Classifications

• • 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
  • G01J3/42—Absorption spectrometry; Double beam spectrometry; Flicker spectrometry; Reflection spectrometry
• • 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
  • G01J3/0256—Compact construction
• • 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
  • G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
  • G01N21/274—Calibration, base line adjustment, drift correction
• • 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
  • G01J2003/2866—Markers; Calibrating of scan
  • G01J2003/2873—Storing reference spectrum
• • 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
  • G01J2003/2866—Markers; Calibrating of scan
  • G01J2003/2879—Calibrating scan, e.g. Fabry Perot interferometer

Definitions

• the present invention relates to a method for configuring a target spectrometry device using a reference spectrometry device. It also relates to a spectrometry device configured according to this method.
• the field of the invention is, without limitation, that of the field of spectrometric methods.
• Spectrometry is an essential tool in the identification, quantification and characterization of substances, compounds or molecules. It is used in many scientific fields, such as physics, organic chemistry, the pharmaceutical field or medicine. Spectrometry is also very important in the industrial field, for example for quality control in production, control of mixtures, in-line cleaning or monitoring in anaerobic digestion centers.
• the response of a spectrometric device consists of an electrical signal proportional to the amplitude of absorption or reflection of a light beam emitted towards the sample or the object to be analyzed and absorbed or reflected by the latter.
• the properties of the samples to be analyzed may include, for example, the concentration of any chemical elements (sugar, lipid, contaminants, etc.), the level of humidity in a matrix or protein in wheat, texture, temperature carbohydrates, sugars, etc.
• a relationship must be established between the measured signal and the property of the sample.
• the set of samples may include, for example, a range of different flours, textiles, liquids, etc. These samples are clearly identifiable and can be stored.
• An aim of the present invention is to improve the existing techniques.
• An aim of the present invention is to provide a method for configuring a target spectrometry device by means of a reference spectrometry device making it possible to choose only a subset of the reference samples to carry out the configuration of the target spectrometer, that is to say, without it being necessary that all of the samples measured by the reference device are also measured by the target device. At least one of these goals is achieved with a method for configuring a target spectrometry device by means of a reference spectrometric device, each spectrometry device comprising a spectrometer, each spectrometer comprising a light source and a detector adapted to detect light.
• the process comprises the steps of:
• the determination steps are carried out by means of a calculation module.
• the method according to the present invention eliminates the need to measure all the samples of a reference set with a target spectrometer before being able to configure said spectrometer. Thanks to the method according to the invention, a spectral database containing all the spectral measurements of all the samples can be recorded for the target spectrometer, starting from a small volume of measurements and using the measurements made by a spectrometer reference.
• the method can be applied to any type of target spectrometer.
• the reference spectrometer also called a master spectrometer, can be, for example, a laboratory spectrometer, or any other type of spectrometer which serves as a reference spectrometer.
• the target spectrometer also called a slave spectrometer, can be a spectrometer of the same type as the reference spectrometer.
• the target spectrometer corresponds to a production version of the reference spectrometer.
• the target spectrometer can also be a device having technical characteristics different from those of the reference device.
• the two spectrometers can be distinguished, in particular, by their method of measurement (reflection, transmission or transflexion), by their spectral range, their resolution, sensitivity or dynamic range.
• the second spectrometer can be, for example, a miniaturized spectrometer.
• the reference spectrometer is a device whose technical specifications are better than those of the target spectrometer.
• the two spectrometers are sensitive in the visible and / or infrared range of the light spectrum, between approximately 400 nm and 2500 nm.
• the optical transfer function of a spectrometer corresponds to its impulse response, that is to say, the response of a spectrometer at a given wavelength.
• the application of the optical transfer function to each average spectrum of the reference spectrometer is carried out by calculating a product of convolution between the optical transfer function and each average spectrum.
• the method may further comprise a step of minimizing the difference between the determined mean spectrum and the mean spectrum measured by the target spectrometer for each sample of the subset of reference samples.
• the optical transfer function is determined by at least one technical characteristic of the target spectrometer. This technical characteristic is chosen from among the sensitivity, the spectral range or the resolution. Preferably, these three technical characteristics of the target spectrometer are used to determine the optical transfer function.
• the step of determining a series of n spectra comprises the steps of:
• the covariance matrix is estimated from the spectra measured by the target spectrometer and the noise associated with these measurements.
• the estimation of the covariance matrix is more reliable by taking into account the high frequency noise, or measurement noise, present in all the physical measurements.
• the noise dependence of the measured optical signal strength can be modeled and used to refine the estimate of the covariance matrix.
• the invention relates to a spectrometry device comprising a spectrometer comprising a light source, a light radiation detector and an electronic module, the spectrometry device being configured according to the method according to the invention.
• the target spectral database can in particular be recorded in an electronic module forming part of the spectrometer or being linked to the latter.
• This electronic module can comprise, for example, an internal memory of the spectrometer or an on-board platform, such as a microcomputer, a smartphone, and / or a remote server.
• the electronic module can be directly or indirectly connected to the spectrometer, for example via a cloud or any other communication device.
• the calculation module carrying out the determination and estimation steps of the method according to the invention can form part of the spectrometer or be connected to it.
• These two modules can be formed either by a single and unique module, or by two distinct modules.
• the spectrometer can be a miniaturized spectrometer.
• it can include a fiber probe suitable for carrying out measurements at a distance.
• FIG. 1 is a schematic representation of a spectrometry device according to one embodiment of the invention
• FIG. 2 is a schematic representation of a method according to one embodiment of the invention.
• a method for configuring a target spectrometry device using a reference spectrometry device is provided.
• Figure 1 schematically shows a spectrometry device 100, comprising a spectrometer 110 and an electronic module 120.
• Each spectrometer 110 is equipped, at least, with a light source and a detector. Light is directed at an object or sample to be analyzed, and the transmitted or reflected radiation is picked up by the detector.
• the spectrometry device 100 can be controlled by means of an external control unit 130.
• the electronic module 120 is configured to process the optical signals detected and thus analyze the sample by means of a database stored therein and notably comprising calibration relationships.
• Each spectrometer 110 can be characterized by an optical transfer function or, equivalently, by its impulse response.
• a spectral measurement corresponds to the measurement of the absorbance of light for each wavelength l in a spectral range A.
• To obtain the absorbance of a material we measure the intensity / (l) reflected or transmitted by the sample and compares it to a reference intensity / o (l) according to the following equation:
• the reference intensity / o (l) is measured on a reference sample of inert material.
• This reference sample is generally a material used to measure the spectral distribution of the light source of the spectrometer.
• FIG. 2 schematically illustrates the steps of a method 1 according to one embodiment of the invention.
• a first spectrometer M the Si spectra of a set A of samples are measured.
• This first spectrometer M is called a master spectrometer or a reference spectrometer. These measurements are stored or recorded in a spectral database, called reference, BAM.
• the Si spectra for a subset B of samples are measured with a second spectrometer S, called the target or slave spectrometer. These measurements are stored or recorded in a spectral database, called target, BBS.
• the subset B is part of the set A of samples.
• the reference samples of set A are preferably made of an inert material, for example wood, flour, wheat, plastics or oils. It is assumed that for the reference samples of Set A, only few chemical properties vary from sample to sample. It is in fact preferable for the reference samples to have similar chemical properties so that their spectra are not subjected to too many independent sources of variability. For example, a set of flours with different protein levels can present a source of variability which is the protein level. If the set of flours contains different types of flours (e.g. T45, T55), there is a source of variability additional. The greater the number of sources of variability, the larger the size of the subset B must be.
• an inert material for example wood, flour, wheat, plastics or oils.
• the measurements 10, 12 of the spectra are carried out under the same conditions.
• the samples are measured using M and S spectrometers by performing n repeated scans at different physical points of each sample.
• the measurement method (reflectance, transmittance or transflectance) can be different for the two spectrometers M and S.
• each spectral measurement is a matrix of m columns and rows, where m corresponds to the number of wavelengths used for the measurement of a spectrum and n is the number of spectra measured per sample.
• m corresponds to the number of wavelengths used for the measurement of a spectrum
• n is the number of spectra measured per sample.
• the shape of an average spectrum s's () for each sample in the set A is determined for the spectrometer S.
• the average spectra obtained at starting from the spectral measurements by the reference spectrometer called mean reference spectra, in order to re-sample them.
• each M and S spectrometer it is necessary to know the following technical characteristics and parameters of each M and S spectrometer: the spectral range AM, AS, the resolution GM, rs and the sensitivity SM, SS.
• the spectral range A is the set of all wavelengths used to make a spectral measurement. This information can be found, for example, in measurement files or on a spectrometer data sheet, provided by the manufacturer.
• the spectral range can be expressed in wavelengths l with the nanometer (nm) as unit or in wavenumbers a with cm T as the unit.
• the two units used must be identical between the two spectrometers M and S.
• the units can be converted according to the following relation: [Math 2] l - 107
• an estimate of the optical transfer function, or of the impulse response, CMS of the target spectrometer is performed.
• the resolution of the target spectrometer is simulated from the mean reference spectra.
• a convolution product between the transfer function of the target spectrometer and the mean reference spectra is calculated.
• Average target spectra are thus obtained which can be measured with the target spectrometer.
• the impulse response can, for example, have a Gaussian form, according to the following general equation:
• x represents a wavelength
• the shape of the impulse response is characterized by the three constants a, b and c. These constants will be determined subsequently using the technical information from the spectrometers M and S. The parameters a, b and c then intervene in the determination of the mean spectrum s s (l) (step 15 of FIG. 2).
• the impulse response is used as follows. For each wavelength Xs in the spectral range As, the value closest to the wavelength lM in the spectral range AM should be found. The constants a, b and c are then calculated for each wavelength thus identified in the range As. Finally, the CMS function is applied to the mean reference spectra.
• the pulse function is defined for each wavelength Xs in the spectral range As, just like the constants a, b and c.
• the 3 l parameter is proportional to the sensitivity of the target spectrometer S.
• the 3 l parameter can be calculated using spectra measured as follows:
• ss (X) corresponds to an average spectrum measured by the target spectrometer
• SM ( ⁇ ) corresponds to an average spectrum measured by the reference spectrometer, called the average reference spectrum.
• the first of these relations takes into account the fact that the impulse response of the target spectrometer does not necessarily have an identical gain over the entire spectral range. There may, for example, be a loss of sensitivity at the end of the spectral range.
• the parameter b % represents the wavelength in the reference spectral range AM which is closest to the wavelength Xs considered:
• the c % parameter is determined using the resolution of the target spectrometer at each wavelength. Resolution is defined as the width at half height (FWHM) of an assumed Gaussian impulse response of the target spectrometer. It is often given by the manufacturer on the spectrometer data sheet. It can also be obtained by measuring a monochromatic light source with the spectrometer. The FWHM can vary within the spectral range.
• the parameter c % can be obtained by performing the following calculation:
• the mean spectrum determined for the target spectrometer S is obtained using the CMS impulse response of the target spectrometer and the mean reference spectrum SM at each wavelength Xs of the spectral range As.
• an average spectrum simulated s'sM can be obtained by:
• SM (I) represents a point in the mean reference spectrum.
• the Math 8 calculation is repeated for each wavelength in the spectral range As of the target spectrometer S to obtain the full mean spectrum for the target spectrometer.
• the calculation is repeated for each reference sample in set A, in order to obtain a calculated average spectrum s's for each sample.
• the quality of the determination or simulation of the mean spectra depends on the quality of the determination or the estimation of the values of the three parameters a % , b % and c % . In fact, it may happen that the technical information available for the target spectrometer S is insufficient to determine these values with satisfactory precision. It is then advisable to define a criterion of good modeling of the spectra starting from the database of reference.
• the method comprises a step of adjustment, for each sample of the subset B, between the average spectrum calculated s's and the average spectrum measured ss by the target spectrometer.
• the reference and target databases BAM and BBS are two coherent databases, that is to say, based on measurements made on the same samples.
• This function can be optimized by using a brute force strategy for the parameters a % , b % and c % , exhaustively checking a set of values for each parameter.
• Step 14 of determining the mean spectra ends after the adjustment step.
• a target database, BAS stored in the electronic module of the target device, which is populated by an average spectrum for each sample of the set A.
• n spectra are generated from each mean spectrum s's.
• the probability density function is a Gaussian function defined by:
• m is defined by the mean spectrum s's calculated according to Math 8.
• the covariance matrix ⁇ is unknown.
• the covariance matrix is estimated.
• this database can be represented by a matrix having / rows and j columns .
• the spectral measurements are organized in rows so that the variables (i.e., the wavelength Xs) are in columns.
• the column / of the matrix is denoted by, and mi expresses the mean of the column / of the matrix according to the following relation:
• Math Equation 11 represents the absorbance of the average spectrum at the / th wavelength.
• the covariance matrix ⁇ is a square matrix of size N x N. It is estimated using the matrix according to the following relation:
• the values of the probability density function can be determined according to Math Equation 11. This determination can be made, for example, using suitable software.
• suitable software capable of generating a multivariate normal (or Gaussian) distribution or languages of Programming such as Matlab or C can be used to perform this generation of values.
• Gaussian vectors represent spectra s' k) simulating spectra measured with the target spectrometer S.
• the covariance matrix ⁇ contains all the information concerning the variability of the spectrometric measurements from one scan to another for a sample.
• the estimate of ⁇ is based on all the samples of the subset B to be of sufficient quality. Depending on the nature of the samples, however, only a few samples may be sufficient to obtain a good estimate of ⁇ .
• the complete set A of samples contains very different materials or chemicals, it may be useful to measure more samples with the target spectrometer for the estimation of ⁇ .
• the measurement noise can be recognized in the measured spectral data by its high frequency signal which is modulated by the spectral signal itself.
• This type of noise can be calculated using the diagonal terms of the covariance matrix.
• the noise depends on the measured absorbance level. The smaller the signal from the detector, the higher the absorbance and the greater the influence of the measurement noise.
• V E [(b - E [b]) 2 ]
• the noise signal must be separated from the measurement signal using a signal processing technique suitable for this purpose.
• the technique may involve, for example, a low pass filter, a band pass filter or a Savitsky-Golay filter. Any other filter capable of reducing high frequency noise can also be used.
• the exponential curve has the form described in the following equation:
• parameters a and b can be calculated using standard statistical software adapted to optimize the modeling of V (A) in order to best fit the measurement data.
• n spectra will be added to the BAS database.
• the BAS database is then recorded in the electronic module of the target device.
• the recorded BAS database can then be used for other configuration operations of the target spectrometer S.
• a step 20 of calibrating the spectrometer can be carried out, according to a calibration method using the spectra. calculated s' ⁇ (l) present in the database.
• This calculation module comprises at least one computer (as illustrated in Figure 1 with reference 130), a central or calculation unit, an analog electronic circuit (preferably dedicated), a digital electronic circuit (preferably dedicated) , and / or a microprocessor (preferably dedicated), and / or software means.

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• 2019
  • 2019-04-04 FR FR1903613A patent/FR3094791B1/fr not_active Expired - Fee Related
• 2020
  • 2020-04-03 CA CA3135861A patent/CA3135861A1/fr active Pending
  • 2020-04-03 CN CN202080033682.XA patent/CN113795748A/zh active Pending
  • 2020-04-03 WO PCT/EP2020/059507 patent/WO2020201484A1/fr not_active Ceased
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