WO2017084119A1 - Procédé de mesure de paramètres physiques dans les infrarouges proches assurant une fonction de compensation de température indépendante du point de mesure - Google Patents

Procédé de mesure de paramètres physiques dans les infrarouges proches assurant une fonction de compensation de température indépendante du point de mesure Download PDF

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WO2017084119A1
WO2017084119A1 PCT/CN2015/096376 CN2015096376W WO2017084119A1 WO 2017084119 A1 WO2017084119 A1 WO 2017084119A1 CN 2015096376 W CN2015096376 W CN 2015096376W WO 2017084119 A1 WO2017084119 A1 WO 2017084119A1
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temperature
infrared
physical property
point
sample
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Chinese (zh)
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栾小丽
赵忠盖
刘飞
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Jiangnan University
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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
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
    • G01N2201/12—Circuits of general importance; Signal processing
    • G01N2201/129—Using chemometrical methods

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  • the invention relates to a method for predicting sample physical property parameters by using a near-infrared spectroscopy model with no measuring point temperature compensation function, and is suitable for viscosity of a substance susceptible to environmental temperature, alanine concentration in a fermentation process, food quality, quality of agricultural products, Rapid detection of drug quality, gasoline oil, etc.
  • This method can also be used for the measurement of non-invasive blood glucose concentration, soil composition and mineral composition.
  • Near-infrared spectroscopy is a modern analytical technique that combines the spectral measurement technology, chemometrics technology and computer technology of the near-infrared spectrum region. It has been widely used in the fields of chemical industry, food, petroleum, medicine, agriculture and so on. In particular, near-infrared spectroscopy has the advantages of fast analysis speed, low destructiveness to samples, no chemical pollution, simultaneous analysis of various components, and simple sample preparation, making it more and more popular and the fastest growing. One of qualitative and quantitative analysis techniques.
  • the absorption in the near-infrared spectral region is mainly due to state changes caused by molecular vibration or rotation.
  • the internal molecular motion of the sample increases, and the interaction between the molecules changes, which inevitably affects the transition of the molecular vibration or the rotating state between different energy levels, thereby affecting the vibrational spectrum of the molecule, making the spectrum Both the peak position and the band width change and affect the performance of the model. Therefore, in the near-infrared spectroscopy, an important reason that affects the measurement accuracy is the interference caused by the temperature change to the spectrum, which makes the model established at different temperatures not universal, which affects the accuracy and robustness of the model.
  • the method proposed by the invention establishes a temperature correction model by using temperature as an explicit factor variable, and thus, when using near-infrared measurement, it can rely on the response of the spectrum itself to temperature, and complete physical property parameter measurement at different temperatures, thereby eliminating the need for direct temperature measurement. Information and related calculations.
  • the steps of the invention are divided into two parts.
  • the first part the experimental design of the modeling data and the near-infrared spectroscopy collection;
  • the second part the preprocessing of the near-infrared spectrum and the establishment of the calibration model.
  • the experimental equipment for modeling data includes (1) a sample cell that can adjust the temperature of the sample; (2) a temperature measurer that can display the temperature change; (3) a near-infrared spectrum collecting instrument; (4) does not significantly affect the temperature of the sample. Affected optical probes; (5) Computer recording devices connected to near-infrared spectroscopy collection instruments.
  • Experimental step 1 Confirm the maximum and minimum temperature values of the sample. Divide the temperature range into multiple levels. Each temperature level is generally greater than 5 times the resolution of the temperature measuring instrument to achieve effective discrimination accuracy.
  • Experimental step 2 In the sample measurement temperature range, the original standard data is obtained for all sample physical parameters under the standard temperature specified by the measurement of a sample physical property parameter.
  • Experimental step 3 Near-infrared spectroscopy data were collected for each sample at different temperature levels. Record the corresponding sample temperature value at the same time. This temperature value is used to establish the temperature correction model.
  • temperature as an explicit factor variable modeling step is as follows:
  • Modeling step 1 Preprocessing of the near-infrared spectrum with the aim of temperature mode: The first-order derivative or the second-order derivative operation is performed on the original near-infrared spectrum to generate a first-order derivative spectrum or a second-order derivative spectrum.
  • the determination of the derivative order differs depending on the characteristics of the physical property parameter.
  • the second derivative is preferred; for the low-viscosity sample, the first derivative is preferred.
  • Modeling step 2 Perform a principal component analysis (PCA) on the derivative spectrum generated in the above modeling step 1, and eliminate the statistical outliers so that the principal element patterns of the entire derivative spectral data are within a statistical credibility.
  • PCA principal component analysis
  • Modeling step 3 Using temperature as the predictor, the derivative spectral wavenumber is used as the independent variable.
  • the partial correction model is established using the partial least squares algorithm (PLS):
  • T c A 1 x 1 +A 2 x 2 +...A n x n
  • Fig. 4 shows an example of a temperature prediction model of a polymer compound.
  • Modeling step 4 Preprocessing the original spectrum with the target physical property parameter pattern as the target.
  • These pre-processings include superposition operations of one or more of the following algorithms: first derivative, second derivative, maximum-minimum normalization, basic baseline correction, scatter correction, constant offset correction, and the like.
  • the determination of the preprocessing algorithm here varies depending on the physical property parameters to be tested.
  • Modeling step 5 Performing a principal component analysis (PCA) on the pre-processed spectrum generated above, and eliminating the statistical outliers, so that the pre-processed spectral data pivot mode is within a statistical credibility.
  • PCA principal component analysis
  • Modeling step 6 The data set corresponding to the highest experimental temperature is selected, and the physical property parameter of the sample to be tested is used as a predictor, and the spectral wave number after preprocessing is taken as an independent variable.
  • the partial temperature least squares algorithm (PLS) is used to establish a high temperature physical property parameter correction model as follows:
  • Fig. 5 is an example of a low temperature point prediction model of a polymer compound.
  • Modeling step 7 Select the data set corresponding to the lowest experimental temperature, take the physical property parameter to be used as the predictor, and pre-process the spectral wave number as the independent variable.
  • the partial temperature least squares algorithm (PLS) is used to establish a high temperature physical property parameter correction model as follows:
  • Modeling Step 8 Construct the following formulas for the physical properties of the predicted values based on the low temperature model at any temperature:
  • Fig. 7 is an illustration of an effect of viscosity measurement temperature compensation of a polymer compound.
  • the method of the invention establishes a temperature correction model by using temperature as an explicit factor variable, so that when using near-infrared measurement, it can rely on the response of the spectrum itself to temperature, and complete physical property measurement at different temperatures, thereby eliminating the need for direct temperature measurement information and related calculations. It is possible to measure the physical properties of the sample. Moreover, the no-point temperature compensation method proposed by the present invention is more robust to temperature changes.
  • Figure 1 is a schematic diagram of an experimental device without temperature measurement.
  • Figure 2 is a second derivative spectrum of a polymer material.
  • Figure 3 is a schematic diagram of the main elements produced by the second derivative spectrum.
  • Fig. 4 is a graph showing a temperature prediction model of a polymer compound.
  • Fig. 5 is a graph showing a low temperature point prediction model of a polymer compound.
  • Fig. 6 is a graph showing a viscosity high temperature point prediction model of a polymer compound.
  • Fig. 7 is a view showing the effect of viscosity measurement of the polymer compound.
  • Figure 8 is a comparison of temperature measured and model predicted values.
  • Figure 9 is a plot of the wavenumber range of the low temperature modeling spectrum.
  • Figure 10 is a plot of the wavenumber range of the high temperature modeling spectrum.
  • Figure 11 is a block diagram showing the implementation steps of the near-infrared no-point temperature compensation method.
  • the implementation flow chart of the non-measurement temperature compensation method for near-infrared measurement is shown in FIG. 11 , and specifically includes the following steps:
  • Step 1 Collect a representative sample, and ensure that the physical property parameters of the sample can cover the measurement requirements.
  • the total number of samples is 40-60.
  • Step 2 Using the laboratory equipment shown in Figure 1, the near-infrared spectra of each sample were collected at five different temperature levels of 24 ° C, 35 ° C, 50 ° C, 60 ° C, and 70 ° C, and the experimental conditions such as temperature were recorded. .
  • Step 3 Pre-processing and principal component analysis of the collected near-infrared spectrum.
  • the spectra are pre-processed and compared to determine the final pre-treatment method.
  • a second-order derivative treatment was performed on a high-viscosity polymer sample.
  • the processing effect is shown in Figure 2.
  • the processed spectrum eliminates spectral up-and-down drift due to aging of the source, probe vibration, and probe-to-sample contact, while retaining effective information on the effects of temperature on the peak and shape of the spectrum.
  • the main element pattern generated by the second derivative spectrum is shown in Fig. 3. In the PCA pattern shown in Fig. 3, there is a singular point which is eliminated, so that the pre-processed spectral data principal element mode is in a statistical Within the credibility.
  • Step 4 Establish a near-infrared prediction model of the sample temperature.
  • Figure 4 is an example of a temperature model that takes the temperature value of the sample directly from the spectrum with a selected modeling band of 7305-6880 cm -1 .
  • Figure 8 is a comparison of the measured temperature and the predicted values of the model. It can be seen from Fig. 8 that the correlation between the predicted value of the model and the measured value is 0.99, and the model accuracy R 2 is 0.98.
  • Step 5 Establish a near-infrared prediction model for low temperature and high temperature points, respectively.
  • Figures 5 and 6 show the results of the low temperature and high temperature models, respectively.
  • Figures 9 and 10 are examples of modeled spectral wavenumber ranges used. Selection wavelength range shown in FIG. 9 8770-4497cm -1 cryogenic model modeled in FIG. 5, selected wavelength range shown in FIG. 10 8955-4497cm -1 modeled temperature model FIG. It can be seen from Fig. 5 that the correlation between the predicted value of the low temperature model and the measured value is 0.99, and the model accuracy R 2 is 0.98. It can be seen from Fig. 6 that the correlation between the predicted value of the high temperature model and the measured value is 0.98, and the model accuracy R 2 is 0.95.
  • Step 6 Pay attention to the established low temperature near-infrared physical parameter model, which is more accurate in the low temperature range.
  • the high temperature near-infrared physical parameter model is more accurate in the high temperature range. Correction of different temperatures from low temperature or high temperature Calculate, the method is exactly the same.
  • the formula for the physical property parameters at any temperature using the predicted values based on the low temperature model is as follows:
  • P l0 and P h0 are model prediction values of the same sample at the lowest temperature point and the highest temperature point of the low temperature model and the high temperature model, respectively.
  • T h , T l are the temperature model prediction values of the highest and lowest temperature points of the experiment, respectively, and P c is the physical property measurement at the temperature T c .
  • Figure 7 shows a comparison of the viscosity measurement effect of a polymerized composite after temperature compensation with the temperature-free viscosity measurement at 50 °C. It can be seen from the graph that the measured value of the 50 degree fixed temperature model is compared with the temperature. Sensitive, the no-point temperature compensation method proposed by the present invention is more robust to temperature changes.

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  • Health & Medical Sciences (AREA)
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Abstract

L'invention concerne un procédé de mesure de paramètres physiques dans les infrarouges proches assurant une fonction de compensation de température indépendante du point de mesure, consistant : à collecter des échantillons représentatifs, à collecter des spectres infrarouges proches de tous les échantillons à des températures différentes, et à enregistrer des conditions expérimentales de type température ; puis à prétraiter les spectres collectés, à établir un modèle de prédiction dans les infrarouges proches pour des températures d'échantillons, et à établir des modèles de prédiction de paramètres physiques dans les infrarouges proches pour des points à basse température et des points à haute température respectivement ; et enfin à procéder à un calcul de correction sur différentes températures depuis les points à basse température ou les points à haute température et à construire un modèle de correction de paramètres physiques applicable à n'importe quelle température. Le procédé utilise la température en tant que variable de facteur explicite dans la construction d'un modèle de correction de température, si bien que la mesure physique à des températures différentes peut être achevée en vertu de la réponse des spectres à des températures pendant la mesure dans les infrarouges proches ; ainsi, des informations de mesure directe de température et un calcul associé ne sont pas requis.
PCT/CN2015/096376 2015-11-19 2015-12-04 Procédé de mesure de paramètres physiques dans les infrarouges proches assurant une fonction de compensation de température indépendante du point de mesure Ceased WO2017084119A1 (fr)

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CN109145403A (zh) * 2018-07-31 2019-01-04 温州大学 一种基于样本共识的近红外光谱建模方法
CN109583087A (zh) * 2018-11-30 2019-04-05 重庆邮电大学 一种基于多方位融合的回转窑表面温度补偿方法
CN112084737A (zh) * 2020-08-28 2020-12-15 上海华力微电子有限公司 一种高精度温度模型校准方法及系统
CN113960256A (zh) * 2021-10-21 2022-01-21 上海朝辉压力仪器有限公司 一种含水仪的温度补偿方法
CN114112101A (zh) * 2021-11-17 2022-03-01 中国航发沈阳发动机研究所 一种示温漆光谱自动判读方法及装置
CN114170119A (zh) * 2021-11-23 2022-03-11 西安理工大学 基于数字孪生的高温红热目标视觉测量场景还原方法
CN114184576A (zh) * 2021-10-19 2022-03-15 北京蓝星清洗有限公司 一种基于分子光谱流程工业在线测量方法及系统
CN117388209A (zh) * 2023-11-23 2024-01-12 蓝星智云(山东)智能科技有限公司 即时参比反馈的在线近红外光谱仪测量方法
CN119985393A (zh) * 2025-04-17 2025-05-13 中检易兴元科技(北京)有限公司 基于近红外光谱的车用汽油中苯胺类添加物检测方法

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CN106546558A (zh) * 2016-11-07 2017-03-29 江南大学 一种基于特征波段的近红外在线检测模型的更新方法
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CN112432917B (zh) * 2019-08-08 2023-02-28 北京蓝星清洗有限公司 一种光谱差异校正方法及系统
CN113624717B (zh) * 2021-09-14 2023-07-11 四川启睿克科技有限公司 基于近红外光谱数据预测样品成分的模型建立及使用方法
CN114739945B (zh) * 2022-05-16 2025-03-18 四川启睿克科技有限公司 一种双向修正无创检测血糖近红外光谱数据的方法

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CN109145403A (zh) * 2018-07-31 2019-01-04 温州大学 一种基于样本共识的近红外光谱建模方法
CN109145403B (zh) * 2018-07-31 2022-12-13 温州大学 一种基于样本共识的近红外光谱建模方法
CN109583087A (zh) * 2018-11-30 2019-04-05 重庆邮电大学 一种基于多方位融合的回转窑表面温度补偿方法
CN109583087B (zh) * 2018-11-30 2023-05-30 重庆邮电大学 一种基于多方位融合的回转窑表面温度补偿方法
CN112084737A (zh) * 2020-08-28 2020-12-15 上海华力微电子有限公司 一种高精度温度模型校准方法及系统
CN114184576A (zh) * 2021-10-19 2022-03-15 北京蓝星清洗有限公司 一种基于分子光谱流程工业在线测量方法及系统
CN113960256A (zh) * 2021-10-21 2022-01-21 上海朝辉压力仪器有限公司 一种含水仪的温度补偿方法
CN113960256B (zh) * 2021-10-21 2023-11-21 上海朝辉压力仪器有限公司 一种含水仪的温度补偿方法
CN114112101A (zh) * 2021-11-17 2022-03-01 中国航发沈阳发动机研究所 一种示温漆光谱自动判读方法及装置
CN114170119A (zh) * 2021-11-23 2022-03-11 西安理工大学 基于数字孪生的高温红热目标视觉测量场景还原方法
CN117388209A (zh) * 2023-11-23 2024-01-12 蓝星智云(山东)智能科技有限公司 即时参比反馈的在线近红外光谱仪测量方法
CN117388209B (zh) * 2023-11-23 2024-03-15 蓝星智云(山东)智能科技有限公司 即时参比反馈的在线近红外光谱仪测量方法
CN119985393A (zh) * 2025-04-17 2025-05-13 中检易兴元科技(北京)有限公司 基于近红外光谱的车用汽油中苯胺类添加物检测方法

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