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
• • 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
  • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
  • G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
• • 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
  • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared 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
  • G01N21/62—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
• • 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
  • G01N2021/1734—Sequential different kinds of measurements; Combining two or more methods
  • G01N2021/1736—Sequential different kinds of measurements; Combining two or more methods with two or more light sources
• • 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
  • G01N2021/6421—Measuring 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
  • G01N21/6486—Measuring fluorescence of biological material, e.g. DNA, RNA, cells
• • 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

• Spectroscopic analysis method and apparatus using multichannel spectral data processing in infrared and fluorescence.
• the present invention relates to a method and apparatus for spectroscopic analysis. More particularly, the invention relates to a method of analyzing at least one sample by applying multi-channel statistical processing to a set of spectral data from different spectroscopic analysis techniques.
• the invention can be applied particularly, but not exclusively, to the food industry, the pharmaceutical industry, or the environmental industry. In the food industry, it allows for example the study of technological, nutritional and / or toxicological properties of a food product during its preparation, or the agricultural, biological or technological processes to which the product is subject. More generally, the invention can be applied to the determination of any quality indicator of a sample, and / or of any parameter characterizing a process to which said sample has been subjected.
• absorption spectroscopy methods including transmittance spectroscopy and / or reflectance spectroscopy, are at the basis of many devices used in agrifood factories and reception sites for agricultural raw materials.
• Absorption spectroscopy in the field of infrared (IR) and / or near infrared (NIR) allows, in particular, to evaluate measurements of the content of food products in high concentration constituents such as proteins, fat, water content or total sugars.
• Multivariate analysis is the natural extension of multivariate analysis when the data are multidimensional as in the case of fluorescence (excitation and emission matrices), and is then based on the use of multi-channel statistical models such as PARAFAC "(" Parallel Factor ”) and NPLS (" N-ways Partial Least Squares Regression ", ie partial least squares regression with n-ways).
• a known solution of the state of the art to quantify more precisely the quality of the analyzed products is to use a fluorescence technology.
• a sample subjected to light radiation at a specific wavelength for example in the visible (Vis) and / or ultraviolet (UV) domains, emits in response emission radiation depending on the components contained in this sample. Based on the measurement of this radiation emission, it is possible to obtain the corresponding fluorescence spectrum, depending on the wavelengths. Fluorescence spectroscopy thus makes it possible to characterize phenomena such as pH change, the heating of food matrices as is the case for vegetable oils, or the analysis of contaminants or the characterization of the growth of a plant. and the germination of a seed. The information obtained also makes it possible to evaluate different markers of the technological quality of the sample (s) analyzed.
• fluorescence spectroscopy can not accurately determine the same quality parameters that absorption spectroscopy allows to access.
• absorption spectroscopy provides information on interatomic bonds while fluorescence is concerned with molecular composition.
• sugars can be characterized by intermolecular carbonyl binding, quantifiable in infrared, but they are not fluorescent, and therefore not quantifiable by fluorescence.
• the proteins can possibly be visible by the two technologies but through different structures: the amide group for the infrared and the aromatic ring of amino acids, such as tryptophan fluorescence.
• the invention aims at providing a method for analyzing at least one sample using a spectroscopic data analysis method based on a multi-channel statistical model, characterized in that what it includes:
• said first light source is a source of light radiation at respective illumination wavelengths.
• said second light source is a continuous source.
• said fluorescence spectra are spectra acquired over a spectral range between 250 nm and 800 nm.
• said transmittance and / or reflectance spectra are spectra acquired over a spectral range between 400 nm and 2500 nm, and preferably over a spectral range between 400 nm and 1100 nm.
• the number of light rays emitted by the first light source is between one and eight, and preferably between two and five.
• the fluorescence spectra are fluorescence spectra acquired in the frontal mode.
• said step d) also comprises a preliminary step of normalizing said fluorescence spectra and / or said transmittance and / or reflectance spectra.
• said multi-channel statistical model implemented is a Tucker type model.
• said determination of an indicator characterizing said or each sample is carried out by applying a calibration model linking the decomposition data to said indicator.
• the invention also aims at providing an apparatus for analyzing at least one sample for implementing a method according to the invention, characterized in that it comprises:
• said lighting means comprising a first light source and at least a second light source, said at least one second light source being distinct from said first light source;
• FIG. 1 a block diagram of an analysis apparatus according to one embodiment of the invention.
• FIG. 2 a diagram describing the organization of the spectral data into data acquisition cubes
• FIGS. 3, 4 and 5 diagrams describing the organization and the merging of the acquisition data into at least one merged data cube according to different embodiments.
• a first step a) of the method according to the invention comprises illuminating a sample or several samples by a plurality of light sources.
• Figure 1 shows a simplified diagram of an apparatus A for implementing a method according to the invention.
• a sample E is disposed on a support H.
• Said sample may be a solid, a powder, a liquid contained in a transparent container, etc.
• the support H may be transparent or partially transparent to light radiation.
• the apparatus A comprises a first light source S1 disposed on one side of said support H, and configured to illuminate E.
• said first light source is a source of excitation light radiation at wavelengths of respective lighting.
• each of said light sources emits a monochromatic radiation beam at a different wavelength.
• the illumination of E by the first light source makes it possible to generate a fluorescence spectrum.
• Fluorescence spectroscopy consists in sending to a sample a light radiation at a specific wavelength. This light radiation typically has at least one wavelength in the visible range (Vis) and / or ultraviolet (UV) to cause excitation of the components contained in this sample.
• the wavelengths characterizing said light radiation extend over a spectral range typically between 250 nm and 800 nm.
• the considered sample emits a full spectrum, said fluorescence spectrum comprising a plurality of corresponding emission radiation at several wavelengths A réelle.
• These radiations generally comprise two contributions: one, at the same wavelength as the illumination radiation, due to the elastic diffusion; the other, polychromatic, due to fluorescence, the corresponding emission radiations being characterized by a wavelength A em ission greater than Aexcitat n-
• the fluorescence spectra may also include autofluorescence spectra or, in in some cases, fluorescence spectra induced by a marker added to the sample.
• said first light source may comprise a single source of monochromatic radiation, a number of monochromatic radiation sources greater than two, or one or more sources of polychromatic light generating illumination radiation from said first source of radiation.
• the first light source comprises one or more electroluminescent diodes.
• S1 can thus also include one or more laser sources if higher intensities are required.
• S1 may comprise another light source S12, or more generally several other light sources distinct from S1.
• said wavelengths of the light beams are between 250 nm and 800 nm.
• the excitation light rays may have selected wavelengths so as to cover the UV-visible spectrum as widely as possible.
• these excitation radiations can roughly sample (several tens of wavelengths) and / or finely (several hundreds of wavelengths) a spectral range covering the infrared domains, visible and ultraviolet.
• the number of light rays emitted by the first light source is between one and eight, and preferably between two and five.
• the fluorescence spectra are fluorescence spectra acquired in the frontal mode. The specific use of fluorescence in the frontal mode has the advantage of being able to apply the process in real time.
• the acquisition of the frontal fluorescence spectra emitted by the or each sample does not generate an analytical error related to the preparation of the sample. The results obtained by the process according to the invention are therefore more precise and determined more rapidly.
• the apparatus A also comprises a second light source S2 configured to illuminate the sample E.
• This illumination of E by the source S2 can occur before or after the illumination of E by the source S1 as detailed above.
• S2 is a continuous light source, for example a polychromatic source such as a tungsten, halogen or halogen-tungsten lamp.
• the source S2 is configured to emit continuous radiation whose wavelengths can be distributed over a wide spectral range of the electromagnetic spectrum.
• S1 is configured to illuminate the sample over a spectral range between 400 and 2500 nm, and preferably between 400 nm and 1100 nm. This spectral range may include the visible, infrared and / or near infrared domains.
• An illumination module Ml can also be added to the source S2 to direct the radiation emitted by S2 to the sample E. These rays are absorbed by the sample, before being detected by the acquisition means MA, as detailed below.
• the illumination of E by the source S2 makes it possible to generate an absorption spectrum.
• These absorption signals may, in particular, include transmittance and / or reflectance signals.
• Absorption spectroscopy is based on the principle that any material subjected to incident radiation, for example infrared radiation, may either reflect some of these rays, absorb some of these rays, or transmit some of these rays. More particularly, absorption spectroscopy is based on the property of the atomic bonds to absorb light energy at a wavelength of interest. It will be appreciated that the second light source may be disposed on the same side as the first light source relative to the sample, or in any other direction.
• the first light source and the second light source are arranged on two different sides of the sample E and / or the support H.
• a single device comprising for example the same measurement chamber and a single spectrometer configured to analyze a set of spectra acquired in the ultraviolet, visible, infrared and / or near infrared domains makes it possible to facilitate the consistency of the data obtained on the same sample.
• a second step b) and a third process step c) according to the invention comprises the acquisition of fluorescence spectra and absorption of said or each sample.
• all the fluorescence spectra and absorption spectra from the sample are captured by the acquisition means MA.
• Said means MA detect and measure any light radiation emitted, reflected or transmitted by the sample, and resulting from illumination of said sample.
• the means MA comprise, for example, one or more measurement stations, distinct physically or otherwise, and making it possible to acquire the fluorescence spectra and the absorption spectra from the sample.
• the MA means are collocated in a single measuring station, and arranged appropriately so as to receive optimally any type of radiation from the sample E. This facilitates the analysis of the same sample of the material, making the process more efficient, reducing the time required for analysis, and allowing a better correlation of the spectroscopic data relating to the material.
• the fluorescence signals and the absorption signals emitted by E are then transported via communication means MC to one or more processors P.
• Said communication means MC may comprise a wired connection, for example of the optical fiber, Ethernet type. , CPL, or even a wireless connection for example of WiFi or Bluetooth type, or any other type of connection that may vary depending on the preferred hardware for the implementation of the invention.
• the processor or processors P can in turn comprise a signal processing device, a spectrometer configured to decompose the light radiation emitted into a spectrum, or any other processing equipment adapted to the process. More generally, P includes data processing means (for example, a computer programmed in a timely manner) for extracting chemometric information from spectra acquired by the apparatus A.
• the signals are, typically, analyzed by chemometric methods that extract the information correlated to the quality parameters that are to be measured.
• chemometric methods that extract the information correlated to the quality parameters that are to be measured.
• a fourth step d) and a fifth process step e) according to the invention comprises the organization of the acquired fluorescence spectra and absorbance spectra acquired in a first cube and in a second cube of acquisition data, respectively.
• the collected data are organized in cubes of data.
• said data cubes comprise several matrices called “excitation-emission matrices” (MEEs), said matrices being constructed to contain all the spectra acquired on a sample.
• MEE may be a two-way array, said array being able to be represented by a three-dimensional spectrum in the form "Excitation x Emission x Intensity".
• a Acquisition data cube will typically include three dimensions, "Excitation x Emission x Sample”.
• FIG. 2 The modes of organization of the data cubes spectral data according to the invention are illustrated in FIG. 2.
• the organization of the data acquired in cubes of data will make it possible, in the following steps of the method, to apply methods of analysis. multipath much more powerful than multivariate decomposition tools.
• the fluorescence measurements are organized in a three-dimensional data cube "I x J x K", said first cube of acquisition data C1, or "cube of fluorescence".
• the mode I of C1 comprising an "i" number of inputs, is associated with the number of samples illuminated by the second light source during the step of acquiring fluorescence spectra of said or each sample.
• the mode J of C1 comprising a number "j" of inputs, is associated with the number "j" of emission wavelengths, each of these wavelengths corresponding to one of the components of the radiation emitted by said sample or samples after illumination thereof or thereof by the first light source.
• the K mode of C1 comprising a number "k” of inputs, is associated with the number "k” of excitation wavelengths, each of these wavelengths corresponding to a light radiation used for the illumination of sample or samples.
• the fluorescence data obtained are thus organized into a three-dimensional cube, these dimensions corresponding to the three modes "Excitations x Emissions x Samples”.
• the absorption measurements are organized in a two-dimensional data cube "I x L", said second cube of acquisition data C2, or "absorption cube” .
• Each of said two dimensions corresponds to a given mode.
• the mode I of C2, comprising an "i" number of inputs is associated with the number of samples illuminated by the first light source during the step of acquiring transmittance and / or reflectance spectra of said of each sample.
• the mode L of C2, comprising an "I" number of inputs is associated with the number "I" of absorption wavelengths, each of these wavelengths corresponding to one of the components of the radiation emitted by said sample or samples after illumination thereof or thereof by the second light source.
• the fluorescence data obtained are thus organized in a two-dimensional cube "I x L", corresponding to the modes "Emissions x Samples".
• a sixth process step f) comprises merging the data of the first cube and the data of the second cube into a third cube called merged data.
• the first mode of data organization has the advantage of respecting the physics of the data acquired.
• An important technical effect of the first and third modes of data organization described below is that they preserve the linearity of the spectral data acquired separately by each of the two spectroscopy techniques.
• These embodiments also make it possible to preserve the correlations between the absorption data and the fluorescence data during the merging of the first data cube and the second data cube. These correlations are important because they may include information linking the UV-visible fluorescence spectra and the visible-near-infrared transmittance spectra for a given sample.
• This information is for example: concentrations in analytes, the physico-chemical structure, or the functionality and sensoriality of the product. This information can be particularly useful for defining quality criteria specific to the sample analyzed, and is difficult to access via the use of absorption spectroscopy alone or fluorescence only spectroscopy.
• an embodiment according to the invention consists of registering the first acquisition data cube and the second acquisition data cube in the same third data cube C31, referred to as merged data.
• the cube C2 is transformed into a three-dimensional cube I x L x L, so that the cube I x L constitutes a diagonal plane of a cube I x L x L according to the modes L x L, whose diagonal has a dimension equal to the diagonal formed by the elastic diffusion of the k th source of the fluorescence cube.
• this cube I x L x L is concatenated with the cube C1 of dimensions I x J x K to form the cube C31.
• the other C31 entries are filled with values all equal to zero. This concatenation is performed in order to align the mode L with the modes J and K to form said cube C3 of merged data.
• the cube C31 is, thus, a cube of dimensions I x (K + L) x (K + L) whose upper left contains the fluorescence data in the form of a three-dimensional sub-cube, and one of which part of the diagonal plane contains the absorption data in the form of a diagonal subplane according to the (K + L) x (K + L) modes.
• This organization of the data has the advantage of respecting the initial common modes of the cubes C1 and C2, since the mode L of C2 is aligned with the modes J and K of C1. Since these modes correspond respectively to the emission wavelengths and excitation wavelengths, the correlation between the data acquired by the fluorescence spectroscopy and the data acquired by the absorption spectroscopy is preserved.
• two other embodiments according to the invention consist in replicating cube C2.
• cube C2 is replicated a number "k" of times to form a three-dimensional intermediate cube I x L x K.
• Said intermediate cube I x L x K thus comprises two types of common inputs with the cube C1 of dimensions I x J x K.
• a first possibility consists in proceeding according to steps 4.2 and 4.3, to juxtapose the intermediate cube I x L x K with cube C1.
• steps 4.2 and 4.3 By aligning the J and L modes of these two three-dimensional cubes, we obtain a three-dimensional C32 cube I x K x (J + L) containing the set of fluorescence and absorption data.
• Another possibility consists in proceeding according to steps 5.2 and 5.3, to juxtapose the intermediate cube I ⁇ L ⁇ K with the cube C1. Aligning these two cubes in the common K mode results in a three-dimensional cube C3 I x L x J, containing the set of fluorescence and absorption data. This can be done, for example, by performing a number "k" of matrix products.
• the organization of the acquired spectroscopic data can be preceded by different preprocessing sub-steps.
• the fluorescence spectra may, for example, be pretreated to take account of the contributions due to elastic scattering, also known as Rayleigh scattering. These contributions can be calculated using generalized linear models, then subtracted from the acquired spectra. Subtraction of Rayleigh scattering is generally required in most analytical methods, and may be applied in the process of the present invention. However, the subtraction of the diffusion is not necessarily desirable in the present invention.
• the contributions of elastic scattering can be eliminated by means of mathematical processing, in order to exploit the "pure" fluorescence spectra.
• the elastic scattering intensities can be adjoined for later use, for example when calculating indicators characterizing the sample.
• the initial intensities of elastic diffusion corresponding to the different excitation wavelengths can indeed be reused in combination with the information from the subsequent steps of the method.
• the acquired spectra can be pretreated by performing a normalization, or by performing a multiplicative dispersion correction MSC (Multiplicative Scatter Correction), or SNV (Standard Normal Variate).
• MSC Multiplicative Scatter Correction
• SNV Standard Normal Variate
• the pretreatments described can also be applied to the data cubes according to the invention.
• a seventh step g) of the method according to the invention comprises the decomposition of the merged data of the third cube by application of a multi-channel statistical model.
• the decomposition of the data can proceed according to different types of chemometric treatments.
• the multivariate methods of the multichannel methods can be distinguished.
• Multivariate methods such as PLS or PCA are, typically, data reduction methods adapted for data organized in two-dimensional cubes. They conventionally involve a prior unfolding of the initial cube according to one of the dimensions, a concatenation of the data obtained, and then the actual analysis.
• Multichannel methods such as Tucker, NPLS, or mPCA are suitable data reduction methods for data organized in cubes with more than two dimensions. They are therefore intrinsically multidimensional and can be used directly on the data cubes resulting from the analysis method according to the steps previously described.
• Intrinsic correlations can also be used to infer more precise information about the analyzed sample (s).
• the invention thus provides a faster and more efficient analysis method. Similarly, an analysis apparatus implementing such a method requires equipment that is simpler, less expensive, and therefore better suited to industrial requirements than current technologies. The invention It also facilitates the speed and rationalization of decision-making in the production of food products.
• a multichannel processing applied to effect the decomposition of the three-dimensional fused data cube is a Tucker3 type model.
• the Tucker3 model decomposes a tensor X "I x J x K" into three cubes with two dimensions, and into two cubes of data.
• each element x, j, k is decomposed as follows:
• one of the matrices A, B or C is a matrix called “scores” or reduced data, while the others are called matrices of "loadings". If for example the mode I is that of the samples, the matrix A “I x P" will then be the matrix of "scores", said matrix of "scores” making it possible to describe each sample “i” by a number “p” of " representative scores. Said “scores” are used in the rest of the invention.
• the loadings matrices B and C respectively represent the contributions of the modes J and K, whereas the cube G represents the interactions between the 3 modes.
• the invention can also apply a multichannel decomposition of Tucker2 or PARAFAC type, these two models constituting special cases of Tucker3.
• An eighth process step h) according to the invention comprises the determination of at least one indicator characterizing said or each sample, from the data resulting from the application of said multi-channel statistical model to said merged data.
• the matrix of "scores" from step g) according to the invention makes it possible to characterize the sample analyzed or the samples analyzed by a set of variables.
• the variables may in turn relate to the at least one indicator via a regression model.
• the application of said regression model on the "scores" obtained on one or more new samples then makes it possible to obtain the value of said indicator on these samples.
• the first example relates to the result obtained by a multilinear combination of scores obtained via the combined analysis of fluorescence spectra and fluorescence spectra to obtain the prediction of a protein level in wheat samples, for example gluten.
• each sample is illuminated by 4 light-emitting diodes, or LEDs, emitting respective light rays at 280 nm, 340 nm, 385 nm and 450 nm. Illumination by such light radiation leads to the acquisition of a complete emission spectrum over a range of the electromagnetic spectrum ranging from 250 nm to 800 nm, and including the fluorescence spectra associated with the wheat samples. Each sample is then illuminated by a halogen tungsten lamp emitting continuous radiation spread over a spectral range from 800 nm to 2500 nm.
• the illumination by this radiation leads to the acquisition of a complete emission spectrum over the same range of the electromagnetic spectrum, extending from 250 nm to 800 nm and including the transmittance and / or reflectance spectra or spectra of the electromagnetic spectrum. 20 wheat samples.
• a processing of the acquired spectra is then performed by the signal analyzer, in particular via one or more processors.
• the fluorescence spectra can be cleaned of the elastic diffusion and then pretreated via normalization. This normalization is for example of the SNV type. It will be understood that the pretreatment of the spectra can be carried out at any time preceding the organization of the fluorescence spectra and absorption spectra in cubes of data, according to the best way to implement the method.
• the fluorescence spectra are organized into a three-dimensional CF1 cube, called the first acquisition data cube, the number of entries associated with said dimensions respectively corresponding to the number of samples, the number of excited radiations and the number of emitted emissions radiation, ie a cube of "Samples x Excitations x Emissions" modes.
• the CF1 cube comprises 20 x 4 x 550, or 44000 entries.
• Absorption spectra, possibly pretreated using standard normal variate (SNV) normalization, are organized into a two-dimensional CAD cube, called the second acquisition data cube, the number of inputs associated with said dimensions.
• SNV standard normal variate
• said CAD cube comprises 20 x 1700 entries, or 34000 entries.
• the CAD cube is then duplicated 4 times to form a cube CA1 of size 20 x 4 x 1700, that is to say consisting of 136000 entries.
• said cubes CF1 and CA1 are then matched according to the emission mode to obtain a CFA1 cube of Samples x Excitation x Emissions modes, of size 20 x 4 x 2250.
• the CFA1 cube is then decomposed by application of a algorithm, for example of the Tucker 2 type, to obtain a matrix of scores of size 20 x 15, that is to say leading to obtaining 15 score factors for each of the 20 samples.
• the score matrix is then correlated with a vector of size 20 x 1, said vector containing the results of analysis of gluten levels (in percent) measured in each of the samples, obtained via multiple linear regression.
• This information includes not only the quality of the calibration on a wheat quality parameter by infrared alone and the quality of the calibration obtained by the fluorescence alone, but also the calibration obtained by the conjunction of the scores obtained for the two technologies separately, as well as that the calibration obtained using the three-dimensional structure explained above.
• the statistical performance of this regression is provided in the table below.
• Table 1 shows a table characterizing the performances resulting from a typical process according to the current state of the art, through the value of R 2 and the calibration error (RMSEC and RMSECV).
• the performances of the regressions thus obtained are compared with the approach that is the subject of the invention to obtain the prediction of a level of proteins in each of the wheat samples.
• the comparison of these performances is presented in Table 2 below, showing a clear improvement in prediction performance.
• the respective values of R 2 , RMSEC, R 2 CV and RMSECB obtained by applying the method according to the invention for analyzing the spectroscopic data obtained from the acquisition of the fluorescence spectra and the transmittance spectra of the samples considered are all greater than those obtained by applying traditional methods to analyze data from the acquisition of fluorescence spectra alone or the acquisition of transmittance spectra alone.
• the second example of application close but distinct from the first example described above, relates to the result obtained by a multilinear combination of the scores obtained via the combined analysis of the fluorescence spectra and the fluorescence spectra to obtain the prediction of a protein levels in wheat samples.
• Each sample is successively illuminated by 4 LEDs emitting respective light rays at 280 nm, 340 nm, 385 nm and 450 nm. For each of said light beams, a complete emission spectrum was acquired over the 250 nm-800 nm range. Each sample is then illuminated by a halogen-tungsten lamp over a spectral range from 800 nm to 2500 nm, and the corresponding absorption spectrum is acquired over the same range.
• the fluorescence spectra are cleaned of the elastic scattering, then pretreated via SNV normalization (Standard normal variat ⁇ ), and organized into a first cubic cube of CF2 data of modes "Samples x Excitations x Emissions ", and of size 20 x 4 x 550.
• Absorption spectra are preprocessed via normal standard variate (SNV) normalization, and organized into a second cube of" Sample x Emissions "modes of size 20. x 1700.
• This absorption data table is duplicated 4 times and the 4 tables thus obtained are matched to form a new CA2 cube of size 20 x 4 x 1700.
• a matrix product according to the Excitations mode is then performed between the cubes.
• the present invention relates to an analysis method for optimizing the joint processing of spectral data from two different spectroscopic technologies for the analysis of one or more samples.
• the analysis method described, and its various embodiments aim to reconcile the constraints resulting from the simultaneous use of these two technologies, including absorption spectroscopy and fluorescence spectroscopy.
• the invention thus proposes an innovative analysis method for obtaining more precise indicators characterizing the quality of one or more samples.
• the present invention also provides an analysis apparatus for implementing such a method of analysis.

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• Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
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• 2016
  • 2016-02-02 FR FR1650830A patent/FR3047313B1/fr not_active Expired - Fee Related
• 2017
  • 2017-01-31 CN CN201780012576.1A patent/CN109313127A/zh active Pending
  • 2017-01-31 JP JP2018539897A patent/JP2019503490A/ja active Pending
  • 2017-01-31 US US16/073,750 patent/US20190369013A1/en not_active Abandoned
  • 2017-01-31 WO PCT/EP2017/052046 patent/WO2017134050A1/fr active Application Filing
  • 2017-01-31 EP EP17701899.1A patent/EP3411691A1/de not_active Withdrawn
  • 2017-01-31 CA CA3013301A patent/CA3013301A1/fr not_active Abandoned

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