Introduction to Analytical Chemistry
Lecture Date: January 14, 2013
What is Analytical Chemistry?
Analytical chemistry is the science of obtaining,
processing, and communicating information about
the composition and structure of matter.
In other words, it is the art and science of
determining what matter is and how much of it
exists.
Qualitative: provides information about the identity of
an atomic, molecular or biomolecular species
Quantitative: provides numerical information as to the
relative amounts of species
Definitions from www.acs.org
The Role of Analytical Chemistry
They adapt proven methodologies to new materials/systems or to answer
new questions about their composition.
Analytical chemists work to improve existing techniques to meet the
demands of for faster, cheaper, more sensitive chemical measurements
Analytical chemists research to completely new types of measurements
and are at the forefront of the utilization of major discoveries in fields as
diverse as photonics and implantable microchip sensors.
Analytical chemistry is applied to many branches of science
Medicine
Industry
Environmental
Food and Agriculture
Forensics
Archaeology
Space science
History of Analytical Methods
Classical methods:
Separation via precipitation, extraction or distillation
Qualitative: recognized by color, boiling point, solubility,
taste
Quantitative: gravimetric or titrimetric measurements
Instrumental Methods:
Separation via chromatography and electrophoresis
Qualitative and Quantitative: recognized by their
interaction with radiation (spectroscopy), their mass (mass
spectrometry), their electrical properties, or their
interaction with environment (temperature, humidity)
Modern Instrumental Techniques
Separation Techniques
Gas chromatography
High performance liquid chromatography
Ion chromatography
Super critical fluid chromatography
Capillary electrophoresis
Planar chromatography
Mass Spectrometry
Electron ionization MS
Chemical ionization MS
High resolution MS
Gas chromatography MS
Fast atom bombardment MS
Liquid chromatography MS
Laser MS
Ambient ionization MS
Modern Instrumental Techniques
Spectroscopic techniques
Infrared spectrometry
Raman spectrometry
Nuclear magnetic resonance (NMR)
X-ray spectrometry
Atomic absorption spectrometry
Inductively coupled plasma atomic emission spectrometry
Inductively coupled plasma MS
Atomic fluorescence spectrometry
Ultraviolet/visible spectrometry (CD)
Molecular fluorescence spectrometry
Chemiluminescence spectrometry
X-Ray Fluorescence spectrometry
Electrochemical techniques
Amperometry
Voltammetry
Potentiometry
Conductiometry
Microscopic and surface techniques
Atomic force microscopy
Scanning tunneling microscopy
Auger electron spectrometry
X-ray photon electron spectrometry
Major Steps in Solving an Analytical Problem
1. Understanding and defining the problem, by looking at
the history of the material to be analyzed and background
of the problem
2. Choosing your analytical technique(s) and running the
experiments (or developing a new analytical technique)
3. Data analysis and interpretation, validation of results (if
needed), and reporting of results
1. Understanding and Defining
the Problem
•
•
•
•
•
•
What is it that you want to know?
What accuracy is required?
Is there a time (or money) limit?
How much sample is available?
What is the concentration range of the analyte?
What components of the sample may cause an
interference?
• What are the physical and chemical properties
of the sample matrix?
• How many samples are to be analyzed?
History of sample and background
of the problem
Background information can originate from many sources
• The client and competitor’s products
• Literature searches on related systems
• Sample history:
• How was the sample collected, transported, and stored?
• How was it sampled?
• If synthesized, by what synthetic route?
• What was the source of the raw materials used to make
the sample?
• What analysis has already been performed?
2. Choosing the Analytical Technique
Consider the sample characteristics
Choose an instrument (and ultimately a method) that can
obtain the desired information
Evaluate the performance characteristics of that instrument
and method
Does an entirely new technique need to be developed?
Technique Selection
Analysis type
Quantitative, Qualitative
Location of sample
bulk or surface
Physical state of sample
gas, liquid, solid, dissolved solid, dissolved gas
Amount of Sample
macro, micro, nano, …
Fate of sample
destructive, non destructive
Estimated purity of sample
pure, simple mixture, complex mixture
Analyte concentration
major or minor component, trace or ultra trace
Elemental information
total analysis, speciation, isotopic and mass analysis
Qualitative Molecular information
compounds present, polyatomic ionic species, functional
group, structure, molecular weight, physical property
Comparing Two Analytical Techniques:
High pressure Liquid Chromatography (HPLC)
vs. Nuclear Magnetic Resonance (NMR)
HPLC
NMR
B
B
L,Ds
L,S,Ds
macro, micro
Ma, Mi
Ma, Mi
pure, simple mixture, complex mixture
Sm,M
P,Sm
Location of sample
bulk or surface
Physical state of sample
gas, liquid, solid, dissolved solid, dissolved gas
Amount of Sample
Estimated purity of sample
Fate of sample
destructive, non destructive
Elemental information
N,D
N
T,S (ion)
limited
Cp,Io,St
Cp,Fn,St
Ql,Qt
Ql,Qt
total analysis, speciation, isotopic and mass analysis
Molecular information
Compounds present, Polyatomic ionic species,
Functional group, Structural, MW, Physical prop
Analysis type
Quantitative, Qualitative
Components of an Analytical Method
Obtain and store sample
Extract data
from sample
Pretreat and prepare sample
Perform measurement(s)/
experiment and process raw
data (if needed)
Compare results
with standards
Covert data
into information
Apply
Statistics (Quantitative)
Interpret Data (Qualitative)
Transform
information into
knowledge
Present/Report information
in a understandable form
After reviewing results
might be necessary
to modify and repeat
procedure
3. Analyzing Data and Reporting Results
• Analytical data analysis takes many forms: statistics,
chemometrics, simulations, empirical interpretation, etc…
• Analytical results can be reported in
• Peer-reviewed papers
• Technical reports
• Laboratory notebook records
• Analytical results can be subject to extreme scrutiny and
can be challenged by other experts
Basis Quantitative Analysis
 Precision refers to the reproducibility of analytical results.
When a result is precise, numerical results agree closely.
Precision can be estimated by repeating the measurement
n times (when possible).
 Accuracy describes the correctness of a result by its
closeness to an accepted or true value.
Precise, not accurate
Accurate, not precise
Accurate and precise
See pg. 967 of Skoog et al., Principles of Instrumental Analysis, Thomson Brooks/Cole, New York, 2007.
Basis Quantitative Analysis

Selectivity: the extent to which a technique or method can
determine particular analytes under given conditions in
mixtures or matrices, simple or complex, without
interferences from other components.


Also referred to as “specificity”
Sensitivity: the ability of a technique or method
discriminate between small differences in level of an
analyte
Basis Quantitative Analysis
amount of an analyte that can be
detected at a known confidence level
 Signal-to-noise: ratio of the average
signal to the average level of noise.
 Limit of quantitation (LOQ): the range
over which quantitative measurements
can be made (usually the linear range),
often defined by detector dynamic
range
Detector response
 Limit of detection (LOD): the lowest
Limit of linearity
Slope relates to
sensitivity
LOQ
LOD
Dynamic range
Concentration
 Linearity: the degree to which a response of an analytical detector to
analyte concentration/mass approximates a linear function
 Dynamic range: range between the LOQ and limit of linearity
Significant Figures





All nonzero digits are significant:
 1.234 g has 4 significant figures
Zeroes between nonzero digits are significant:
 1002 kg has 4 significant figures
Leading zeros to the left of the first nonzero digits are not significant; such
zeroes merely indicate the position of the decimal point:
 0.001 oC has only 1 significant figure
Trailing zeroes that are also to the right of a decimal point in a number are
significant:
 0.0230 mL has 3 significant figures
When a number ends in zeroes that are not to the right of a decimal point, the
zeroes are not necessarily significant:
 190 miles may be 2 or 3 significant figures
Significant Figures
 Addition and subtraction, the result is rounded off so that it has the same
number of digits as the measurement having the fewest decimal places
100 (no decimal places) + 23.643 (3 places) = 123.643, which
should be rounded to 124 (no places).
 In multiplication and division, the result should be rounded off so as to
have the same number of significant figures as in the input value with
the least number of significant figures
3.0 (2 significant figures ) × 12.60 (4 significant figures) = 37.8000
which should be rounded to 38 (2 significant figures).
Scientific Notation and Prefixes
0.00000356 M
3.56 x 10-6 M
3.56 mM
or …. ppm
Prefixes for SI Units
gigaG
megaM
kilok
decid
centic
millim
microµ
nanon
picop
femtof
attoa
109
106
103
10-1
10-2
10-3
10-6
10-9
10-12
10-15
10-18
Working with Numbers: Analytical
Concentrations
Molarity
Moles of solute / L
Parts per Million (ppm)
cppm = mass of solute X 106 ppm
mass of solution
For dilute aqueous solutions whose densities are approximately 1.00
g/mL
1 ppm = 1 mg/L =1 µg/mL
Parts per Billon (ppb)
cppb = mass of solute X 109 ppb
mass of solution
or
1 µg/L
Basic Statistics
 Mean (average) of a population:
 Mean (average) of a sample:
The Standard Deviation
 The standard deviation indicates the spread of data
 The sample standard deviation (for a data set of limited
size) is given by s:
 Relative standard deviation (RSD) (%)
See pg. 971-972 of Skoog et al., Principles of Instrumental Analysis, Thomson Brooks/Cole, New York, 2007.
The Gaussian Probability Distribution
 If you take a large number
600
500
Response

of measurements, the
values with be distributed
around the expected
value, or mean
The likelihood of a result
will become lower the
farther away the result is
from the mean
400
300
200
100
Measurement (e.g. spectral frequency)
 Many physical phenomena studied in analytical chemistry
result in measurements that can be modeled as Gaussian
distributions
See pg. 971-972 of Skoog et al., Principles of Instrumental Analysis, Thomson Brooks/Cole, New York, 2007.
Probability Distributions and Measurement
Confidence Intervals
Consider the following eight results :
2.1
2.3
2.6
2.1
1.9
2.2
1.8
3.8
mean = 2.35 and std deviation = 0.635
The question is, what is the chance that the large value of 3.8
occurred by random chance assuming a Gaussian
distribution?
Confidence Intervals – An example
 N = 8, mean = 2.35, and s = 0.635
 For this example, choose a 95% confidence level.
 Use Skoog et al. table A1-5 to obtain t (95% CI, dof
=7)
2.35 ± (2.36*0.635)/Sqrt[8]
2.35 ± 0.53
 Result:
3.8 is outside the range of 2.35 ± 0.53. We
can be 95% confident that the value of 3.8 is from a
different system, etc…
Calibration Curves


•

analyte concentration
The data is plotted and fit to a function to
obtain the equation of the “best” fit and the
uncertainty in the fit.
Typically the best fit is linear
y = mx+b
response = slope [c] + intercept
m is related to the method sensitivity
Measure the sample response to
determine the concentration
Matrix effects must be minimal
Response
 Measure signal response vs. known
Concentration
Linear Least Squares or Linear Regression
 Method to minimize the residual of the
experimental values and fitted line
 Appendix 1 shows the how to do linear regression
by hand
 However, typically this is done with software
Correlation Coefficient (R2 )
• A fraction between 0.0 and 1.0
• Dimensionless – it has no units
Sum of
residuals
associated
with a linear
relationship
SSreg
Sum, of
residuals
associated
with the null
hypothesis –
average of the
y values
SStot
If R2 is near 1.0, the regression model
fits the data much better than the null
hypothesis
If the regression model were not much
better than the null hypothesis, R2
would be near zero
Using a Calibration Curve
4.5
4
3.5
Peak Area (au)
What is the mole % of
isooctane in the sample
with a peak area of 2.65?
y = 2.0925x + 0.2567
R² = 0.9877
3
2.5
2
What is the standard
deviation?
1.5
1
0.5
0
0
sr
stdev 
m
0.5
1
1.5
Concentration isooctane mol%
2
1 1  yc - y 
 
M N
m 2 S xx
2
0.1442 1 1 2.65 - 12.51 / 5
 
2.0925 1 5 2.09252 1.145
 0.076 mol%
2

y - b y - 0.2567
x

m
2.0925
2.65 - 0.2567

 1.14 mol%
2.0925
sr= stdev of regression line
M= number of sample measurements
N= number of samples for calibration
y= average peak area of the calibration
Sxx = sum of squares of the deviation for x
The Standard Addition Method
 Known quantities of analyte are added to a sample of
unknown concentration
 Good for systems with significant matrix effects
(“interferences”), where selectivity or specificity is lacking
0.6
y = 0.1794x + 0.1988
0.5
0.4
0.3
[X] = concentration of analyte
[S] = concentration of standard
I = signal
i = initial, f = final
0.2
[x]f
0.1
0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
The Standard Addition Method: An Example
Example: atomic absorbance
measurements of a Zn spectral
line for determination of Zn2+
 Three standard additions are made
Added [Zn2+] ppm
0.0
0.5
1.0
2.0
Slope:
Intercept:
0.179
0.199
A = 0.179[c] + 0.199
0.5
(A = absorbance, [c] = concentration)
0.4
Solve for [X]f , where A = 0
 Zn2+ in sample =1.11 ppm
Absorbance
0.196
0.289
0.383
0.555
0.6
Absorbance

to the actual sample, which is also
analyzed (four samples)
Data analyzed by linear regression
Sample
1
2
3
4
0.3
0.2
0.1
0
0.0
0.5
1.0
1.5
2.0
Zinc Concentration (ppm )
Example from H. A. Strobel and W. R. Heineman, Chemical Instrumentation: A Systematic Approach,
Wiley: New York, 1989, p. 393.
2.5
Non-Linear Fitting
Function
Form
Example
Logarithmic
S=a+blnC
Nernst Equation
Exponential
S=aebC
Healy's model for
immunoassay
Power
S=a+bCn
Kohlrausch’s Law
Polynomial
S=a+bC2+ cC3...
Immunometric
assays
The Nernst equation is an equation that can be used (in conjunction with other
information) to determine the equilibrium reduction potential of a half-cell in an
electrochemical cell.
Kohlrausch’s Law states that the conductivity of a dilute solution is the sum of
independent values: the molar conductivity of the cations and the molar
conductivity of the anion. The law is based on the independent migration of ions.
Simple Chemical Tests


While most of this class is focused on instrumental
methods, a very large number of simple chemical tests
have been developed over the past ~300 years
Examples:
– Barium: solutions of barium salts yield a white precipitate with 2
N sulfuric acid. This precipitate is insoluble in hydrochloric acid
and in nitric acid. Barium salts impart a yellowish-green color to
a non-luminous flame that appears blue when viewed through
green glass.
– Phosphate: With silver nitrate TS, neutral solutions of
orthophosphates yield a yellow precipitate that is soluble in 2 N
nitric acid and in 6 N ammonium hydroxide. With ammonium
molybdate TS, acidified solutions of orthophosphates yield a
yellow precipitate that is soluble in 6 N ammonium hydroxide.
Examples are from US Pharmacopeia and National Formulary USP/NF
A Colormetric Test for Mercury

A modern example of a
“spot” test: a test for Hg2+
developed using DNA and
relying on the formation of
a thymidine-Hg2+thymidine complex
 LOD = 100 nM (20 ppb) in
aqueous solution
 Linearity from the high
nanomolar to low micromolar
range
 Selective for Hg2+ and
insensitive to Mg2+, Pb2+, Cd2+,
Co2+, Zn2+, Ni2+, and other
metal ions
Angew. Chem. Int. Ed., DOI: 10.1002/anie.200700269
http://pubs.acs.org/cen/news/85/i19/8519news6.html
Further Reading
 Optional Reading:
Skoog et al. Chapter 1 and Appendix 1
Skoog et al. Chapters 6 and 7 (Spectroscopy)