## Analytical Chemistry Lesson of the Day – Accuracy in Method Validation and Quality Assurance

In pharmaceutical chemistry, one of the requirements for method validation is accuracy, the ability of an analytical method to obtain a value of a measurement that is close to the true value. There are several ways of assessing an analytical method for accuracy.

1. Compare the value from your analytical method with an established or reference method.
2. Use your analytical method to obtain a measurement from a sample with a known quantity (i.e. a reference material), and compare the measured value with the true value.
3. If you don’t have a reference material for the second way, you can make your own by spiking a blank matrix with a measured quantity of the analyte.
4. If your matrix may interfere with the analytical signal, then you cannot spike a blank matrix as described in the third way.  Instead, spike your sample with an known quantity of the standard.  I elaborate on this in a separate tutorial on standard addition, a common technique in analytical chemistry for determining the quantity of a substance when matrix interference exists.  Standard addition is an example of the second way of assessing accuracy as I mentioned above.  You can view the original post of this tutorial on the official JMP blog.

## Analytical Chemistry Lesson of the Day – Linearity in Method Validation and Quality Assurance

In analytical chemistry, the quantity of interest is often estimated from a calibration line.  A technique or instrument generates the analytical response for the quantity of interest, so a calibration line is constructed from generating multiple responses from multiple standard samples of known quantities.  Linearity refers to how well a plot of the analytical response versus the quantity of interest follows a straight line.  If this relationship holds, then an analytical response can be generated from a sample containing an unknown quantity, and the calibration line can be used to estimate the unknown quantity with a confidence interval.

Note that this concept of “linear” is different from the “linear” in “linear regression” in statistics.

This is the the second blog post in a series of Chemistry Lessons of the Day on method validation in analytical chemistry.  Read the previous post on specificity, and stay tuned for future posts!

## Analytical Chemistry Lesson of the Day – Specificity in Method Validation and Quality Assurance

In pharmaceutical chemistry, one of the requirements for method validation is specificity, the ability of an analytical method to distinguish the analyte from other chemicals in the sample.  The specificity of the method may be assessed by deliberately adding impurities into a sample containing the analyte and testing how well the method can identify the analyte.

Statistics is an important tool in analytical chemistry, and, ideally, there is no overlap in the vocabulary that is used between the 2 fields.  Unfortunately, the above definition of specificity is different from that in statistics.  In a previous Machine Learning and Applied Statistics Lesson of the Day, I introduced the concepts of sensitivity and specificity in binary classification.  In the context of assessing the predictive accuracy of a binary classifier, its specificity is the proportion of truly negative cases among the classified negative cases.

## Analytical Chemistry Lesson of the Day – Method Validation in Quality Assurance

When developing any method in analytical chemistry, it must meet several criteria to ensure that it accomplishes its intended objective at or above an acceptable standard.  This process is called method validation, and it has the following criteria* in the pharmaceutical industry:

• specificity
• linearity
• accuracy
• precision
• range
• limit of detection
• limit of quantitation
• robustness**

As I will note in future Chemistry Lessons of the Day, these words are used differently between statistics and chemistry.

*These criteria are taken from Page 723 of the 6th edition of “Quantitative Chemical Analysis” by Daniel C. Harris (2003).

**The Food and Drug Administration does not list robustness as a typical characteristic of method validation.  (See Section B on Page 7 of its “Guidance for Industry Analytical Procedures and Methods Validation for Drugs and Biologics“.)  However, it does mention robustness several times as an important characteristic that “should be evaluated” during the “early stages of method development”.