Standard Practice for Validation of the Performance of Multivariate Online, At-Line, Field and Laboratory Infrared Spectrophotometer, and Raman Spectrometer Based Analyzer Systems

Importancia y uso:

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 prediction interval (PI) 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 the expected calibration space, but will not have specific information as to the cause of the outlier without additional testing.

Subcomité:

D02.25

Referida por:

D7165-22, D7453-22, D6624-20, D8470-22, D6621-21, D4814-25, C1307-21, D7166-23, D7278-21, D7825-24, D7164-21, D7314-21, E2898-20A, D8340-22, D7808-22, D3764-23, E1655-17R24, D7235-21A, D4814-25, D3764-23, D8321-25, D4814-25

Volúmen:

05.02

Número ICS:

17.180.30 (Optical measuring instruments)

Palabras clave:

control chart; infrared analyzer; infrared spectrophotometers; IR spectroscopy; multivariate process; NIR spectroscopy; Raman analyzer; Raman spectrometer; Raman spectroscopy; statistical quality assurance; validation;