How do you handle GC-MS calibration drift and bias over time?

1 What is calibration drift and bias?

Calibration drift and bias are terms that describe the deviation of the instrument response from the expected value based on a calibration curve or standard. Drift is the gradual change of the response over time, while bias is the systematic difference between the response and the true value. Both drift and bias can result from various factors, such as instrument wear and tear, environmental conditions, sample matrix effects, or operator errors.

2 How to detect calibration drift and bias?

The best way to detect calibration drift and bias is to use quality control (QC) samples, which are known samples with known concentrations of the target analytes. You should analyze QC samples before, during, and after each analytical run, and compare the results with the acceptance criteria. You can also use control charts, which are graphical tools that show the variation of the QC results over time and indicate if they are within the control limits.

3 How to correct calibration drift and bias?

If you detect calibration drift and bias, you need to correct them as soon as possible to avoid compromising your data quality. The most common method to correct calibration drift and bias is to perform a new calibration or update the existing calibration curve with fresh standards. You should also check and maintain your instrument components, such as the injector, column, detector, and ion source, and ensure that they are clean and functioning properly. Additionally, you should optimize your method parameters, such as the carrier gas flow, temperature, and injection volume, and minimize the sample matrix effects by using appropriate sample preparation techniques.

5 How to evaluate calibration drift and bias?

To evaluate the impact of calibration drift and bias on your data quality, you need to calculate and report some performance indicators, such as the calibration slope, intercept, correlation coefficient, residual standard deviation, and relative standard error. These indicators can help you assess how well your calibration curve fits your data, how much variation and uncertainty are present in your measurements, and how close your results are to the true values. You should also compare your performance indicators with the method specifications or the regulatory requirements.

6 How to improve calibration drift and bias?

To improve your calibration drift and bias, you need to continuously monitor and review your QC results and performance indicators, and identify any trends or outliers that may indicate a problem. You should also perform regular audits and validations of your instrument, method, and data, and implement corrective and preventive actions if needed. Moreover, you should seek feedback and guidance from your peers, supervisors, or experts, and participate in proficiency testing or interlaboratory comparisons to benchmark your performance and improve your skills.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?