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    • 专利摘要:
    A method for analyzing at least one sample, implementing a spectroscopic data analysis method based on a multi-channel statistical model, characterized in that it comprises: a) lighting said or each sample to be analyzed by a first light source and a second light source, said at least one second light source being distinct from said first light source; b) the acquisition of fluorescence spectra of said or each sample, said fluorescence spectra resulting from the illumination of said or each sample by one or more light rays emitted by said first light source; c) the acquisition of transmittance and / or reflectance spectra of said or each sample, said transmittance and / or reflectance spectra resulting from the illumination of said or each sample by one or more light radiations emitted by said second source; from light ; d) the organization of said fluorescence spectra acquired in a first cube of acquisition data; e) arranging said acquired transmittance and / or reflectance spectra into a second acquisition data cube; f) merging acquisition data of said first cube and acquisition data of said second cube into a third merged data cube; g) decomposing the merged data of said third cube by applying said multi-channel statistical model; h) the determination of at least one indicator characterizing said or each sample, from data derived from the application of said multi-channel statistical model to said merged data. Apparatus for implementing such a method
    公开号:FR3047313A1
    申请号:FR1650830
    申请日:2016-02-02
    公开日:2017-08-04
    发明作者:Ines Birlouez-Aragon；Pierre Lacotte；Abdelhaq Acharid；Fatma Allouche；Jad Rizkallah
    申请人:Spectralys Innovation；
    IPC主号:
    专利说明:

    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.
    To determine parameters indicative of the quality of a food product, known as quality parameters, it is known spectroscopic analyzes using methods of chemometrics. In this context, 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.
    Conventionally, known and used methods for absorption spectroscopy are based on multivariate statistical analysis methods of spectroscopic data. 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).
    However, many industrial procedures today require a precise knowledge of the raw material constituting these samples, in particular to carry out detailed analyzes of the technological, nutritional and / or toxicological properties of a given product. For example, it may be necessary to know various parameters such as the level of contamination of these samples by unwanted chemical molecules (acrylamide, mycotoxins ...), the structure of the proteins that condition their functionality (denaturation rate, size of the aggregates. ..), or the state of germination of a seed (Hagberg drop index of wheat, germination potential of malting barley, ..). To be measured accurately, these parameters require spectroscopic data processing over as wide a range of electromagnetic spectrum as possible, including infrared, visible, and ultraviolet. However, the quality parameters determined using absorption spectroscopy alone, generally applied in the infrared range, provide inaccurate information on the samples analyzed. For example, this type of spectroscopy does not make it possible to quantify trace molecules (<0.5%), as is the case for mycotoxins or acrylamide.
    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 emission radiation, it is possible to obtain the corresponding fluorescence spectrum, as a function of 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.
    Despite its sensitivity, it is known that fluorescence spectroscopy can not accurately determine the same quality parameters that absorption spectroscopy allows to access. In particular, absorption spectroscopy provides information on interatomic bonds while fluorescence is concerned with molecular composition. For example, 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. It would therefore be logical to use the fluorescence signals and the absorption signals emitted on the surface of a given sample together to determine a greater number of information relating to the physicochemical state of a sample, illuminating it by light beams at specific wavelengths of the electromagnetic spectrum.
    However, the joint processing of data from these two technologies, for example absorption in the IR and NIR domains and fluorescence in the Vis and UV domains, remains problematic today because limited by the efficiency of the methods of analysis current. Typically, the processing of data from two different technologies is done separately. The results obtained from the two types of spectroscopy therefore do not benefit from the synergies and complementarities resulting from their joint use. In addition, the manipulation and combination of data acquired by two different spectroscopy technologies are subject to numerous technical constraints, limiting the performance of associated analysis methods. Obtaining reduced variables obtained through the application of multivariate decomposition tools for each technology solves some of this complementarity, but does not allow to extract accurately and robustly all original and unreduced spectral information. Moreover, this information is not correlated, since it does not confer a power advantage to the applied treatments given the redundancy provided by the two technologies. This information is not complementary either, since it does not confer enrichment. extracted information. Finally, these approaches only allow at best a classification or comparison of samples, while the quantification of indicators is much more interesting and more useful for professionals. In order to benefit from both types of measure, producers, manufacturers and cooperatives usually equip themselves with two different types of analyzers, which represents investment and person costs! and potentially high logistics.
    To overcome these difficulties or exploitation limits of the various technologies, 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 it comprises: a) illuminating said or each sample to be analyzed by a first light source and a second light source, said at least one second light source being distinct from said first light source; b) the acquisition of fluorescence spectra of said or each sample, said fluorescence spectra resulting from the illumination of said or each sample by one or more light rays emitted by said first light source; c) acquiring transmittance and / or reflectance spectra of said or each sample, said transmittance and / or reflectance spectra resulting from illumination of said or each sample by one or more light rays emitted by said second source; from light ; d) the organization of said fluorescence spectra acquired in a first cube of acquisition data; e) the organization of said transmittance and / or reflectance spectra acquired in a second cube of acquisition data; f) merging acquisition data of said first cube and acquisition data of said second cube into a third merged data cube; g) decomposing the merged data of said third cube by applying said multi-channel statistical model; h) the determination of at least one indicator characterizing said or each sample, from data derived from the application of said multi-channel statistical model to said merged data.
    According to various additional features that may be taken together or separately: 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 the implementation of a method according to the invention, characterized in that it comprises: lighting means of said or of each sample to be analyzed, said illumination 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; a first means for acquiring fluorescence spectra of said or each sample, said fluorescence spectra resulting from the illumination of said or each sample by one or more light rays emitted by said first light source; a second means of acquisition of transmittance and / or reflectance spectra of said or each sample, said transmittance and / or reflectance spectra resulting from the illumination of said or each sample by one or more light rays emitted by said second light source; and one or more processors configured to implement at least steps d) to h). Other characteristics, details and advantages of the invention will emerge on reading the description given with reference to the appended drawings given by way of example and which represent, respectively: FIG. 1, a schematic diagram of a device analysis 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 carrying out a method according to the invention. As shown, 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. Advantageously, said first light source is a source of excitation light radiation at wavelengths of respective lighting. Preferably, each of said light sources emits a monochromatic radiation beam at a different wavelength. According to the invention, 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 range of visible (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. For each excitation light radiation corresponding to an excitation wavelength, the sample in question emits a complete spectrum, called the fluorescence spectrum, comprising a plurality of corresponding emission radiations at several emission wavelengths. 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 higher emission wavelength · Fluorescence spectra may also include autofluorescence spectra or, in some cases, fluorescence spectra induced by a marker added to the sample.
    In a nonlimiting manner, said first light source may comprise a single monochromatic radiation source, a number of monochromatic radiation sources greater than two, or one or more polychromatic light sources generating light radiation from said first source of light. light. Advantageously, 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. As illustrated in FIG. 1, S1 may comprise another light source S12, or even more generally several other distinct light sources of S1. Preferably, but not limited to, said wavelengths of the light beams are between 250 nm and 800 nm. In general, the excitation light rays may have selected wavelengths so as to cover the UV-visible spectrum as widely as possible. Depending on the number of radiation sources, these excitation radiations can roughly sample (several tens of wavelengths) and / or finely (several hundred wavelengths) a spectral range covering the infrared domains, visible and ultraviolet. Advantageously, the number of light rays emitted by the first light source is between one and eight, and preferably between two and five.
    Advantageously, 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. In addition, 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. Advantageously, 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. Advantageously, 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.
    According to the invention, 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. Advantageously, the first light source and the second light source are arranged on two different sides of the sample E and / or the support H. Finally, the use of 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.
    According to the invention, 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. Advantageously, 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 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. These correlations are present in many food products and appear because of the evolution of their contents. For example, the intrinsic fluorescence of the natural constituents of a food (vitamins, proteins and other natural constituents or added intentionally or not intentionally), as well as their reflectance, can evolve over time, while at the same time, new signals may appear due to the formation of new molecules. These correlations therefore play an important role in the context of their characterization by spectroscopic analyzes.
    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.
    Once the fluorescence, transmittance and / or reflectance spectra are acquired for the analyzed sample (s), the collected data are organized in cubes of data. By definition, said data cubes comprise several matrices called "excitation-emission matrices" (MEEs), said matrices being constructed to contain all the spectra acquired on a sample. In particular, a MEE can be a two-vote array, said array being able to be represented by a three-dimensional spectrum in the form "Excitation x Emission x Intensity". For the particular case of an acquisition of one or more fluorescence spectra, an acquisition data cube will typically comprise three dimensions, "Excitation x Emission x Sample".
    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.
    During the acquisition of the fluorescence data, 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". Each of said three dimensions corresponds to a given mode. 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".
    When acquiring the absorption data, 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, including an "I" number of entries, is associated with the number "! Of absorption wavelengths, each of these wavelengths corresponding to one of the components of the radiation emitted by said sample or said 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) according to the invention comprises merging the data of the first cube and the data of the second cube into a third cube called merged data.
    Three modes of organization and data fusion are thus proposed. As described, 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 can include information linking the fluorescence spectra in the uitraviolet-visible and the transmittance spectra in the visible-near-infrared 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.
    As illustrated in FIG. 3, 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. .
    In step 3.1, 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 keme source of the fluorescence cube. In step 3.2, this cube I x L x L is concatenated with cube C1 of dimensions IxJxK to form 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.
    According to FIGS. 4 and 5, two other embodiments according to the invention consist in replicating cube C2. According to step 4.1 or step 5.1, 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 C1 cube of dimensions lx J x K. These two three-dimensional cubes having two common modes, it is possible to combine them in several ways so as to preserve the mode I corresponding to the number "i" of samples analyzed in a single, three-dimensional cube of merged data.
    As illustrated in FIG. 4, a first possibility consists in proceeding according to steps 4.2 and 4.3, to juxtapose the intermediate cube I × L × K with the cube C1. 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.
    As illustrated in FIG. 5, 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. By aligning these two cubes according to the common K mode, we obtain a cube C3 three-dimensional! 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.
    It will be understood that other modes of fusion can also be used to form a three-dimensional fused data cube and be characterized by similar technical advantages.
    It will be noted that according to the invention, the organization of the acquired spectroscopic data can be preceded by different preprocessing sub-steps. Advantageously, 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. In addition, the contributions of elastic scattering can be eliminated by means of mathematical processing, in order to exploit the "pure" fluorescence spectra. Alternatively, 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.
    Advantageously, 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). Advantageously, 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. Depending on the size and dimensions of the data cubes to be decomposed, 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.
    In addition to the greater software efficiency of the analysis methods enabled by the application of a multi-channel statistical model to a single merged data cube rather than two data cubes, the possibility of decomposing said data while preserving 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 also facilitates the speed and rationalization of decision making during the production of food products.
    In an innovative way, the invention applies a new multichannel decomposition technique of all the merged raw data. Advantageously, 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. In particular, each element xy, k is decomposed as follows:
    with - aiiP, bj, q, ck, r the elements of the respective matrixes A "I x P", B "J x Q" and C "K x R" - ey, k is an element of the cube of residues E "I x J x K "- gPiqir is an element of the interaction cube G" P x Q x R ", also called" core-array "
    In any case, 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.
    Preferably, but in a nonlimiting manner, 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.
    Some technical results of the invention will be described below with the aid of two application examples. These two examples demonstrate the improvement in the performance of prediction of the characteristics of a sample by means of a method according to the invention with respect to the performances obtained without implementing said method.
    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.
    For this first example, we consider the analysis of 20 wheat samples. 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 samples of bié. A processing of the acquired spectra is then performed by the signal analyzer, in particular via one or more processors. In particular, 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. After this pretreatment, 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. For the example considered, 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. corresponding respectively to the number of samples and the number of transmissions, ie a cube of modes "Samples x Emissions". For the example considered, 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.
    For the present application, 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 an 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 a multiple linear regression. The application of these particular modes of organization allows to extract a larger amount of information. 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 below shows a table characterizing the performances resulting from a typical process according to the current state of the art, through the value of R2 and the calibration error (RMSEC and RMSECV).
    Table 1
    In order to characterize the technical improvement provided by this method, similar regressions were obtained by methods conventionally used in the literature. In particular: a decomposition of the pretreated two-way array of PCR uptake data, providing a MA1 matrix of 20 x 5 scores, followed by multilinear regression is then performed. Also: a decomposition of the pre-processed CF1 cube of PARAFAC fluorescence data, providing an MF1 matrix of 20 x 6 scores, followed by a multilinear regression is then performed. Finally, a concatenation of the two matrices MA1 and MF1 to form a matrix MFA1 of size 20 x 11, followed by a multilinear regression. 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 of the prediction performances. The respective values of R2, RMSEC, RZCV 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 application of traditional methods to analyze the data from the acquisition of fluorescence spectra alone or the acquisition of only the transmittance spectra.
    Table 2
    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 diffusion, then pretreated via normal standard variate (SNV) normalization, and organized into a first cubic cube of CF2 data of modes "Samples x Excitations x
    Emissions ", and size 20 x 4 x 550. Absorption spectra are preprocessed via standard normal 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. CF2 and CA2, to obtain a CFA2 cube of modes "Samples x Emissions x Emissions", size 20 x 550 x 1700. Then, this cube is decomposed by application of a PARAFAC algorithm, allowing to obtain a matrix of "Sample x Factor" scores, size 20 x 15. The score matrix is then correlated to a vector of size 20 x 1 containing the results of analysis of the protein levels {%) measured in each of the samples, via a regressor. multiple linear pressure. The statistical performance of this regression is provided in Table 3 below. The table below shows a table characterizing the performances resulting from a typical process according to the current state of the art, through the value of R2 and the calibration error (RMSEC and RMSECV).
    Table 3
    In order to characterize the technical improvement provided by this method, similar regressions were obtained by the methods conventionally used in the current state of the art literature: a decomposition of the two-way array pretreated of PCA absorption data, providing a MA1 matrix of 20 x 5 scores, followed by a multilinear regression. Also; a decomposition of the pre-processed CF1 cube of PARAFAC fluorescence data, providing an MF1 matrix of 20 x 6 scores. A multilinear regression is then performed. Finally: a simple concatenation of the two matrices MA1 and MF1 to form a matrix MFA1 of size 20 x 11, followed by a multilinear regression. The performances of the regressions thus obtained are compared with the approach that is the subject of the invention, thus demonstrating an improvement in the prediction performance, as indicated in Table 4 below.
    Table 4
    In summary, 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. In particular, 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 carrying out such a method of analysis.
    Naturally, to meet specific needs, a person skilled in the field of the invention may apply modifications in the foregoing description.
    Although the present invention has been described above with reference to specific embodiments, the present invention is not limited to specific embodiments, and modifications that are within the scope of the present invention will be obvious to someone skilled in the art.
    权利要求:
    Claims (11)
    [1" id="c-fr-0001]
    A method for analyzing at least one sample, implementing a spectroscopic data analysis method based on a multi-channel statistical model, characterized in that it comprises: a) lighting said or each sample analyzing by a first light source and a second light source, said at least one second light source being distinct from said first light source; - b) the acquisition of fluorescence spectra of said or each sample, said fluorescence spectra resulting from the illumination of said or each sample by one or more light rays emitted by said first light source; c) acquiring transmittance and / or reflectance spectra of said or each sample, said transmittance and / or reflectance spectra resulting from the illumination of said or each sample by one or more light radiations emitted by said second or each sample; light source ; - d) the organization of said fluorescence spectra acquired in a first cube of acquisition data; e) organizing said acquired transmittance and / or reflectance spectra into a second acquisition data cube; f) merging the acquisition data of said first cube and acquisition data of said second cube into a third cube of merged data; g) decomposing said merged data of said third cube by applying said multi-channel statistical model; h) determining at least one indicator characterizing said or each sample, based on data derived from the application of said multi-channel statistical model to said merged data.
    [2" id="c-fr-0002]
    The method of claim 1, wherein said first light source is a source of light radiation at respective illumination wavelengths.
    [3" id="c-fr-0003]
    The method of claim 1 or 2, wherein said second light source is a continuous source.
    [4" id="c-fr-0004]
    4. Method according to any one of the preceding claims, wherein said fluorescence spectra are spectra acquired over a spectral range between 250 nm and 800 nm.
    [5" id="c-fr-0005]
    5. Method according to any one of the preceding claims, wherein 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.
    [6" id="c-fr-0006]
    6. Method according to any one of the preceding claims, wherein the number of light rays emitted by the first light source is between one and eight, and preferably between two and five.
    [7" id="c-fr-0007]
    The method according to any of the preceding claims, wherein the fluorescence spectra are fluorescence spectra acquired in the frontal mode.
    [8" id="c-fr-0008]
    The method according to any one of the preceding claims wherein said step d) also comprises a prior step of normalizing said fluorescence spectra and / or said transmittance and / or reflectance spectra.
    [9" id="c-fr-0009]
    9. Method according to any one of the preceding claims, wherein said multi-channel statistical model implemented is a Tucker type model.
    [10" id="c-fr-0010]
    The method according to any one of the preceding claims, wherein said determining an indicator characterizing said or each sample is performed by applying a calibration model connecting the decomposition data to said indicator.
    [11" id="c-fr-0011]
    11. Apparatus for analyzing at least one sample for the implementation of a method according to any one of the preceding claims, characterized in that it comprises: - lighting means of said or each sample to analyzing, said illumination 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; a first means for acquiring fluorescence spectra of said or each sample, said fluorescence spectra resulting from the illumination of said or each sample by one or more light rays emitted by said first light source; a second means of acquisition of transmittance and / or reflectance spectra of said or each sample, said transmittance and / or reflectance spectra resulting from the illumination of said or each sample by one or more light rays emitted by said second light source; and one or more processors configured to implement at least steps d) to h).
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    同族专利:
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    EP3411691A1|2018-12-12|
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    优先权:
    申请号 | 申请日 | 专利标题
    FR1650830A|FR3047313B1|2016-02-02|2016-02-02|METHOD AND APPARATUS FOR SPECTROSCOPIC ANALYSIS USING MULTIVOUS PROCESSING OF SPECTRAL DATA IN INFRARED AND FLUORESCENCE|
    FR1650830|2016-02-02|FR1650830A| FR3047313B1|2016-02-02|2016-02-02|METHOD AND APPARATUS FOR SPECTROSCOPIC ANALYSIS USING MULTIVOUS PROCESSING OF SPECTRAL DATA IN INFRARED AND FLUORESCENCE|
    CN201780012576.1A| CN109313127A|2016-02-02|2017-01-31|The method and apparatus for implementing the infrared and fluorescence multichannel process to spectroscopic data to carry out spectrum analysis|
    US16/073,750| US20190369013A1|2016-02-02|2017-01-31|Method and apparatus for spectroscopic analysis, implementing infrared and fluorescence multichannel processing of spectral data|
    JP2018539897A| JP2019503490A|2016-02-02|2017-01-31|Spectroscopic analysis method and apparatus using multiple processing of infrared and fluorescent spectral data|
    EP17701899.1A| EP3411691A1|2016-02-02|2017-01-31|Method and apparatus for spectroscopic analysis, implementng infrared and fluorescence multichannel processing of spectral data|
    PCT/EP2017/052046| WO2017134050A1|2016-02-02|2017-01-31|Method and apparatus for spectroscopic analysis, implementng infrared and fluorescence multichannel processing of spectral data|
    CA3013301A| CA3013301A1|2016-02-02|2017-01-31|Method and apparatus for spectroscopic analysis, implementng infrared and fluorescence multichannel processing of spectral data|
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