**6. Method validation and statistical interpretation of the analytical method**

The function of the analyst is to obtain a result as near to the true value as possible by the correct application of the analytical procedure employed. Quantitative analysis is not simply a case of taking sample, carrying out a single determination and then claim that the value obtained is irrefutable. It also requires knowledge of the chemistry involvements and the possibilities of the interferences from other ions, elements and compounds as well as of the statistical distribution of values [27].

Different errors may occur during the analysis process which should be well noticed and overcome. There are three types of errors:


Validation of methods for the quantitative analysis of drugs involves determining as a minimum, their selectivity, and limit of detection, limit of quantification, linearity, working range, accuracy and precision [28].

#### **6.1 Accuracy**

Accuracy is a measure of how closely the result of an experiment agrees with the expected result. The difference between the obtained result and the expected

**37**

*Drug Analysis*

error [29].

**6.2 Precision**

**6.3 Linearity**

the line.

errors.

**6.4 Limit of detection**

calculated by the reduced formula:

*DOI: http://dx.doi.org/10.5772/intechopen.88739*

or UV-absorption in spectrophotometry.

result is usually divided by the expected result and reported as a percent relative

Precision is a measure of how close a set of results are to each other [30]. It is often measured under repeatable (same analyst, same day, same instruments and same materials) and reproducible conditions. Precision always accompanies

For any developed analytical method, standard curve is constructed to verify the linear relationship between the concentration and a characteristic parameter for a component such as peak area, peak height or peak ratio in chromatographic analysis

Most analytical methods are based on processes where the method produces a response that is linear and which increases or decreases linearly with analyte concentration. In other words, it is the ability of the method to elicit test results that

Statistical application is important in evaluating calibration graphs in instru-

Where a is the intercept of the straight line with the y axis and b is the slope of

The statistical measure of the goodness of the fit of the line through the data is the correlation coefficient "r". It falls in the range −1 ≤ r ≥+1. Negative r-values indicate negative slope and vice-versa. It is important to note that calculated r-values can be sometimes misleading and a calibration curve must be physically plotted to ensure the shape of the plot. From the calculated regression line data, the concentration of the analyte can be estimated by interpolation. Each value of y is subjected to random error and likely an error in the slope and intercept values can occur. This can be resolved by calculating standard deviations of the slope (Sb) and intercept (Sa). Sb and Sa are obtained from a calculated statistic value Sy/x [29]. The values of Sb and Sa are used to calculate the confidence limits for the slope and intercept using a *t*-value at a desired confidence level, normally 95% level. These limits are important to indicate if there is a significant difference between these values and certain true values, which reflects the effect of random or systemic

The limit of detection is the lowest content of analyte that can be distinguished from background noise and measured with reasonable statistical certainty. It can be

Where SB = Sy/x (calculated from the regression analysis data), b is slope [29].

y = a + bx (3)

3SB/b (4)

directly proportional to the concentration of analyte within a given range.

mental analysis. The equation of a straight line takes the form:

accuracy, but a high degree of precision does not imply accuracy.

result is usually divided by the expected result and reported as a percent relative error [29].
