EP4058783A1 - Commande de processus en boucle ouverte/boucle fermée sur la base d'une détermination spectroscopique de concentrations de substances non déterminées - Google Patents

Commande de processus en boucle ouverte/boucle fermée sur la base d'une détermination spectroscopique de concentrations de substances non déterminées

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Publication number
EP4058783A1
EP4058783A1 EP21706559.8A EP21706559A EP4058783A1 EP 4058783 A1 EP4058783 A1 EP 4058783A1 EP 21706559 A EP21706559 A EP 21706559A EP 4058783 A1 EP4058783 A1 EP 4058783A1
Authority
EP
European Patent Office
Prior art keywords
concentration
sample
models
spectra
model
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP21706559.8A
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German (de)
English (en)
Inventor
Marek HÖHSE
Alexander Graf
Christian Grimm
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Sartorius Stedim Biotech GmbH
Original Assignee
Sartorius Stedim Biotech GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Sartorius Stedim Biotech GmbH filed Critical Sartorius Stedim Biotech GmbH
Publication of EP4058783A1 publication Critical patent/EP4058783A1/fr
Pending legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/33—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using ultraviolet light
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/85—Investigating moving fluids or granular solids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G01N2021/6417—Spectrofluorimetric devices
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N2021/8411—Application to online plant, process monitoring
    • G01N2021/8416—Application to online plant, process monitoring and process controlling, not otherwise provided for
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/65—Raman scattering
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
    • G01N2201/12—Circuits of general importance; Signal processing
    • G01N2201/129—Using chemometrical methods

Definitions

  • the present invention relates to a method, a device and a computer program product for determining an undetermined concentration of at least one substance, in particular a biological substance such as a protein or a DNA in a sample by means of spectroscopy, in particular by means of UV / vis spectroscopy or by means of infrared , Raman and / or fluorescence spectroscopy.
  • the invention can be practiced in the broad bioprocess industries, including the environmental, agricultural and food industries, biofuels and pharmaceuticals.
  • concentrations of substances are important parameters or variables that are used throughout Process chain must be followed and determined again and again. This determination usually takes place offline, in particular as a random sample outside the container in which a fermentation broth is subjected to chemical and / or biochemical processes and not in real time.
  • a fermentation broth can be understood, for example, as a biotechnological medium, in particular as a product that can be a medium that contains at least one protein and / or one vaccine and / or one hormone.
  • the determination or measurement of the concentrations of substances is often carried out by means of optical spectroscopy, in particular by means of UV / vis absorption spectroscopy.
  • optical spectroscopy in particular by means of UV / vis absorption spectroscopy.
  • the molecules of the substance of a sample of the fermentation broth with individual, in particular essentially predetermined discrete wavelengths, the excitation wavelengths or excitation frequencies i correspond, excited and the absorbance or extinction or alternatively the intensity is determined at these essentially discrete excitation wavelengths in order to determine the concentration.
  • narrow-band sensors are often used for this purpose. However, the sensors do not necessarily have to be narrow-band. In the case of sensors, the excitation is usually essentially narrow-band, whereas the wavelength range can be detected or recorded relatively broadly.
  • a dispersive element such as a grating, a prism, etc. is often used.
  • broadband emitting light sources also called broadband light sources
  • broadband detecting sensors also called broadband sensors
  • a broadband light source can be, for example, a deuterium light source or a halogen light source.
  • the substance - in particular the protein concentration - can differ considerably.
  • the values for substance concentrations can vary, for example, between approximately 0 and approximately 200 g / l.
  • the hardware for example the measuring cell or the sensor, is often not suitable for all sub-ranges of the concentrations achieved. For example, because of the limited dynamic range of the detectors, many available online sensors only allow substance concentrations of up to about 10 g / l to be determined.
  • the optical path or path length must be adapted depending on the concentration range in order to be able to shift the detection range if necessary or to also be able to measure other substance concentrations. It is therefore possible that one and the same hardware, in particular not the same measuring cell, can be used for all applications, in particular for all cleaning steps.
  • UV sensors can often have significant undesirable cross-sensitivity between DNA and protein. This is the case, for example, when an increase in the DNA concentration leads to a supposedly higher one measured protein concentration leads. For example, it can happen that an absorption band for DNA overlaps with an absorption band for a protein. Since the bands are not isolated and cannot be recorded separately and independently using sensors, the problem often arises to clearly differentiate and identify which substances (here the protein or the DNA) have changed to what extent in terms of concentration.
  • UV-Vis spectrum can accordingly have a large number of other excitation wavelengths or frequencies.
  • a spectrum that is emitted by a light source can by all means be wider or more extensive than the part that is measured or detected by the sensor. It depends on the choice of sensors and the light source (s).
  • Such UV / vis spectrometers can accordingly comprise one or about two (possibly also several) light sources, which accordingly generate one or two excitation wavelengths.
  • corresponding sensors can each be designed to detect electromagnetic radiation in the wavelength range that is of interest.
  • UV sensors can detect a few narrow-band excitation wavelengths, in particular one or two, each having a wavelength of around 10 nm.
  • the central wavelengths that is to say the wavelengths at which the maximum lies, can be approximately 250 nm and / or approximately 280 nm.
  • different substances in particular different proteins, can have absorption bands which each have a maximum essentially centered around different excitation wavelengths.
  • the central wavelength of an absorption band or an absorption maximum can vary for different proteins.
  • the commercially available UV / vis spectrometers do not adapt the excitation and / or detection wavelength to the substances currently to be measured, in particular the protein currently to be measured. It is therefore a matter of chance whether a protein is measured by chance at the maximum or in the vicinity of the maximum of the absorption wavelength or whether the molecules of a protein are excited with the excitation wavelength around which the absorption band of the protein is centered or a maximum at this excitation wavelength having.
  • the previously determined excitation wavelength can lie beyond the wavelength range in which the absorption band has an absorption maximum.
  • the previously fixed excitation wavelength can overlap with a flank region of an absorption band.
  • a flank range that is to say a wavelength range for which the absorption levels off to higher or lower wavelengths around the absorption maximum, usually has a low sensitivity to changes in the concentration.
  • the degree of cross-sensitivity also depends in particular on the wavelength-dependent extinction coefficient e A of the target protein and the impurities (HCP) and their concentration ratio. For this reason, absorption values of host cell proteins (HCPs) are often recorded together with those for target proteins (cross-sensitivity is close to 100%) and it is hardly possible to isolate the respective contributions of both absorption values to the analysis of the respective concentration of both substances.
  • the object is to provide a method, a device and a computer program product for determining an undetermined concentration of at least one substance in a sample over a large concentration range.
  • the present invention is based on the generation of quantitative models, in particular on multivariate data analysis.
  • Essential concepts of multivariate data analysis such as PLS, OPLS, multivariate calibration, multivariate process modeling, hierarchical modeling, etc., are in the book “Multi- and Megavariate Data Analysis - Basic Principles and Applications” (3rd Edition) by L. Eriksson et ai. described in detail.
  • spectra in particular optical spectra, preferably absorption spectra and / or excitation spectra of a plurality of concentration samples, at least two concentration samples having mutually different concentrations of the substance;
  • the models each comprising an assignment and / or a plot of at least one spectral measured variable of the spectra to concentrations in concentration ranges, the spectral measured variable in particular comprising an absorbance and / or an intensity of the spectra and wherein the concentration ranges of two models are not identical, in particular two concentration ranges overlap or are adjacent to one another;
  • an undetermined concentration relates in particular to an unknown concentration or a concentration that has not yet been determined by means of reference analysis.
  • the prediction by means of the method achieves in particular a determination of the concentration.
  • a whole series or set of absorption values for a wavelength range can be recorded under this aspect.
  • entire spectra, in particular absorption or intensity spectra, of dilution samples of a dilution series and a sample are recorded or recorded.
  • spectra for example an absorption spectrum, are recorded instead of individual values such as an absorbance for an excitation wavelength.
  • a flow cell with a constant optical path length is particularly suitable.
  • a flow cell which has a sample layer thickness or an optical path length or a width of the interior between approximately 10 ⁇ m and approximately 10 cm, in particular between approximately 15 ⁇ m and approximately 5 mm and particularly preferably between approximately 50 ⁇ m and approximately 3 mm having.
  • a particularly preferred sample layer thickness is around 1 mm. This has the advantage that one or more samples can be examined several times or repeatedly with one and the same measuring cell.
  • the method can be adapted comprehensively to the substance in question for the respective medium to be examined.
  • the method can be used in particular for monitoring a downstream process.
  • the method also find application for monitoring a fermentation and / or an upstream process.
  • the medium to be separated and / or the medium to be purified for example the fermented broth with the target protein and / or the target DNA, can be used in a purification step, for example before, in and / or after a Filtration system and / or a dialysis unit to be monitored.
  • the method can be carried out particularly advantageously for analyzing a medium, in particular a fluid medium, in an SU bioreactor if a process in the bioreactor is to be monitored.
  • an SU bioreactor which for example can have an SU bag and / or another SU container and / or a hose with a flow measuring cell, preferably an SU flow cell, contains in particular a fermentation broth that goes through chemical and / or biological and / or biochemical processes .
  • the fermentation broth is then monitored downstream, preferably during purification.
  • the content is repeatedly analyzed over time and / or analyzed permanently at times.
  • a container can be equipped with a sensor, for example.
  • a container can be equipped with a bypass line or sampling line and a flow cell.
  • a wall projection with a measuring area can be arranged or provided on the container.
  • a spectrometer can be arranged directly or indirectly on the flow cell and / or on the wall projection, which spectrometer is designed to repeatedly or permanently or permanently record data, in particular measured absorption values. In this way, spectra can be recorded over a longer period of time, for example over many hours, in order to follow the processes over this period. For example, the concentration of a protein and / or a DNA can thus be followed and / or monitored.
  • the following downstream steps can occur, for example:
  • polishing steps (cation and / or anion exchange chromatography and / or size exclusion chromatography)
  • Concentration and / or buffer exchange such as ultrafiltration and / or diafiltration in the crossflow setup.
  • an advantage of this aspect is that different substances or substances can be analyzed in isolation from one another, provided that their bands do not have their local absorption or intensity maximum at identical wavelengths. If, for example, the absorption maximum of a “host cell protein” (HCPs) differs in its excitation wavelength and / or in its shape from that of the target protein, an analytical differentiation can be made possible by recording an entire spectrum.
  • HCPs host cell protein
  • the absorption spectrum in comparison to the spectrum of the target analyte is also important. In particular, the width of the peak, the maximum and the shape of the peak are important.
  • a superposition of several protein peaks of the HCPs can then result in a structure in the region of the protein absorption which does not necessarily correspond to a Gaussian-like absorption band.
  • so-called cross-sensitivities of the measurement method can be suppressed and / or bypassed.
  • An effective quality control of the medium can thus take place, so that it can be determined as precisely as possible how high the respective proportion of, for example, HCP and / or target protein is present in the medium. Based on this, it can be estimated whether further cleaning steps are necessary.
  • the aspect mentioned allows in particular the measurement of essentially continuous absorption spectra or absorption spectra comprising discrete measurement points, instead of measuring just individual absorption values at essentially singular wavelengths or measuring individual absorption values at relatively narrow wavelength ranges.
  • a continuous absorption spectrum between a wavelength range of approximately 280 nm and approximately 750 nm can be recorded, whereas a customary known method allows, for example, a measurement between approximately 310 nm and 320 nm.
  • the full width at half maximum (FWHM) can be relatively narrow-band in the case of LEDs and can be around 10 nm, for example.
  • the absorption spectroscopy in particular the UV absorption spectroscopy and the corresponding sensors, is preferably based on a comparatively broadband excitation or on a broadband emitted and correspondingly recorded spectrum.
  • Light sources can represent, for example, a deuterium lamp and / or a xenon lamp and / or one or more LEDs.
  • the invention is based on the Lambert-Beer law. In particular, it is avoided that the path length has to be varied in the case of fixed waves (or in the case of a few wavelengths that can be selected beforehand via the light source or detector / filter).
  • the invention allows the entire spectrum to be recorded and thus allows the possibility of choosing the effective e with a fixed or unchanged path length d. This is done via the selection of the wavelengths l to be used explicitly or using a multivariate algorithm.
  • said aspect allows the system to be monitored using a single flow cell with a constant optical path length.
  • various multivariate models e.g. PLS with substance concentration, in particular protein concentration as target variable and the absorption values of the respective spectral range as input variables
  • PLS concentration range
  • MLR Multiple Linear Regression
  • the selected wavelength ranges being determined by the absorbance of the target analyte (e.g. DNA or protein) or change the matrix can be influenced.
  • be at least two non-identical sub-ranges, in particular excitation wavelengths of a range are selected according to the criterion whether the maximum intensity of the absorption band is still within the usable dynamic range of the detector.
  • the individual wavelength ranges have different sensitivities with regard to the detection of the substance concentration for the absorption values selected for the models.
  • the dynamic range differs depending on the model. The same applies analogously to the achievable analytical accuracy of the measurement.
  • the spectra preferably include absorption spectra and / or intensity spectra, the absorption spectra and / or intensity spectra having a one-to-one assignment, in particular a one-to-one plot of the spectral measured variable, for example the absorption value and / or the intensity value, at several wavelengths, with preferably at least a subset of the absorption values or Intensity values that are assigned to the corresponding wavelengths have no saturation on the detector side.
  • the spectral measured variable can in particular be the absorption value or the intensity value.
  • a saturation on the detector side occurs when there is no longer a concentration-dependent change in the detector signal at a specific wavelength or a wavelength range.
  • a separate weighting or weighting of each wavelength can also take place. In this case it is usually not formally necessary to select wavelengths. A weighting close to 0 does essentially the same thing.
  • a detector signal can enter a saturation range if the electromagnetic radiation has too high an intensity or too high an absorbance at a certain wavelength or in a wavelength range, so that this corresponding value is outside the dynamic range of the detector.
  • saturation can take place in both directions of the “detector filling”, ie with regard to an “overflow” or an “underflow”.
  • UV spectroscopy if the sample is saturated, it absorbs too much light, so that the light arriving at the detector can no longer be distinguished from the dark noise of the detector.
  • the detector cannot provide a resolution of the intensity or the absorbance within the saturated area. For example, this can be the case if there is a very high concentration in a sample and / or a long optical path length is due to a very thick measuring cell, so that the absolute number of molecules that interact with the light or the electromagnetic radiation is particularly important is high.
  • the wavelength range to be investigated has essentially no saturation for all concentrations to be investigated, so that the differences in absorbance and / or intensity are resolved and / or the dependence on the concentration a concentration series can be displayed if necessary. If a wavelength range experiences saturation, the detector is blind to the actual spectral measured variable, that is to say the absorbance and / or the intensity.
  • the quantitative models preferably include at least one global model comprising a plurality of sub-models that do not overlap one another.
  • a global model can have values from about 0 g to about 150 g.
  • Possible local models have values from about 2 g to about 10 g, from about 10 g to about 50 g and from about 30 g to about 80 g.
  • a sub-model does not necessarily have to have a common limit value with the global model. Limit values can be identical, but this case is an exception.
  • a global model allows a process unit to estimate a rough dependency between the spectral measurand of a spectrum and different concentration ranges.
  • the sub-models can essentially comprise relatively roughly graded concentration ranges that are limited to ever smaller ranges.
  • the sub-models of a global model can be graded, for example, into a first range from about 0 g / l to about 5 g / l, in a second range from about 0 g / l to about 25 g / l and in a third range from about 0 g / l to about 75 g / l.
  • sub-models do not necessarily have to start at 0. This can possibly even be unfavorable, since in this case the model area has to be kept unnecessarily wide.
  • the process step contains concentrations from about 50 to about 75 g
  • an optimal model will contain a slightly larger range (e.g. 45-80 g / l).
  • the quantitative models preferably each include at least one individually selected sub-model.
  • a sub-model can include any concentration range.
  • several sub-models can be created, each with different concentration ranges.
  • at least two concentration ranges can essentially overlap, essentially adjoin one another and / or be spaced apart from one another.
  • a sub-model can in particular be a real subset (e.g. with a restricted y-range) of the superordinate model spectrum set.
  • the quantitative models preferably include hierarchically organized models corresponding to at least a first hierarchical level and a second hierarchical level, the first hierarchical level in particular comprising a global model and the second hierarchical level comprising a local model.
  • a global model of the process unit and / or the user allows a rough dependency between the spectral measured variable of a spectrum and different concentration ranges to be recognized and / or assessed, for example automatically or automatically.
  • the sub-models can essentially comprise relatively roughly graded concentration ranges that are limited to ever smaller ranges.
  • the sub-models of a global model can, for example, be graded into a first range between approximately 0 g / l and 5 g / l, a second range between approximately 0 g / l and 25 g / l and a third range between approximately 0 g / l l and 75 g / l.
  • a local model of the process unit and / or the user allows a more finely resolved dependency between the spectral measurement of a spectrum and different concentration ranges to be recognized and / or assessed, in particular recognized automatically or automatically.
  • the sub-models can essentially comprise relatively finely graduated concentration ranges.
  • the sub-models of a local model can, for example, be graded into a first range between approximately 0 g / l and 5 g / l, a second range between approximately 5 g / l and 25 g / l and a third range between approximately 25 g / l l and 75 g / l.
  • the concentration areas of a local model are essentially adjacent to one another.
  • the assignment of the sample spectrum to at least one quantitative sub-model preferably includes an estimate based on determined and in particular based on displayed data, in particular based on displayed spectra.
  • a quick, uncomplicated estimation for example “by hand” and / or on the basis of a rough approximation, can provide a first approximate result, which can then be verified and / or specified more precisely using a fit, for example.
  • the assessment by the user occurs in particular when selecting the sub-models.
  • the user can choose the 0-50 g / l model because, for example, a content of around 40 g / l is expected at the time of measurement.
  • a classification can be carried out via PLS-DA or SIMCA or Euclidean distances or Mahalanobis distance between model and current spectra can be determined. This shows which of the models - based on different concentration ranges - the current spectrum best fits.
  • the sample spectrum is preferably assigned to at least one quantitative model (M) by means of Euclidean distance and / or Mahalanobis and / or PLS-DA and / or SIMCA.
  • Assigning the sample spectrum to at least one quantitative model by means of Euclidean distance and / or Mahalanobis and / or PLS-DA and / or SIMCA has the advantage that precise predictions about the concentration of a substance in a medium or in a sample can be taken. Accuracy of the concentration prediction achieved.
  • some processes can have a very wide range of protein concentrations that should preferably be covered. It may be that the model areas are too small and, as a result, it may be necessary to switch between the models within a process
  • the accuracy of the prediction depends in particular on the area of the model. It may be that this range is too small in relation to the error of the reference and as a result an algorithm cannot determine which wavelengths are required to determine the analyte concentration. It may also be that the range is too large and, as a result, it will not always be the highest, which may cause a jump in the forecast.
  • the control and / or regulation is preferably carried out with regard to the at least one parameter and the parameter comprises at least one of the following parameters: a valve circuit, a pump control, a media flow, a media pressure, a gas pressure, a pH value, a filtration step, a fractionation step, a time control, a process time setting, a temperature, an ion concentration, a chromatography step.
  • the process control to which the forecast value is passed on can influence the process to the desired extent on the basis of the forecast value determined.
  • a process can thus be controlled and / or regulated efficiently based on a specific prediction value.
  • the feedback can in particular serve to make the process more efficient and / or to obtain the product or the substance particularly gently and / or to protect it from possible denaturation and / or damage due to undesired values of the process parameters.
  • the process parameters that can be influenced include the following: a valve circuit, a flow pressure, a gas pressure, a pH value, a filtration step, a fractionation step, a time control, a process time setting, a temperature, an ion concentration or a salt concentration or a titer, a gradient, a chromatography step.
  • the at least one sample spectrum of the sample is preferably recorded online or in real time, in particular during or during the process, and the control and / or regulation also preferably takes place during the process as a direct consequence of the determined prediction value for the undetermined concentration.
  • Real-time monitoring allows the process unit and / or the user and / or an automated apparatus to intervene immediately in the process parameters on the basis of the determined prediction value or on the basis of the determined concentration of the substance in question. Immediate intervention in the process allows, in particular, the regulation and / or control of the process. In other words, feedback can take place in such a way that at least one process parameter is monitored and / or controlled and / or regulated on the basis of the determined concentration value of at least one substance. This also avoids having to take a random sample of the medium and measure it ex situ. The sample or the medium can take place in situ, in particular during a process within a system, for example within a fermentation vessel and / or within a downstream system, for example upstream and / or downstream of a chromatography unit.
  • Quantitative models are preferably generated by means of multivariate model creation, in particular by means of a multivariate regression method, such as preferably by means of multiple linear regression (MLR), and / or partial least squares (PLS) and / or orthogonal partial least squares (OPLS).
  • MLR multiple linear regression
  • PLS partial least squares
  • OPLS orthogonal partial least squares
  • a multivariate regression method allows efficient automated determination or creation of models with a high degree of precision.
  • Parameters which determine the desired conditions in the automation for example maximum and / or minimum values for process parameters, can be specified by the process unit and / or the user.
  • the process can thus be automated or automatically monitored in real time even in the absence of a user.
  • at least a partial range, in particular individual wavelengths of the spectra of the majority of the concentration samples, is weakened or excluded by weighting.
  • At least one sub-range in particular individual wavelengths of the spectra of the majority of the concentration samples and preferably sub-ranges for which the spectra are saturated, are weakened and in particular excluded for use on the sample spectrum.
  • Weighting can for example take place automatically on the basis of predetermined criteria and / or can be predetermined by the process unit and / or the user.
  • At least one sub-range in particular individual wavelengths of the spectra of the majority of the concentration samples, preferably sub-ranges for which there is a linear dependence between wavelengths and measured values, are weighted more heavily for application to the sample spectrum, the weighting being manual or by means of an algorithm, in particular Factor analysis, factor loadings or RLS loadings, regressions in RLS and / or OPLS takes place.
  • the acquisition of a large number of sample measured values is preferably carried out with a single, essentially identical gap width of one or more measuring cells.
  • Maintaining a single measuring cell enables the process unit and / or the user to determine the concentration particularly efficiently, since the replacement of hardware, in particular measuring cells, can be reduced and possibly completely avoided.
  • the circumstances under which measurements are carried out are thus constant and variations in the measurement configuration and the resulting incorrect settings are avoided.
  • the reversible setting of a measuring cell to a certain layer thickness can be flawed.
  • an additional method step or several additional method steps, which include setting the layer thickness are avoided, which makes the method particularly efficient.
  • a device for controlling and / or regulating a process, in particular a downstream biopnocess, based on the prediction of an undetermined concentration of at least one substance in a sample or a medium by means of spectroscopy, in particular UV / vis spectroscopy comprises:
  • At least one interface for obtaining spectra of a plurality of concentration samples of the at least one substance and of at least one sample spectrum of the sample or of the medium;
  • At least one process unit for controlling and / or regulating the process with respect to at least one parameter on the basis of the prediction value for the undetermined concentration.
  • the device preferably comprises a spectrometer for recording the spectra of the plurality of concentration samples of the at least one substance and / or of the at least one sample spectrum of the sample.
  • a computer program product comprises computer-readable instructions which, when the program is executed by a suitable computer-aided system, cause the program to carry out the steps of the method according to the above aspect or one of the embodiments.
  • the invention can be implemented with single-use (SU) - or disposable systems, comprising, for example, SU downstream elements, SU fermentation containers, SU bags, SU containers each with a wall projection which comprises a gap and window through which the The contents of the container can be analyzed spectroscopically in situ, i.e. in the container in real time.
  • Tube lines with SU flow lines can also be used with preference, which can also be combined or directly connected to SU containers.
  • Add 1 is a schematic illustration of exemplary process monitoring for a downstream process
  • FIG. 2 is an illustration of an exemplary spectrum of a protein band with marked wavelength ranges for various models
  • FIG. 3 is an illustration of an exemplary plot of absorption values of the exemplary protein band shown in FIG. 2 corresponding to different concentration samples against the corresponding concentration at three different excitation wavelengths;
  • FIG. 5 is a schematic illustration of an exemplary method for controlling a process
  • the process monitoring is implemented in this example by means of a UV / vis spectrometer 4.
  • the two representations of UV / vis spectrometers 4 shown in FIG. 1 can also be only a single UV / vis spectrometer 4.
  • the at least one UV / vis spectrometer 4 is connected or coupled to an evaluation unit.
  • the evaluation unit 9 in the present case comprises a computer with a desktop b or an output unit or a screen 9 a on which the measured spectra 1 a , 1 b can be displayed or represented.
  • the evaluation unit 9 can comprise an interface for receiving spectra, such as an interface for connecting a data cable.
  • the UV / vis spectrometer 4 can also have a Include an interface for obtaining spectra, so that the data can be tapped, for example, by a device and / or an evaluation unit 9 and / or a data carrier.
  • a réellerundes medium 5 a 7a can pass through a first measuring cell before it passes through the purification unit 6 and passes through.
  • the first measurement line 7a allows the process unit and / or the user to record a first spectrum 5a of the medium.
  • the first measuring cell 7a comprises in particular a flow cell.
  • the medium 5a can in particular comprise one or more substances to be monitored.
  • the medium 5 a can in particular comprise impurities or substances that are to be separated from the medium 5 a.
  • the medium 5 a can comprise HCPs which are to be separated or separated or isolated from the medium 5 a.
  • the medium 5a to be purified can also be examined inside a container.
  • a range of 1 a of the medium to be purified 5a can be at least partially received or measured or detected within a fermentation vessel.
  • the medium 5 passes through a downstream of the first cell 7a, a purification unit 6.
  • the purification unit 6 is particularly designed to isolate or separate the medium 5 a from impurities and / or undesired substances, so that the medium 5b after passing through the purification unit 6 at least essentially no longer includes the undesired substance, for example HPCs.
  • the essentially purified medium 5b only includes what is desired Substances such as target molecules, in particular target proteins and / or salts, in particular buffer substances.
  • the at least partially purified medium 5b leaves the purification unit 6 after the purification step and passes or passes through a further measuring cell 7b.
  • it can also be the first measuring cell 7a, in particular it can be the first cleaned measuring cell 7a, but it is preferred that the cleaned medium 5b passes a non-identical further or second measuring cell 7b, since it may be in the first measuring cell 7a could be contaminated with the impurities.
  • the second measuring cell 7b can, however, be an identical model of the first measuring cell 7a.
  • a further spectrum 1b can be recorded by the UV / vis spectrometer 4.
  • the screen 9 a in FIG. 1 exemplifies a spectrum 1 b, in particular an absorption spectrum of the at least partially purified medium 5 b, which essentially only has band b.
  • the first band a which was still visible in the first spectrum 1 a and which was caused, for example, by the contamination, is at least no longer visible in the displayed spectrum 1 b of the purified medium 5 b.
  • the second band b which is caused, for example, by the target substance, for example by the target protein, is still visible, however. This indicates that the substance which generates the first band a has largely been separated from the medium 5b in the purification unit 6.
  • Two optical light guides 8 are arranged on and / or in the measuring cells 7a, 7b.
  • electromagnetic radiation can be coupled into one of the two optical light guides 8, for example from the side of the UV / vis spectrometer, which emerges from the optical light guide 8 on the side of the measuring cell 7a, 7b and at least partially passes through the measuring cell 7a, 7b, so that an optical light guide 8 on the opposite side of the respective measuring cell 7a, 7b can at least partially detect the transmitted electromagnetic radiation and at least partially transmit or guide it to the UV / vis spectrometer 4.
  • the process monitoring can also take place on and / or in a container.
  • At least one optical light guide can be guided to and / or into a fermentation container so that it can stimulate the medium in the fermentation container by means of coupled light and / or the light that passes or has passed through and possibly stimulated at least part of the medium, can capture or lead to a detector or a sensor.
  • the monitoring can take place at least partially in situ and / or ex situ.
  • elements that may be required for monitoring such as sensors and / or light guides, protrude into the interior of the fermentation vessel and in particular are in direct physical contact with the medium, in particular the fermentation broth (in situ), and / or from the outside , for example on a window with optical access to the interior, so that direct physical contact with the medium can be avoided (ex situ).
  • the measuring cells 7a, 7b in particular have an essentially constant optical path length or thickness.
  • the optical path length of the light or the thickness of the measuring cell 7a, 7b can be between approximately 100 nm and approximately 10 cm, in particular between approximately 500 nm and approximately 5 mm and particularly preferably between approximately 800 nm and approximately 3 mm.
  • FIG. 2 is an illustration of an exemplary spectrum of a protein band with marked wavelength ranges for various models.
  • the absorbance 2 is plotted between about 240 nm and about 360 nm against the wavelength l and thus the spectrum 1 results, in particular the absorption spectrum for a band that has its local maximum between about 270 nm and 280 nm and one on both sides showing falling edge.
  • the absorption band is caused by an exemplary protein.
  • the invention is based on the measurement or acquisition of absorption spectra instead of individual wavelengths and / or wavelength ranges.
  • a medium in a measuring cell for example in a flow cell, which has an essentially constant thickness between approximately 100 nm and approximately 10 cm, in particular between approximately 500 nm and approximately 5 mm and particularly preferably between approximately 800 nm and approximately 3 mm, for example of about 1 mm.
  • various multivariate models are created that are based on the evaluation of different wavelength ranges.
  • the models can be based on PLS, for example, with the concentration of a predetermined protein being defined as the target variable and the absorption values of the respective spectral range serving as input variables.
  • the wavelength ranges can either be selected manually and / or determined using an algorithm and / or weighted differently. All selected wavelength ranges can be influenced by the absorbance of the target analyte (e.g. DNA or protein), the preferably linear range of the detector response or the changes in the matrix, e.g. due to particle scattering or changes in the buffer composition.
  • the target analyte e.g. DNA or protein
  • the preferably linear range of the detector response or the changes in the matrix e.g. due to particle scattering or changes in the buffer composition.
  • individual wavelength ranges are indicated which are each assigned to a model M1, M2, M3, M4.
  • the wavelength range assigned to model M1 is approximately between 270 nm and 280 nm.
  • the wavelength range assigned to model M2 is between approximately 280 nm and 290 nm.
  • the wavelength range assigned to model M3 is between approximately 290 nm and 300 nm and the wavelength range assigned to model M4 is between approximately 300 nm and 310 nm.
  • the individual wavelength ranges have different sensitivities with regard to the detection of the analyte concentration or the concentration of the substance in question.
  • model 1 which is assigned to the wavelength range in which the maximum of the absorption band falls, has a particularly high sensitivity compared to the other models.
  • the models that are assigned to the wavelength ranges that are essentially in the flank of the absorption band each show a decreasing sensitivity compared to one another from model 2 to model 4.
  • the sensitivity decreases with increasing wavelength.
  • the dynamic range thus differs depending on which model M1, M2, M3, M4 is selected. The same applies to the achievable analytical accuracy of the measurement. Compared to the trend in sensitivity, the opposite is true for the absolute measurable concentration of a substance.
  • Model 1 which is assigned to the wavelength range in which the maximum of the absorption band falls, has a low potential for detecting high concentrations. In contrast, this potential increases with increasing wavelength in the present case.
  • the model is based on the wavelengths with the highest absorbance or the maximum of a band, this model has the highest sensitivity, but the upper detection limit is reached quickly even at low substance concentrations.
  • the M4 model is mainly based on wavelength ranges that lie in the flank of the band. This means that the sensitivity is lower, but much higher substance concentrations can be measured. In particular, because of the high sensitivity, the maximum of an absorption band at a relatively low concentration reaches very high values which in particular exceed the dynamic range of a sensor or detector.
  • the individual wavelength ranges have different sensitivities with regard to the detection of the analyte concentration.
  • the dynamic range thus differs depending on the model, the same applies to the achievable analytical accuracy of the measurement.
  • Figure 2 illustrates this. It shows an example of an absorption band of a protein or a protein species. If the model in the core is based on the wavelengths with the highest absorption, this model has the highest sensitivity, but the upper detection limit is reached quickly even at low protein concentrations.
  • model 4 is mainly based on wavelength ranges that lie in the flank of the band. This means that the sensitivity is lower, but much higher protein concentrations can be measured.
  • FIG. 3 shows how the absorbance of the models based on different wavelength ranges of a single band behaves with changing protein concentrations.
  • FIG. 3 is a representation of the plot of absorption values 2 of the protein band shown in FIG. 2 for different concentrations c in each case at three different excitation wavelengths.
  • the protein band shown in FIG. 2 corresponds to only a single selected concentration.
  • the absorption 2 is plotted against the substance concentration c for different wavelength ranges of the protein band with the maximum at about 280 nm.
  • FIG. 3 it is shown in particular how the absorbance 2 of three models based on different wavelength ranges of a single band behaves with changing substance concentrations c.
  • the three models that were selected by way of example with regard to FIG. 3 are assigned wavelength ranges which each include the value of approximately 300 nm, 290 nm and 280 nm.
  • the recorded neighboring data points of a model were each connected with a linear connecting line.
  • models in areas that show essentially linear dependencies between absorbance 2 and concentration c are suitable for the reliable prediction of indeterminate concentrations.
  • the wavelength ranges or the models have different, essentially linear ranges.
  • the plot for about 300 nm shows a linear range over a particularly broad concentration c, namely between about 0 g / l and 150 g / l.
  • the entire range can be used to predict an indeterminate concentration, although the sensitivity or sensitivity of this wavelength to the concentration c is not high compared with the other models.
  • the plot for about 290 nm shows a high sensitivity compared to the plot for about 300 nm.
  • the plot for 280 nm shows a comparatively high sensitivity to changes in the concentration, but only the concentration range below about 10 g / l is linear. With regard to the spectrum shown in FIG. 2, the value of approximately 280 nm is relatively close to the maximum, whereas the other two values of approximately 290 nm and 300 nm lie in the region of the falling edge of spectrum 1.
  • linear areas of different sizes can be used for the evaluation.
  • the three models shown have different sensitivities, especially at low substance concentrations.
  • the model for 280 nm shows the highest sensitivity and thus also measurement accuracy at low substance concentrations, but the model at 300 nm has a much larger linear range up to 150 g / l protein. In this way it is possible to do justice to different applications, for example the monitoring of a DNA concentration and / or the concentration of different proteins, whereby the hardware can be retained and in particular no variation of the optical path length is assumed.
  • FIG. 4 is an illustration of the plot of absorption values for two exemplary bands at two different excitation wavelengths for different concentrations.
  • FIG. 4 shows the respective absorbance of two protein bands at different concentrations.
  • the band at 224 nm changes much more strongly with increasing substance concentration than the band at 280 nm and thus has a higher sensitivity.
  • the linear range for the band at 280 nm is much larger.
  • FIG. 5 is a schematic illustration of an exemplary method 100 for controlling a process. The method begins with step 101, in which a plurality of spectra of a concentration series of a specific substance, the concentration of which is to be determined in a medium or in a sample.
  • the protein to be separated can be a photosynthesis protein.
  • a dilution series can include staggered concentration ranges.
  • the concentrations of a substance can be at constant intervals for predetermined areas, wherein they can increase and / or decrease the constant intervals in areas.
  • the distances can also be selected to be non-constant for at least one area.
  • Such an example of a concentration series is given below: 100 mg / l; 400 mg / l; 700 mg / l; 1g / l; 1.5 g / l; 2 g / l; 2.5 g / l; 5 g / l; 7.5 g / l; 10 g / l; 20 g / l; 30 g / l; 40 g / l; 50 g / l; 75 g / l; 100 g / l; 150 g / l etc.
  • the concentration series preferably only comprises the substance to be determined in different concentrations dissolved in a suitable, essentially physiological solvent, in particular in an aqueous buffer solution, for example in a phosphate-buffered saline solution or a TBS buffer.
  • a suitable, essentially physiological solvent in particular in an aqueous buffer solution, for example in a phosphate-buffered saline solution or a TBS buffer.
  • the solution can also comprise a detergent and / or an addition of another substance to prevent possible denaturation of the substance.
  • a measuring cell with a constant gap width or sample layer thickness is used to record the spectra of the concentration series.
  • two optical light guides can be arranged at a constant distance at least partially within a container in which the medium is at least partially, one non-conductor being designed to emit light or electromagnetic radiation into the medium and the other light guide designed to do so is to at least partially absorb the transmitted radiation and forward it to an evaluation unit and / or the UV / vis spectrometer.
  • At least one spectrum is recorded, in particular in a range between approximately 200 nm and 700 nm.
  • suitable models for different concentration ranges are created in step 102.
  • multivariate models are created, preferably using partial least square (PLS) regression, orthogonal RLS (OPLS) regression and comparable regression concepts.
  • hierarchical models can be selected, each model covering a specific concentration range.
  • the rule is that the precision is high for low concentrations and decreases at higher concentrations and, as a result, model errors may occur.
  • the method 100 offers two combinable options, namely option A, a global model and option B, a local model.
  • the global model of option A includes the exemplary sub-models, namely models M1, M2 and M3, each based on spectra measured, for example, between approximately 0 g / l and 5 g / l, between approximately 0 g / l and 25 g / l and between about 0 g / l and 75 g / l. Additionally or alternatively, a local model can also be used.
  • the local model of option B includes the exemplary sub-models, namely models M4, M5 and M6, each based on spectra measured, for example, between approximately 0 g / l and 5 g / l, between approximately 5 g / l and 25 g / l and between about 25 g / l and 75 g / l.
  • the main difference between option A and option B is that for option B, the models M4, M5 and M6 do not all start at around 0 g / l, only the first model M4.
  • a hierarchical model can be selected that includes a global model (option A) over the entire concentration range (for example between about 0 g / l and 75 g / l) and represents a top hierarchy and further sub-models M4, M5, M6 (option B ) for individual concentration ranges (for example staggered between about 0 g / l and 75 g / l).
  • Sub-models are generally based on unsaturated spectra or spectral ranges, which are determined, for example, by means of an OPLS / PLS algorithm and / or by plotting the absorbance against the concentration of the relevant wavelengths. In other words, is the goal of a model in particular, to build on unsaturated spectral ranges. This applies to both the global model and the sub-models.
  • the linear range of each plot is determined with respect to each selected wavelength, which is followed by a decision as to whether the selected wavelength of a model can be used for a specific concentration range. In the linear range, the slope of the detector signal is usually maximum when the concentration changes.
  • the slope gradually decreases until there is essentially no change in the detector signal with a further increase in concentration.
  • the non-linear area can still be evaluated, but the prediction accuracy is lower in this area and the model is more complex, for example due to additional main components.
  • Standard MVDA methods are usually multilinear methods and therefore these non-linearities or non-linear areas cannot be optimally modeled. Therefore, linear areas of the plot are particularly suitable for determining an undetermined concentration of a substance.
  • a data set for modeling comprises a large number of spectra of samples with a known analyte content (eg protein).
  • Input variables are a large number of intensities at different wavelengths (X values) as well as reference values such as protein content or concentration (Y values).
  • the algorithm finds correlations in the multivariate space of the X values, which correspond to the course of the Y values. It is in particular the reference book “Multi- and Megavariate Data Analysis - Basic Principles and Applications” (3rd Edition) by L. Eriksson et al. referenced, in which (especially the 4th chapter) a detailed introduction to modeling, especially using PLS, can be found.
  • step 103 an online measurement or a real-time measurement of at least one sample spectrum of a sample or a medium, which in particular includes the substance to be determined, is recorded.
  • the term online measurement or real-time measurement relates to the fact that the medium or the sample is examined immediately before and / or after and / or during a process, so that a dynamic or direct based on the determined concentration of the substance is examined or several process parameters can be influenced.
  • step 104 the sample spectrum that was recorded in step 103 is classified in or assigned to at least one suitable model. Possible methods for classification include, for example, PLS-DA (Partial Least Squares Discriminant Analysis), SIMCA (Soft Independent Modeling of Class Analogy) calculations of the Euclidean distances or a dendrogram.
  • the sample spectrum is compared with the spectrum sets of the individual models and a classification according to the best fit or best fit result is made.
  • a classification according to the best fit or best fit result is made.
  • at least one, but preferably several absorption values of a sample spectrum are compared with the corresponding absorption values of the spectra of different models.
  • the classification can also be based on process knowledge or known process parameters, e.g. on known concentration ranges of the process step.
  • the sample spectrum results in particular from a measurement, in particular from an online measurement in the process.
  • an optimally applicable model can be selected.
  • One criterion can, for example, be the expected concentration of the sample. Due to the process, the user can therefore classify relatively roughly whether the concentration of the sample is, for example, around 15 g / l or around 150 g / l, but a fine classification can only be made using the correspondingly selected model. It may therefore not be possible to estimate whether the concentration is around 12 g / l, 15 g / l or 17 g / l without a suitable model.
  • a suitable model would be, for example, a model between 5 and 20 g / l.
  • step 105 the model is applied to the sample spectrum in order to essentially at least approximately determine or predict the unknown concentration of the substance.
  • the prediction value or the determined concentration of the substance is then output by the computing unit in step 106 and passed on or sent to the process control in step 107.
  • the process control On the basis of the concentration value determined, the process control, to which this value is passed, can, in step 108, refer to the process in the desired Take influence. In this way, a process can be controlled and / or regulated based on a specific prediction value.
  • step 104 a current spectrum or a sample spectrum is assigned to a suitable model. With the aid of the selected model, the current or the concentration of the substance to be determined is determined in step 105.
  • step 106 the determined value for the concentration of the substance is output to an interface or an interface and passed on to a control unit in step 107.
  • the process control which receives the prediction value, can influence the process, in particular one or more process parameters, to the desired extent in step 108.
  • the modeling in particular the generation of several quantitative models based on the spectra of the concentration samples, is expressly different from a usual calibration.
  • a typical calibration can essentially be based on a linear regression between a wavelength and a protein concentration.
  • a linear regression is essentially based on a fit or a comparison calculation with a linear model.
  • a calibration can also be based on non-linear models.
  • the modeling described here is based in particular on PLS models.
  • PLS models are based on the main components of two matrices X and Y.
  • the main components should be calculated separately for the matrices X and Y in order to create a regression model between the entries or scores of the main components (and not the original data).
  • the matrix X is broken down into a matrix T (the "Score” matrix) and a matrix P "(the” Loadings "matrix) plus an error matrix E.
  • the matrix Y is broken down into the matrices U and Q and the error term F.
  • This equation can also be referred to as the "inner relationship".
  • the control can in particular have valves and pumps. Otherwise, the corresponding collected volume can be discarded, especially if it contains impurities.
  • changes in the pH value, conductivity, concentration and / or other parameters can be achieved by means of appropriate additions of substances and, in particular, these and / or other parameters can be regulated.
  • spectrum essentially means the absorption spectrum.
  • the term can also refer to an intensity spectrum, provided that absorption spectroscopy is not involved. This is the case, for example, for Raman spectroscopy, which does not measure the absorbance of light, but rather light with a “shifted” wavelength, triggered by the inelastic scattering of light on molecules. Without being restricted to UV / vis spectroscopy, however, the present embodiments essentially relate to the absorption spectra generated thereby.
  • sample is not limited to a random sample that can be examined ex-situ outside a container, for example outside a bioreactor. Rather, the term sample relates to the medium, in particular a liquid, a gas, a solid and / or any mixture thereof, which comprises the substance and is essentially located within the container during the measurement.
  • the sample is measured in particular in situ, i.e. essentially within the container in which the process also takes place. In particular, the sample is also not discarded but essentially remains with the entire medium in the container and, if necessary, remains exposed to all further process steps.
  • a sample may not be an identical medium. That is, the sample can mean the medium that happens to get into the measuring cell, for example into a flow cell, and is analyzed there.
  • the molecules and or atoms must must not necessarily be the same for each measurement of a permanent monitoring, rather the sample is determined by chance with each measurement.
  • the sample is particularly representative of the bulk.
  • the sample is particularly representative of the amount in the corresponding hose area.
  • HCPs Host cell proteins
  • the host cell can express HCPs or, on the other hand, it itself consists of HCPs, which are released upon cell disruption or cell death. During the cleaning process, most of the HCPs are usually removed (especially> 99%). If necessary, however, the remaining amounts of HCP can remain in the products, such as monoclonal antibodies (mAbs), antibody-drug conjugates (ADCs), therapeutic proteins, vaccines and other protein-based biopharmaceuticals, among others.
  • mAbs monoclonal antibodies
  • ADCs antibody-drug conjugates
  • a UV / vis spectrometer which is designed to detect essentially spectral information, can have a broadband light source.
  • an essentially broadband emission spectrum can also be generated through the use of several narrow and / or broadband emitting light sources.
  • An example of a light source emitting essentially broadband is a deuterium lamp (D2 lamp) or a halogen lamp.
  • narrowband ig with regard to the emission spectrum of an LED light source relates to light sources that emit electromagnetic radiation with an FWHM (spectral half-width) of values that are less than about 50 nm, in particular less than about 30 nm and particularly preferably less than are about 15 nm.
  • a light source with an FWHM of approximately 10 nm is a narrow-band light source.
  • one or more narrow-band emitting light sources are used to determine the extinction and, accordingly, a concentration of a very specific substance that absorbs electromagnetic radiation at a specific wavelength.
  • An example of the use of several such light sources is based on two light sources with each one FWHM of about 10 nm, each of which has maximum emission values or peak wavelengths at about 250 nm and about 280 nm.
  • the sensor can be selected in accordance with the selected light source and the corresponding emission spectrum, regardless of whether it emits narrow or broadband, in order to be able to detect at least part of the emission spectrum. In other words, it makes sense that the emission spectrum of the light source overlaps with the sensitive area of the sensor, in particular by more than about 50% and preferably by more than about 70%.
  • Sensors can also have, in particular, sensor elements with different sensitive wavelength ranges, which are designed to be able to detect electromagnetic radiation in wavelength ranges that are not identical to one another.
  • the light source or light sources is or are preferably designed to emit electromagnetic radiation in a wavelength range that is visible to humans, i.e. between about 380 nm and about 780 nm and in the UV range is between about 10 nm and about 380 nm.
  • the light source can also be designed to emit electromagnetic radiation in the infrared range, that is to say between approximately 780 nm and approximately 50 mhti.
  • the senor can be matched to the emission range.
  • An exemplary method for controlling or regulating a process can proceed as follows: 1. Detection of UV-Vis spectra from a concentration series
  • the highest concentration of a sample within a model is determined by the maximum absorbance in the corresponding wavelength range (model 1 with focus on 224 nm only includes samples from 0-2 g / l, model 5 with Focus on 300 nm includes all samples between 0 and 200 g / l b.
  • various calibration data sets can be created from the concentration series. From these, various models are then created using statistical methods (e.g. PLS). In this case, the Algorithm to redefine the wavelength ranges for each calibration data set (and will thus mainly select wavelengths in the flank of the protein band for models with high protein concentrations (and also not use the band at 224 nm)
  • the protein concentrations differ considerably (0-200 g / l), which is why the samples can usually only be measured conventionally in different dilution stages.
  • the optical path length must be changed depending on the concentration range in order to be able to expand the detection range if necessary. This means that one and the same hardware cannot be used for all applications.
  • existing UV sensors show considerable cross-sensitivity between DNA and protein. An increase in the DNA concentration therefore also leads to a higher measured protein content.
  • a sensor can also detect concentrations of about 50 g / l in the usual way if the path length is reduced accordingly. It must therefore be decided in advance whether a higher sensitivity and a limited concentration range or a broader concentration range and lower sensitivity is to be achieved. This is then determined in advance by choosing the path length.
  • the method according to the invention has the advantage that, without changing the hardware, a decision can be made retrospectively as to whether a high sensitivity or a higher concentration range or higher concentrations should generally be measured.
  • HCPs host cell proteins
  • a fiber was alternatively designed to be movable beforehand for the UV transmission measurement, so that the path length can be varied reproducibly and can also be chosen to be much smaller than the minimum path length of, for example, about 1 mm that is usual for UV sensors.
  • protein concentrations above 100 g / l can also be recorded.
  • the distance between the fibers must be adjustable in a highly precise and reproducible manner.
  • implementation can only be transferred to use in single-use systems under certain circumstances.
  • the method described herein for controlling or regulating a process is based in particular on the measurement of complete absorption spectra - instead of individual wavelength ranges - preferably using a flow cell with a constant optical path length.
  • a preferred flow cell thickness or sample layer thickness is approximately 1 mm.
  • various multivariate models e.g. PLS with protein as the target variable and the absorption values of the respective spectral range as input variables
  • PLS protein as the target variable and the absorption values of the respective spectral range as input variables
  • wavelength ranges can either be selected yourself or, with all selected wavelength ranges being influenced by the absorbance of the target analyte (DNA or protein).
  • the invention allows a precise determination of concentrations for DNA and / or proteins.
  • absorption spectra instead of values at discrete wavelengths
  • a data set is compiled into a concentration range and multivariate models are generated with regard to the results within different wavelength ranges.
  • Selected wavelength ranges show different sensitivities to DNA and / or protein. Individual wavelength ranges have different sensitivities.
  • the dynamic range (dynamic ranks) is different.
  • model 1 is assigned to the absorbance at the highest sensitivity, the dynamic range being correspondingly limited.
  • Model 4 is generated with reduced sensitivity, but the concentration range to be used can be expanded. The concentration values are determined by comparing the collected data with predefined models.
  • the invention makes it possible to avoid the usually required dilution of the samples during the measurement with regard to the limited dynamic range of the sensor.
  • cross-sensitivity when measuring between DNA and protein can be reduced or avoided.

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Abstract

L'invention se rapporte à un procédé de commande en boucle ouverte ou en boucle fermée d'un processus (3), plus particulièrement à un bioprocédé en aval, sur la base de la prédiction d'une concentration inconnue d'au moins une substance dans un échantillon (5a, 5b) par spectroscopie, plus particulièrement par spectroscopie UV-vis, comprenant les étapes consistant : à capturer (101) des spectres (1) d'une pluralité d'échantillons de concentration, au moins deux échantillons de concentration ayant différentes concentrations de la substance ; à générer (102) une pluralité de modèles quantitatifs (M) sur la base des spectres des échantillons de concentration, les modèles (M) comprenant chacun une association entre au moins une variable de mesure spectrale (2) des spectres (1) et des concentrations dans des plages de concentration, les plages de concentration de deux modèles (M) étant non identique ; à capturer (103) au moins un spectre d'échantillon (1; 1a, 1b) de l'échantillon (5a, 5b) ; à attribuer (104) le spectre d'échantillon (1; 1a, 1b) à au moins un modèle quantitatif (M) des modèles quantitatifs générés (M) ; à appliquer (105) le ou les modèles quantitatifs (M) attribué(s) au spectre de l'échantillon (1; 1a, 1b) sur le spectre d'échantillon (1; 1a, 1b) afin de déterminer une valeur de prédiction relative à la concentration non déterminée ; et à commander en boucle ouverte et/ou en boucle fermée (108) le processus (3) par rapport à au moins un paramètre sur la base de la valeur de prédiction relative à la concentration non déterminée.
EP21706559.8A 2020-04-09 2021-02-18 Commande de processus en boucle ouverte/boucle fermée sur la base d'une détermination spectroscopique de concentrations de substances non déterminées Pending EP4058783A1 (fr)

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US5532487A (en) 1994-11-23 1996-07-02 E. I. Du Pont De Nemours And Company Near-infrared measurement and control of polyamide processes
US6087662A (en) 1998-05-22 2000-07-11 Marathon Ashland Petroleum Llc Process for analysis of asphaltene content in hydrocarbon mixtures by middle infrared spectroscopy
FI991542A (fi) 1999-07-06 2001-01-07 Neste Chemicals Oy Menetelmä moniarvoisten alkoholien valmistusprosessin ohjaamiseksi
DE202008003790U1 (de) * 2008-03-18 2008-05-15 Buck, Christian, Dr. Vorrichtung zur schnellen Onlinemessung in Biogas
FR3060123B1 (fr) * 2016-12-08 2021-12-03 Ifp Energies Now Methode amelioree de mesure en ligne sur les unites en lit mobile simule ou les unites de separation hybrides par lit mobile simule et cristallisation et application au controle et regulation desdites unites.
KR20200125978A (ko) * 2018-03-02 2020-11-05 젠자임 코포레이션 생물 제조의 다변량 스펙트럼 분석 및 모니터링

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