• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
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    • 专利摘要:
    abstract system and method for determining the properties of a sample the invention relates to a system (102) for determining the properties of a sample (114) comprising a libs detector (104,106) and an infrared absorption detector (108,110) for determining a sample (114) to generate, respectively, spectral data libs and spectral data of infrared absorption; and a data processor (112) adapted to apply at least one chemometric prediction model, each of which is built to link, preferably quantitative binding, the resources of both libs and spectral absorption data to a specific property different from sample, for a set of combined data derived from at least portions of both the libs and the absorption data to generate, from it, a determination, preferably a quantitative determination, of the specific property linked by that model. 1/1
    公开号:BR112015023894B1
    申请号:R112015023894
    申请日:2013-03-22
    公开日:2020-05-05
    发明作者:Vilstrup Juhl Henrik；Kirstine Elsoee Maja；Nikolajsen Thomas
    申请人:Foss Analytical As；
    IPC主号:
    专利说明:

    SYSTEM AND METHOD FOR DETERMINING PROPERTIES OF A SAMPLE [001] The present invention relates to the determination of sample properties using laser-induced plasma emission spectroscopy (LIBS) in conjunction with infrared absorption (IR) spectroscopy ).
    [002] LIBS is a well-known technique that has the ability to provide an elementary ”fingerprint of a sample with high sensitivity. The use of such a technique in soil analysis is also well known and has been reported, for example, in the Development of a Laser-Induced Breakdown Spectroscopy Method for Soil Analysis (Review) ”, VS Burakov et al., Journal of Applied Spectroscopy, Vol 77, N :: 5, 2010, Pages 595 to 608.
    [003] LIBS operates by placing the focus of a laser on a small area on the surface of the sample material to thereby ablate a very small amount of material and generate a plasma column. The material extracted from the plasma column breaks down into excited ionic and atomic species. The atomic emission lines characteristic of the elements can be observed in the electromagnetic spectrum of the plasma column, which is typically recorded by the use of a spectrometer and analyzed in a data processor to provide information regarding the relative amount of chemical species present in the sample as a measurement of sample properties.
    [004] It is known to combine LIBS with other optical energy measurement techniques in order to provide an improved compositional determination. The combination of X-ray fluorescent measurements with LIBS, for
    2/21 example, is disclosed in US 6,801,595 to Grodzins et al. LIBS is typically employed to receive information about the relatively lighter elements and typically provides data with respect to the relative concentrations of elements in a sample matrix while X-ray fluorescence provides information about the relatively heavier elements and provides information about absolute concentration. According to US 6,801,595, the spectra of the two techniques are combined and the information from the two techniques referring to the same element is used to produce a result indicative of absolute concentrations of elements in the sample matrix from the combination of both LIBS and X-ray fluorescence data.
    [005] A combination of Raman scattering and LIBS emission detection is revealed by Beckstead et al. in US 7,999,928. Raman spectroscopy is based on the scattering of light by vibrant molecules and the spectral shift (anti-Stokes or Stokes shifts) from the light source (typically a laser) caused by the loss of energy due to inflexible collisions between photons and molecules is what is detected. The advantage of this combination is the high similarity of these measurements, both depending on the detection of radiation that results from the interaction of laser material so that the revealed measurement system can use many of the same hardware components for both LIBS measurements and measurements. Raman. Furthermore, in addition to the complementary use of the hardware, it was also demonstrated, in US 7,999,928, that the subsequent PCA analysis of the combined spectral information leads to a better classification than each of the techniques alone, which leads to a reduction
    3/21 in the number of false positive determinations in relation to each technique alone and an improved system for the identification (as opposed to quantification) of target species in a sample.
    [006] Infrared absorption spectroscopy is another well-known technique for determining compositional properties of a sample, such as identification and quantification of target species, for example, in food and medicines, or quality parameters, such as wheat hardness, cooking properties of flour or wine quality. Since the absorption of IR by species of interest in the sample matrix generally follows the law of Lambert Beer (ie, a linear relationship between absorption and the number of species of absorption), the detection of IR absorption allows, more quickly , a quantitative determination of target species. In particular, IR absorption data in combination with the treatment of sophisticated chemometric data can be used to provide this quantitative information for various types of sample matrix. The IR absorption technique is fundamentally different from Raman spectroscopy in the sense that the latter depends on a polarizability of the molecule under study and the former depends on changes in dipole moment during vibration. Consequently, species that are detectable by Raman are generally not readily detectable by IR absorption and vice versa. In addition, Raman spectroscopy tends to be less sensitive than infrared absorption spectroscopy and is therefore employed in determining qualitative rather than quantitative properties, such as the presence of target species in a sample.
    4/21 [007] A restriction on the use of IR, particularly the proximal infrared ('NIR'), spectroscopy for the quantitative analysis of one or more target species is its sensitivity to matrix effects that interfere with the weak molecular overtones probed in this wavelength range. In a way, this sensitivity can be compensated using a large data set from a large sample matrix rate, which are chosen to cover these variations in combination with chemometric data analysis methods, such as PLS. However, for very complex matrices, such an approach can sometimes prove to be insufficient. This is the case when trying to use the NIR for soil analysis, when it was concluded that only local calibrations of soil parameters in soils of similar material and climatic impacts can be performed (Visible and Near Infrared Spectroscopy in Soil Science ”, B Stenberg et al., Advances in Agronomy ', Vol. 107, 2010, Pages 163 to 215).
    [008] The present invention aims to mitigate at least one of the problems associated with the well-known spectroscopic infrared absorption technique and to provide a system and method with the capacity to allow quantitative determination of properties in a complex sample matrix, such as those related to species target or physical quality properties, for example, of the soil.
    [009] In accordance with an aspect of the present invention, a system is provided for determining the properties of a sample comprising a LIBS detector, which has a laser to ablate a portion of the sample and an optical spectrophotometer to generate data from LIBS that
    5/21 represent a variation of intensity dependent on the wavelength in optical energy emitted from the portion extracted by ablation; an infrared absorption detector that has an infrared energy source to illuminate at least a portion of the sample with infrared energy and an optical spectrophotometer to generate illumination data that represents an intensity variation dependent on the infrared wavelength after its sample lighting; at least one chemometric prediction model built to link the resources of both LIBS data and lighting data to a specific property different from the sample and executable by a data processor; and a data processor configured to receive LIBS data and lighting data; to build a combined data set derived from at least a portion of the LIBS data and at least a portion of the lighting data and to apply to the constructed data set the at least one chemometric prediction model to generate from this point, a determination of the specific property. It has been found that certain properties whose prediction was the use of a combined data set that comprise both LIBS data and lighting data are more adequately predicted and have a better repeatability than the same properties whose prediction was the use of LIBS data or lighting data alone. The present invention, therefore, provides a system for determining the properties of a sample that performs better relative to a system that employs individual techniques in isolation.
    6/21 [010] In a system modality according to the present invention, at least one chemometric prediction model is constructed so that, when applied to the data processor, a quantitative determination of the property is generated.
    [011] In a further modality of the system, a sample stage adapted for movement, preferably for rotational movement, is provided to effect a movement of a sample which thus exposes different portions for extraction by laser ablation and illumination by infrared energy and wherein the data processor is configured to receive LIBS data and lighting data from a plurality of portions as the sample is moved; to generate a set of average LIBS data and a set of average lighting data from the respective LIBS data and lighting data; and applying at least one prediction model to the combined data set derived from the data from both the average LIBS data set and the average lighting data set. Using a data set derived from LIBS and lighting data obtained from a plurality of different portions of the sample, then any adverse effects on the data due to the lack of homogeneity of the sample can be mitigated and the data that is more representative of the sample can be obtained.
    [012] In accordance with a second aspect of the present invention, a method of determining the properties of a sample is provided, comprising the steps of: acquiring, in a data processor, the LIBS data corresponding to the intensity-dependent variations of the
    7/21 wavelengths of optical radiation that were emitted from at least a portion of the sample as a result of extraction by laser-induced ablation of the portion; acquire, in the data processor, the illumination data corresponding to the intensity variation depending on the wavelengths of infrared radiation of illumination after its interaction with at least a portion of the sample; on the data processor, apply at least one chemometric prediction model, each of which is built to link the resources of both LIBS data and lighting data to a specific sample property, to a combination of both LIBS data and lighting data to generate a determination of the specific property linked by the prediction model.
    [013] These, as well as the additional objects, features and advantages of the invention, will be better understood through the description of illustrative and non-limiting modalities of the present invention, made with reference to the drawings of the attached figures, of which:
    [014] DESCRIPTION OF THE FIGURES [015] Figure 1 shows a schematic block representation of an embodiment of a system according to the present invention;
    [016] Figure 2 shows a flow chart that illustrates a method of establishing a prediction model that can be used in the system in Figure 1;
    [017] Figure 3 shows a calibration curve for a prediction model for clay in the soil established following the methodology illustrated in Figure 2 and a combined data set;
    8/21 [018] Figure 4 shows a calibration curve for a prediction model for clay in the soil established using only NIR absorption data;
    [019] Figure 5 shows a calibration curve for a soil clay prediction model established using LIBS emission data only; and [020] Figure 6 shows a calibration curve for a prediction model for TOC in soil established using a combined data set and the methodology illustrated in Figure 2.
    [021] An exemplary non-limiting embodiment of a system 102 according to the present invention is illustrated in Figure 1. System 102 comprises a LIBS detector, which includes a laser source 104 for ablating a region of the sample and a optical spectrophotometer 106; an infrared absorption detector, which includes an infrared (IR) energy source 108 to illuminate a sample region and an optical spectrophotometer 110; and a data processor 112 in an operable connection to the outputs of both optical spectrophotometers 106,110. It will be seen from a consideration of the following that, although data processor 112 is illustrated as a single unit, the present invention can also be accomplished using a data processor that comprises physically separate elements to perform different functions assigned to the only data processor 112 of the present modalities and that these elements may be in remote locations from each other and, for example, interconnected through a telecommunication link.
    [022] 106,110 optical spectrophotometers are
    9/21 illustrated in the present modality as being separate instruments, but this is not essential and, in other modalities, these can be combined into a single spectrophotometric instrument that uses the same optical dispersion elements and / or detection arrangements. Each spectrophotometer 106, 110 (or, alternatively, the only spectrophotometer) is adapted to generate a result that represents a variation in intensity dependent on the input wavelength of optical energy from a sample 114 to the input to the processor data 112 (hereinafter referred to as LIBS data ”when generated using the LIBS detector and as lighting data” when generated using the infrared absorption detector). As will be seen, spectrophotometers 106,110 can be performed in a variety of known ways, but, by way of example only, each spectrophotometer 106,110 of the present embodiment comprises a wavelength dispersion device 106a, 110a that has an output for optical energy to provide optical radiation in a detection device 106b, 110b that converts optical intensity into a corresponding electrical signal for the output to data processor 112. It may be that the wavelength dispersing device 106a, 110a of one or both spectrophotometers 106, 110 include a mobile dispersion element (such as a diffraction grating) which, as it moves (more typically is rotated), scans the wavelengths of incident optical radiation through an output groove and over a single element detection device 106b, 110b. In a provision that is
    10/21 Less sensitive to physical vibrations, the wavelength dispersion device 106a, 110a of one or both of the spectrophotometers 106, 110 includes a static wavelength dispersion element that provides an optical output that is spatially dispersed over lengths wavelength for an array of detection elements of the detection device 106b, 110b in which each element, or perhaps subgroup of elements of the array, receives a separate and individually identifiable portion of the dispersed wavelengths spatially dispersed by the fixed dispersion elements dispersion devices 106a, 110a and converts these into individually identifiable electrical signals that correspond to the radiation intensities in the separate elements or in the element subgroups.
    [023] Each of the source laser 104 and the source IF power 108 is willing to generate an output for focus on an matrix of sample 114 that can to be prepared or not prepared and that is located in a
    sample stage 116, for example, and not necessarily in a container 118. This arrangement of laser outputs and IR energy can be achieved in several ways, for example, as illustrated for the present embodiment, the laser source 104 and the IR 108 power source can be positioned to provide outputs that are orthogonal and intersect. An optical arrangement 120, here in the form of a semi-silvered mirror positioned at the junction of the outlets and oriented at 45 o each, can be provided to direct the incident exits towards the sample matrix 114. In another embodiment, the optical arrangement 120 may have an optical fiber arrangement to guide the exits to the matrix of
    11/21 sample 114. In an additional embodiment, each of the laser source 104 and the IF 108 energy source can be arranged to direct their outputs to be incident on the sample matrix 114 without the need for an optical arrangement. Although achieved, it is preferable, but not essential, that the outputs from each of the laser source 104 and the IF 108 power source be made incidentally in substantially the same region of the sample matrix 114 to interact with substantially the same matrix material of sample 114.
    [024] Collection optics 122 can be provided to collect radiation from sample matrix 114 that results from an interaction between incident outputs from sources 104, 108 and sample matrix material 114 and to provide this radiation for detection by one of the spectrophotometers 106,110, or both, as well as by the detection devices 106b, 110b. The collection optics 122 can be performed in a variety of ways and, for example, and without limitation, the collection optics can comprise, as illustrated in the present modality, a collection lens 122a that collects and focuses radiation from the matrix sample 114 over a fiber optic port 122b. Optical fiber 122b conducts this radiation to spectrophotometers 106,110, whose optical fiber 122b, in the present embodiment, comprises a bifurcated output, one for each of the spectrophotometers 106,110.
    [025] In an exemplary mode of operation of a system according to the present invention, which will now be illustrated as an example only, with reference to system 102 of Figure 1, a sample matrix 114 is
    12/21 located in a measurement location inside system 102, where the output of laser 104 and the power source IR 108 can each interact with sample 114. The measurement location in this mode is defined by a sample receiving platform 116 which can advantageously be movable, here rotatable, to minimize size overloads in relation to the displacement directions of the outputs from the laser 104 and the IF 108 energy source. relative to sample matrix 114 LIBS data and lighting data can be obtained from different regions of sample matrix 114 and data from each region can be combined to provide a mean data set for a larger region sample matrix 114 than those regions used to provide any data set. Sample 114, in some cases, can be positioned in a sample holder, such as a sample cup 118 shown in Figure 1 in which it is then positioned on the sample receiving platform 116 (or more generally) at the measurement site). In other cases, the sample may be positioned in a non-retained manner on the sample receiving platform 116 (or more generally, at the measurement site). Sample 114 may also undergo some treatment prior to its investigation with the use of LIBS detectors 104,106 and IR absorption detectors 108,110, for example, when sample 114 is soil or other particulate material, the sample material can be pressed in order to avoid voids in the sample.
    [026] After sample 114 is located at the measurement site (sample receiving platform 116), each of the laser 104 and the IR 108 energy source is operated
    13/21 to illuminate a region, preferably the same region, of sample 114. The operation of the 104,108 sources can be done simultaneously or sequentially. Simultaneous operation can cause the radiation problem from a source that creates an unwanted background signal for the detector that incorporates the other source. Preferably, but not essentially, the two sources 104,106 are operated sequentially, more preferably the laser 104 is operated after the IR 108 energy source, so that the lighting data and LIBS data will originate substantially from the same material from the same region. This will provide a better correlation of data from the LIBS detectors (104,106) and infrared absorption detectors (108,110) since both sets of data are therefore generated from substantially identical material. The IR 108 energy source is configured to generate IR energy that extends at least through the wavelength regions expected to be absorbed by the sample 114. The IR 108 energy source can be broadband or can be arranged to emit energy IR in a plurality of narrow wavelength bands, which possibly overlap or are consecutive. In the operation of system 102, the IF 108 energy source is energized and its IR energy output is made incident in a region of the sample 114 that absorbs a particular wavelength dependent on the composition of the sample 114. This IR energy, after its interaction with the sample is collected through the collection optics 122, passed to the infrared absorption detector, in the present mode, to the detector's spectrophotometer 110, and an output is generated that
    14/21 corresponds to a variation of intensity depending on the wavelengths of the interacted IR energy (optical absorption spectrum). This output is passed to the data processor 112 as lighting data, for example, and as an illustration mode only, which represents intensity values measured in a plurality, m, of different wavelengths. Next, the power source IF 108 is de-energized and laser 104 is energized. The laser beam is made incident on the sample 114 and a portion of it is extracted by ablation to form a plasma. The optical radiation that is generated as excited species in the plasma returns to its lower energy state, which emits characteristic photons in the process, is collected by the collection optics 122, passed to the LIBS detector, in the present mode for the detector 106 spectrophotometer , and an output is generated that corresponds to an intensity variation dependent on the wavelengths (optical emission spectrum) of the plasma emissions and is passed to the data processor 112 as LIBS data, for example, and as an illustration mode only, which represents the intensity values measured in the same plurality or in different pluralities, n, of different wavelengths.
    [027] Data processor 112 is configured to combine LIBS data and lighting data into a single data set (combined data set ”). In the present modality and as a non-limiting example only, this combined data set consists of m + n data points that contain all the lighting and LIBS point data. The intensity values for each of these points can also undergo normalization or other
    15/21 pre-processing of data in data processor 112.
    [028] A computer executable algorithm that describes a multivariable chemometric prediction model that is built to link resources from both LIBS data and lighting data to a sample property is made available to data processor 112, for example, from a computer memory or integral data storage device with and a data processor component 112 or from a remote storage device (not shown) which may, in some embodiments, be accessible to the data processor 112 through a telecommunication connection. The data processor 112 is adapted to operate to apply the prediction model to the combined data set to generate, from there, a determination of the sample property that is linked by the prediction model. One or more additional prediction models can be accessible for data processor 112, each model linking a different property to the combined LIBS and lighting data, and the data processor being adapted to apply one or more of these models to the combined data set with the objective of obtaining determinations of the linked properties for each corresponding prediction model. The results of each of these determinations can be provided by the data processor 112 as an output 124, for example, as an output to a screen, printer or other human-readable format or as an output in machine-readable format.
    [029] Such prediction models are established with the use of known chemometric techniques that employ
    16/21 linear or non-linear multivariate static analysis, for example, Partial Minimum Squares (PLS); Multiple Linear Regression (MLR); or Artificial Neural Network (ANN), to generate a mathematical relationship by which the combined data set, derived from the LIBS and spectral illumination data, can be quantitatively correlated with the sample's properties of interest.
    [030] The chemometric prediction model that is used in the data processor 112 can be constructed according to the flowchart illustrated in Figure 2. A first step 202 in establishing such a prediction model is the generation of a database ( or information matrix) where each recording represents the data from a calibration sample. In this database, LIBS data and lighting data are stored from calibration samples (that is, samples that have the same matrix as the samples whose properties will be predicted) indexed with other information obtained from the same sample. calibration that identifies the presence and / or, more usefully, the quantities of a species whose presence and / or quantity will be determined in a test sample. This other information can be obtained using direct compositional analysis methods, such as, for example, liquid or gas chromatography, in each of the calibration samples. Such other methods of analysis, while providing a direct measurement of species of interest present in the sample matrix, are typically time consuming and costly to perform.
    [031] In step 204, the contents of the database are subjected to a multivariate statistical analysis. At the
    In the present example this comprises step 204a of dividing the database from step 202 into two parts. The first part is subjected to multivariate analysis in step 204b. The second part is employed in step 204c as an independent validation set. It will be noted that the precise use and division of the database content may vary.
    [032] In step 206, a prediction model is established, by which a mathematical relationship is provided between the LIBS input and the combined lighting data (the combined data set) and a sample property whose quantitative indication is to be predicted ( general relation: Property = Function {LIBS spectral data, spectral illumination data}). This model is for use in data processor 112 for application to LIBS data and lighting data combined to form a combined data set for an unknown sample.
    [033] It will be noted that the prediction model according to the present invention can be established with the use, in addition, of other data, such as information regarding the estimates of physical qualities of the calibration samples such as hardness or texture; information regarding temperature, physical location, sample pre-treatment conditions.
    [034] EXEMPLE: SOIL ANALYSIS [035] The combined information in the LIBS spectrum and the IFP absorption spectrum is used to develop mathematical prediction models, each model being useful for the quantitative determination of a different sample property of soil. The soil samples were homogenized
    18/21 and pressed into blocks about 40 mm in diameter and about 5 mm thick with the use of a simple hydraulic compressor. In the present example, 5 tons were applied for 30 seconds and then 11 tons for an additional 30 seconds to produce blocks in which substantially all air pockets are removed. The pressed blocks demonstrated much less fluctuations in their LIBS spectra as compared to unpressed samples. Measurements were made on one hundred and six soil samples obtained from locations across North America. The resulting diversity of soil matrices and the limited number of samples used to generate a useful prediction model illustrate that the present inventive combination of measurement modalities according to the present invention is advantageously able to compensate for complex matrices that no measurement methodology can do it in isolation. These samples were formed in blocks as described above and the measurements obtained using a system generally as described in relation to that of Figure 1 with the objective of generating LIBS data sets and lighting data sets. The combined data set, derived using data from both the LIBS data sets and the lighting data set, is used to generate one or more prediction models generally according to the process described in relation to the Figure 2.
    [036] In the specific example of clay content or texture prediction (a quantitative prediction typically expressed as a percentage), about 50 soil calibration samples and 1 replica are used in order to establish the calibration and the fifty-six
    19/21 remaining calibrations employed as the validation set. Each had its combined data set (LIBS data + lighting data) indexed against the clay content that was derived using the sedimentation reference method. The PLS prediction model was built using the combined data set (Figure 3) and compared with the models built using only lighting data (here the IFP absorption data) (Figure 4) and LIBS (Figure 5).
    [037] The PLS prediction model generated from the curve (straight line) illustrated in Figure 3 that employs the combined data set provides a model that has a prediction accuracy of 4.12 and a correlation of 0.91. As used here, accuracy is defined as a measurement of the standard deviation of the predicted values from the middle of the reference measurements (the reference method itself has an accuracy of 3.5 for clay), while the correlation is a measurement of the linear dependence between the plotted variables and the ranges between -1 and +1 (+1 indicates a stronger correlation. This can be compared to the PLS prediction model generated from the curve illustrated in Figure 4 that uses only the lighting data set ( IFP Absorption), whose model has a prediction accuracy of 6.37 and a correlation of 0.76 The PLS prediction model generates from the curve illustrated in Figure 5 that uses only the LIBS data set, and is, likewise, worse than that generated using the combined data set and has a prediction accuracy of 5.25 and a correlation of 0.89.
    [038] Total organic carbon (TOC) is another important parameter to determine the amount in the soil
    20/21 as it characterizes the humus content and, therefore, the innate fertility of the soil. A second prediction model for OCD was also constructed (a quantitative prediction typically expressed as a percentage) in a manner described above in relation to the clay content of the prediction model. The reference method against which the TOC was calibrated was the dry combustion method in which the amount of CO2 released to a heated soil sample is monitored. In the present example, the same one hundred and six soil calibration samples were measured using the system generally described in relation to Figure 1. Again, the combined data set from fifty samples was used to establish a prediction model PLS and the combined data set from the remaining fifty-six was used as the validation data set.
    [039] The PLS prediction model generated from the curve illustrated in Figure 6 that uses the combined data set has a prediction accuracy of 0.678 (as compared to the reference accuracy of 0.7) and a correlation of 0, 79.
    [040] Other prediction models for soil properties, such as cation exchange capacity or 'CEC' (a quantitative prediction typically expressed in cmol (+) / kg), calcium or potassium contents (quantitative predictions typically expressed in parts per million 'ppm'), can be built in a similar way and some, or all, of them are available for data processor 112 in Figure 1 for application to the combined data set obtained for unknown soil samples
    21/21 using a system according to the present invention, such as that illustrated in Figure 1.
    [041] Thus, as a form of the present example, it was illustrated that the system according to the present invention can be used to make quantitative measurements even in a highly complex sample matrix.
    [042] It will be noted that, while the system and method according to the present invention have a particular application in soil analysis, the present invention is intended to be limited to use in that field. Indeed, the present invention can find uses and bring its advantages to different fields, such as explosives detection or other types of danger detection; monitoring and control of food, drink and animal food; and biological fluid investigations. It will also be verified that the choice of analysis methodology is not limited to PLUS, but, as it is known in the chemometrics technique, it can be selected after considering one or more, for example, the linearity of the data set, in the size and the diversity of this data set and whether a quantitative prediction or a qualitative prediction is necessary.
    权利要求:
    Claims (7)
    [1]
    1. SYSTEM (102) FOR DETERMINING PROPERTIES OF A SAMPLE, characterized by comprising a laser-induced plasma emission spectroscopy detector (LIBS) (104,106), which has a laser (104) to extract a portion of the sample and a optical spectrophotometer (106) for generating LIBS data that represent a variation of intensity dependent on the wavelengths in optical energy emitted from a portion extracted by ablation; an infrared absorption detector (108,110), which has an infrared energy source (108) for lighting at least a portion of the sample with infrared energy and an optical spectrophotometer (110) for generating lighting data that represent a variation of intensity dependent on the wavelengths of infrared energy after lighting the sample; at least one chemometric prediction model in a form usable by a data processor (112) and each chemometric prediction model is built to link resources from both LIBS data and illumination data in combination with a different specific property of the sample; and a data processor (112) configured to receive LIBS data and lighting data; to build a combined data set derived from at least a portion of the LIBS data and at least a portion of the lighting data; and applying at least one chemometric prediction model to the combined data set to generate, from there, a determination of the specific property linked by the chemometric prediction model.
    Petition 870200021512, of 13/02/2020, p. 12/8
    [2]
    2/3
    2. SYSTEM (102), according to claim 1, characterized in that at least one chemometric prediction model is constructed in such a way that, when applied to the data processor (112), a quantitative determination of the specific property is generated.
    [3]
    3. SYSTEM (102) according to claim 2, characterized in that the quantitative determination comprises a determination of the quantity of a target species within the sample.
    [4]
    4. SYSTEM (102) according to claim 1, characterized in that the prediction model is constructed to establish a connection with a soil property.
    [5]
    5. SYSTEM (102), according to claim 1, characterized in that the optical spectrophotometer (106) of the LIBS detector (104,106) and the optical spectrophotometer (110) of the infrared absorption detector (108,110) are the same.
    [6]
    6. METHOD OF DETERMINING PROPERTIES OF A SAMPLE, characterized by understanding the steps of: acquiring, in a data processor (112), the LIBS data that correspond to the intensity variation depending on the wavelengths of optical radiation that were emitted from at least a portion of the sample as a result of laser-induced extraction of the portion; acquire, in the data processor (112), the illumination data that correspond to the intensity variation depending on the wavelengths of infrared radiation of illumination after its interaction with at least a portion of the sample; apply, in the data processor (112), at least one chemometric prediction model, with each prediction model
    Petition 870200021512, of 13/02/2020, p. 9/12
    3/3 chemometric is built to link resources from both LIBS data and lighting data to a specific property other than the sample, for a combined data set derived from at least a portion of the LIBS data and at least a portion of the lighting data to generate, from it, a determination of the specific property linked by the chemometric prediction model.
    [7]
    7. METHOD according to claim 6, characterized by the step of applying at least one chemometric prediction model comprising applying a generated chemometric prediction model to provide a quantitative determination of a target species within the sample.
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    同族专利:
    公开号 | 公开日
    BR112015023894A2|2017-07-18|
    EP2976620A1|2016-01-27|
    US20160018325A1|2016-01-21|
    CN105008898B|2018-09-11|
    ES2638965T3|2017-10-24|
    EP2976620B1|2017-08-09|
    WO2014146719A1|2014-09-25|
    RU2616777C1|2017-04-18|
    US9625376B2|2017-04-18|
    CN105008898A|2015-10-28|
    引用文献:
    公开号 | 申请日 | 公开日 | 申请人 | 专利标题
    EP1386145B8|2001-05-04|2012-08-29|Thermo Scientific Portable Analytical Instruments Inc.|X-ray fluorescence combined with laser induced photon spectroscopy|
    US7945393B2|2002-01-10|2011-05-17|Chemimage Corporation|Detection of pathogenic microorganisms using fused sensor data|
    US20110237446A1|2006-06-09|2011-09-29|Chemlmage Corporation|Detection of Pathogenic Microorganisms Using Fused Raman, SWIR and LIBS Sensor Data|
    US7092087B2|2003-09-16|2006-08-15|Mississippi State University|Laser-induced breakdown spectroscopy for specimen analysis|
    WO2007123555A2|2005-07-14|2007-11-01|Chemimage Corporation|Time and space resolved standoff hyperspectral ied explosives lidar detector|
    US8553210B2|2007-01-23|2013-10-08|Chemimage Corporation|System and method for combined Raman and LIBS detection with targeting|
    US8547540B2|2005-07-14|2013-10-01|Chemimage Corporation|System and method for combined raman and LIBS detection with targeting|
    US8582089B2|2006-06-09|2013-11-12|Chemimage Corporation|System and method for combined raman, SWIR and LIBS detection|
    WO2007087315A2|2006-01-23|2007-08-02|Chemimage Corporation|Method and system for combined raman and libs detection|
    US8158957B2|2006-03-02|2012-04-17|Chemimage Corporation|System and method for structured illumination and collection for improved optical confocality of raman fiber array spectral translator imaging and interactive raman probing|
    EP1862795A1|2006-05-10|2007-12-05|ABB Schweiz AG|Bulk Material Analyzer System|
    US8018647B2|2007-03-12|2011-09-13|Northrop Grumman Systems Corporation|Imaging systems and methods|
    US8125627B2|2007-04-27|2012-02-28|Alakai Defense Systems, Inc.|Laser spectroscopy system|
    WO2009137494A1|2008-05-05|2009-11-12|Applied Spectra, Inc.|Laser ablation apparatus and method|
    JP5615941B2|2011-02-10|2014-10-29|株式会社日立ハイテクノロジーズ|Foreign object detection device and foreign object detection method|
    US8830476B2|2012-03-19|2014-09-09|The United States Of America As Represented By The Secretary Of The Army|Methods and apparatuses for contact-free holographic imaging of aerosol particles|
    US9970876B2|2012-07-17|2018-05-15|Sciaps, Inc.|Dual source analyzer with single detector|
    US9285272B2|2012-07-17|2016-03-15|Sciaps, Inc.|Dual source system and method|
    WO2014031591A1|2012-08-22|2014-02-27|Sackett Donald W|Dual source analyzer with single detector|DE112013004743T5|2012-09-26|2015-09-24|Panalytical Inc.|Multi-sensor analysis of a complex geological material|
    US11080798B2|2014-09-12|2021-08-03|The Climate Corporation|Methods and systems for managing crop harvesting activities|
    US10667456B2|2014-09-12|2020-06-02|The Climate Corporation|Methods and systems for managing agricultural activities|
    US11113649B2|2014-09-12|2021-09-07|The Climate Corporation|Methods and systems for recommending agricultural activities|
    US11069005B2|2014-09-12|2021-07-20|The Climate Corporation|Methods and systems for determining agricultural revenue|
    US10564316B2|2014-09-12|2020-02-18|The Climate Corporation|Forecasting national crop yield during the growing season|
    ES2532032B2|2014-10-05|2015-10-08|Universidad Complutense De Madrid|Method of alcoholic beverage analysis|
    US10113952B2|2015-06-01|2018-10-30|Ingrain, Inc.|Combined vibrational spectroscopy and laser induced breakdown spectroscopy for improved mineralogical and geochemical characterization of petroleum source or reservoir rocks|
    DE102015122408A1|2015-12-21|2017-06-22|Thyssenkrupp Ag|Method and installation for analyzing a sample material|
    CN109073560B|2016-03-31|2021-09-28|福斯分析仪器公司|System and method for performing laser induced breakdown spectroscopy|
    CN106226282A|2016-06-24|2016-12-14|北京华泰诺安探测技术有限公司|A kind of laser Raman spectrometer is used to carry out the device and method sampled|
    US10429240B2|2016-07-29|2019-10-01|Viavi Solutions Inc.|Transfer of a calibration model using a sparse transfer set|
    CN106501237A|2016-10-31|2017-03-15|清华大学深圳研究生院|A kind of composite insulating material case hardness method of testing|
    US10677733B2|2017-04-07|2020-06-09|Thermo Scientific Portable Analytical Instruments Inc.|Device for positioning and isolating a sample in a sealed environment|
    CA3063930A1|2017-05-19|2018-11-22|National Research Council Of Canada|Characterization of a material using combined laser-based ir spectroscopy and laser-induced breakdown spectroscopy|
    WO2019142699A1|2018-01-16|2019-07-25|株式会社ニコン|Spectral diffraction data management device, measurement device, and measurement system|
    US11156556B1|2019-02-13|2021-10-26|Creative LIBS Solutions LLC|Method and apparatus for detecting pathogens and chemicals in complex matrices using light emissions from a laser spark|
    CN110873709B|2019-11-11|2021-12-31|中国科学院上海技术物理研究所|LIBS active and passive dual-mode spectrum detection method|
    CN110873708B|2019-11-11|2022-02-18|中国科学院上海技术物理研究所|LIBS active and passive dual-mode spectrum detection system|
    US20210172800A1|2019-12-10|2021-06-10|Perkinelmer Health Sciences Canada, Inc.|Systems and Methods for Analyzing Unknown Sample Compositions Using a Prediction Model Based On Optical Emission Spectra|
    CN112730383A|2020-12-28|2021-04-30|西安交通大学|Optical fiber array LIBS detection system for online detection|
    法律状态:
    2018-12-04| B06F| Objections, documents and/or translations needed after an examination request according art. 34 industrial property law|
    2019-11-19| B06U| Preliminary requirement: requests with searches performed by other patent offices: suspension of the patent application procedure|
    2020-03-17| B09A| Decision: intention to grant|
    2020-05-05| B16A| Patent or certificate of addition of invention granted|Free format text: PRAZO DE VALIDADE: 20 (VINTE) ANOS CONTADOS A PARTIR DE 22/03/2013, OBSERVADAS AS CONDICOES LEGAIS. |
    优先权:
    申请号 | 申请日 | 专利标题
    PCT/EP2013/056091|WO2014146719A1|2013-03-22|2013-03-22|System for and method of combined libs and ir absorption spectroscopy investigations|
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