1.1 本规程涵盖了实验室、现场或过程（在线或在线）红外（近红外或中红外分析仪，或两者兼有）和拉曼分析仪进行测量的验证要求，用于计算液体石油产品和燃料的物理、化学或质量参数（即特性）。使用多变量建模方法从光谱数据中计算这些特性。这些要求包括验证适当的仪器性能、验证校准模型对测试样品光谱的适用性，以及验证与红外或拉曼测量计算的结果与用于开发校准模型的PTM产生的结果之间的一致程度相关的不确定性是否符合用户要求- 规定的要求。最初，代表当前生产的有限数量的验证样本用于进行本地验证。当有足够数量的验证样本，且性质水平和样本组成都有足够的变化，以跨越模型校准空间时，实践的统计方法 D6708 可用于在分析仪的整个操作范围内提供这种等效性的一般验证。对于未实现充分性能和成分变化的情况，应继续使用局部验证。 1.1.1 对于某些应用，分析仪和PTM应用于相同的材料。将多变量模型应用于分析仪输出（光谱）直接产生测量光谱的相同材料的PPTMR。 将PPTMR与在相同材料上测量的PTMR进行比较，以确定一致程度。 1.1.2 对于其他应用，由分析仪系统测量的材料在由PTM分析之前要经过一致的添加剂处理。将多变量模型应用于分析仪输出（光谱）可产生处理材料的PPTMR。将基于分析仪输出的PPTMR与在处理过的材料上测量的PTMR进行比较，以确定一致程度。 1.1.3 在某些情况下，采用两步程序。在第一步中，将分析仪和PTM应用于混合料材料的测量。在第二步骤中，将步骤1中产生的PPTMR用作第二模型的输入，该第二模型预测当PTM应用于通过添加到混合料中而产生的最终混合产物的分析时所获得的结果。 如果第一步中使用的分析仪是基于多变量光谱的分析仪，则此实践用于获取PPTMR和PTMR之间的一致程度。否则，练习 D3764 用于将该混合料的PPTMR与PTMR进行比较，以确定一致程度。由于第二步不使用光谱数据，因此第二步的验证是使用实践进行的 D3764 如果第一步骤使用多变量分光光度分析仪，则在第二步骤中仅使用光谱相对于多变量模型不是异常值的样品。请注意，第二个模型可以适应添加到混合原料中的不同水平的添加剂材料。 1.2 被测样品的多种物理、化学或质量特性通常通过单个光谱测量进行预测。在应用这一实践时，每个特性预测都是单独验证的。每个特性的单独验证程序可能具有共同的特征，并受到共同影响，但每个特性预测的性能是独立评估的。用户通常会同时并行运行多个验证过程。 1.3 分析仪验证中使用的结果适用于多变量模型开发中未使用的样本，以及相对于多变量模型不是异常值或最近邻的谱线。 1.4 当可用验证样品的数量、组成范围或性质范围不超过模型校准范围时，使用代表当前生产的可用样品进行局部验证。当可用验证样本的数量、组成范围和性质范围与模型校准集的样本数量、组成和性质范围相当时，可以进行一般验证。 1.4.1 本地验证: 1.4.1.1 开发多变量模型时使用的校准样本必须显示出足够的成分和性质变化，以实现有意义的相关性的开发，并且必须跨越使用该模型进行分析的样本的成分范围，以确保通过插值而非外推进行此类分析。 标准校准误差（SEC）是衡量PTMR和PPTMR对这组校准样本的一致性的指标。SEC包括频谱测量误差、PTM测量误差和模型误差的贡献。样本（类型）特定偏差是模型误差的一部分。通常，光谱分析仪非常精确，因此光谱测量误差相对于其他类型的误差较小。 1.4.1.2 在初始分析仪验证期间，可用样品的成分范围可能相对于校准集的范围较小。由于光谱测量的高精度，PTMR和PPTMR之间的平均差可以反映出样品（类型）特异性偏差，该偏差在统计上是可观察的，但小于PPTMR的不确定性， U（PPTMR） 因此，PTMR/PPTMR差异的偏差和精度不作为局部验证的基础。 1.4.1.3 基于SEC和杠杆统计，每个PPTMR的不确定性， U（PPTMR） 计算。在验证过程中，对于每个非异常样本，确定PPTMR和PTMR之间的绝对差值|δ|是否小于或等于 U（PPTMR） 。对非异常值验证样本的总数以及|δ|小于或等于的样本数量进行计数 U（PPTMR） 给定非异常值验证样本的总数，使用反二项式分布来计算|δ|必须小于的最小结果数 U（PPTMR） .如果|δ|小于的结果数 U（PPTMR） 大于或等于该最小值，则结果与多变量模型的预期一致，并且分析器通过了局部验证。有关计算的详细说明见第节 11 和 附件A4 . 1.4.1.4 用户必须确定与基于多元模型的预期一致的结果将足以用于预期应用。A 95 % 建议使用概率进行反二项式分布计算。用户可以基于应用程序的关键性来对此进行调整。看见 附件A4 详细信息。 1.4.2 一般验证: 1.4.2.1 当验证样品数量足够，且其组成和性质范围与模型校准集的组成和性质相当时，可以进行一般验证。 1.4.2.2 一般验证通过执行 D6708 基于由多变量模型的应用产生的来自分析系统（或子系统）的结果（这种结果在本文中被称为PPTMR）与相同样本集的PTMR之间的评估。如果 D6708 满足以下条件: (1) 无偏差校正可以在统计上改善PPTMR与PTMR之间的一致性，以及 (2) R xy 根据计算 D6708 满足用户指定的要求。 1.4.2.3 对于产品发布或产品质量认证申请中使用的分析仪，一致度的精度和偏差要求通常基于PTM的现场或公布精度。 注1: 在大多数此类应用中，PTM是引用规范的测试方法。 1.4.2.4 本规程未描述分析仪系统应用的精度和偏差要求的制定程序。此类要求必须基于结果对预期业务应用程序的关键性以及合同和监管要求。在开始本文所述的验证程序之前，用户必须确定精度和偏差要求。 1.5 本规程不包括建立分析仪使用的校准模型（相关性）的程序。校准程序包含在实践中 D8321 以及其中的参考文献。 1.6 此做法旨在对有经验的人员进行审查。 对于新手来说，本实践将作为用于验证仪器性能、验证模型对测试样品光谱的适用性以及验证PPTMR和PTMR之间的一致性程度是否满足用户要求的技术概述。 1.7 本规程规定了适当的统计工具，即异常值检测方法，用于确定被测样品的光谱是否是用于分析仪校准的光谱群体中的一员。统计工具用于确定红外测量是否导致有效的特性或参数估计。 1.8 异常值检测方法没有定义标准来确定样本或仪器是否是异常值测量的原因。 因此，在常规基础上测量样本的操作员将找到标准来确定光谱测量位于校准之外，但不会具有关于异常值原因的特定信息。该实践确实建议了一些方法，通过这些方法可以使用仪器性能测试来指示异常值方法是否对仪器响应的变化做出了响应。 1.9 本规程并非用于比较不同设计分析仪的定量性能标准。 1.10 尽管本规程主要涉及红外和拉曼分析仪的验证，但本文所述的程序和统计测试也适用于采用多变量模型的其他类型的分析仪。 1.11 本标准并不旨在解决与其使用相关的所有安全问题（如有）。本标准的使用者有责任在使用前制定适当的安全、健康和环境实践，并确定监管限制的适用性。 1.12 本国际标准是根据世界贸易组织技术性贸易壁垒委员会发布的《关于制定国际标准、指南和建议的原则的决定》中确立的国际公认的标准化原则制定的。 ====意义和用途====== 5.1 这种做法的主要目的是允许用户验证多元、红外或近红外产生的数值- 校准用于测量特定化学浓度、化学性质或物理性质的红外实验室或过程（在线或在线）分析仪。如果分析仪结果与基于用户预先指定的统计置信水平的多变量模型的主要测试方法一致，在限值范围内，则这些结果可以被视为“验证”到用户为特定应用预先指定的置信限，因此可以被认为对该特定应用有用。 5.2 描述了验证仪器、模型和分析仪系统稳定且正常运行的程序。 5.3 多变量分析器系统固有地利用多变量校准模型。 在实践中，该模型隐式和显式地跨越了可能在完整的多变量样本空间中的所有可能样本的总体的某个子集。该模型仅适用于模型构建中使用的子集总体内的样本。除非确定了适用性，否则无法验证样品测量值。不能假设适用性。 5.3.1 使用异常值检测方法来证明校准模型对过程样本光谱分析的适用性。异常值检测限值基于历史标准和理论标准。异常值检测方法用于确定分析器获得的结果是否潜在有效。 验证程序基于数学测试标准，该标准指示过程样品光谱是否在分析仪系统校准模型所涵盖的范围内。如果样本光谱是异常值，则分析仪结果无效。如果样本光谱不是异常值，那么分析仪结果是有效的，前提是满足所有其他有效性要求。可以执行额外的可选测试，以确定过程样本光谱是否落在校准集覆盖的多元空间的稀疏区域中，离相邻的校准光谱太远，从而确保良好的插值。例如，如果校准样品光谱高度聚集，则建议进行这种最近邻测试。 5.3.2 该实践没有定义数学标准，以根据样本的光谱测量来确定样本、模型或仪器是否是异常值测量的原因。因此，在常规基础上测量样本的操作员将在异常值检测方法中找到标准，以确定样本测量值是否位于预期校准空间内，但在没有额外测试的情况下，将不具有关于异常值原因的特定信息。

1.1 This practice covers requirements for the validation of measurements made by laboratory, field, or process (online or at-line) infrared (near- or mid-infrared analyzers, or both), and Raman analyzers, used in the calculation of physical, chemical, or quality parameters (that is, properties) of liquid petroleum products and fuels. The properties are calculated from spectroscopic data using multivariate modeling methods. The requirements include verification of adequate instrument performance, verification of the applicability of the calibration model to the spectrum of the sample under test, and verification that the uncertainties associated with the degree of agreement between the results calculated from the infrared or Raman measurements and the results produced by the PTM used for the development of the calibration model meets user-specified requirements. Initially, a limited number of validation samples representative of current production are used to do a local validation. When there is an adequate number of validation samples with sufficient variation in both property level and sample composition to span the model calibration space, the statistical methodology of Practice D6708 can be used to provide general validation of this equivalence over the complete operating range of the analyzer. For cases where adequate property and composition variation is not achieved, local validation shall continue to be used. 1.1.1 For some applications, the analyzer and PTM are applied to the same material. The application of the multivariate model to the analyzer output (spectrum) directly produces a PPTMR for the same material for which the spectrum was measured. The PPTMRs are compared to the PTMRs measured on the same materials to determine the degree of agreement. 1.1.2 For other applications, the material measured by the analyzer system is subjected to a consistent additive treatment prior to being analyzed by the PTM. The application of the multivariate model to the analyzer output (spectrum) produces a PPTMR for the treated material. The PPTMRs based on the analyzer outputs are compared to the PTMRs measured on the treated materials to determine the degree of agreement. 1.1.3 In some cases, a two-step procedure is employed. In the first step, the analyzer and PTM are applied to the measurement of a blendstock material. In a second step, the PPTMRs produced in Step 1 are used as inputs to a second model that predicts the results obtained when the PTM is applied to the analysis of the finished blended product produced by additivation to the blendstock. If the analyzer used in the first step is a multivariate spectroscopic based analyzer, then this practice is used to access the degree of agreement between PPTMRs and PTMRs. Otherwise, Practice D3764 is used to compare the PPTMRs to the PTMRs for this blendstock to determine the degree of agreement. Since this second step does not use spectroscopic data, the validation of the second step is done using Practice D3764 . If the first step uses a multivariate spectrophotometric analyzer, then only samples for which the spectra are not outliers relative to the multivariate model are used in the second step. Note that the second model might accommodate variable levels of additive material addition to the blend stock. 1.2 Multiple physical, chemical, or quality properties of the sample under test are typically predicted from a single spectral measurement. In applying this practice, each property prediction is validated separately. The separate validation procedures for each property may share common features, and be affected by common effects, but the performance of each property prediction is evaluated independently. The user will typically have multiple validation procedures running simultaneously in parallel. 1.3 Results used in analyzer validation are for samples that were not used in the development of the multivariate model, and for spectra which are not outliers or nearest neighbor inliers relative to the multivariate model. 1.4 When the number, composition range or property range of available validation samples do not span the model calibration range, a local validation is done using available samples representative of current production. When the number, composition range and property range of available validation samples becomes comparable to those of the model calibration set, a general validation can be done. 1.4.1 Local Validation: 1.4.1.1 The calibration samples used in developing the multivariate model must show adequate compositional and property variation to enable the development of a meaningful correlation, and must span the compositional range of samples to be analyzed using the model to ensure that such analyses are done via interpolation rather than extrapolation. The Standard Error of Calibration (SEC) is a measure of how well the PTMRs and PPTMRs agree for this set of calibration samples. SEC includes contributions from spectrum measurement error, PTM measurement error, and model error. Sample (type) specific biases are a part of the model error. Typically, spectroscopic analyzers are very precise, so that spectral measurement error is small relative to the other types of error. 1.4.1.2 During initial analyzer validation, the compositional range of available samples may be small relative to the range of the calibration set. Because of the high precision of the spectroscopic measurement, the average difference between the PTMRs and PPTMRs may reflect a sample (type) specific bias which is statistically observable, but which are less than the uncertainty of PPTMR, U(PPTMR) . Therefore, the bias and precision of the PTMR/PPTMR differences are not used as the basis for local validation. 1.4.1.3 Based on SEC, and the leverage statistic, the uncertainty of each PPTMR, U(PPTMR) is calculated. During validation, for each non-outlier sample, a determination is made as to whether the absolute difference between PPTMR and PTMR, |δ|, is less than or equal to U(PPTMR) . Counts are maintained as to the total number of non-outlier validation samples, and the number of samples for which |δ| is less than or equal to U(PPTMR) . Given the total number of non-outlier validation samples, an inverse binomial distribution is used to calculate the minimum number of results for which |δ| must be less than U(PPTMR) . If the number of results for which |δ| is less than U(PPTMR) is greater than or equal to this minimum, then the results are consistent with the expectations of the multivariate model, and the analyzer passes local validation. The calculations involved are described in detail in Section 11 and Annex A4 . 1.4.1.4 The user must establish that results that are consistent with the expectations based on the multivariate model will be adequate for the intended application. A 95 % probability is recommended for the inverse binomial distribution calculation. The user may adjust this based on the criticality of the application. See Annex A4 for details. 1.4.2 General Validation: 1.4.2.1 When the validation samples are of sufficient number, and their compositional and property ranges are comparable to that of the model calibration set, then a General Validation can be done. 1.4.2.2 General Validation is conducted by doing a D6708 based assessment between results from the analyzer system (or subsystem) produced by application of the multivariate model, (such results are herein referred to as PPTMRs), versus the PTMRs for the same sample set. The system (or subsystem) is considered to be validated if the D6708 meets the following condition: (1) No bias correction can statistically improve the agreement between the PPTMRs versus the PTMRs, and (2) R xy computed as per D6708 meets user-specified requirements. 1.4.2.3 For analyzers used in product release or product quality certification applications, the precision and bias requirement for the degree of agreement are typically based on the site or published precision of the PTM. Note 1: In most applications of this type, the PTM is the specification-cited test method. 1.4.2.4 This practice does not describe procedures for establishing precision and bias requirements for analyzer system applications. Such requirements must be based on the criticality of the results to the intended business application and on contractual and regulatory requirements. The user must establish precision and bias requirements prior to initiating the validation procedures described herein. 1.5 This practice does not cover procedures for establishing the calibration model (correlation) used by the analyzer. Calibration procedures are covered in Practice D8321 and references therein. 1.6 This practice is intended as a review for experienced persons. For novices, this practice will serve as an overview of techniques used to verify instrument performance, to verify model applicability to the spectrum of the sample under test, and to verify that the degree of agreement between PPTMRs and PTMRs meet user requirements. 1.7 This practice specifies appropriate statistical tools, outlier detection methods, for determining whether the spectrum of the sample under test is a member of the population of spectra used for the analyzer calibration. The statistical tools are used to determine if the infrared measurement results in a valid property or parameter estimate. 1.8 The outlier detection methods do not define criteria to determine whether the sample or the instrument is the cause of an outlier measurement. Thus, the operator who is measuring samples on a routine basis will find criteria to determine that a spectral measurement lies outside the calibration, but will not have specific information on the cause of the outlier. This practice does suggest methods by which instrument performance tests can be used to indicate if the outlier methods are responding to changes in the instrument response. 1.9 This practice is not intended as a quantitative performance standard for the comparison of analyzers of different design. 1.10 Although this practice deals primarily with validation of infrared and Raman analyzers, the procedures and statistical tests described herein are also applicable to other types of analyzers which employ multivariate models. 1.11 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use. 1.12 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee. ====== Significance And Use ====== 5.1 The primary purpose of this practice is to permit the user to validate numerical values produced by a multivariate, infrared or near-infrared laboratory or process (online or at-line) analyzer calibrated to measure a specific chemical concentration, chemical property, or physical property. If the analyzer results agree with the primary test method to within limits based on the multivariate model for the user-prespecified statistical confidence level, these results can be considered ’validated’ to the user pre-specified confidence limit for a specific application, and hence can be considered useful for that specific application. 5.2 Procedures are described for verifying that the instrument, the model, and the analyzer system are stable and properly operating. 5.3 A multivariate analyzer system inherently utilizes a multivariate calibration model. In practice, the model both implicitly and explicitly spans some subset of the population of all possible samples that could be in the complete multivariate sample space. The model is applicable only to samples that fall within the subset population used in the model construction. A sample measurement cannot be validated unless applicability is established. Applicability cannot be assumed. 5.3.1 Outlier detection methods are used to demonstrate applicability of the calibration model for the analysis of the process sample spectrum. The outlier detection limits are based on historical as well as theoretical criteria. The outlier detection methods are used to establish whether the results obtained by an analyzer are potentially valid. The validation procedures are based on mathematical test criteria that indicate whether the process sample spectrum is within the range spanned by the analyzer system calibration model. If the sample spectrum is an outlier, the analyzer result is invalid. If the sample spectrum is not an outlier, then the analyzer result is valid providing that all other requirements for validity are met. Additional, optional tests may be performed to determine if the process sample spectrum falls in a sparsely populated region of the multivariate space covered by the calibration set, too far from neighboring calibration spectra to ensure good interpolation. For example, such nearest neighbor tests are recommended if the calibration sample spectra are highly clustered. 5.3.2 This practice does not define mathematical criteria to determine from a spectroscopic measurement of a sample whether the sample, the model, or the instrument is the cause of an outlier measurement. Thus, the operator who is measuring samples on a routine basis will find criteria in the outlier detection method to determine whether a sample measurement lies within t he expected calibration space, but will not have specific information as to the cause of the outlier without additional testing.