Process sensors calibration and validation

In this chapter we address the issues stemming from calibration and validation of process sensors that will be used or interchanged with analyzers. Technically speaking, the establishment treats them rather differently. For cahbration and validation issues, in most cases a sensor element may make up one component of the analyzer, and the overriding issue is where these instruments are going to be used (e.g., in the manufacturing suite). It is from this vantage point that we will treat sensors and sensor systems equally with analyzers. 

The objective of this chapter will be to propose a scheme for demonstrating the accuracy, precision, and suitability of sensors. As will be seen, no matter what sensor, device, or measurement approach is chosen, these three simple criteria will be at the heart of the calibration/validation exercise. Depending on the complexity of the process, the sensor system, and process requirements necessary to achieve a mechanistic understanding, more parameters may be necessary. For example, linearity, range, specificity, robustness, raggedness, detection limit, and quantification hmit may also need to be investigated. By definition (see calibration and validation definition in this chapter), accuracy, precision, and suitability will be the minimum requirements for the cahbration/validation exercise of process sensors. 

This chapter explores the fundamental meaning and rationale behind measurement and control mechanisms and hopefully will lead the reader to view calibration and validation of process sensors as a very different exercise than the one that most analytical minds working in regulated industries have previously experienced in the laboratory. 

Under these practices then, calibration and validation of process sensor elements, sensors, and sensor systems used in a pharmaceutical manufacturing facility would have to be... 

The elements of calibration and validation of sensor systems are thus composed of all of the physical attributes that make up sensor systems, the functional aspects that determine the operational capability of these systems, and finally the parameters that determine the frequency of verification, the sensor system variables that determine the operational range, and the process/sensor standards that will be used during the verification phases of the sensor systems. 

With respect to cGMPs that will be used to regulate how process sensors will be calibrated and validated, new application and architectural schemes are being proposed for the design, qualification, installation, operation, and maintenance of in-line and on-line sensors. 

Put in the context of calibration and validation requirements, the answers to the four W s and H, and their objective of achieving an able and reliable sensor that can be depended on to also control a process, will be needed. As it has been stated in the PAT guideline, Design and construction of the process equipment, the analyzer, and their interface are critical to ensuring that collected data are relevant and representative of process and product attributes. Robust design, reliability, and ease of operation are important considerations. With respect to reliability, which in this context means assurance as demonstrated by way of calibration and validation tests performed on sensors or analyzers, consideration should be given to ... 

Comparison between Traditional Calibration and Validation Strategy for Laboratory-Based Instruments and Process Sensors and Analyzers... 

Provides new chapters on diffuse reflection theories, the calibration and validation of process sensors, and the NIR spectra of gases... 

Abstract Validation of analytical methods of well-characterised systems, such as are found in the pharmaceutical industry, is based on a series of experimental procedures to establish selectivity, sensitivity, repeatability, reproducibility, linearity of calibration, detection limit and limit of determination, and robustness. It is argued that these headings become more difficult to apply as the complexity of the analysis increases. Analysis of environmental samples is given as an example. Modern methods of analysis that use arrays of sensors challenge validation. The output may be a classification rather than a concentration of analyte, it may have been established by imprecise methods such as the responses of human taste panels, and the state space of possible responses is too large to cover in any experimental-design procedure. Moreover the process of data analysis may be done by non-linear methods such as neural networks. Validation of systems that rely on computer software is well established. 

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