Principles of HPLC Validation - Training Module 5: Data Analysis

A short course in the systematic validation of HPLC methods. This section covers various aspects of data analysis including how and when external standardization, internal standardization, and the method of standard additions should be used for calibration. We consider how to properly measure peaks and how to evaluate the quality of the data gathered.

This course is designed for laboratory personnel responsible for validating HPLC methods. It will also be useful for managers and quality assurance staff involved in the method validation process. For workers who develop, but do not validate methods, this class will give insight into how to develop methods that will be easier to validate. No prior experience is needed, although those with some laboratory experience will certainly benefit more than those with no experience at all.

This course covers:

This section covers various aspects of data analysis including how and when external standardization, internal standardization, and the method of standard additions should be used for calibration. We consider how to properly measure peaks and how to evaluate the quality of the data gathered. Control charts are not often used in the analytical laboratory, but we consider how they may provide an additional degree of method control – from data you are gathering anyway. Finally we consider how much a method can be adjusted before it must be re-validated.

• Quantitative Analysis – External Standardization
• Other Standardization Techniques
• Evaluating Data Quality
• Integration & Peak Measurement
• Control Charts & Method Adjustment

By attending this online training course you will understand how to organize a validation project. By planning ahead, you will see how to develop better methods that will validate more easily and will function more reliably in routine use. You will realize how by using Quality by Design principles during development, the methods will be easier to validate and be more robust in routine use. You will learn how to decide which variables are important and which ones are not. You will see how software tools can help you to get much more mileage out of your experimental runs. You will gain a better understanding of the calibration process and how to examine data for problems. Learn when method adjustments are allowed without re-validating the method. Find out why uncertainty plays such a big role in validation.

You will be able to answer these questions upon completion of the course:

• Describe the quantitative analysis by HPLC?
• Describe the quantification by external standardization?
• How does the use of internal standardization (IS) differ from external standardization (ES)?
• When would the method of standard additions be useful?
• When we describe the “quality” of an analytical result, what do we mean?
• Describe types of error?
• For a method that detects both very large peaks and very small peaks for the same analyte, how is the precision of the method correlated with the analyte response?
• When trying to reduce overall method uncertainty (error), and the contributions to uncertainty are both independent and random, what is the best approach?
• When setting up an integrator or data processing system, what is the best approach?
• Describe the manual integration or adjusting the integration of a peak after the data system has first processed it?
• What technique will give the most accurate results when a minor peak appears on the tail of a large peak?
• Why might it be useful to plot the results of system suitability or quality control samples over time (a control chart)?
• When using the USP’s guidelines (USP chapter <621>) for method adjustment, how should these be applied?