fermentation using Corynebacterium strain, which is mainly used as a production strain of amino acid fermentation and nucleic acid fermentation produced at research stage or industrial scale until now, Ammonia concentration, target product material concentration, co-produced product, growth state of the strain, etc.) in real time to monitor the result of biological reaction immediately and convert the generated analysis signal into electric signal, The present invention relates to a method for optimally managing a process for fermenting amino acids (threonine, glutamine, lysine) using corynebacteria, In the laboratory, concentrations of per fermentation broth, ammonia concentration, amino acid concentration, (Threonine, glutamine, lysine) fermentation solution using NIRS instead of the conventional method of analyzing and managing the non-real-time and offline methods using the physicochemical method, ie, the sugar concentration, the ammonia concentration, (On-line) monitoring of amino acid concentration, cell concentration, and the like, and using the measured signal for automatic control of the amino acid fermentation process.
In order to achieve the above object, a method for real-time management of an amino acid fermentation process using a near-infrared spectroscopic analyzer according to the present invention comprises: collecting 300 samples of fermentation broth of amino acid fermentation process at intervals of 2 hours; Measuring 250 samples of the 300 samples using the NIR spectrometer; Performing physicochemical standard analysis on the 250 samples; Pre-processing the measured source spectrum; Selecting a spectral region specific to a concentration of a component to be quantified among the pre-processed spectra and developing a calibration calibration model through spectral analysis chemistry of the spectrum of the selected region; The developed calibration calibration model was compared with the 50 samples which were not subjected to the step of measuring the raw spectrum among the samples collected at the step of collecting the sample, and the analysis results obtained by physicochemical analysis were compared with each other Wherein the calibration and calibration model obtained through the calibration and optimization is measured by a near infrared spectrometer, wherein a plurality of fermentation process control factors are simultaneously measured and monitored in real time, By utilizing each electrical analysis signal generated during the measurement, the fermentation process is automatically controlled in real time to maximize the process control and productivity.
In addition, the characteristics of near-infrared spectroscopic analysis are as follows: First, non-destructive analysis is possible without liquid pretreatment for liquid, solid, and semi-solid materials. Second, the analysis time for the sample is very short, so it is possible to qualify and quantify an unknown sample very fast. This method can be used as an on-line analyzer in various process control and optimization stages of an industry using optical fiber coupled with complex field production processes. Third, simultaneous analysis of several physicochemical components from one spectrum is possible. Fourth, the near-infrared spectroscopy has excellent repeatability with respect to the sample, and it is also advantageous to obtain quantitative information as well as qualitative information. Therefore, it is possible to predict the physical properties such as chemical composition and concentration through the calibration model. The introduction of (FT) -NIRS with these characteristics can be fully utilized as a real-time process analyzer for automatic control simultaneously with process monitoring.
In particular, the recent development of electronics and information and communication technologies and the remarkable development of analytical chemometrics (FT) have made it easy to select and process important information from complex and diverse absorption spectra obtained from -NIRS (FT-) The application of NIRS to the fermentation industry can reduce the number of human and physical resources required to manage the fermentation process, and furthermore, it is possible to identify and solve the physiological phenomena of microorganisms that could not be solved by the input of resources, This is a very promising way to prevent the occurrence of the fermentation process in advance.
Therefore, in order to achieve the object of the present invention, as shown in Fig. 1, a fermentation medium capable of producing the above amino acids can be sterilized in a fermenter equipped with NIRS, inoculated with a strain capable of producing each amino acid from the sugar, (2 ~ 4 hours interval), and immediately after obtaining the spectrum by NIRS, the same sample was analyzed by using the conventional method of analyzing each amino acid concentration, sugar concentration, ammonia nitrogen concentration, And the degree of growth of each strain is separately analyzed by HPLC, Kjeldahl, and spectrophotometer, and utilized as a standard analysis value. A calibration calibration model was developed by applying the measured spectrophotometric absorption spectra and chemometrics to the major components analyzed by physicochemical standard analysis method, and then the optimal calibration equation for each component The validation model was developed to validate the applicability of the calibration calibration model to the unknown sample by selecting the model and the accurate and reproducible monitoring of the trend of major components in the fermentation broth. And a method of simultaneously controlling the process in real time by using the generated electrical signal can be developed.
Statistical analysis of the above analytical chemistry can be performed using multiple linear regression (MLR), Plinciple Component Regression (PCR), Partial Least Square (PLS), Modified Least Squares (MPLS) Modified Partial Least Squares). The measurement of the (FT-) NIR of the sample was performed by scanning the wavelengths in the entire range of near infrared of 1,100 to 2,500 nm at intervals of 2 nm, and the transmission distance of the light was 2 mm. In the measurement mode (NIR mode) Were used as raw spectra.
In the real-time management method of the amino acid fermentation process using the NIR spectrometer according to the present invention, the plurality of fermentation process control factors are simultaneously measured and monitored in real time, and the fermentation process So that accurate process control and productivity can be maximized.
The efficiency of the fermentation process creates an environment in which the microorganism used can produce the target product as much as possible. In the conventional fermentation management, the sample is taken from the fermentation tank and then the main factor, sugar, The chromatographic analysis (hereinafter referred to as HPLC) was conducted by analyzing the Kallard analyzer for the nitrogen component (ammonia nitrogene), the spectrophotometer for the growth of the microorganism, the high-performance liquid chromatography (HPLC) for the target product such as amino acid, The existing method of feeding back the analysis results to the fermentation process management has been a major obstacle to maximizing the fermentation process efficiency because it is separated temporally and spatially from the management of actual phenomena.
In other words, the existing method is as follows: 1) After the sample is sampled, it has to be analyzed using a separate analyzer. Since the analysis result is not the current time but the analysis time is less than 30 minutes and more than one day is necessary, It is impossible to monitor and control the accurate and practical fermentation state because the analysis of the fermentation state has passed since the analysis time has elapsed since the analysis time, and 2) the resultant data is applied to the experimental and production sites (Try and error) method has been applied due to its low reproducibility and many experiments and experience. 3) Analysis method of fermentation component is different, and human and material resources are consumed in most cases. Which led to an increase in product cost including an increase in analysis cost.
In particular, the analysis of the phenomenon before the time spent in the analysis is used to solve the problem of the low reliability of the monitoring and control of the fermentation process without the real-time concept that can only be used as a reference value for judging the current fermentation state It was the biggest challenge.
According to the present invention, the application of the near infrared spectroscopic method can accurately and reproducibly measure the composition of the medium (sugar concentration, concentration of AN, concentration of the target production material) and the growth of the strain, It is possible to continuously monitor changes in fermentation over time and to convert the analysis signals generated at this time into electrical signals so that the main control factors of the fermentation process can be automatically controlled on- It is possible to control the fermentation process very accurately and reproducibly as compared with the existing methods that were managed through offline and non-real-time analysis after collecting the samples, thus contributing to the reduction of production cost and technological competitiveness by maximizing productivity through process efficiency. It was confirmed in a way that could be done.
In addition, by improving the reliability of process management data, it was possible to shorten the development period such as research and experiment and to minimize the human, material, temporal and spatial resources that were put into process control. Prompt-maintenance), minimizing the occurrence of unknown losses, thus contributing to enhancement of competitiveness of products.
(FT-) NIRS has improved the disadvantages of these conventional methods and it has become possible to monitor reproducible and efficient fermentation process by accurate real-time simultaneous multi-analysis on the trend of important fermentation factors. This method is a new method that can realize continuous and automatic control of the fermentation process by feedback in real time, thereby maximizing the productivity and efficiency of the product.
It is an additional effect of the present invention that improvement of working environment and improvement of work capacity of the production site operator through operation or remote automatic control of the night unmanned fermentation process can be judged as an additional effect of the present invention.
1, A schematic diagram showing a near infrared spectrometer and a fermentation process
FIG. 2 is a graph showing the relationship between the original spectrum (Glutamine Raw Spectrum: Wave number cm-1) of the glutamine fermentation broth,
FIG. 3 shows the pretreatment spectrum (1st derivative: 1st Derivative / Wave length cm-1) of the fermentation broth of glutamine (Gln)
FIG. 4 is a graph showing the relationship between the selection range of the glutamine (Gln) concentration spectrum of the glutamine fermentation broth (Calibration Wave number: cm-1)
FIG. 5 shows a calibration calibration model for the concentration of glutamine (Gln) in fermentation broth
FIG. 6 is a graph showing the test set validation of the glutamine (Gln)
FIG. 7 is a graph showing the relationship between the selection area of the glucose spectrum of the glutamine fermentation broth (Calibration Wave number: cm-1)
8 is a calibration calibration model of glucose concentration of glutamine (Gln) fermentation broth.
9 shows the validation model of the glucose concentration of the glutamine (Gln) fermentation broth.
FIG. 10 is a graph showing the relationship between the selection range (calibration wave number: cm-1) of the AN concentration spectrum of the glutamine fermentation broth,
FIG. 11 shows a calibrated calibration model of the AN concentration of the glutamine fermentation broth.
12 is a graph showing a test set validation of an AN concentration calibration model in a glutamine fermentation broth.
13 is a graph showing the relationship between the selection area (Calibration Wave number: cm < -1 >) of the glutamine fermentation broth growth absorbance spectrum
FIG. 14 is a graph showing the OD of the fermentation broth of the glutamine fermentation broth,
Fig. 15 shows the results of test set validation of the OD of the fermentation broth of glutamic acid
FIG. 16 is a graph showing the results of real-time automatic control of glucose (glucose), ammonia (AN) and glutamine concentration and glucose and AN concentration in online state by connecting a fermentation tank with a calibration model equipped NIR spectrometer
FIG. 17 shows the raw spectrum (Threonine Raw Spectrum: Wave number cm-1) of the threonine fermentation broth.
18 shows the pretreatment spectrum (1st derivative: 1st Derivative / Wave length cm-1) of threonine (Thr)
FIG. 19 shows the results of real-time automatic control of concentrations of sucrose, ammonia (AN) and threonine and concentrations of sucrose and AN in a online state by connecting a calibration-type NIR spectrometer with a fermenter
20 shows the pretreatment spectrum (1st derivative: 1st Derivative / Wave length cm-1) of the lysine fermentation broth,
FIG. 21 shows the results of real-time automatic control of concentrations of sucrose, ammonia (AN) and lysine concentration and concentrations of sucrose and AN in an on-line state by connecting a calibration model equipped NIR spectrometer with a fermenter
Hereinafter, preferred embodiments of the present invention will be described in detail. These examples are for further illustrating the present invention, and the scope of rights of the present invention is not limited to these examples.
The present invention relates to a method of real-time management of an amino acid fermentation process using a near-infrared spectroscopic analyzer, comprising the steps of: collecting 300 samples of fermentation broth of amino acid fermentation process every 2 hours; Measuring 250 samples of the 300 samples using the NIR spectrometer; Performing physicochemical standard analysis on the 250 samples; Pre-processing the measured source spectrum; Selecting a spectral region specific to a concentration of a component to be quantified among the pre-processed spectra and developing a calibration calibration model through spectral analysis chemistry of the spectrum of the selected region; The developed calibration calibration model was compared with the 50 samples which were not subjected to the step of measuring the raw spectrum among the samples collected at the step of collecting the sample, and the analysis results obtained by physicochemical analysis were compared with each other ≪ / RTI >
In this case, the near-infrared spectroscopic analyzer is characterized in that the near infrared ray has a range of 1,100 to 2,500 nm or 12,000 to 4,000 cm -1, and the measurement mode is a transmission state.
In the amino acid fermentation process, the strain used for fermentation is a bacterium, and the amino acid is one of glutamine, threonine, and lysine, and the glutamine, threonine, and lysine are fermented using bacteria.
The real-time control method of the amino acid fermentation process using the NIR spectroscopic analyzer is based on the real-time monitoring factor and the real-time automatic control factor of the NIR spectroscope, and the concentration of the amino acid, the sugar and the ammonia nitrogene in the amino acid fermentation broth and the absorbance of the fermentation broth, Measuring in real time, multiple measurements are monitored, and the concentration of residual sugar and ammonia nitrogen in the fermentation broth is controlled automatically in real time using the electrical signals from the measurement.
Wherein the saccharide is any one of sucrose, glucose and fructose.
(FT-) NIRS is a non-destructive analytical method that can rapidly analyze various components without destroying the sample. By measuring the absorption spectrum of the sample, it is possible to obtain the intrinsic spectrum of a substance in the near infrared region .
In other words, the near infrared absorption spectrum for the same sample as the physicochemical analysis (wet data) data of various samples is secured and their derivatives are obtained and statistical analysis is performed using Chemometrics, , The concentration of ammonia (AN), the concentration of amino acids (glutamine, threonine, lysine), and the absorbance, which indicates the degree of growth of bacteria, were developed and tested. Is the main principle.
Utilizing the above-mentioned NIRS principle, active application to various industries and research fields such as agricultural, fishery, food, feed, chemical, cosmetic, pharmaceutical, polymer,
The present invention relates to a fermentation process for fermenting glutamine threonine and lysine, which are amino acids produced through microbial fermentation, using NIRS to obtain raw spectra of major control factors of the fermentation process, Calibration Model Calibration Model for the determination of each major fermentation control factor was obtained through statistical analysis of the analysis value by physicochemical standard analysis method, and the calibration model was used to determine the amount of amino acid fermentation liquid sample , We developed a validation model of each of the major fermentation factors to develop a simultaneous analysis method for on-line and real-time multi-components that was not possible with the conventional physicochemical standard analysis method. Thus, amino acid fermentation Real-time monitoring and control of the process has become possible.
Once again, the main management parameters for real-time monitoring and control of the amino acid fermentation using bacteria at laboratory or industrial scale are temperature, pH, stirred water, dissolved oxygen concentration, oxygen and carbon dioxide concentrations in the exhaust gas , And the amount of aeration. However, factors that are essential for monitoring and control of fermentation process, such as sugar (sucrose, glucose, fructose) concentration, AN concentration, bacterial growth, And concentrations of major co-products.
Until now, real-time monitoring and control of essential factors of fermentation process control was impossible because of analyzer or analysis that can accurately and reproducibly measure changes in sugar concentration, AN concentration, growth rate of bacteria, It is considered that the products that have not been developed or developed have low reproducibility, robustness, and effectiveness, which makes it difficult to apply them in actual field.
Accordingly, the present inventor has recently applied the NIR technology which is being used for nondestructive measurement of a concentrated (axial) product component, real-time online measurement of a chemical process, a process analytical technology (PAT) The concentration of sugar, the concentration of amino acid, the concentration of amino acid as a target product, the concentration of major co-product (co-product), the degree of growth of bacteria, etc., The monitoring signal is utilized for automatic control of the fermentation process, thereby optimizing the process efficiently from the research stage, maximizing the productivity, and drastically reducing and shortening the development period and cost, thereby completing the present invention.
Hereinafter, a process for developing a calibration model of the main control factors applied to the present invention will be described in detail as an example of lysine fermentation.
(P1) Lysine Fermented lysine Concentration, AN concentration and absorbance ( Cell growth rate ) relation Near infrared Acquisition of absorption spectrum
300 consecutive samples of the fermentation broth of a continuous lysine fermentation process were collected at regular intervals (2 hours apart), and then 250 samples were collected from 12,000 cm-1 to 4,000 cm-1 using a near infrared spectrophotometer (NIRS) 1, and the raw spectrum is obtained by scanning with a constant wavelength unit of 2 nm.
At this time, the passing distance of the light source was 2 mm and the measurement mode was the transmission mode. FT-NIR (FT-NIR Matrix-F Bruker Optics: Germany) was used for the FT-NIR device used.
The 250 samples of the same samples were subjected to physicochemical standard analysis and the concentrations of the saccharides and lysine were measured by High Performance Liquid Chromatography (hereinafter, referred to as HPLC, Japan Shimadzu LC-20A) Buchi-Buchi-K-370) was measured at 560 nm using a spectrophotometer (Beckman-390, USA) and used as a standard sample for the development of a calibration model.
The fermentation broth of the fermentation broth can be used by collecting the fermentation broth in the order of 2 hours. In order to broaden the calibration equation and develop a significant calibration model, 300 specimens were collected. 24 to 30 samples were collected per fermentation cycle. 250 samples per fermentation broth were used for raw spectrum measurement to develop a calibration calibration model, and the remaining 50 samples were subjected to a validation calibration equation It is used as a test sample for a model (Validation Model).
Fifty validation samples were selected as well as evenly distributed samples throughout the fermentation process.
(P2) Analysis of physicochemical standard of sample
About 25 lysine fermentation (total 300 fermented fermentation broths) were collected from the fermentation broth and fermented for a certain period of time with addition of additional sugars.
30 ml of the sample is taken and the spectrum of NIR is immediately measured in 10 ml, and the remaining sample is kept frozen.
The cryopreservation samples were dissolved at room temperature and analyzed using conventional standard analyzers. That is, the sugar concentration and lysine concentration were analyzed by high performance liquid chromatography (HPLC: SHIMADZU LC-20A), and the concentration of AN was measured with a Kjeldahl analyzer (Buchi K-370) using a spectrophotometer Beckman DU730) at 560 nm and used as a standard analytical value for the calibration equation development of NIRS.
(P3) Raw Spectrum Preprocessing
In order to obtain an accurate quantitative calibration model from the raw spectrum of the fermentation liquid obtained in the above (P1), it is necessary to remove the variable of the spectrum according to the apparatus condition and the sample, and remove the noise included in the measurement spectrum.
Spectrum correction and preprocessing were performed using WinISI II (Ver.1.50, Foss and Infra soft international LLC, Stage College, PA, USA). This preprocessing is a necessary process to secure the quality spectrum necessary to develop robust, accurate and reproducible calibration models by eliminating the noise of the raw spectrum and matching the baseline.
In the present invention, a first order differentiation method such as a first order differentiation method, a second order differentiation method, Norris-Williams derivatives, Savitzky-Golay polynomial derivatives, which are generally applied to the transmission spectra, By applying mathematical treatment to the raw spectrum, noise is removed and the baseline is matched to improve the utilization of the useful information of the spectrum, thereby providing a more accurate and reproducible calibration model development spectrum ) Were selected and utilized.
(P4) Preprocessed Through spectrum selection and regression analysis Calibration Calibration equation Model Development
In the case of the fermentation process solution used in the present invention, a specific wavelength region having a spectrum containing a large amount of impurities and foreign substances is selected, and the spectrum of the selected region is measured by a calibration calibration chemistry (Chemometrics) Model will be developed. The statistical analysis methods applicable in this case are multiple linear regression (MLR), principal component regression (PCL), partial least square (PLS) And Modified partial least squares (MPLS). In the present invention, a calibration model is developed using a partial least squares method (PLS).
The optimal calibration calibration model was selected using statistics such as standard error of calibration (SEC) and coefficient of determination (R2).
(P5) selection Calibration Calibration equation Model Validation and Optimization
In order to verify the applicability of the selected calibration calibration model to the unknown sample, the spectra of 50 unknown new samples not used as samples in the calibrated calibration model were compared with the calibrated calibration model and the analytical values and physicochemical The results are compared with the standard analysis results.
The applicability of the calibrated calibration model was evaluated by using the standard error of prediction (SEP), coefficient of determination in r2, bias (average difference between reference and NIRS values) and SD (standard deviation) Cross-validate the accuracy and reproducibility by applying unknown samples (50) to the calibration model.
The final validation model, which is optimized as described above, measures the concentration (glucose), lysine concentration, AN concentration and growth rate of the bacteria per fermentation liquid to be analyzed in the present invention at the same time in an accurate and reproducible manner (Concentration of sugar, concentration of AN, growth rate of bacteria, concentration of target product material), and the measurement time is completed in seconds or tens of seconds, Can be monitored in real time by measuring the concentration of various substances at the same time with only one spectral measurement within 30 seconds.
In addition, real-time simultaneous multi-material analysis of NIR-based fermentation process real-time monitoring of the concentration of each component concentration signal is converted to electrical signals and then used in real-time control management of fermentation process, It is confirmed that this is a revolutionary method that can be controlled.
Hereinafter, the details of the present invention will be described concretely with respect to examples of the type of amano acid (glutamine, threonine, lysine).
Example 1: Using microorganisms Glutamine (Gln) Fermentation broth Glutamine Development of concentration measurement method
1) Installation and sampling of NIR
In order to monitor the fermentation state of the fermentation tube on-line and monitor it in real time, the NIR probe is directly installed in the fermentation tube to control the fermentation process, , The glutamine fermentation process monitoring and control system is configured as shown in Fig. 1 so that the measurement signal of NIR can be used for controlling the fermentation process.
In the continuous fermentation process, the raw spectrum of NIRS was measured, and about 30 ml of sample for physico-chemical standard analysis was collected at intervals of 1 to 2 hours. A part of this sample (10 ml) And the remainder was frozen immediately and used as a sample for physico-chemical standard analysis.
Samples were collected during 10 ~ 15 fermentation experiments and the total number of samples was 300, and it is of course possible to collect more samples by repeating fermentation experiments when necessary.
2) Measurement of raw spectrum and analysis of physicochemical standard of samples
2-1) Spectrum measurement of sample: When the fermentation tube was operated once (the whole process of fed batch culture for 40 to 48 hours by inoculating the strain into the fermentation medium), about 25 to 30 samples were collected, (10ml) was set to FT-NIR with a beam passing distance of 2 mm and a wavelength interval of 2 nm in the entire wavelength range of 12,000 cm-1 to 4,000 cm-1, and the measurement mode of the spectrum was set to a transmission mode The raw spectrum was measured and collected. The NIR instrument used was Matrix F-NIR (FT-NIR Matrix-F Bruker Optics, Germany).
2-2) Analysis of physicochemical standard of glutamine concentration: After cryopreservation samples were dissolved at room temperature, each component was analyzed using a conventional standard analyzer. Glutamine concentration was analyzed by HPLC (Shimadzu LC-20A, Shimadzu) and used as a standard assay.
3) Development of Calibration Model of Water Pre-treatment and Calibration of Raw Spectrum
3-1) Water Pretreatment of Raw Spectrum: As described in P3) above, to remove noise from the original spectrum obtained in the glutamine fermentation process and to match the baseline, Respectively.
3-2) Screening and Calibration of Glutamine Spectral Domain Development of a Calibration Model: The results of the measurement of the glutamine concentration primer spectrophotometer and the physicochemical standard analysis of the sample glutamine in the above 2) are referred to above (P2, P3, P4) One method was used to derive the calibration curve model. More specifically, first, a raw spectrum of 250 samples is pretreated with a first derivative to obtain a stable spectrum (see FIG. 3), and a glutamine-specific spectral region, that is, one spectral region is selected (See FIG. 4).
The calibration chemistry method used to develop the calibration calibration model was PLS, and the optimal calibration calibration model was the standard error of calibration (SEC) and the correlation coefficient (R2: coefficient of determination in calibration). And statistical values were used to select them. As shown in FIG. 5, the calibration curve of the calibration curve shows that the glutamine and Y-axis in the separated solution by the NIRS analysis on the X-axis are R2 99.87 and RMSEE value 0.109, and RPD 27.3, respectively.
4) Developed Glutamine (Gln) Calibration Validation of the calibration model.
The calibrated calibration model developed using the spectra of 250 samples was used to compare the spectra of 50 new samples prepared beforehand and the glutamine (Gln) concentration by standard analytical method to test the applicability to unknown samples. As a result of the test, as shown in FIG. 6 and Table 1, it was confirmed that there is no statistical significance as shown in R2 value, RMSEP value and Bias value. In other words, the results of the physicochemical analysis of the standard and the analytical value measured by NIRS are very similar, so that the development of the validation calibration model has been successfully developed and it can be confirmed that the fermentation liquid Gln can be accurately and reproducibly measured And the development of a real-time continuous monitoring method of glutamine (Gln) during fermentation by NIRS that can replace the existing offline analysis method (HPLC or other enzymatic analysis method) has been completed.
(Table 1) Calibration of glutamine (Gln) Validation result of calibration model
Example 2: Glutamine using microorganisms Gln ) Per fermentation Development of measurement method
1) Installation and sampling of NIRS
The procedure of Example 1 was repeated. At this time, the spectrum of the same sample as used in Example 1 is used.
2) Measurement of spectrum and pretreatment and analysis of physicochemical standard of sample
The same procedure as in Example 1 was carried out. The original spectra for sugar analysis also use the same spectrum as the original spectra for Gln analysis. Pretreatment of the primordial spectrum by the first derivative also used the same spectrum as for the glutamine (Gln) measurement.
That is, it is the greatest advantage and characteristic of NIRS analysis that can simultaneously analyze various components with one spectrum analysis. Also, in the present invention, a representative sugar component of the glutamine (Gln) fermentation broth was previously analyzed using glucose as a glucose, and the glucose component was defined as a main sugar component. The physicochemical standard analysis of the glucose was analyzed using an HPLC analyzer (Shimadzu LC-20A).
3) Selection of Glucose Spectral Domain and Development of Calibration Calibration Model for Glucose
As shown in FIG. 7, the spectral region for measuring glucose was selected as two wavelength regions, and the sample glucose was processed and calculated by the method described in the physicochemical standard analysis and the above (P2, P3 P4) Calibration model was derived. The calibration chemistry method used to develop the calibration calibration model was PLS, and the optimal calibration calibration model was the standard error of calibration (SEC) and the correlation coefficient (R2: coefficient of determination in calibration). And statistical values were used to select them.
As shown in FIG. 8, the calibration curve of the calibration curve shows that the X-axis shows a very high correlation between the glucose standard data obtained by the actual physicochemical analysis, that is, the HPLC analysis, on the glucose and Y-axis of the fermentation broth obtained by the NIRS analysis .
4) Validation of Calibration Calibration Model for Glutamine Fermentation Glucose
The calibrated calibration model developed using the spectra of 250 samples (FIG. 8) was used to compare the spectra of 50 new samples already prepared and the standard analysis method to prepare the unknown samples for comparison As a result of the test, it was confirmed that there is no statistical difference such as R2 value, RMSEP value, and Bias value as shown in FIG. 9 and Table 2. In other words, the results of the physicochemical analysis of the standards and the analytical values measured by the calibration model in NIRS are very similar, so that the development of the validation calibration model has been successful and it has been confirmed that the glutamine fermentation broth can be accurately and reproducibly measured . In other words, the correlation between the physicochemical standard analysis and the NIRS - fermented glucose concentration showed a very significant correlation, and it was confirmed that the NIRS can continuously monitor the change of glucose in the fermented milk of glutamine in real time. Therefore, monitoring and control of glucose, which is one of the biggest problems of real time monitoring and control of fermentation phenomenon over time, has been solved, and online real-time monitoring of glutamine fermentation process and development of automatic control process have become possible.
(Table 2) Calibration of Glucose Concentration per Glutamine Fermentation Solution Validation Result of Calibration Model
Example 3: Glutamine Fermentation solution Ammonia Nitrogen (AN) Measurement of
1) Installation and sampling of FT-NIR
The procedure of Example 1 was repeated. The sample used at this time is the same sample as that of Example 1.
2) Measurement of spectrum and pretreatment and analysis of physicochemical standard of sample
The same procedure as in Example 1 was carried out and the original spectrum for AN analysis also uses the same spectrum as the original spectrum for Gln analysis. Pretreatment of the original spectrum by the first derivative also used the same spectrum as the Gln measurement spectrum. AN analysis of the physicochemical standard was performed using a Kjeldahl analyzer (BUCHI K-307).
3) Selection of Spectral Domain and Development of Calibration Calibration Model for AN Concentration
The spectral range for measuring the AN concentration was selected as three wavelength ranges as shown in FIG. 12, and the sample AN was processed and calculated by the method described in the physicochemical standard analysis and the above (P2, P3 P4) Calibration calibration model was derived as shown in Fig.
The calibration chemistry method used to develop the calibration calibration model was PLS, and the optimal calibration calibration model was the standard error of calibration (SEC) and the correlation coefficient (R2: coefficient of determination in calibration). And statistical values were used to select them. As shown in FIG. 11, the calibrated calibration model curve developed in this way shows that the AN and Y axes in the separated solution by the NIRS analysis on the X axis show a very high level of correlation with the AN standard analytical value obtained by the actual physicochemical analysis, I could.
4) Calibration of AN Concentration Validation of calibration model
The calibrated calibration model developed using the spectra of 250 samples was used to compare the spectra of 50 new samples and the AN concentration by the standard analysis method to test the applicability to the unknown new sample As shown in FIG. 12 and Table 3, it was confirmed that there is no statistical difference in value such as R2 value and RMSEP value. In other words, the results of the physicochemical analysis of the standard and the analytical value measured by the NIR were very similar, so that the development of the validation calibration model was successful and it was confirmed that the concentration of the Gln fermentation broth can be accurately and reproducibly measured. Again, the measured values of the AN concentration of the fermentation broth measured by the physicochemical standard analysis and the NIRS showed a very high correlation with the NIRS, so that the change of the AN concentration of the Gln fermentation by the NIR was continuously monitored in real time And that it is accurate and reproducible. Therefore, real-time monitoring of AN concentration, which is one of the biggest obstacles of real-time monitoring and control of fermentation phenomenon over time, has become possible and online real-time monitoring and automatic control process of Gln fermentation process can be developed.
(Table 3) Calibration of glutamine fermentation broth AN and validation result of calibration model
Example 4: Glutamine ( Gln ) Fermentation broth Production strain Measurement of optical density (OD) of fermentation broth for growth analysis
1) Installation and sampling of FT-NIR
The procedure of Example 1 was repeated. The sample used at this time is the same sample as that of Example 1.
2) Measurement of spectrum and pretreatment and analysis of physicochemical standard of sample
The same procedure as in Example 1 was carried out, and the original spectra for absorbance analysis were analyzed using the same 250 spectra as the original spectra for the Gln concentration analysis. The absorbance was measured at 550 nm using a spectrophotometer (BECKMAN DU-730) and used as a standard analytical value.
3) Screening of bacterial growth spectrum and development of calibrated calibration model for absorbance
As shown in FIG. 13, two selective regions were selected and used as a spectrum region for measuring the absorbance indicating cell growth.
The spectral measurements by NIRS and the absorbance analysis values measured from the spectrophotometer were processed and calculated by the method described in (P2, P3 P4) above to obtain a calibrated calibration model as shown in FIG. The calibration chemistry method used to develop the calibration calibration model was PLS, and the optimal calibration calibration model was the standard error of calibration (SEC) and the correlation coefficient (R2: coefficient of determination in calibration). And statistical values were used to select them. As shown in FIG. 14, the X-axis shows a very high level of correlation between the absorbance of the fermentation broth obtained by the FT-NIR analysis and the Y-axis, as well as the spectrophotometer obtained from the actual physicochemical analysis there was.
4) Validation of Calibration Calibration Model for Glutamine Fermentation Bacterial Growth Rate (Validation)
The calibrated calibration model developed using the spectra of 250 samples (FIG. 14) was used to substitute the absorbance of 50 new sample spectra and standard analytes for comparison to the unknown sample As a result, as shown in FIG. 15 and Table 3, it was confirmed that there is no statistical difference such as R2 value, RMSEP value, and Bias value. In other words, the validation model of the validation model has been successfully developed because of the similarity between the results of the OD analysis and the standard physicochemical analysis, which are measured by NIRS. And the growth rate of the seedlings can be measured accurately and reproducibly. Therefore, real-time monitoring of the fermentation process and real-time monitoring of the growth of the strain, which is one of the biggest problems of real-time monitoring and control of the fermentation phenomenon, have been solved and online real-time monitoring and automatic control process of the glutamine fermentation process have become possible.
(Table 4) Calibration of the absorbance of the glutamine fermentation broth and the validation result of the calibration model
Example 5: NIRS Used glutamine fermentation process Monitoring Department CONTROL result
The concentration of glucose (glucose), the concentration of ammonia (AN), and the absorbance (OD) indicating the growth of the cells, which were obtained through the above Examples 1, 2, 3 and 4, The NIR system equipped with an expression model was applied to the actual fermentation process, and a plurality of fermentation components were continuously measured and monitored at the same time. Experiments were conducted to see if the glutamine fermentation process could be controlled in real time using the signals generated at this time. It is the same as that shown.
In other words, the spectrum measured in real time by the probe installed in the fermentation tube is calculated by the calibration model and the concentration of glutamine fermentation glutamine, glucose concentration (glucose), ammonia nitrite (AN) concentration, In addition to monitoring accurately and reproducibly in real time, it was able to control the fermentation process in real time in conjunction with PLC (Programmable Logic Controller), which is a conventional fermentation process control program, by converting signals generated during monitoring into electrical signals.
As shown in FIG. 16, samples were taken offline and the results of the physicochemical standard analysis and the NIRS measurements, i.e. glucose concentration, AN concentration, cell growth degree (absorbance: OD) and glutamine concentration, And the real time monitoring of the glutamine fermentation process by NIRS and the real-time automatic control of the fermentation process using the signal proceed very well. Thus, the present invention has been completed.
Example 6: Using microorganisms Threonine (Thr) Fermentation Solution Threonine Development of concentration measurement method
1) Installation and sampling of NIR
The NIRS probe is directly installed in the fermentation tube to control the fermentation state of the fermentation tube on-line and to monitor the fermentation state of each fermentation tube on-line. , The threonine fermentation process monitoring and control system is configured as shown in Fig. 1 so that the measurement signal of NIRS can be used to control the fermentation process. The NIRS raw spectrum of the continuous fermentation process was measured, and about 30 ml of each sample was collected at intervals of 1 to 2 hours for the physicochemical standard analysis. A part (10 ml) of this sample was used for spectral measurement And the remainder was frozen immediately and used as a sample for physico-chemical standard analysis. Samples were collected during 10 ~ 15 fermentation experiments and the total number of samples was 300, and it is of course possible to collect more samples by repeating fermentation experiments when necessary.
2) Measurement of raw spectrum and analysis of physicochemical standard of samples
2-1) Spectrum measurement of sample: When the fermentation tube is operated once (the whole batch of fermentation batch culture for 36 to 40 hours by inoculating the strain into the fermentation medium) A sample of about 20 samples is collected The spectrum was measured and collected in the same manner as in Example 1.
2-2) Analysis of physicochemical standard of threonine concentration: The same analysis as in Example 1 was carried out using HPLC (Japan Shimadzu LC-20A).
3) Development of Calibration Model of Pretreatment and Calibration Primer Spectrum for Threonine Fermentation Threonine Analysis
As described in P3 above, the original spectrum was pretreated with the first derivative to remove the noise of the original spectrum obtained in the threonine (Thr) fermentation process and coincide with the baseline.
4) Screening of threonine concentration spectrum area and development of calibrated calibration model
The results of the physico-chemical standard analysis of the threonine concentration and the sample threonine concentration in Example 6-2) were processed by the method described in (P2, P3.P4) above to derive a calibration curve model.
More specifically, first, a raw spectrum of 250 samples was pretreated with a first-order differential to obtain a stable spectrum (see FIG. 18), and spectral regions specific to threonine concentration, that is, 9025 to 7498 cm -1 and 6101.8 to 5446.1 cm -1, and the calibration calibration model was developed. The Calibration Calibration Model We developed an optimal calibration calibration model using the same method as in Example 1 for the analytical chemistry method used to develop the calibration calibration model. The calibrated calibration model developed in this way showed a very high level of analysis accuracy with R2 value of 99.84, RMSEE value of 0.103, and RPD of 25.2 as a result of comparing NIRS measurements and standard analytical values.
5) Calibration of developed threonine (Thr) fermentation concentration Validation of calibration model
The calibrated calibration model developed using the spectra of 250 samples was used to compare the spectra of 50 new samples and threonine (Thr) concentrations by standard analytical methods to test the applicability of the new samples to unknown samples. As a result of the test, it was confirmed that the R2 value, the RMSEP value, and the Bias value are not statistically significant, as shown in Table 5. In other words, the results of the physicochemical analysis of NIRS and the standard of physicochemical analysis of the standard are very similar, so that the validation calibration model has been successfully developed and the concentration of threonine in the fermentation broth can be accurately and reproducibly measured (Thr) concentration during the fermentation by NIRS, which can replace the conventional off-line analysis method (HPLC or other enzymatic analysis method).
(Table 5) Calibration of threonine (thr) concentration of threonine fermentation solution Validation result of calibration model
Example 7: Threonine ( Thr ) Per fermentation Development of measurement method
1) Installation and sampling of NIRS
The procedure of Example 6 was repeated. In this case, the spectrum of the sample used in Example 6 is used.
2) Measurement of spectrum and pretreatment and analysis of physicochemical standard of sample
Was carried out in the same manner as in Example 6, and the raw spectrum for sugar analysis also uses the same spectrum as the raw spectrum for threonine concentration analysis. Pretreatment of the original spectrum by the first derivative also used the same spectrum as that for the threonine (Thr) concentration measurement. Threonine (Thr) fermentation liquid A typical sugar component was sucrose (Suc), which was pre-analyzed by HPLC and defined sucrose as a main sugar component. The physicochemical standard analysis of the sucrose concentration was analyzed using an HPLC analyzer (Shimadzu LC-20A).
3) Selection of sucrose spectral region and development of calibrated calibration model for sucrose concentration
By selecting the range of 9974.2 ~ 7498cm-1 and 6109.5 ~ 5446.1cm-1 of the spectrum by the same method and procedure as in Example 5 for measuring the sucrose concentration of threonine fermentation broth, As shown in Table 6, the R2 value is 99.64, the RMSEE value is 0.0491, and the RPD is 16.1.
4) Calibration of threonine fermentation broth concentration (Validation)
The calibrated calibration model developed using the spectra of 250 samples was used to assign the spectrum of 50 or more new samples already prepared and the sucrose concentration by the standard analysis method to test the applicability of the unknown sample As a result of the comparison, it was confirmed that there is no statistically significant difference such as R2 value, RMSEP value, and Bias value as shown in Table 6. In other words, the results of the physicochemical analysis of the standards and the analytical values measured by the calibration model in NIRS are very similar, so that the development of the validation calibration model has been successful and the threonine fermentation broth concentration can be measured accurately and reproducibly . In other words, there is a very significant correlation between the physicochemical standard analysis and the NIRS fermentation broth concentration, and it is confirmed that the NIRS can continuously monitor the change of the sucrose concentration of threonine fermentation broiler in real time . Monitoring and control of sugar concentration (sucrose), which was one of the biggest obstacles in real-time monitoring and control of fermentation process, has been solved by applying NIRS, enabling online real-time monitoring and automatic control of threonine fermentation process .
(Table 6) Calibration of sucrose concentration per threonine fermentation liquid Validation result of calibration model
Example 8 : Threonine Fermentation liquid Ammonia Nitrogen (AN) concentration
1) Installation and sampling of FT-NIR
The procedure of Example 5 was repeated. The sample used here was the same sample as that of Example 5.
2) Measurement of spectrum and pretreatment and analysis of physicochemical standard of sample
The same procedure as in Example 5 was carried out and the original spectrum for AN analysis also uses the same spectrum as the source spectrum for threonine concentration analysis. Analyzes of AN concentrations of physicochemical standards were made using a Kjeldahl analyzer (BUCHI K-307).
3) Screening of AN spectral region of threonine fermentation broth and development of calibrated calibration model for AN concentration
The area of the spectrum for measuring the AN concentration was selected from 8865.4 ~ 7503, 4cm-1, 5879.7 ~ 5499.4cm-1, and the sample AN concentration was measured by the method described in the physicochemical standard analysis and the above (P2, P3 P4) As shown in Table 7, R2 value of 99.88, RMSEE value of 0.103, and RPD of 25.2 showed very high level of analysis accuracy.
4) Calibration of AN Concentration Validation of calibration model
The calibrated calibration model developed using the spectra of 250 samples was used to compare the spectra of 50 new or more new samples and the AN concentration by the standard analysis method to test the applicability to unknown samples. As shown in Table 7, it was confirmed that there is no statistical significance such as R2 value and RMSEP value. In other words, the results of the physicochemical analysis of the standard and the analytical value measured by the NIR are very similar, so that the development of the validation calibration model has been successful and it is confirmed that the AN concentration of the threonine fermentation liquid can be accurately and reproducibly measured there was. Again, the measured values of the AN concentration of the fermentation broth measured by the physicochemical standard analysis and the NIRS showed a very high correlation with the NIRS, and the changes in the AN concentration of the threonine fermentation broth by NIRS were continuously We can confirm that monitoring is accurate and reproducible. Thus, real-time monitoring of AN concentration, which is one of the biggest obstacles of real-time monitoring and control of fermentation phenomenon over time, has become possible and online real-time monitoring of the threonine fermentation process and development of automatic control process have become possible.
(Table 7) Calibration Calculation of AN Concentration of Threonine Fermentation Validation Result of Calibration Model
Example 9: Threonine Of fermentation broth producing strain Measurement of optical density (OD) of fermentation broth for growth analysis
1) Installation and sampling of FT-NIR
The procedure of Example 5 was repeated. The sample used here was the same sample as that of Example 5.
2) Measurement of spectrum and pretreatment and analysis of physicochemical standard of sample
The same procedure as in Example 5 was carried out, and the raw spectra for absorbance analysis were analyzed using the same 250 spectra as the source spectrum for threonine concentration analysis. The absorbance was measured at 550 nm using a spectrophotometer (BECKMAN DU-730) and used as a standard analytical value.
3) Development of Calibration Calibration Model for Screening and Absorbance of Growth Absorption (OD) spectral region of threonine fermentation bacteria
The spectral region for measuring the absorbance indicating cell growth was selected and used in two regions of 11907.6 to 7490 cm-1 and 5705.5 to 5446.1 cm-1. The spectral measurements by NIRS and the absorbance (OD) analysis values measured from the spectrophotometer were processed and calculated by the method described in (P2, P3 P4) above to derive a calibrated calibration model as shown in Table 8 8) Calibration The analytical chemistry method used to develop the calibration model was PLS and the optimal calibration calibration model was the standard error of calibration (SEC) and the coefficient of determination (R2) ) Were selected using statistics such as.
4) Validation of Calibration Calibration Model for Growth Rate of Threonine Fermentation Broth (Validation)
The calibrated calibration model developed using the spectra of 250 samples (Table 8) was used to determine the applicability of unknown new samples to the spectra of 50 to 100 new samples and the absorbance (OD) As shown in Table 8, R2 value, RMSEP value, and Bias value were not statistically significant. In other words, the validation model of the validation model has been successfully developed because of the similarity between the results of the OD analysis and the standard physicochemical analysis as measured by NIRS, and the growth of the bacteria in the threonine fermentation process It can be confirmed that the measurement can be performed accurately and reproducibly in a short period of time. Therefore, real - time monitoring and control method of threonine fermentation process has been developed so far, which is one of the biggest problem of real - time monitoring and control of fermentation phenomenon over time.
(Table 8) Calibration of the absorbance of the threonine fermentation broth and the validation result of the calibration model
Example 10: NIRS Used Threonine Fermentation process Monitoring Department CONTROL result
The fermentation main factors such as threonine concentration, sugar (sucrose) concentration, ammonia nitrite (AN) concentration and absorbance (OD) indicating the growth of the cells were measured through the above Examples 5, 6, NIR system equipped with a calibration model can be applied to the real fermentation process to continuously monitor and measure the factors of the fermentation process control factors at the same time and to monitor the threonine fermentation process in real time The result of the experiment is shown in Fig.
That is, the spectrum measured in real time by the probe installed in the fermentation tube was calculated by the calibration model and the concentration of threonine fermentation solution threonine, sugar concentration (sucrose), ammonia nitrite (AN) concentration, Is monitored in real time in an accurate and reproducible manner. In addition, the signal generated during monitoring is converted into an electrical signal, and the fermentation process is automatically controlled in real time in conjunction with a PLC (Programmable Logic Controller) Could.
As shown in FIG. 19, samples were taken offline, and the results of the physicochemical standard analysis and the NIRS measurements, ie, the sugar concentration (sucrose), the AN concentration, the degree of cell growth (absorbance: OD) The results show that the real - time monitoring of the threonine fermentation process by NIRS and the real - time automatic control of the fermentation process using the signal are very satisfactory.
Example 11: Using microorganisms Lysine (Lys) Fermentation Lysine Development of concentration measurement method
1) Installation and sampling of NIR
The NIRS probe is directly installed in the fermentation tube to control the fermentation state of the fermentation tube on-line and to monitor the fermentation state of each fermentation tube on-line. , The lysine fermentation process monitoring and control system is configured as shown in Fig. 1 so that the measurement signal of NIRS can be used for fermentation process control
The NIRS raw spectrum of the continuous fermentation process was measured, and about 30 ml of each sample was sampled at intervals of 1 to 2 hours for the physicochemical standard analysis. A part (10 ml) of this sample was used for spectral measurement And the remainder was frozen immediately and used as a sample for physico-chemical standard analysis. Samples were collected during 10 ~ 15 fermentation experiments and the total number of samples was 300, and it is of course possible to collect more samples by repeating fermentation experiments when necessary.
2) Measurement of raw spectrum of lysine fermentation broth and analysis of physicochemical standard
2-1) Spectrum measurement of lysine fermentation sample: When the lysine fermentation is run once (the entire process of fed batch culture for 42-48 hours by inoculating the strain into the fermentation medium) And the spectrum was measured and collected in the same manner as in Example 1. [
2-2) Analysis of physicochemical standard of lysine concentration of fermentation broth: The same analysis as in Example 1 was carried out using HPLC (Japan Shimadzu LC-20A).
3) Development of Calibration Model of Pretreatment and Calibration Primer Spectrum for Analysis of Lysine Concentration of Lysine Fermentation Solution
As described in P3) above, the raw spectrum was pretreated with the first derivative to remove the noise of the original spectrum obtained in the lysine fermentation process and to match the baseline.
4) Screening and calibration of lysine concentration spectral region Development of calibration model
The results of the measurement of the lysine concentration and the lysine concentration of the lysine concentration in Example 11 (2) were analyzed by the method described in (P2, P3.P4) above to derive a calibration curve model. More specifically, first, the raw spectrum of 250 samples was pretreated with a first-order differential to obtain a stable spectrum (see FIG. 20), and spectral regions specific to lysine concentration, that is, 8454.6 to 7650.3 cm -1 and 5998.0 to 5457.0 cm- 1 wavelength region was selected to develop the calibrated calibration model.
The Calibration Calibration Model We developed an optimal calibration calibration model using the same method as in Example 1 for the analytical chemistry method used to develop the calibration calibration model. The calibrated calibration model thus developed showed a very high level of accuracy of analysis with R2 value of 99.93, RMSEE value of 0.092, and RPD of 36.5 as a result of comparison between the measured values and the standard analytical values of NIRS (see Table 9).
5) Calibration of the developed lysine (Lys) fermentation concentration Validation of the calibration model
The calibrated calibration model developed using the spectra of 250 samples was used to test the spectra of 50 ~ 100 new samples and the lysine (Lys) concentration analyzed by standard analytical methods to test the applicability of the new samples. As shown in Table 9, R2 value, RMSEP value, and Bias value were not statistically significant. In other words, the results of the physicochemical analysis of NIRS were very similar to those of the standards. Therefore, the validation calibration model was successfully developed and the lysine concentration of the fermentation broth can be measured accurately and reproducibly in a short period of time (Lys) concentration during fermentation by NIRS, which can replace the conventional off-line analysis method (HPLC or other enzymatic analysis method).
(Table 9) Calibration of lysine fermentation lysine (Lys) concentration Validation result of calibration model
Example 12: Lysine ( Lys ) Development of measurement method per fermentation solution
1) Installation and sampling of NIRS
The procedure of Example 11 was repeated. In this case, the spectrum of the sample used in Example 11 is used.
2) Measurement of spectrum and pretreatment and analysis of physicochemical standard of sample
Was carried out in the same manner as in Example 11, and the same primer spectrum as the raw spectrum for analysis of lysine concentration was used as the primer spectrum for sugar analysis.
Pretreatment of the original spectrum by the first derivative also used the same spectrum as that for the threonine (Thr) concentration measurement. Lysine (Lys) fermentation liquid A representative sugar component was sucrose (Suc), which was preliminarily analyzed by HPLC and defined sucrose as a main sugar component. The physicochemical standard analysis of the sucrose concentration was analyzed using an HPLC analyzer (Shimadzu LC-20A).
3) Selection of sucrose spectral region and development of calibrated calibration model for sucrose concentration
The area of 8750.2 ~ 7355.1cm-1 and 6120.1 ~ 5385.1cm-1 of the spectrum was selected by the same method and procedure as in Example 11 to measure the sucrose concentration of the lysine fermentation broth and treated by a weighing chemical method, 10, R2 value of 99.65, RMSEE value of 0.051, RPD of 16.9 and so on.
4) Calibration of sucrose concentration of lysine fermentation broth Validation of calibration model
The calibrated calibration model developed using the spectra of 250 samples was used to assign the spectrum of 50 or more new samples already prepared and the sucrose concentration by the standard analysis method to test the applicability of the unknown sample As a result of the comparison, it was confirmed that there is no statistical difference in values such as R2 value, RMSEP value, and Bias value as shown in Table 10. In other words, the results of the physicochemical analysis of the standards and the analytical values measured by the calibration model in NIRS are very similar, so that the development of the validation calibration model has been successful and the concentration of lysine fermented sucrose is measured accurately and reproducibly I could confirm that it was possible. In other words, there is a very high correlation between the physicochemical standard analytical value and the measured value of lysine fermented sucrose concentration by NIRS, and it has been confirmed that the NIRS can continuously monitor the change of the lysine fermentation sucrose concentration over time in real time . Therefore, monitoring and control of sugar concentration (sucrose), which is one of the biggest obstacles to real-time monitoring and control of lysine fermentation process, has been solved by applying NIRS, enabling online real-time monitoring and automatic control of lysine fermentation process.
(Table 10) Calibration of sucrose concentration per lysine fermentation solution Validation result of calibration model
Example 13: Lysine Fermentation solution Ammonia Nitrogen (AN) Measurement of concentration
1) Installation and sampling of FT-NIR
The procedure of Example 11 was repeated. The sample used at this time is the same sample as that of Example 11.
2) Measurement of spectrum and pretreatment and analysis of physicochemical standard of sample
The same procedure as in Example 11 was carried out and the original spectrum for AN analysis also used the same spectrum as the raw spectrum for lysine concentration analysis. Analyzes of AN concentrations of physicochemical standards were made using a Kjeldahl analyzer (BUCHI K-307).
3) Selection of AN spectral region of lysine fermentation broth and development of calibrated calibration equation model for AN concentration
(
4) Calibration of AN Concentration Validation of calibration model
The calibrated calibration model developed using the spectra of 250 samples was used to compare the spectra of 50 new or more new samples and the AN concentration by the standard analysis method to test the applicability to unknown samples. As shown in Table 11, it was confirmed that there is no statistical significance such as R2 value and RMSEP value. In other words, the results of the physicochemical analysis of the standards and the analytical values measured by the NIR were very similar, so that the validation calibration model was successfully developed and the lysine fermentation AN concentration was measured accurately and reproducibly in a short period of time I can confirm that I can. Again, the measured values of the fermentation broth AN concentration measured by the physicochemical standard assay and the NIRS showed a very high correlation with the NIRS, so that the change of the AN concentration of the lysine fermentation solution by NIRS was continuously monitored in real time It was confirmed that it was accurate and reproducible. Therefore, real-time monitoring of AN concentration, which is one of the biggest obstacles of real-time monitoring and control of change of fermentation over time, has become possible and online real-time monitoring and automatic control process of lysine fermentation process can be developed.
(Table 11) Calibration Calibration of AN Concentration of Lysine Fermentation Result of Validation of Calibration Model
Example 14: Lysine Of fermentation broth producing strain Measurement of optical density (OD) of fermentation broth for growth analysis
1) Installation and sampling of FT-NIR
The procedure of Example 11 was repeated. The sample used at this time is the same sample as that of Example 11.
2) Measurement of spectrum and pretreatment and analysis of physicochemical standard of sample
The same procedure as in Example 11 was carried out, and the original spectra for the absorbance analysis were analyzed using the same 250 spectra as the raw spectrum for lysine concentration analysis. The absorbance was measured at 550 nm using a spectrophotometer (BECKMAN DU-730) and used as a standard analytical value.
3) Selection of the spectral range of the growth absorbance (OD) of the lysine fermentation broth and development of calibrated calibration model for the absorbance
The spectral region for measuring the absorbance indicating cell growth was selected from 11086.6 to 7504.1.cm-1 and 6071.1 to 5442.3 cm-1. The spectral measurements by NIRS and the absorbance (OD) analysis values measured from the spectrophotometer were processed and calculated by the method described in (P2, P3 P4) above to derive a calibrated calibration equation model as shown in Table 12. 12)
The calibration chemistry method used to develop the calibration calibration model was PLS, and the optimal calibration calibration model was the standard error of calibration (SEC) and the correlation coefficient (R2: coefficient of determination in calibration). And statistical values were used to select them.
4) Validation of Calibration Calibration Model for Growth Rate of Threonine Fermentation Broth (Validation)
The calibrated calibration model developed using the spectra of 250 samples (Table 8) was used to determine the applicability of unknown new samples to the spectra of 50 to 100 new samples and the absorbance (OD) As shown in Table 12, R2 value, RMSEP value, and Bias value were not statistically significant. In other words, the validation model of the validation model was successfully developed because the results of the OD analysis and the standard physicochemical analysis were very similar, and the growth of the bacteria in the lysine fermentation process It was confirmed that it was possible to measure accurately and reproducibly within a short period of time. Therefore, real - time monitoring and control method of lysine fermentation process has been developed by real - time measurement method of bacterial growth, which is one of the problem of real - time monitoring and control of change over time of fermentation phenomenon.
(Table 12) Calibration of absorbance (OD) of lysine fermentation broth and validation result of calibration model
Example 15: NIRS Used Lysine Fermentation process Monitoring Department CONTROL result
The main fermentation factors such as lysine concentration, sugar (sucrose) concentration, ammonium nitrate (AN) concentration and absorbance (OD) indicating cell growth can be measured through the above Examples 11, 12, 13 and 14 The NIR system equipped with a calibration model is installed in an actual fermentation process, and the above factors such as a plurality of factors for controlling the fermentation process are continuously measured and monitored, and the lysine fermentation process is controlled in real time The results are shown in FIG.
That is, the spectrum measured in real time through the probe installed in the fermentation tube was calculated by the calibration model and the concentration of lysine accumulated lysine, sugar concentration (sucrose), ammonia nitrite (AN) concentration, (Absorbance: OD) are measured and reproduced accurately in real time. In addition to monitoring, the signal generated during monitoring is converted into an electrical signal, and the fermentation process is performed in conjunction with a PLC (Programmable Logic Controller) It was able to control automatically in real time.
As shown in FIG. 21, samples were taken offline, and the results of the physicochemical standard analysis and the NIRS measurements, ie, the sugar concentration (sucrose), the AN concentration, the degree of cell growth (absorbance: OD) And the real-time monitoring of the lysine fermentation process by NIRS and the real-time automatic control of the fermentation process using the signal were very satisfactory.
Claims (5)
Measuring 250 samples of the 300 samples using the NIR spectrometer;
Performing physicochemical standard analysis on the 250 samples;
Pre-processing the measured source spectrum;
Selecting a spectral region specific to a concentration of a component to be quantified among the pre-processed spectra and developing a calibration calibration model through spectral analysis chemistry of the spectrum of the selected region;
The developed calibration calibration model was compared with the 50 samples which were not subjected to the step of measuring the raw spectrum among the samples collected at the step of collecting the sample, and the analysis results obtained by physicochemical analysis were compared with each other ≪ / RTI >
The validation calibration model obtained through the above-described assay and optimization is measured by a near-infrared spectroscopic analyzer, and a plurality of fermentation process control factors are simultaneously measured and monitored in real time, and the respective electrical analysis signals A real - time management method of amino acid fermentation process using near - infrared spectroscopic analyzer to control the fermentation process in real time and maximize productivity and process control.
The near-infrared spectroscopic analyzer comprises:
A method for real-time management of an amino acid fermentation process using a near-infrared spectroscope, wherein a near infrared range is from 1,100 to 2,500 nm or from 12,000 to 4,000 cm-1 and a measurement mode is a transmission state.
In the amino acid fermentation process,
Wherein the strain used for fermentation is a bacterium and the amino acid is selected from the group consisting of glutamine, threonine and lysine, and wherein one of the glutamine, threonine and lysine is fermented using bacteria. Real time management method of amino acid fermentation process.
Wherein the plurality of fermentation process managers include:
Wherein the concentration of the amino acid, sugar, and ammonia nitrogene to be produced, and the growth of the strain, is a real-time management method of an amino acid fermentation process using a near-infrared spectroscopic analyzer.
The sugar may be,
Sucrose, glucose, and fructose,
The degree of growth of the strain is,
A method for real-time management of an amino acid fermentation process using a near-infrared spectroscope.
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| JP2020162549A (en) * | 2019-03-29 | 2020-10-08 | 味の素株式会社 | Control device, control method and program, and method for producing organic compound |
| CN111929274A (en) * | 2020-08-13 | 2020-11-13 | 山东寿光巨能金玉米开发有限公司 | Method for detecting indexes of amino acid fermentation process based on near infrared spectrum analysis |
| CN114166782A (en) * | 2021-11-16 | 2022-03-11 | 华中农业大学 | Intermediate infrared rapid batch detection method for content of free valine in milk |
| CN116793991A (en) * | 2023-08-22 | 2023-09-22 | 青岛理工大学 | Glutamic acid concentration measurement method based on near infrared spectrum and mixing loss |
| WO2024061126A1 (en) * | 2022-09-23 | 2024-03-28 | 北京蓝晶微生物科技有限公司 | Method, apparatus, and system for evaluating polyhydroxyalkanoate content, and device |
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| CN103487398B (en) * | 2013-09-30 | 2016-05-25 | 中粮生物化学（安徽）股份有限公司 | A kind of analytical method of lysine fermentation liquor |
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| JP2020162549A (en) * | 2019-03-29 | 2020-10-08 | 味の素株式会社 | Control device, control method and program, and method for producing organic compound |
| CN111929274A (en) * | 2020-08-13 | 2020-11-13 | 山东寿光巨能金玉米开发有限公司 | Method for detecting indexes of amino acid fermentation process based on near infrared spectrum analysis |
| CN114166782A (en) * | 2021-11-16 | 2022-03-11 | 华中农业大学 | Intermediate infrared rapid batch detection method for content of free valine in milk |
| CN114166782B (en) * | 2021-11-16 | 2024-02-13 | 华中农业大学 | Mid-infrared rapid batch detection method for content of free valine in milk |
| WO2024061126A1 (en) * | 2022-09-23 | 2024-03-28 | 北京蓝晶微生物科技有限公司 | Method, apparatus, and system for evaluating polyhydroxyalkanoate content, and device |
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| CN116793991B (en) * | 2023-08-22 | 2023-11-10 | 青岛理工大学 | Glutamic acid concentration measurement method based on near infrared spectrum and mixing loss |
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