Standard Addition Method

Introduction

In analytical chemistry, a calibration equation generally serves in determining the unknown concentration of an analyte. Developing the equation entails evaluating the sensor’s response to a series of standards with known concentrations of the same analyte. The calibration equation method normally requires an identical matrix for both sample and standards without interference with the analyte signal. Whereas within complex or unknown sample matrices such as biological fluids, liquids with heavy metals, and soils etc., components other than the analyte itself can interfere with the analyte signal. A name for this occurrence is the matrix effect. Thus, the direct comparison between sample and standards becomes invalid. Adopting the standard addition method reduces the discrepancies between samples and standard, thus, ensuring better accuracy in the analysis of such samples.

The standard addition method, a.k.a. “spiking”, can also account for similar matrix effects in the calibration equation by adding known amounts of analytes to the samples. In doing so, a change in the sensor response between a sample and a spiked sample is assumed to be due only to the change in the analyte concentration.

Standard Addition Procedure

This entails preparing equal volumes of a series of test solutions (V_T), which also contain equal volumes of the sample (V_X) with unknown concentration (C_X). Then, increasing volumes of analyte standards with known concentration (C_S) are also spiked into all solutions except one, which comprises sample and solvent only. Next, plot a calibration curve of the sensor response to these solutions by placing the spiked volume of the analyte standards (V_S) on the x-axis. While the corresponding sensor response (S) can be on the y-axis, as shown in the graph below. 

A linear regression can help to acquire the slope (m) and y-intercept (b) of the calibration curve. Whereas, computing Equation 1 serves in establishing the relationship between the sensor response and the spiked volume of analyte standard. 

   

Conceptually, if the curve is extrapolated to where the sensor response is zero (S = 0), the volume of analyte standard [(V_S)0 = b/m] from that point to the point of the first solution on the curve ((V_S)0 = 0) will contain the same amount of analyte as the sample added into each solution. Equation 2 highlights the mathematical relationship.

   

Combining Equation 1 and 2 can easily solve for the analyte concentration in the sample. Note, both C_S and V_X are values of the user’s selection.

   

Alternative Procedure

Alternatively, it is possible to use the concentration of the spiked analyte standard in the test solution (C_SA) instead of its volume in the regression analysis, where C_SA = C_S * V_S / V_T. Both m and b still designate the slope and y-intercept of the regression, respectively. Equation 4 below, gives the relationship of the sensor response to this concentration.

   

Based on the same theory, Equation 2 still holds, and Equation 5 can also be established.

   

Combining Equation 2 and 5, the analyte concentration in the sample can, thus, be solved as Equation 6 highlights. Note, both V_T and V_X are values of user’s selection.