WO2022112125A1 - Procédé d'identification d'une substance sur une surface - Google Patents

Procédé d'identification d'une substance sur une surface Download PDF

Info

Publication number
WO2022112125A1
WO2022112125A1 PCT/EP2021/082282 EP2021082282W WO2022112125A1 WO 2022112125 A1 WO2022112125 A1 WO 2022112125A1 EP 2021082282 W EP2021082282 W EP 2021082282W WO 2022112125 A1 WO2022112125 A1 WO 2022112125A1
Authority
WO
WIPO (PCT)
Prior art keywords
substance
classifier
detection device
response signal
response signals
Prior art date
Application number
PCT/EP2021/082282
Other languages
German (de)
English (en)
Inventor
Arne Walter
Marian Kraus
Jonas Grzesiak
Frank DUSCHEK
Original Assignee
Deutsches Zentrum für Luft- und Raumfahrt e.V.
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 Deutsches Zentrum für Luft- und Raumfahrt e.V. filed Critical Deutsches Zentrum für Luft- und Raumfahrt e.V.
Publication of WO2022112125A1 publication Critical patent/WO2022112125A1/fr

Links

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
    • G01N21/62—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
    • G01N21/6402—Atomic fluorescence; Laser induced fluorescence
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/28—Investigating the spectrum
    • G01J3/44—Raman spectrometry; Scattering spectrometry ; Fluorescence spectrometry
    • G01J3/4406—Fluorescence 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
    • G01N21/62—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
    • G01N21/6408—Fluorescence; Phosphorescence with measurement of decay time, time resolved fluorescence
    • 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
    • G01N21/645—Specially adapted constructive features of fluorimeters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/02—Details
    • G01J3/0264—Electrical interface; User interface
    • 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
    • G01N2021/6419—Excitation at two or more wavelengths
    • 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
    • G01N2021/6423—Spectral mapping, video display
    • 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
    • G01N21/6428—Measuring fluorescence of fluorescent products of reactions or of fluorochrome labelled reactive substances, e.g. measuring quenching effects, using measuring "optrodes"
    • G01N2021/6439—Measuring fluorescence of fluorescent products of reactions or of fluorochrome labelled reactive substances, e.g. measuring quenching effects, using measuring "optrodes" with indicators, stains, dyes, tags, labels, marks
    • G01N2021/6441—Measuring fluorescence of fluorescent products of reactions or of fluorochrome labelled reactive substances, e.g. measuring quenching effects, using measuring "optrodes" with indicators, stains, dyes, tags, labels, marks with two or more labels
    • 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
    • G01N21/645—Specially adapted constructive features of fluorimeters
    • G01N21/6456—Spatial resolved fluorescence measurements; Imaging
    • G01N2021/646—Detecting fluorescent inhomogeneities at a position, e.g. for detecting defects
    • 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
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/94—Investigating contamination, e.g. dust
    • 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
    • G01N2201/1296—Using chemometrical methods using neural networks

Definitions

  • the invention relates to a method for identifying a substance on a surface using a light-based detection device and a computer program product, a data processing system and an evaluation unit for identifying a substance on a surface using such a method and a data processing system for such a method.
  • the automatic identification of substances on surfaces based on the classification of their spectral signature can be carried out in real time with light-based methods for the detection and identification or classification of substances, mixtures of substances and microorganisms on surfaces. These methods are active, the signal is induced by a light source, usually a laser pulse, and can be carried out directly, i.e. without sampling and processing.
  • the signal response of a substance is at best spectral, temporal or otherwise characteristic, so that the substance can be identified or at least classified based on these spectra.
  • Classification is a grouping of objects into classes (or groups). This classification is part of the input during training of the predictive model. Identification is a special case here: in classification, each substance is assigned its own class. The predictive model is what is commonly referred to as the "classifier”. For an input, the model predicts a class according to the classification. This process of classifying objects in existing (trained) class systems is called classification
  • a classifier is an instance that carries out the classification, but also a classification. In both cases, pre-processing of the data is usually necessary.
  • the signal contribution of the surface may have changed due to interaction with the substance and, together with the signal from the substance, results in a mixed signal with different compositions.
  • LIF laser-induced fluorescence
  • the automatic identification or differentiation of substances based on their fluorescence spectra can be implemented with classification algorithms such as decision trees, support vector machines or neural networks. Regardless of the algorithm used, training a predictive model of a classifier follows the same pattern. Measurement data is linked to the desired output of the model. A set of such data from different substances on different surfaces forms a training data set. The prediction model is thus generated and can then be used to make a prediction about the substance on which the spectrum is based from a mixed spectrum measured during use.
  • a further object is to provide an improved detection device for identifying a substance on a surface with such a
  • a further object is to specify a computer program product for carrying out such an improved method.
  • a further object is to specify a data processing system for such an improved method.
  • a further object is to specify an evaluation unit for such a detection device.
  • Proposed detection device wherein the detection device irradiates a light beam on the substance located on the surface at least partially covering and receives a response signal emanating from the substance and / or the surface in a receiving unit and the received response signal in a
  • the method comprises at least the steps of training the classifier with training data sets of at least one substance located on different surfaces, the training data sets receiving response signals from the substance on the surfaces and receiving response signals from the pure
  • the substance can be present directly on the surface. However, it can also be rubbed into or drawn into the surface.
  • the substance can comprise one component, but it can also have a mixture of two or more different components. In this case, according to the method according to the invention, not the individual
  • the light source can be in the form of a laser, for example, so that the incident light beam is a laser beam.
  • a fluorescence signal for example, can be received as a response signal.
  • the method can thus advantageously be based on laser-induced fluorescence spectroscopy.
  • the response signal can be received with spectral resolution and processed with the classifier.
  • Other measuring methods can also be advantageously used in the method according to the invention.
  • mixed response signals i.e. response signals from the substance and the surface
  • Substances on surfaces can be identified or classified, for example, by an artificial neural network.
  • the mixed response signals are combined with the response signals from the clean surfaces to form an input for the classifier and linked to the desired classification result.
  • This linking of mixed response signals is used both for the training phase of the classifier and for the classification of the measurement data set of the substance to be identified. This leads to a significant improvement in the quality of substance detection on both known and unknown surfaces. A clear improvement can also be achieved with a pronounced filter effect, such as partial absorption of the surface signal by the substance.
  • the method for improving the detection of substances on surfaces can advantageously be used wherever desired or undesired substances have to be detected on known or unknown surfaces and there are also clean surface areas.
  • Analogous to the substance detection of a pure substance on a surface for example, two pure substances can also be detected, with the second substance then being treated in the method according to the invention instead of the surface. In this case, separate recording signals of the pure substances must be available separately for identification, im
  • the method can be used advantageously for different types of detection methods in which measurement data from the substance and background are superimposed.
  • Fluorescence spectroscopy in which, for example, pulsed lasers with a wavelength of 266 nm and detection using a spectrometer and a photomultiplier array are used, represents an advantageous embodiment of the method.
  • Some possible application examples are, for example, detection of hazardous substances by emergency services (police, fire brigade, military),
  • a method of machine learning can be used as the algorithm of the classifier.
  • Various classification algorithms such as decision trees, support vector machines or neural networks can be used to advantage in order to process combined training data sets from mixed response signals and response signals from the clean surfaces as well as combined measurement data sets of the substance to be identified from mixed response signals and response signals from the clean surfaces and the To predict the substance with great accuracy or at least to rule out other substances.
  • a neural network can be used as the algorithm of the classifier.
  • Neural networks can be used particularly advantageously to process combined training data sets from mixed response signals and response signals from the clean surfaces as well as combined measurement data sets of the substance to be identified from mixed response signals and response signals from the clean surfaces and to predict the substance accordingly with great quality.
  • a fluorescence signal of the substance and the surface can be recorded as the outgoing response signal.
  • fluorescence spectroscopy LIF is particularly suitable as an active and direct method without taking or preparing samples.
  • Output receiving unit spectrally resolved response signals received.
  • the receiving unit can be designed as a spectrometer.
  • An optical spectrometer or a spectral analysis of the outgoing response signals are not absolutely necessary.
  • the method can advantageously provide for the use of wavelength-selective input channels.
  • a spectrometer with a detector supplies such spectrally resolved response signals.
  • spectrally resolved response signals can also be implemented using photodiodes with filters.
  • a laser in particular a pulsed laser, can be used as the at least one light source.
  • a narrow-band light source such as an LED is sufficient.
  • a laser has the advantage over an LED that the losses due to divergence on the way to the sample are low. Both lasers and LEDs can be operated in pulsed mode. This is not absolutely necessary for LIF, but with suitable detection it can improve the signal-to-noise ratio.
  • a pulsed light source is required for time-resolved signals, since the decay of the response signal is recorded after a time-defined excitation.
  • the light beam can be used with at least two wavelengths, in particular in the ultraviolet.
  • information from different fluorophores can advantageously be included in the classification of the measured response signals.
  • wavelengths in the UV-C range and in the UV-A range for example wavelengths of 266 nm and 355 nm, can advantageously be used.
  • UV radiation in the UV-A range has a wavelength in the range from 315 nm to 380 nm.
  • UV radiation in the UV-B range has a wavelength in the range from 280 nm to 315 nm.
  • Radiation in the UV-C range has a wavelength of 100 nm to 280 nm.
  • the excitation wavelength required for the method according to the invention depends on the substance. Many substances have absorption bands in the UV range, including those that also absorb in the visible range. In systems that are used in biological detection, the wavelengths of 266 nm (UV-C) and 355 nm (UV-A) that are frequently used in biology and are readily available can be used to advantage. Alternatively, 280 nm (UV-B / UV-C) and 355 nm can also be used. After a favorable embodiment of the method, at least one
  • Part of the outgoing response signal can be recorded as a time signal. In this way, a temporal signature of the outgoing response signal can be recorded via the time signal. In this way, additional information can advantageously be included in the classification of the measured response signals.
  • a detection device for identifying a substance on a surface comprising at least one light source which at least partially covers the substance
  • an optical receiving unit which is designed to receive a response signal emanating from the substance and/or the surface, and a classification which is designed to process a received response signal of the outgoing response signal and which is designed for training with training data sets and for identifying a substance from measurement data sets.
  • response signals from the non-contaminated surfaces are recorded.
  • a light beam is radiated onto the substance located on the surface so that the light beam covers both the substance and the surface at least in certain areas.
  • response signals from the clean surface are recorded.
  • the mixed response signals are combined with the response signals of the clean surfaces to form an input for the classifier and linked to the desired classification result.
  • the classifier is used for both the training phase and the
  • Classification of the measurement data set of the substance to be identified is used.
  • the combination of mixed response signals with response signals from the pure surface leads to a significant improvement in the quality of substance detection on both known and unknown surfaces.
  • the algorithm of the classifier can be based on a machine learning method.
  • Various classification algorithms such as so-called decision trees, usually referred to as decision trees, so-called support vector machines, usually referred to as support vector machines, or neural networks can be used to advantage to combine training data sets from mixed response signals and response signals of the pure surfaces as well as combined measurement data sets of the to process the substance to be identified from mixed response signals and response signals from the pure surfaces and to predict the substance accordingly with great accuracy or at least to exclude other substances.
  • an algorithm of the classifier can be designed as a neural network.
  • Neural networks can be used particularly advantageously to process combined training data sets from mixed response signals and response signals from the clean surfaces as well as combined measurement data sets of the substance to be identified from mixed response signals and response signals from the clean surfaces and to predict the substance accordingly with great quality.
  • the receiving unit can be designed to receive fluorescence signals.
  • LIF light-based methods for the detection and identification or classification of substances, mixtures of substances and microorganisms on surfaces in real time, LIF is particularly suitable as an active and direct method.
  • a spectrometer with a PMT (photo multiplier tube) array for example, can be used as the receiving unit.
  • a system of bandpass filters, a monochromator, a tunable optical filter, or the like can also be used.
  • the detection device can comprise at least two light sources of different wavelengths, which are designed to radiate a light beam onto the substance located on the surface.
  • the detection device can comprise at least two light sources of different wavelengths, which are designed to radiate a light beam onto the substance located on the surface.
  • Light sources have wavelengths in the ultraviolet, in particular wavelengths of 266 nm and 355 nm. In this way, information from different fluorophores can advantageously be included in the classification of the measured response signals.
  • a light source for example a laser, can also be used which emits at least two different wavelengths.
  • Output mirror can be recorded as a beam splitter as a time signal. In this way, a temporal signature of the outgoing response signal can be recorded via the time signal. In this way, additional information can be advantageous in the
  • the at least one light source can be designed as a laser, in particular as a pulsed laser.
  • a narrow-band light source such as an LED is sufficient.
  • a laser has the advantage over an LED that the losses due to divergence on the way to the sample are low. Both lasers and LEDs can be operated in pulsed mode. This is not absolutely necessary for LIF, but with suitable detection it can improve the signal-to-noise ratio.
  • a pulsed light source is required for time-resolved signals, since the decay of the response signal is recorded after a time-defined excitation
  • pulsed light sources increase the signal to ambient light quality, but are not absolutely necessary for spectral detection. Pulsed light beams are required to evaluate time signals.
  • a computer program product for identifying a substance on a surface using a light-based detection device, the computer program product comprising at least one computer-readable storage medium which comprises program instructions which can be executed on a computer system and cause the computer system to perform a method as described above.
  • At least the following steps are carried out: training the classifier with training data sets of at least one substance located on different surfaces, the training data sets comprising received response signals of the substance on the surfaces and received response signals of the clean surfaces, which are linked to the desired prediction of the respective substance; Entering at least one measurement data set with at least one received response signal of the substance to be identified and located on a surface and at least one received response signal of the clean surface in the classifier; and classifying the measurement data set in the classifier and predicting the substance.
  • the computer program product advantageously serves to implement the method according to the invention. To avoid unnecessary repetition, reference is made to the description of the method.
  • a data processing system for executing a data processing program which is computer-readable
  • Program instructions includes a method for identifying a substance on a surface using a laser-based
  • the data processing system is advantageously used to carry out the method according to the invention. To avoid unnecessary repetition, reference is made to the description of the method.
  • an evaluation unit with at least one classifier for identifying a substance on a surface using a light-based detection device is proposed.
  • the evaluation unit with the classifier can have hardware modules such as FPGAs (field programmable gate arrays) or components produced by 3D printing, on which the method for identifying a substance on a surface using a light-based detection device is advantageously implemented. It is possible to build the predictive model in hardware instead of as part of a software solution.
  • FPGAs field programmable gate arrays
  • a simple and realistic example is an FPGA, where the logic is implemented in physical elements.
  • Such a hardware implementation directly links a received response signal as input with a prediction as output.
  • the method according to the invention can be implemented with a high processing speed of the received response signals.
  • 1, 2 show a schematic representation of the measuring principle of the method according to the invention, in which a light beam is radiated onto a substance located on a surface, covering it at least in some areas, and the outgoing response signal is recorded;
  • 3 shows a schematic structure of a light-based
  • Detection device for the method according to the invention
  • 4 shows a procedural model of the method according to the invention for identifying a substance on a surface
  • FIG. 5 classification results for the identification of different substances on different surfaces.
  • FIG. 1 shows a plan view of a surface 52 with a substance 50 thereon
  • FIG. 2 shows a longitudinal section through the arrangement, in which the outgoing
  • Response signal 40, 42, 44 is shown symbolically in the form of an arrow. 1 shows three surfaces illuminated by light beams 30, of which one light beam 30 impinges directly on the substance 50, while the other two light beams 30 each hit the edge of the substance 50 and part of the light beam 30 hits the surface 52.
  • the light beam 30 excites the irradiated areas, for example
  • the signal response of the outgoing response signal 30 consists of the
  • the signals 40, 42, 44 of the outgoing response signal can, for example, advantageously by means of laser-induced
  • the incident light beam 30 can, for example, have at least two
  • wavelengths especially in the ultraviolet in the UV-C and UV-A range, in order to have interactions between the substance that are as favorable as possible 50 and surface 52 to detect.
  • laser light with wavelengths of 266 nm and 355 nm can be used.
  • light with wavelengths of 280 nm and 355 nm can also be used.
  • the various recorded signals of the outgoing response signal 40, 42, 44 can be input into a classifier 70 in a suitable combination, as shown later in FIG.
  • Figure 3 shows a schematic structure of a light-based detection device 100 for the method according to the invention for identifying a substance 50 on a surface 52.
  • the detection device 100 comprises at least one light source 10, 12, which at least partially covers the substance 50 for irradiating a light beam 30, 32 the substance 50 located on the surface 52 is formed.
  • the detection device 100 further comprises an optical receiving unit 20 which is used to receive the substance 50 and/or the surface 52 emanating from it
  • a classifier 70 which is designed to process a received response signal 64, 66, 74, 76 (recognizable in Figure 3) of the outgoing response signal 34, and whose prediction model is a product of training with training data sets 60 and, for Identifying a substance 50 from
  • Measurement data sets 78 is formed.
  • the light beam 30, 32 of the at least one light source 10, 12 is directed onto the substance 50 via deflection mirrors 14, 16.
  • the receiving unit 20 can be designed, for example, to analyze signals from laser-induced fluorescence spectroscopy. That The outgoing response signal 34 can be coupled into optical fibers, for example, via a collecting mirror 18 and can be received in a spectrally resolved manner in the receiving unit 20 .
  • An algorithm 90 of the classifier 70 (FIG. 4) can advantageously be based on a machine learning method. In particular, a neural network can be used for this.
  • the illustrated in the embodiment in the figure second light source 10, 12 can optionally to increase the accuracy of the
  • the light beam 32 of the second light source 12 is coupled into the beam path 30 of the first light source 10 via a deflection mirror 22 and a dichroic mirror 24 .
  • the two light sources 10, 12 can advantageously have wavelengths in the ultraviolet, in particular wavelengths of 266 nm and 355 nm, i.e. in the UV-C range and in the UV-A range.
  • a part 36 of the outgoing response signal 34 can optionally be recorded as a time signal, in particular via an output mirror 26 as a beam splitter.
  • a temporal signature of the outgoing response signal can be determined for the optional improvement of the prediction when classifying the measurement data sets.
  • Classifier 70 whose mode of operation is described in detail in Figure 4, can be arranged in an evaluation unit 38 of detection device 100, which also records the data from receiving unit 20, and optionally data of a time signal which is determined in time signal evaluation unit 28.
  • Evaluation unit 38 can, for example, also control the at least one light source 10, 12 during the measurement.
  • the evaluation unit 38 can further advantageously as a data processing system for executing a
  • Be formed data processing program which includes computer-readable program instructions to the method for
  • On the data processing system can advantageously a computer program product for identifying a substance 50 at a
  • Surface 52 be implemented by means of a laser-based detection device 100, wherein the computer program product comprises at least one computer-readable storage medium, which includes program instructions that are executable on the computer system and cause the computer system to the inventive
  • the evaluation unit 38 with the classifier 70 can also have hardware modules such as FPGAs (field programmable gate arrays) or components produced by 3D printing, on which the method according to the invention is advantageously implemented.
  • the method can be implemented with a high processing speed of the received response signals.
  • FIG. 3 A possible embodiment of the method according to the invention for identifying a substance 50 on a surface 52 using a light-based detection device 100 is shown in FIG.
  • a detection device 100 (FIG. 3) radiates a light beam 30, 32 onto the substance 50 located on the surface 52, at least partially covering it.
  • One of Substance 50 and/or Der The response signal 34 emanating from the surface 52 is received in a receiving unit 20 and the received response signal 64, 66, 74, 76 is processed in a classifier 70.
  • the classifier 70 is first trained with training data records 60 of a large number of different substances 50 (FIG. 3) located on different surfaces 52 .
  • response signals from the non-contaminated surfaces 52 are recorded.
  • the light beam 30 is radiated onto the substance 50 located on the surface 52 so that the light beam 30 at least partially covers both the substance 50 and the surface 52 (FIG. 3).
  • outgoing response signals of the clean surface 52 are recorded.
  • the training data sets 60 include response signals 64 from the substances 50 on the surfaces 52 and response signals 66 from the clean surfaces 52, which are combined to form an input 62 in the classifier 70.
  • the input 62 is linked to the desired classification result 72 as an output 68 to form a training data set 60 .
  • the training data records 60 are thus permanently linked to the desired prediction of the respective substance 50 (FIG. 3).
  • the classifier 70 thus goes through a series of training runs until a desired prediction quality of the respective substance 50 is reached.
  • the measurement data set 78 which includes at least one response signal 74 of the substance 50 to be identified and located on a surface 52 and at least one response signal 76 of the clean surface 52, is used as input 62 entered into the classifier 70.
  • the measurement data set 78 is then classified in the classifier 70 and a prediction 72 of the substance 50 is output as an output 68 .
  • a method of machine learning can advantageously be used as the algorithm 90 of the classifier 70 .
  • Various classification algorithms such as e.g. Decision Trees, Support Vector Machines or Neural Networks can be used advantageously in order to generate combined training data sets 60 from mixed outgoing response signals and outgoing response signals from the clean surfaces 52 as well as combined measurement data sets 78 for the substance to be identified 50 from mixed response signals and response signals from the to process clean surfaces 52 and to predict the substance 50 accordingly with great quality.
  • a neural network can be used for this.
  • the combination of mixed response signals with response signals from the clean surface 52 is used both for the training phase of the classifier 70 and for the classification of the measurement data record 78 of the substance 50 to be identified. This leads to a significant improvement in the quality of the substance detection both on known and on unknown surfaces 52. A significant improvement can also be achieved with a pronounced filter effect, such as partial absorption of the surface signal with the substance 50.
  • FIG. 5 shows classification results for the identification of different substances on different surfaces. The method was applied with measurement data on 13 different substances on 13 different surfaces each.
  • pressboard (painted white) 224 pressboard (painted white) 224.
  • Two types of training data sets were generated from 80% of the measurement data.
  • One type of training data set contained the channels of the mixed response signal and appended the channels of an associated surface measurement.
  • the other type of training data set contained only the channels of the mixed response signal.
  • To determine the quality For the detection of substances on unknown surfaces, a training data set was created for each surface that did not contain the respective surface. The remaining 20% of the measurement data were used to evaluate the procedure and were not part of the training.
  • Respective columns 82 show results of classifications on known surface with a mixed response and surface response as input to the classifiers.
  • Respective columns 84 show results of classification on known surface with only mixed response as input
  • the respective columns 86 show results of classifications on an unknown surface with a mixed response signal and a surface response signal as input to the classifiers.
  • the respective columns 84 show results of classifications on a known surface with only a mixed response signal as input to the classifiers
  • the improvement in the quality of the prediction on known surfaces is approximately of the order of magnitude that is also achieved by equipment supplements, which are sometimes associated with considerable additional costs, such as the use of a second excitation wavelength or measurement of the temporal signature of the
  • the overall accuracy of the forecasts can be further increased.

Landscapes

  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Optics & Photonics (AREA)
  • Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)

Abstract

L'invention concerne un procédé, un dispositif de détection et un produit de programme informatique pour identifier une substance (50) sur une surface (52) en utilisant un dispositif de détection (100) qui émet un faisceau lumineux (30, 32) sur la substance de manière à couvrir au moins certaines régions de la substance, qui reçoit un signal de réponse (34) dans une unité de réception (20) et qui traite les signaux de réponse reçus (64, 66, 74, 76) dans un classifieur (70). Au moins les étapes suivantes sont réalisées : (i) l'entraînement du classifieur (70) à l'aide d'ensembles de données d'entraînement (60) d'au moins une substance (50) qui peut être trouvée sur différentes surfaces (52), les ensembles de données d'entraînement (60) comprenant des signaux de réponse reçus (64, 66) de la substance (50) sur les surfaces (52) et des surfaces propres (52), lesdits signaux de réponse étant liés à la prédiction souhaitée de la substance respective (50) ; (ii) l'entrée d'au moins un ensemble de données de mesure (78) comprenant un signal de réponse reçu (74, 76) de la substance (50) à identifier qui peut être trouvé sur une surface (52) et de la surface propre (52) dans le classifieur (70) ; et (iii) la classification de l'ensemble de données de mesure (78) dans le classifieur (70) et la prédiction de la substance (50).
PCT/EP2021/082282 2020-11-24 2021-11-19 Procédé d'identification d'une substance sur une surface WO2022112125A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102020131014.5A DE102020131014A1 (de) 2020-11-24 2020-11-24 Verfahren zum identifizieren einer substanz an einer oberfläche
DE102020131014.5 2020-11-24

Publications (1)

Publication Number Publication Date
WO2022112125A1 true WO2022112125A1 (fr) 2022-06-02

Family

ID=78821126

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2021/082282 WO2022112125A1 (fr) 2020-11-24 2021-11-19 Procédé d'identification d'une substance sur une surface

Country Status (2)

Country Link
DE (1) DE102020131014A1 (fr)
WO (1) WO2022112125A1 (fr)

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6485872B1 (en) 1999-12-03 2002-11-26 Mks Instruments, Inc. Method and apparatus for measuring the composition and other properties of thin films utilizing infrared radiation
US20060191319A1 (en) 2004-12-17 2006-08-31 Kurup Pradeep U Electronic nose for chemical sensing
US11105801B2 (en) 2017-09-09 2021-08-31 Neil Gordon Bioanalyte signal amplification and detection with artificial intelligence diagnosis
CN114026458A (zh) 2019-04-17 2022-02-08 密歇根大学董事会 多维材料感测系统和方法
DE102019205903A1 (de) 2019-04-25 2020-10-29 Robert Bosch Gmbh Verfahren und Vorrichtung zum Ermitteln einer festen Zustandsform von Wasser auf einer Fahrbahnoberfläche
DE102019116750A1 (de) 2019-06-20 2020-12-24 Deutsches Zentrum für Luft- und Raumfahrt e.V. Verfahren und Vorrichtung zum Ermitteln von Fehlstellen an einem Fasermaterial sowie Faserlegeanlage hierzu
EP3767403B1 (fr) 2019-07-16 2022-09-07 Carl Zeiss Industrielle Messtechnik GmbH Mesure de forme et de surface assistée par apprentissage automatique destinée à la surveillance de production

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
BABICHENKO S ET AL: "Non-Contact, Real-Time Laser-Induced Fluorescence Detection and Monitoring of Microbial Contaminants on Solid Surfaces Before, During and After Decontamination", JOURNAL OF BIOSENSORS & BIOELECTRONICS, vol. 9, no. 2, 18 June 2018 (2018-06-18), pages 1 - 9, XP055736984, ISSN: 2155-6210, DOI: 10.4172/2155-6210.1000255 *
HAUSMANN ANITA ET AL: "Standoff detection: classification of biological aerosols using laser induced fluorescence (LIF) technique", PROCEEDINGS OF SPIE, IEEE, US, vol. 9073, 29 May 2014 (2014-05-29), pages 90730Z - 90730Z, XP060036835, ISBN: 978-1-62841-730-2, DOI: 10.1117/12.2049923 *

Also Published As

Publication number Publication date
DE102020131014A1 (de) 2022-05-25

Similar Documents

Publication Publication Date Title
DE1498824C3 (de) Vorrichtung zur maschinellen Krebsdiagnose
DE112011100038T5 (de) Verfahren zur Entwicklung von Erkennungsalgorithmen für die laserinduzierte PlasmaEmissionsspektroskopie
EP0822395A2 (fr) Procédé et dispositif de spectroscopie à corrélation d'émission Raman
WO2003098313A2 (fr) Procede et systeme pour analyser des echantillons
EP3494385B1 (fr) Dispositif et procédé d'analyse d'alliage de fragments de ferraille en métal
DE102007053574A1 (de) Farbmessgerät
EP2625673A2 (fr) Procédé pour contrôler un signe de sécurité optique d'un document de valeur
DE19653477C2 (de) Verfahren und Vorrichtung zur Prozeßführung bei der Herstellung von Papier
EP2120682B1 (fr) Procédé et dispositif d'évaluation d'ensembles d'images de fluorescence
DE10222359B4 (de) Verfahren zur spektral differenzierenden, bildgebenden Messung von Fluoreszenzlicht
DE102017221187A1 (de) Verfahren zur Bestimmung der Konzentration von verschiedenen, in einem Objekt enthaltenen Fluoreszenzemittern und Mikroskopiesystem
DE10339312A1 (de) Verfahren zur Trennung von Fluoreszenzspektren von in einer Probe vorhandenen Farbstoffen
EP2110187B1 (fr) Procédé d'identification, de classification et de tri d'objets, de matériaux et système de reconnaissance destiné à l'exécution de ce procédé
DE102013022026A1 (de) Mehrfarben-Scanning-Mikroskop
WO2022112125A1 (fr) Procédé d'identification d'une substance sur une surface
DE102009036562B4 (de) Verfahren zur Bestimmung der Wasserqualität eines Gewässers
EP3612835B1 (fr) Procédé permettant de détecter la rancidité de fruits oléagineux, de graines et de noix
DE102016105102A1 (de) Verfahren zur Untersuchung verteilter Objekte
DE10317669A1 (de) Verfahren zur Separierung von Detektionskanälen eines mikroskopischen Systems
DE10218706B4 (de) Verfahren zur zeitoptimierten Erfassung von speziellen Spektren mit einem Scanmikroskop
WO2002014811A1 (fr) Unite de detection de lumiere et procede de detection de lumiere luminescente, et microscope confocal pour microscopie par luminescence
DE10065783B4 (de) Verfahren, Anordnung und System zur Ermittlung von Prozessgrößen
DE10355150B4 (de) Verfahren und System zur Analyse von Co-Lokalisationen
DE102021200894B3 (de) Optisches Untersuchen von Objekten eines Materialstroms wie beispielsweise Schüttgut
DE102019203850A1 (de) Optische Analyseeinrichtung zum Analysieren von Licht von einer Probe und Verfahren zum Betreiben einer optischen Analyseeinrichtung

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21819357

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21819357

Country of ref document: EP

Kind code of ref document: A1