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Chapter 5: Calibrations, Standardizations, and Blank Corrections

External, internal and standard additions methods. Regression analysis and blank corrections

Neil Fitzgerald

on 21 August 2014

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Transcript of Chapter 5: Calibrations, Standardizations, and Blank Corrections

Chapter 5: Calibrations, Standardizations, and Blank Corrections
By measuring the signal for an
unknown sample
we can use the calibration curve
To find its concentration
The equation for this line is:

The concentration of an unknown can be found by measuring its signal and solving for concentration.
Copper Standards
External Standards
The slope of the line is the sensitivity
To determine the concentration, or amount, of an analyte in a sample, we must determine the relationship between the signal and concentration or amount
External standards is the simplest and most common method
Signals are obtained for a blank and standard solutions
A blank contains all the reagents and no analyte
Standards contain known concentrations of the analyte
calibration curve
is a plot of signal (y-axis) against concentration or amount (x-axis)
Straight line calibration curves are preferred as they are simpler to fit to a line and have constant slope (sensitivity)
External Standards
Not preferred: assumes linear relationship and any error in the standard measurement has significant effect on result
If reagent blank is measured accurately and subtracted from the standard and sample measurements (known as
blank subtraction
) the concentration of an analyte CA can be found by finding the sensitivity, k, for the standard and using it to calculate the concentration of analyte in the sample.
Single Point
The preferred method
Three or more standards and a blank are needed to accurately describe the relationship
If the calibration curve is curved (it’s called a calibration curve even when it is a straight line) we use the linear region (preferred) or fit a polynomial best-fit line
Multiple Standards
Assumes analyte in standard behaves in the similar way to analyte in samples
If analyte responds to something in the sample matrix it may give a different result, in this case the interfering matrix components should be added at a similar level to the standards (known as
matrix matching
Assumes we know the identity and level of interfering matrix components
Standard Additions
We can find this the concentration of sample (CA) by rearranging this equation to:

and using known values of the volume of sample (Vo), concentration of standard (Cstd) and x-intercept.
The x-intercept is found from the equation of the line by setting the y value equal to zero.
Used when matrix matching is not possible
A known amount of standard is added (spiked) into one or more sample solutions
Can be single addition or multiple addition (preferred)
Standard Additions
A known volume of sample (V0) is added to two flasks
Into one a known amount of standard (Vstd) of known concentration (Cstd) is added (spiked)
Both flask are diluted to the same volume (Vf)
Signals are measured for both solutions (Ssamp and Sspike)
Concentration of analyte (CA) is found by:
Single Standard Addition
Multiple standards are made by spiking increasing volumes of a standard into solutions of the sample
Volume of standard is plotted against signal
The concentration of analyte is found from the x-intercept of a straight line graph.
Multiple Standard Additions
Multiple standard additions requires a straight line graph
Assumes matric interferent acts with analyte in standard the same as analyte in sample
Uses more sample
Slow. Graph required for every sample for multiple standard additions
Accounts for fluctuations in the sample
For example:
- Solvent evaporates causing fluctuations in the analyte concentration
- The method requires a very small volume that is difficult measure with accuracy causing fluctuations in signals

To overcome fluctuations we add a known amount of internal standard
An internal standard is not the analyte but has similar behavior to the analyte e.g. lithium might be a good internal standard for sodium (both are group I metals)
We measure the ratio of analyte signal to internal standard signal which is independent of method fluctuations
Internal Standards
We prepare a single standard with known concentration of analyte, CA, and known concentration of internal standard, CIS, and measure the signal for the analyte, SA, and internal standard, SIS. The value of K can then be determined by:

K is then used to find the concentration of analyte in a sample containing a known concentration of internal standard by measurined signals for the analyte and internal standard in the sample
Single Standard
By measuring the analyte and internal standard signals for a series of standards, we can plot a calibration curve of SA/SIS versus CA/CIS
If concentration of internal standard in samples and standards is kept constants, we can plot SA/SIS against CA
Internal standard is added to samples, SA/SIS found, and the calibration curve used to find the analyte concentration
Multiple Standards
The internal standard must have similar behavior to the analyte e.g. if the analyte reacts with a matrix component, the internal standard should react to the same extent.
Internal Standards
by determining the ratio of analyte to internal standard signal in a sample we can be find using this equation to solve for x, the analyte concentration
A perfect straight line has the mathematical expression:

Due to measurement uncertainties, calibration curves are not perfect straight lines.
A linear regression is a method for fitting a straight line to a set of data in order to get good estimates for the slope and intercepts
It does this by minimizing residuals (the distances between the data point and the best-fit line in the y direction)
The R^2 value (where R is the Pearson correlation coefficient) can used as a measure of fit. The closer to 1, the better fit

Calibration curves normally:
- assume all errors affecting the x values (amount or concentration) are insignificant
- assume errors affecting y are normally distributed
- assume errors in y are independent of the value of x
Linear regression
Obtaining Analyte Concentration
A linear calibration is preferred, however a curved calibration is sometimes unavoidable.
A straight line should not be fitted to a curve
Sometimes a mathematical function can be used to generate a straight line
If this is not possible we fit a polynomial function to the curve e.g.

Solving this equation for x will give two solutions, however, normally only one will make sense.
Curvilinear and Multivariate Regression
Signal can originate from the reagents and solvent and can be corrected by subtracting the reagent blank or calibration blank
The reagent blank is the signal resulting from the reagents and solvents only i.e. in the absence of sample
The calibration blank is the y-intercept value found from the regression equation of the calibration curve
Reagent blank and calibration blank differ only by indeterminate errors
Typically analytical chemist use the calibration blank when using a calibration curve and reagent blank when using a single point standardization
Neither account for interactions between analyte and sample matrix
Blank Corrections
Regression and Blank Correction
Uncertainty in Regression Analysis
The error in the slope is given by:

The error in the intercept is given by:


We can use these values to provide confidence intervals:

Uncertainties are minimized by evenly spacing standards over a wide range of analyte concentrations (provided that the concentration curve is still linear)
The concentration of an analyte, CA, can be determined from the regressions equation:

To find the error in CA, we use the equation:

The confidence interval of the analyte concentration can be found by:
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