WO2017084118A1 - 一种近红外光谱分析仪在线应用时无测点温度补偿模型修正方法 - Google Patents

一种近红外光谱分析仪在线应用时无测点温度补偿模型修正方法 Download PDF

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WO2017084118A1
WO2017084118A1 PCT/CN2015/096374 CN2015096374W WO2017084118A1 WO 2017084118 A1 WO2017084118 A1 WO 2017084118A1 CN 2015096374 W CN2015096374 W CN 2015096374W WO 2017084118 A1 WO2017084118 A1 WO 2017084118A1
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temperature
infrared spectrum
physical property
derivative
near infrared
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PCT/CN2015/096374
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French (fr)
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栾小丽
赵忠盖
刘飞
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江南大学
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Priority to US15/571,033 priority Critical patent/US10317280B2/en
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/02—Details
    • G01J3/0286—Constructional arrangements for compensating for fluctuations caused by temperature, humidity or pressure, or using cooling or temperature stabilization of parts of the device; Controlling the atmosphere inside a spectrometer, e.g. vacuum
    • 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
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colours
    • G01J3/02—Details
    • G01J3/0275—Details making use of sensor-related data, e.g. for identification of sensor parts or optical elements
    • 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
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/85—Investigating moving fluids or granular solids
    • G01N21/8507—Probe photometers, i.e. with optical measuring part dipped into fluid sample
    • 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
    • G01N2201/121—Correction signals
    • G01N2201/1211—Correction signals for temperature
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2201/00—Features of devices classified in G01N21/00
    • G01N2201/12—Circuits of general importance; Signal processing
    • G01N2201/129—Using chemometrical methods

Definitions

  • the invention relates to a method for correcting a temperature compensation model without a measuring point when the near infrared spectroscopy analyzer is applied online. It is suitable for on-line real-time detection of physical parameters affected by ambient temperature, such as fluid viscosity, material density, component concentration, food quality, agricultural product composition, active ingredient content of pharmaceuticals, and quality of gasoline oil.
  • Near-infrared spectroscopy is a multi-disciplinary modern analytical technique that combines spectroscopy, chemometrics, and computer applications. It is non-radiative, non-polluting, non-destructive, and can simultaneously measure a variety of components and is successfully applied to agriculture, food, and Petrochemical, textile, pharmaceutical and other industries.
  • the online real-time automatic analysis and detection of the near-infrared spectrum analyzer provides the production process.
  • the broad use of space is of great significance to improving the economic and social benefits of enterprises.
  • the method proposed by the invention has a great influence on the near-infrared measurement when the online physical property measurement is performed, and an online recursive algorithm with a temperature compensation mechanism is established.
  • a method for correcting the temperature compensation model of a near-infrared spectrum analyzer with high temperature adaptability, high precision and good robustness is provided.
  • the steps of the invention are divided into three parts.
  • the first part the experimental design and spectral collection of modeling data
  • the second part the preprocessing of the near-infrared spectrum and the establishment of the calibration model
  • the third part constructing the online recursive algorithm to complete the near-infrared with the temperature-free compensation function. Online measurement.
  • 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 under line conditions. 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 Obtain the original standard data for all sample physical parameters at a standard temperature specified by the physical property parameter measurement.
  • Step 1 Pretreatment of the near-infrared spectrum with the target of temperature mode.
  • the original near-infrared spectrum is subjected to a first derivative or a second derivative operation to generate a first derivative spectrum or a second derivative spectrum.
  • the derivative order here may vary depending on the characteristics of the physical property parameters. For example, for high molecular viscosity samples, the second derivative is preferred. The first derivative is preferred for low viscosity samples.
  • Step 2 Perform a principal component analysis (PCA) on the derivative spectrum generated above, 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
  • Step 3 Perform pretreatment on the original near-infrared spectrum with the target parameter model to be tested.
  • 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.
  • Step 4 Perform a principal component analysis (PCA) on the pre-processed spectrum generated above, and eliminate the statistical outliers, so that the pre-processed spectral data pivot mode is within a statistical credibility.
  • PCA principal component analysis
  • Step 5 Combine the temperature-targeted derivative spectrum formed above and the pre-processed spectrum targeted to the physical property to be tested.
  • Step 6 Taking the original analytical value of the physical property parameter to be measured at a specified temperature as a predictor, the pre-processed spectral wave number and the derivative spectral wave number are used as independent variables. Correct the model with partial least squares (PLS) physical property parameters:
  • P is the measured value at the specified temperature of the physical property variable
  • Step 7 Obtain a new near-infrared spectral data set online and construct a recursive correction algorithm using the following method:
  • P r (k) is the current near-infrared physical property correction value with temperature compensation
  • P(k-1) is the near-infrared physical property measurement value not corrected in the previous step
  • L(k-1) is used in the last calculation.
  • the actual physical parameter value, K is the correction factor or low-order filter.
  • the correction factor or the low-order filter may be a more general statistical judgment and a logical judgment, or a combination of the two.
  • the physical property parameter correction model used may be regenerated from the updated spectral data.
  • the entire calculation algorithm forms a recursive form.
  • the invention participates in the near-infrared modeling process as a hidden implied variable of the separation, so that when using the near-infrared measurement, the physical property measurement at different temperatures can be completed depending on the adaptability of the model itself to the temperature, and no direct temperature measurement is required. Information and related calculations make the established model more versatile.
  • the recursive algorithm invented has better adaptability to changes in sample temperature and other measurement conditions.
  • Figure 1 is a schematic diagram of an experimental device without temperature measurement.
  • Figure 2 is an original spectrum of a polymer at different temperatures.
  • Figure 3 is a second-order derivative local spectrum of a high molecular polymer.
  • FIG. 4 is a schematic diagram of a main element of a high molecular polymer.
  • Figure 5 is a combined spectrum of a high molecular polymer.
  • Figure 6 is a partial derivative pretreatment partial spectrum of a high molecular polymer.
  • Fig. 7 is a schematic diagram of a principal component analysis and a mode abnormal point of a polymer.
  • Figure 8 is a combined spectrum PCA pattern of a high molecular polymer.
  • Figure 9 is a graph of the viscosity model produced by the combined spectra.
  • Figure 10 is a wave number map used in the combined spectral model.
  • Figure 11 is a comparison diagram of online recursive implementation results with no point temperature compensation.
  • Figure 12 is a block diagram showing the recursive implementation steps of the no-point temperature compensation method.
  • 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. . The original spectrum acquired is shown in Figure 2.
  • Step 3 Perform temperature-targeted pretreatment and principal component analysis (PCA) on the acquired spectra. Generate derivative spectral data.
  • PCA principal component analysis
  • the second-order derivative preprocessing is based on the first-order derivative, and the temperature-sensitive spectrum is re-extracted, effectively reducing the overlap of temperature and physical parameters in the modeling wavenumber.
  • the treatment effect is shown in Figure 3.
  • the processed spectrum eliminates spectral up-and-down drift due to aging of the light source, probe vibration and probe-to-sample contact, while retaining effective information on the effect of temperature on the peak and shape of the spectrum. .
  • the main element pattern is shown in Figure 4.
  • Step 4 Perform pretreatment and principal component analysis (PCA) on the original spectrum with the target physical property parameter model as the target.
  • PCA principal component analysis
  • spectral data is generated.
  • the first derivative pretreatment and principal component analysis were performed on the polymer sample.
  • the reason for using the first derivative preprocessing is as described in step 3.
  • the pretreatment spectrum is shown in Fig. 6.
  • the main element mode is shown in Fig. 7. There is a singular point in Fig. 7, which should be eliminated and no longer participate in modeling.
  • Step 5 Combine the derivative spectrum generated above with the pre-processed spectrum to produce combined spectral data.
  • Figure 5 is a combined spectrum at different temperatures.
  • the left half, the first derivative part provides effective physical modeling spectral information; the right half, the second derivative part, provides spectral information for temperature compensation.
  • Step 6 Perform a principal component analysis (PCA) on the combined spectrum generated above, and eliminate the statistical outliers so that the principal element patterns of the entire combined spectral data are within one statistical confidence.
  • Figure 8 is a PCA pattern of the combined spectra. There is a singularity in Figure 8, which should be rejected and no longer involved in modeling.
  • Step 7 The original analysis value of the physical property parameter to be tested is used as a predictor, and the combined spectral wave number is used as an independent variable.
  • the partial least squares algorithm (PLS) is used to establish the physical parameter prediction model at that time:
  • P is the measured value at the temperature specified by the physical property variable at the current time
  • Step 8 Obtain 10 new spectral data sets online and obtain corresponding laboratory raw data at the same time.
  • Step 10 Perform a low-pass dynamic filtering operation on the error time series to obtain a one-step predicted value, denoted as B.
  • P is a near-infrared physical property measurement currently having temperature compensation.
  • Step 12 Assign the current correction value P r (k) to the measured value P(k-1) of the previous moment to perform a recursive assignment operation.
  • Fig. 9 is a viscosity model produced by combining spectra
  • Fig. 10 is a wave number used by the model
  • Fig. 11 is a comparative example of results of different algorithms. Select the band range shown in Figure 10.
  • the first derivative spectral wavelength is selected in two segments, 8909-7683cm -1 and 7598-4758cm -1 , respectively.
  • the second derivative spectral wavelength is selected as 6078-4528cm -1 for modeling. From Fig. 9, it can be seen from Fig. 9 that the correlation between the predicted value of the composite spectral model and the measured value is 0.99, and the model accuracy R 2 is 0.98.
  • 11 is a comparison of the no-point temperature compensation algorithm proposed by the present invention with a modeling algorithm fixed at a temperature of 50 degrees. It can be seen from the figure that the measurement result of the fixed temperature model has greater sensitivity to temperature changes, and The model established by the invention method has better compensation effect on temperature, and because of the characteristics of the recursive algorithm, the overall real-time measurement can better conform to the real analysis data.

Abstract

一种近红外光谱分析仪在线应用时的无测点温度补偿模型修正方法，包括在不同温度水平下采集各个样品的近红外光谱，对采集的光谱针对温度和待测物性参数分别做预处理以及主元分析，再将所得光谱进行合并产生新的光谱数据，用偏最小二乘对其建模以获取当前时刻的测量值，最后构造在线递归算法，完成具有无测点温度补偿功能的近红外在线测量。该方法将温度作为分离的隐含因素变量参与到近红外建模过程中，因而在使用近红外测量时，可以依赖模型本身对温度的适应性完成不同温度下的物性测量，不需要直接温度测量信息和相关计算，使得所建立的模型具有更好的通用性。该方法中的在线递归算法具有对样品温度和其它测量条件变化的较佳的适应性。

Description

一种近红外光谱分析仪在线应用时无测点温度补偿模型修正方法 技术领域
本发明涉及近红外光谱分析仪在线应用时，无测点温度补偿模型修正方法。适用于受环境温度影响的物性参数，如流体粘度、物质密度、成分浓度、食品品质、农产品成分、药品有效成分含量、汽油油品质量等的在线实时检测。
背景技术
近红外光谱技术是综合光谱学、化学计量学和计算机应用等多学科交叉的现代分析技术，它无辐射、无污染、无破坏性、可以同时测定多种成份，被成功应用到农业、食品、石油化工、纺织、医药等行业。同时，一方面为了给生产和质检部门提供较全面、实时的样品信息，另一方面为实现计算机在线监测与实时控制的目的，近红外光谱分析仪的在线实时自动分析检测，为生产过程提供了广阔的使用空间，对提高企业的经济效益和社会效益有重要的意义。
然而当近红外光谱分析仪实时在线应用时，测量结果会受环境因素影响。研究表明，温度的变化会产生振动光谱的偏移，使得特定温度下近红外光谱的测量结果，仅适用于该温度下的样品品质分析，而对于样品品质的在线分析效果不理想，此缺点大大限制了近红外光谱分析仪实时在线测量技术的应用。为了克服在线应用时温度对光谱的影响，一些方法被陆续提出，如剔除受温度影响的光谱、选取对温度影响不敏感的波段建立分析模型、在模型中加温度修正项等等。这些方法可以克服温度变化对在线测量带来的干扰，但目前还没有通用的规则来判断何种情况下使用何种方法，而要根据具体问题进行选择。因此，研究温度适应性强、精度高、鲁棒性好、更为通用的实时在线测量技术，成为近红外技术能否有效在线应用的关键。
发明内容
本发明提出的方法，针对在线物性测量时，温度变化对近红外测量有较大的影响，建立具有温度补偿机制的在线递归算法。提供一种温度适应性强、精度高、鲁棒性好的近红外光谱分析仪在线应用时无测点温度补偿模型修正方法。
本发明为实现上述目的，采用如下技术方案：
本发明步骤分为三个部分。第一部分，建模数据的实验设计和光谱收集；第二部分，近红外光谱的预处理和校正模型的建立；第三部分，构造在线的递归算法，完成具有无测点温度补偿功能的近红外在线测量。
建模数据的实验设备包括，(1)可对样品温度进行调节的样品池；(2)可显示温度变化的温度测量器；(3)近红外光谱收集仪器；(4)不对样品温度产生明显影响的光学探头；(5)和近红外光谱收集仪器连接的计算机记录装置。
本发明实验和数据收集步骤如下：
实验步骤一：确认样品在线条件下最大和最小温度值。把温度范围分为多个水平值。每个温度水平一般要大于温度测量仪器分辨率5倍，以达到有效区分精度。
实验步骤二：在物性参数测量所规定的一个标准温度下，对所有样品物性参数取得原始标准数据。
实验步骤三：在不同温度水平下分别收集所有样品近红外光谱数据。温度值作为一个隐含因素，所以温度值本身的精确记录不是必须的。
温度作为分离的隐含因素变量在线修正算法实施步骤如下：
步骤一：对近红外光谱进行以温度模式为目标的预处理。将原始近红外光谱做一阶导数或二阶导数运算，产生一阶导数光谱或者二阶导数光谱。此处导数阶次可能随物性参数的特性而有所不同。例如，对高分子高粘度样品，以二阶导数为较佳。对低粘度样品以一阶导数为较佳。
步骤二：对上面产生的导数光谱做主元分析(PCA)，剔除统计异常值，使得整个导数光谱数据的主元模式都在一个统计可信度之内。
步骤三：对原始近红外光谱进行以待测物性参数模式为目标的预处理。这些预处理包括一种或几种以下算法的叠加运算：一阶导数，二阶导数，最大-最小标准化，基础底线校正，散射校正，常数偏置校正，等等。此处预处理算法的确定以待测物性参数而异。
步骤四：对上面产生的预处理后光谱做主元分析(PCA)，剔除统计异常值，使得整个预处理后的光谱数据主元模式都在一个统计可信度之内。
步骤五：将以上形成的以温度为目标的导数光谱和以物性待测参数为目标的预处理后的光谱进行合并。
步骤六：以待测物性参数在一个规定温度的原始分析值作为预测变量，预处理后光谱波数和导数光谱波数作为自变量。用偏最小二乘算法(PLS)物性参数校正模型：
P＝B1y1+B2y2+…Bnyn+A1x1+A2x2+…Anxn
此处，P是物性变量规定温度下的测量值，Bi,Ai,i＝1,2,…n是回归系数，yi，xi分别是预处理后光谱和导数光谱在波数i＝1,2,…n处的数值。
步骤七：在线获取新的近红外光谱数据集，利用下述方法构成递归修正算法：
(1)以上述步骤六所得结果作为当前值P(k)
(2)计算下一步测量：Pr(k+1)＝P(k)+K[L(k-1)-P(k-1)]
(3)将当前修正后的预测值，Pr(k)赋值给上一时刻的测量值P(k-1)，重复以上步骤，做递归赋值运算。
此处Pr(k)是当前的具有温度补偿的近红外物性测量修正值，P(k-1)是上一步没有修正的近红外物性测量值，L(k-1)是上次计算所用的实际物性参数值，K为修正因子或低阶滤波器。
上述步骤七中，修正因子或低阶滤波器，可以是更较一般的统计判断和逻辑判断，或者是两者的结合。
上述步骤七中，在每一步计算时，所用物性参数校正模型可以是由更新的光谱数据重新产生。整个计算算法构成递归的形式。
本发明把温度作为分离的隐含因素变量参与到近红外建模过程中，因而在使用近红外测量时，可以依赖模型本身对温度的适应性完成不同温度下的物性测量，不需要直接温度测量信息和相关计算，使得所建立的模型具有更好的通用性。所发明的递归算法具有对样品温度和其它测量条件变化的较佳的适应性。
附图说明
图1为无测点温度补偿实验装置示意图。
图2为一种高分子聚合物在不同温度的原始光谱图。
图3为一种高分子聚合物的基于二阶导数局部光谱图。
图4为一种高分子聚合物的主元素模式图。
图5为一种高分子聚合物的合并光谱图。
图6为一种高分子聚合物的一阶导数预处理局部光谱图。
图7为一种高分子聚合物主元分析和模式异常点示意图。
图8为一种高分子聚合物的合并光谱PCA模式图。
图9为合并光谱产生的粘度模型图。
图10为合并光谱模型使用的波数图。
图11为具有无测点温度补偿的在线递归实施结果比较图。
图12为无测点温度补偿方法递归实施步骤框图。
具体实施方式
以下以一种高分子化合物的粘度测量为例，说明具体实施方法。这个示例不构成对本发明方法的范围限制。
整个实施步骤框图如图12所示。
步骤1：采集具有代表性的样品，要保证样品的待测物性参数可以覆盖测量要求的范围。样品总数在40－60个。
步骤2：利用图1所示的实验室设备，分别在24℃、35℃、50℃、60℃、70℃五个不同温度水平下采集各个样品的近红外光谱，同时记录实验条件如温度等。采集的原始光谱见图2。
步骤3：对所采集的光谱做以温度为目标的预处理和主元分析(PCA)。产生导数光谱数据。示例中，对高分子高粘性样品进行了二阶导数处理和主元素分析。二阶导数预处理是在一阶导数的基础上，对温度信息敏感的光谱进行再提取，有效地减少温度和物性参数在建模波数的重叠。处理效果如图3所示，经过处理后的光谱消除了由于光源老化，探头震动以及探头与样品接触度等因素带来的光谱上下漂移，同时又保留了温度对光谱峰值和形状影响的有效信息。主元素模式如图4所示，在图4所示的PCA模式图中，有一个点(代表一段光谱)和其他所有点有很大的距离，此点为奇异点，在建模时予以剔除，使得整个预处理后的光谱数据主元模式都在一个统计可信度之内。
步骤4：对原始光谱进行以待测物性参数模式为目标的预处理和主元分析(PCA)。产生预处理光谱数据。示例中，对高分子样品进行了一阶导数预处理和主元分析。使用一阶导数预处理原因如步骤3中所述。预处理光谱如图6所示，主元素模式如图7所示，图7中有一个奇异点，应予以剔除，不再参与建模。
步骤5：将上面产生的导数光谱和预处理光谱合并，产生合并光谱数据。图5是不同温度下的合并光谱。在图5中的合并光谱中，左半部分即一阶导数部分，提供了有效的物性建模光谱信息；右半部分即二阶导数部分，提供了温度补偿作用的光谱信息。
步骤6：对上面产生的合并光谱做主元分析(PCA)，剔除统计异常值，使得整个合并光谱数据的主元模式都在一个统计可信度之内。图8是合并光谱的PCA模式图，图8中有一个奇异点，应予以剔除，不再参与建模。
步骤7：以待测物性参数原始分析值作为预测变量，以合并光谱波数作为自变量。用偏最小二乘算法(PLS)建立当时时刻的物性参数预测模型：
P＝B1y1+B2y2+…Bnyn+A1x1+A2x2+…Anxn
此处，P是当前时刻物性变量规定温度下的测量值，Bi,Ai,i＝1,2,…n是回归系数，yi，xi分别是预处理后光谱和导数光谱在波数i＝1,2,…n处的数值。
步骤8：在线获取10个新的光谱数据集，并同时获取对应的实验室原始数据。
步骤9：计算过去10个样品的误差E(k)＝L(k)-P(k)，并形成一个误差时间序列
E(k-1),E(k-2),…E(k-10)。
步骤10：对上述误差时间序列做低通动态滤波运算，取得一步预测值，记为B。
步骤11：计算粘度校正测量值：Pr＝P+B
此处P是当前具有温度补偿的近红外物性测量值。
步骤12：将当前的修正值Pr(k)赋值给上一时刻的测量值P(k-1)，做递归赋值运算。
重复以上步骤8-12。
图9是合并光谱产生的粘度模型，图10是模型使用的波数，图11是不同算法的结果比较示例。选择图10中所示的波段范围，一阶导数光谱波长选择两段，分别为8909-7683cm-1及7598-4758cm-1，二阶导数光谱波长选择为6078-4528cm-1，用以建模得到图9，从图9可以看出复合光谱模型预测值与实测值的相关性为0.99，模型精度R2为0.98。图11是本发明提出的无测点温度补偿算法与固定在50度温度下建模算法的比较，从图中可以看出固定温度模型的测量结果对温度变化有较大的敏感性，而本发明方法建立的模型，对温度有较佳的补偿效果，同时因为递归算法的特点，整体实时测量可以较好地符合真实分析数据。

Claims (6)

  1. 一种近红外光谱分析仪在线应用时无测点温度补偿模型修正方法，其特征在于，包括以下步骤：
    步骤一：获取多个样品物性参数的原始标准数据，并在不同温度下采集各个样品的近红外光谱；
    步骤二：对步骤一中所采集的近红外光谱做以温度为目标的导数运算和主元分析，剔除异常值，产生导数光谱；对步骤一中所采集的近红外光谱做以待测物性参数为目标的预处理和主元分析，产生预处理光谱；
    步骤三：将步骤二产生的导数光谱和预处理光谱进行光谱合并，产生合并光谱数据；
    步骤四：对步骤三中产生的合并光谱数据做剔除统计异常值处理；
    步骤五：以待测物性参数原始标准数据作为预测变量，以合并光谱波数作为自变量，建立物性参数预测模型，获取当前时刻规定温度下的测量值；
    步骤六：在线获取新的近红外光谱数据集，利用在线递归修正算法对物性参数测量值进行更新。
  2. 根据权利要求1所述的红外光谱分析仪在线应用时无测点温度补偿模型修正方法，其特征在于：所述步骤一中采集样品近红外光谱的温度范围覆盖实时物性参数测量温度范围；多个样品待测物性参数覆盖测量要求的范围。
  3. 根据权利要求1所述的红外光谱分析仪在线应用时无测点温度补偿模型修正方法，其特征在于：所述步骤二中导数运算对于高分子高粘度样品的近红外光谱，进行二阶导数运算；对于低粘度样品的近红外光谱进行一阶导数运算。
  4. 根据权利要求1所述的红外光谱分析仪在线应用时无测点温度补偿模型修正方法，其特征在于：所述步骤二中以待测物性参数为目标的预处理包括一种或几种以下算法的叠加运算：一阶导数，二阶导数，最大-最小标准化，基础底线校正，散射校正，常数偏置校正。
  5. 根据权利要求1所述的红外光谱分析仪在线应用时无测点温度补偿模型修正方法，其特征在于：所述步骤五中所述物性参数预测模型建立方法采用偏最小二乘算法进行温度为隐含变量的线性回归。
  6. 根据权利要求1所述的红外光谱分析仪在线应用时无测点温度补偿模型修正方法，其特征在于：所述步骤六中所述在线递归修正算法为：
    Pr(k+1)＝P(k)+K[L(k-1)-P(k-1)]
    其中Pr(k)是当前时刻具有温度补偿的测量修正值，P(k-1)是上一时刻的测量值，L(k-1)是上次计算所用的实际物性参考值，K为修正因子或低阶滤波器。
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