WO2020031447A1 - Procédé d'évaluation/estimation d'échantillon par analyse d'empreinte digitale par fluorescence, programme et dispositif - Google Patents

Procédé d'évaluation/estimation d'échantillon par analyse d'empreinte digitale par fluorescence, programme et dispositif Download PDF

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WO2020031447A1
WO2020031447A1 PCT/JP2019/018739 JP2019018739W WO2020031447A1 WO 2020031447 A1 WO2020031447 A1 WO 2020031447A1 JP 2019018739 W JP2019018739 W JP 2019018739W WO 2020031447 A1 WO2020031447 A1 WO 2020031447A1
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fluorescence
sample
test sample
fingerprint information
fluorescent fingerprint
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PCT/JP2019/018739
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English (en)
Japanese (ja)
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啓貴 内藤
瑞樹 蔦
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日本たばこ産業株式会社
国立研究開発法人農業・食品産業技術総合研究機構
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Priority to JP2020536325A priority Critical patent/JP7021755B2/ja
Publication of WO2020031447A1 publication Critical patent/WO2020031447A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence

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  • the present invention relates to a method, a program, and an apparatus for evaluating and estimating a sample using fluorescent fingerprint analysis, and more particularly, to an evaluation and estimating method, a program, and an apparatus that can be suitably used for evaluating and estimating a trace component in a sample. .
  • the excitation wavelength ( ⁇ Ex) and the fluorescence wavelength Corresponding points are plotted in a three-dimensional space having (measurement wavelength) ( ⁇ Em) and fluorescence intensity (IEx, Em) as three orthogonal axes.
  • a visualization of a set of these points is called a fluorescent fingerprint or an Excitation Emission Matrix (EEM).
  • the fluorescent fingerprint can be represented as a three-dimensional graph by displaying the fluorescence intensity of each point in a contour shape, a color distribution, or the like (see FIG. 3), or can be represented as a two-dimensional graph (see FIG. 4). ).
  • Non-Patent Document 1 introduces a technique for estimating the mixing ratio of buckwheat flour using a fluorescent fingerprint.
  • such analysis using fluorescent fingerprints allows characterization of test samples without performing pretreatment such as fluorescent staining on the test samples, and is easier to operate. Measurement can be performed in a short time, the amount of information is large and quantification can be performed relatively easily, nondestructive and non-contact measurement is possible, and the device is relatively It has such advantages as being inexpensive.
  • fluorescence spectrophotometer that scans the excitation wavelength and continuously measures each fluorescence spectrum is required, but a fluorescence spectrophotometer that has such a function is also commercially available. (Hitachi High-Tech Science F-7000, etc.).
  • JP 2017-36991 A JP-A-2017-51162
  • a fluorescent fingerprint information obtaining step of obtaining fluorescent fingerprint information comprising data of excitation wavelength, fluorescent wavelength, and fluorescent intensity for the test sample, and an axis such that a peak value of the fluorescent intensity unique to the test sample appears by a second derivative process.
  • a pre-processing step including at least a secondary differentiation process of the fluorescent fingerprint information along the axis, and at least the fluorescent fingerprint information on which the secondary differentiation process has been performed is used as an explanatory variable.
  • Aspect 2 2.
  • Aspect 5 The method for evaluating a test sample by fluorescent fingerprint analysis according to aspects 1 to 4, wherein the test sample contains chlorogenic acid. (Aspect 6) 5.
  • (Aspect 7) 5. The method for evaluating a test sample by fluorescent fingerprint analysis according to aspects 1 to 4, wherein the test sample contains rutin.
  • (Aspect 8) Any one of the excitation wavelength / fluorescence wavelengths in which the excitation wavelength is near 285 nm and the fluorescence wavelength is at least one of the values near 410, 415, 420, 425, 430, 435, 440, 450 and 460 nm.
  • Aspect 7 The method for evaluating a test sample by fluorescence fingerprint analysis according to aspect 7, wherein a calibration curve of rutin is created by a combination of wavelengths.
  • the test material is a raw material of a tobacco product
  • the harmony condition is a condition of a room at a temperature of 22 ° C. and a humidity of 60%
  • the predetermined time is 24 hours or more.
  • (Aspect 15) Estimating the content of a specific component contained in a sample based on the calibration curve obtained by the method for evaluating a test sample by the fluorescent fingerprint analysis described in Aspects 1 to 14 and the fluorescent fingerprint information of an unknown sample. A method for estimating the characteristic amount of a component.
  • the excitation wavelength is a value near 285 nm and the fluorescence wavelengths are all values near 410, 415, 420, 425, 430, 435, 440, 450 and 460 nm. 18.
  • Preprocessing means including at least a second derivative process of the fluorescent fingerprint information along the at least the fluorescent fingerprint information on which the second derivative process has been performed is used as an explanatory variable, and a known quantitative value of the test sample is used as a target variable.
  • An apparatus for estimating a component amount to be estimated. (Aspect 21) 21. The apparatus according to aspect 20, wherein the fluorescent fingerprint information of the unknown sample is processed by the preprocessing unit, and the processed fluorescent fingerprint information is input to the component amount estimating unit.
  • the term “nearby value” also refers to a wavelength existing in an error range of the excitation wavelength or the fluorescence (measurement) wavelength because the excitation wavelength or the fluorescence (measurement) wavelength necessarily involves an error. It has been added to clarify inclusion.
  • the error range can vary depending on the measuring equipment, the measuring conditions, and the like.
  • a" program is a data processing method described based on an arbitrary language or description method, and does not ask the form of a source code, a binary code, or the like.
  • the “program” may be configured in a single form, but may be configured in a distributed manner as a plurality of modules or libraries, and may be configured to achieve its function in cooperation with other existing programs. It may be configured.
  • the “apparatus” may be configured as hardware, or may be configured as a combination of function realizing means for realizing various functions by computer software.
  • the function realizing means may include, for example, a program module.
  • the present invention it is possible to create a highly accurate estimation model (calibration curve). In particular, it is possible to accurately determine a specific component from a sample containing components having similar chemical structures.
  • FIG. 1 is a flowchart for explaining an outline of an embodiment of the present invention.
  • FIG. 2 is an explanatory diagram illustrating an outline of a spectrum of fluorescence emitted from the measurement target when the measurement target is irradiated with excitation light.
  • FIG. 3 is a contour graph showing a three-dimensional example of a fluorescent fingerprint.
  • FIG. 4 is a contour graph showing a two-dimensional example of a fluorescent fingerprint.
  • FIG. 5 is a diagram illustrating an example of a fluorescent fingerprint of a mixed sample containing scopoletin, chlorogenic acid, and rutin.
  • FIG. 6A is a diagram illustrating an example of a fluorescent fingerprint of a sample that includes a single substance.
  • FIG. 6A is a diagram illustrating an example of a fluorescent fingerprint of a sample that includes a single substance.
  • FIG. 6B is a diagram illustrating an example of a fluorescent fingerprint of a sample containing chlorogenic acid alone.
  • FIG. 6C is a diagram illustrating an example of a fluorescent fingerprint of a sample containing rutin alone.
  • FIG. 7A is a graph plotting points defined by a known content of scopoletin and an estimated value based on fluorescent fingerprint information for a plurality of samples.
  • FIG. 7B is a graph plotting points defined by a known content of chlorogenic acid and an estimated value based on fluorescent fingerprint information for a plurality of samples.
  • FIG. 7C is a graph plotting points defined by a known content of rutin and an estimated value based on fluorescent fingerprint information for a plurality of samples.
  • FIG. 7A is a graph plotting points defined by a known content of scopoletin and an estimated value based on fluorescent fingerprint information for a plurality of samples.
  • FIG. 7B is a graph plotting points defined by a known content of chlorogenic acid and an estimated value based on
  • FIG. 8A is a graph plotting points defined by an actual measurement value (chemical analysis value) of rutin content and an estimated value based on pre-processed fluorescent fingerprint information for a plurality of samples.
  • FIG. 8B is a graph plotting points defined by measured values (chemical analysis values) of chlorogenic acid contents and estimated values based on pre-processed fluorescent fingerprint information for a plurality of samples.
  • FIG. 9 is a graph in which, for a plurality of samples, points defined by actual measured values (chemical analysis values) of rutin content and estimated values based on fluorescent fingerprint information without preprocessing are plotted.
  • FIG. 10 is a block diagram for explaining an outline of another embodiment of the present invention.
  • FIG. 1 is a flowchart for explaining an outline of an embodiment of the present invention.
  • a test sample with a known type and content of components is prepared (S01), and a fluorescent fingerprint is measured on such a known test sample to acquire fluorescent fingerprint information (S02).
  • preprocessing is performed on the acquired fluorescent fingerprint information (S03).
  • One embodiment of the present invention is particularly characterized by this preprocessing, and the details of the preprocessing will be described later.
  • an estimation model (calibration curve) is created (S04). Specifically, this modeling is performed by using various multivariate analysis methods and data mining methods to construct an estimation expression using preprocessed fluorescent fingerprint information as an explanatory variable and a known content as an objective variable.
  • the calibration curve (regression equation) for estimating the content of the specific component in the test sample from the fluorescent fingerprint information is created.
  • the algorithm used for constructing the estimation formula may be a machine learning algorithm that is more general and supports nonlinear phenomena, such as support vector machine (SVM), random forest (RF), and neural network. An example of a multivariate analysis method used for modeling will be described later.
  • the content of the specific component contained in the unknown sample is estimated based on the fluorescent fingerprint information of the unknown sample (S06).
  • FIG. 10 is a block diagram for explaining an outline of another embodiment of the present invention.
  • the apparatus 100 for evaluating and estimating a sample by fluorescence fingerprint analysis inputs fluorescence fingerprint information including data of excitation wavelength, fluorescence wavelength, and fluorescence intensity of the sample, and performs peak differentiation of the fluorescence intensity peculiar to the sample by a second derivative process.
  • An axis is set so that a value appears, and a preprocessing unit 110 including at least a second differentiation process of the fluorescent fingerprint information along the axis, and an output of the preprocessing unit 110 as an input, at least the second differentiation process Is used as an explanatory variable, and a known quantitative value of the test sample is used as a target variable, and an estimation model creating means 120 for obtaining a calibration curve, and the calibration obtained by the estimation model creating means 120 are used.
  • the obtained fluorescent fingerprint information is input to the preprocessing means 110, and preprocessing is performed on the input fluorescent fingerprint information.
  • This aspect also has a feature in this preprocessing, similarly to the above-described embodiment of the present invention, and the details of the preprocessing will be described later.
  • the relationship between the preprocessed fluorescent fingerprint information and the known content of the specific component is modeled by the estimation model creation means 120 to create an estimation model (calibration curve).
  • This modeling is similar to the above-described embodiment of the present invention.
  • the estimation model (calibration curve) thus constructed is verified, its validity is confirmed, and the estimation model (calibration curve) whose validity is confirmed is stored in a memory (not shown) or the like.
  • the component amount estimating means 130 estimates the content of the specific component contained in the unknown sample based on the fluorescent fingerprint information of the unknown sample using the estimation model (calibration curve) whose effectiveness has been confirmed. It is desirable to perform pre-processing by the pre-processing unit 110 also on the fluorescent fingerprint information of the unknown sample (the sample evaluation / estimation device 100 based on the fluorescent fingerprint analysis of FIG. 10 adopts such a configuration. However, such preprocessing can be omitted as necessary.
  • the measured value of the fluorescent fingerprint (fluorescence spectrum for each excitation wavelength) can be used as it is, but it is necessary to perform various pretreatments as necessary.
  • One embodiment of the present invention aims to improve measurement accuracy by adopting a novel method (secondary differential processing unique to the present invention) as described in detail below, particularly in preprocessing. is there.
  • the three orthogonal axes of the excitation wavelength ( ⁇ Ex), the fluorescence wavelength (measurement wavelength) ( ⁇ Em), and the fluorescence intensity (IEx, Em) in the three-dimensional space are set to the y-axis, the x-axis, and the z-axis, respectively, to measure the test sample.
  • the coordinates of each point P i (1 ⁇ i ⁇ N) of the EEM (fluorescent fingerprint) obtained in (1) are represented by (x i , y i , z i ).
  • a plane determined by the x axis and the y axis is an xy plane
  • a plane determined by the x axis and the z axis is an xz plane
  • a plane determined by the y axis and the z axis is a yz plane.
  • a wz ′ plane orthogonal to the xy plane is obtained.
  • z ′ f (w).
  • the steepest change point of the gradient is obtained as a peak.
  • the number of peaks is not limited to one, and there may be a plurality of peaks. In this case, information corresponding to the plurality of peaks is obtained.
  • an optimal w-axis (hereinafter, referred to as an “optimal axis”) for a test sample, a technique exemplified below is effective.
  • a technique exemplified below is effective.
  • the w axis determined by this is set as the optimal axis.
  • the points P i (1 ⁇ i ⁇ N) of the fluorescent fingerprint the sum of squares of distances from points near the bottom surface (points near the xy plane) of the fluorescent fingerprint is minimized.
  • a straight line (least square line) is obtained, a wz 'plane including the least square line is determined. Therefore, the w axis determined by this is set as the optimal axis.
  • the setting of the optimal axis is not limited to the above-described method.
  • the w-axis obtained by the above-described method is set as a temporary optimal axis, and the temporary optimal axis is appropriately rotated on the xy plane. Then, the w-axis may be set so that the peak value of the fluorescence intensity unique to the test sample appears. Further, depending on the test sample, a simplified method of setting the optimum axis as an axis parallel to the x-axis or the y-axis may be employed.
  • Patent Document 2 exemplifies a second derivative as a signal processing operation for a two-dimensionally developed fluorescent fingerprint (see paragraph [0026], etc.). Note that neither is described nor suggested.
  • non-fluorescent component removal processing in order to remove noise from the measured fluorescent fingerprint and obtain effective fluorescent fingerprint information, non-fluorescent component removal processing, scattered light removal processing, low sensitivity
  • One or a combination of the area deletion processing can be adopted as the preprocessing.
  • one or more combinations of arithmetic processing such as centering, standardization, standardization, baseline correction, smoothing, auto scaling, logarithmic conversion (Log10) are pre-processed on the acquired fluorescent fingerprint information. It can also be adopted as.
  • the secondary differential processing that has been used before can also be used together.
  • the order of application can be set as appropriate.
  • non-fluorescent component removal processing is performed. It is desirable to precede processing such as scattered light removal processing and low-sensitivity area deletion processing.
  • Multivariate analysis method used for modeling As a multivariate analysis method used in modeling, various analysis methods such as PLS (Partial Least Squares) regression analysis, multiple regression analysis, principal component regression analysis, and least square method can be used.
  • PLS Partial Least Squares
  • PLS regression analysis is a method of extracting a principal component so that the covariance between the principal component and the objective variable is maximized, and is effective when there is a strong correlation between explanatory variables (when multicollinearity occurs).
  • Principal component regression analysis is a method of extracting principal components so that the variance of the principal components is maximized.Principal component analysis is performed using only explanatory variables, and the minimum between the obtained principal components and the objective variable is calculated. This is to perform multiple regression analysis by the square method. The multiple regression analysis applies the least squares method between the explanatory variable and the objective variable, and has different characteristics from the principal component regression analysis.
  • trace components are often present in the sample as polyphenols having similar chemical structures (for example, tobacco raw materials).
  • polyphenols having similar chemical structures have similar fluorescent fingerprints, even if the fluorescent fingerprint method is applied, it is expected that it will be difficult to extract only a specific trace component and accurately quantify it. .
  • FIG. 5 shows a fluorescent fingerprint of a mixed sample containing chlorogenic acid (10 ppm), rutin (1 ppm), and scopoletin (0.1 ppm).
  • FIG. 6A, FIG. 6B, and FIG. 6C show that each of scopoletin (1 ppm: (A)), chlorogenic acid (1 ppm: (B)), and rutin (1 ppm: (C)) contained in the mixed sample was used alone.
  • 5 shows a fluorescent fingerprint of a test sample containing the sample.
  • Table 1 shows the mixed sample, the wavelength (excitation wavelength / fluorescence wavelength) near the peak position of the fluorescent fingerprint of scopoletin (1 ppm), chlorogenic acid (1 ppm), and rutin (1 ppm), and the corresponding fluorescence intensity. It is a summary of.
  • test sample ⁇ When using a sample in which rutin, chlorogenic acid, and scopoletin are mixed as a sample> Forty samples (solvent: 50% ethanol / water solution) prepared by mixing rutin / chlorogenic acid / scopoletin samples at various ratios were prepared. Each sample was prepared as a test sample by pulverizing to a particle size of 1 mm or less and sufficiently mixed. By such pulverization and mixing, the accuracy of measurement is ensured even in a case where rutin, chlorogenic acid, and scopoletin are not uniformly distributed but localized in the raw material.
  • test samples used were stored in advance to stabilize the water content.
  • Each sample having a known rutin content was pulverized to a particle size of 1 mm or less and sufficiently mixed to prepare a test sample.
  • the sample is a tobacco raw material
  • rutin is not uniformly present but localized in the tobacco raw material.
  • the sample is reduced to a certain particle size (1 mm diameter) or less. It is preferable to obtain a fluorescent fingerprint after pulverizing the mixture and thoroughly mixing the mixture.
  • the rutin content of each sample was determined in advance by high performance liquid chromatography (HPLC).
  • test samples used were stored in advance to stabilize the water content.
  • the measurement conditions were as follows: excitation light 200-600 nm, fluorescence 200-700 nm, resolution 5 nm, slit width 5 nm, photomultiplier sensitivity 700 V. Considering a resolution of 5 nm, the measurement wavelength allows at least an error of about 5 nm.
  • Preprocessing for fluorescent fingerprint information ⁇ When using a sample in which rutin, chlorogenic acid, and scopoletin are mixed as a sample> Preprocessing is performed on fluorescent fingerprint information obtained from test samples in which samples of rutin, chlorogenic acid, and scopoletin are mixed at various ratios.
  • preprocessing for example, dedicated software such as Matlab or PLS_toolbox is used.
  • the pre-processing in addition to the second-order differentiation processing specific to the present invention described in detail in the above-mentioned ⁇ Pre-processing for fluorescent fingerprint information>, preferably, the second-order differentiation processing previously used for each spectrum and the components are preferably used. There is a process of removing a wavelength that does not contribute to information.
  • VIP Variable important projection
  • iPLS interval PLS
  • G Genetic algorithms
  • e Jack-knife analysis
  • F Forward interval PLS
  • BiPLS Backward interval PLS
  • G Synergy interval PLS (siPLS)
  • H LASSO type method
  • the method described in the above-mentioned ⁇ Preprocessing for fluorescent fingerprint information> is applied.
  • the fluorescent fingerprints of rutin, chlorogenic acid, and scopoletin are used.
  • An effective result can be obtained by performing a second derivative process on the information along an axis parallel to the x-axis (fluorescence wavelength axis).
  • the calibration curve uses the acquired fluorescent fingerprint information as an explanatory variable and the content of known components (rutin, chlorogenic acid, scopoletin) as an objective variable, and performs PLS regression analysis (hereinafter simply referred to as “PLS”). There is also).
  • PLS PLS regression analysis
  • the PLS does not directly use the information of the explanatory variable X for modeling the objective variable y, but converts part of the information of the explanatory variable X into a latent variable t, and models the objective variable y using the latent variable t. Is what you do.
  • the number of latent variables can be determined, for example, using a predictive explanatory variance value obtained by cross-validation as an index.
  • Latent variables may also be called principal components.
  • the above (1) and (2) are represented by the following (3) and (4).
  • X t 1 p 1 T + E (3)
  • y t 1 q 1 + f (4)
  • t 1 is a latent variable (vector)
  • p 1 is loading (vector)
  • q 1 is a coefficient (scalar).
  • t 1 Xw 1 (5)
  • w 1 is a standardized weight vector.
  • PLS is a covariance y T t 1 between y and t 1
  • the norm of w 1 is 1 (
  • 1 ) is intended to determine the t 1 that maximizes under the condition that, t 1 May be calculated using the so-called Lagrange undetermined multiplier method. Since the calculation method using the Lagrange's undetermined multiplier method is well known, the details of the calculation are omitted, and only the calculation results regarding w 1 , p 1 , and q 1 are shown as (6) to (8) below.
  • t 1 in the equations (7) and (8) is a vector calculated by substituting w 1 obtained in the equation (6) into the equation (5).
  • the above-described PLS regression analysis is applied to the calibration sample group, and a calibration curve for estimating the content of each component from the acquired fluorescent fingerprint information is created.
  • a calibration curve for estimating the content of each component from the acquired fluorescent fingerprint information is created.
  • pre-processing for the acquired fluorescent fingerprints.
  • (1) second-order differentiation processing unique to the present invention (2) removal processing for wavelengths that do not contribute to component information, and the like. It is desirable to perform pre-processing.
  • the pre-processing of (2) for example, the following processing can be adopted.
  • the content of each component is estimated from the acquired fluorescent fingerprint information using the calibration curve, and the calibration curve is verified.
  • FIG. 7A to 7C are graphs of data of a validation sample group for each component of rutin, chlorogenic acid, and scopoletin.
  • FIG. 7A shows scopoletin
  • FIG. 7B shows chlorogenic acid
  • FIG. 7C shows rutin. ing.
  • FIG. 8A shows the measured value (chemical analysis value) by high-performance liquid chromatography (HPLC) on the horizontal axis and the estimated value by the fluorescent fingerprint on the vertical axis, and the pretreatment was performed for each sample belonging to the validation sample group. It is the graph which plotted the corresponding point of the case.
  • FIG. 8B shows the measured value (chemical analysis value) by high-performance liquid chromatography (HPLC) on the horizontal axis and the estimated value by the fluorescent fingerprint on the vertical axis, and the pretreatment was performed for each sample belonging to the validation sample group. It is the graph which plotted the corresponding point of the case.
  • the content of each component of rutin, chlorogenic acid, and scopoletin is based on the fluorescent fingerprint information of the unknown sample, and the content of each component contained in the sample is determined.
  • the amount can be estimated effectively.
  • This simplified embodiment uses only nine wavelengths of 285/410, 415, 420, 425, 430, 435, 440, 450, and 460 (nm) as excitation wavelength / fluorescence wavelength (measurement wavelength) to obtain fluorescent fingerprint information. Is obtained to determine the amount of rutin.
  • the method for quantification is basically the same as the case where all the fluorescent fingerprint information is used, and details are omitted.
  • the wavelength is limited as described above (generally less than 10 wavelengths)
  • multiple regression analysis (MLR) can be used to generate a calibration curve.
  • MLR multiple regression analysis
  • the nine wavelengths of 285/410, 415, 420, 425, 430, 435, 440, 450, and 460 are specific excitation / fluorescence wavelengths at which the fluorescence intensity takes a maximum value based on the chemical structure of rutin. It is equivalent.
  • the simplified embodiment is not limited to this, and the excitation wavelength / fluorescence wavelength (measurement wavelength) is 285/410, 415, 420, 425, 430, 435, 440, 450, and 460 (nm).
  • An embodiment including at least one can be adopted. In this case, although the measurement accuracy is inferior to the case where only 9 wavelengths are used, an improvement in speed can be realized.

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Abstract

L'invention concerne un procédé d'évaluation/estimation d'échantillon par analyse d'empreinte digitale par fluorescence, dans lequel un modèle d'estimation très précis (courbe d'étalonnage) est obtenu en effectuant une nouvelle forme de traitement différentiel secondaire sur des informations d'empreinte digitale par fluorescence acquises. Un échantillon de test présentant un contenu connu d'un composant spécifique est préparé (S01) et des informations d'empreinte digitale par fluorescence sont acquises (S02). Un prétraitement comprenant le nouveau traitement différentiel secondaire est effectué (S03) sur les informations d'empreinte digitale par fluorescence, et un modèle d'estimation (courbe d'étalonnage), permettant d'estimer le contenu du composant spécifique dans un échantillon de test à partir d'informations d'empreinte digitale par fluorescence, est créé (S04). Après vérification (S05) de la courbe d'étalonnage, ladite courbe d'étalonnage est appliquée à un échantillon inconnu et le contenu du composant spécifique compris dans l'échantillon inconnu est estimé (S06).
PCT/JP2019/018739 2018-08-10 2019-05-10 Procédé d'évaluation/estimation d'échantillon par analyse d'empreinte digitale par fluorescence, programme et dispositif WO2020031447A1 (fr)

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CN116660207A (zh) * 2023-06-20 2023-08-29 北京易兴元石化科技有限公司 一种油品快检中特征谱段确定方法和辛烷含量检测系统
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