Prof.Dr.Cevdet Demir

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INTRODUCTION Prof.Dr.Cevdet Demir cevdet_at_uludag.
edu.tr
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1. INTRODUCTION
2. EXPERIMENTAL DESIGN
3. SIGNAL PROCESSING
4. PATTERN RECOGNITION
5. CALIBRATION

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DIFFERENT GROUPS HAVE DIFFERENT BACKGROUNDS AND
EXPECTATIONS AS TO HOW CHEMOMETRICS SHOULD BE
INTRODUCED Statisticians want to start with
distributions, hypothesis tests etc. and build up
from there. They are dissatisfied if the maths is
not explained. Chemical engineers like to start
with linear algebra such as matrices, and expect
a mathematical approach but are not always so
interested in distributions etc.
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Computer scientists are often most interested in
algorithms. Analytical chemists often know a
little statistics but are not necessarily very
confident in maths and algorithms so like to
approach this via statistical analytical
chemistry. Difficult group because the ability to
run instruments is not necessarily an ability in
maths and computing. Organic chemists do not
like maths and want automated packages they can
use. They often require elaborate courses that
avoid matrices. The course an organic chemist
would regard is good is one a statistician would
regard as bad.
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HIERARCHY OF USERS
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• Balance between levels.
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• No point if no data.
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• No real interest for us if method routine.
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• Big gap between theory and practice.
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• "Bridging the gap".

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CHEMOMETRICS IS NOT A UNITARY SUBJECT LIKE
ORGANIC CHEMISTRY In organic chemistry, a solid
skill base that all organic chemists have is
built upon over the years. All organic chemists
have roughly the same skill base. More
experience ones have a bigger knowledge
base. Good organic chemists read the literature
a lot and know many reactions well.
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ORGANIC CHEMISTRY IS BASICALLY A KNOWLEDGE BASED
SUBJECT certain basic skills and then increase
the knowledge. CHEMOMETRICS IS MORE A SKILLED
BASED SUBJECT not necessary to have a huge
knowledge of named methods, a very few basic
principles but one must have hands-on experience
to expand ones problem solving ability.
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INTEREST IN CHEMOMETRICS IS DIVERSE Practical
chemists may see a package in the lab and be
interested, not much previous knowledge. Chemical
engineers and statisticians interest in
algorithms and computing
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•  
• Four building blocks.
• Methods.
• Software.
• Instrumental techniques.
• Applications.
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• METHODS
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• Main subject of next lectures.
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• Experimental design.
• How to get the best out of your experiment.
  Optimisation, using time efficiently, sensible
  conclusions and modelling.
• Examples.
• Confidence in models.
• Screening for significant factors.

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• Pattern recognition
• Grouping of objects e.g. how similar is the
  behaviour of compounds, how similar are
  chromatographic columns.
• Examples.
• Chromatographic column performance
• Badger urine characterisation

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• Calibration
• Quantitative estimation. Especially mixtures.
• Estimation of bulk parameters.
• Examples.
• PAHs calibrating uv to GCMS
• Linking of taste to chemical composition

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• Signal resolution
• Univariate e.g. smoothing and derivatives.
• Multivariate e.g. coupled chromatography such as
  DAD-HPLC, LCMS.
• Examples.
• DAD-HPLC, unresolved peaks
• Fluorescence excitation-emission of mixtures
• GCMS of complex environmental samples
• Uv/vis to follow reactions

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• SOFTWARE
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• Many approaches according background of user.
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• Programmers
• C / C
• VB and VBA
• Matlab
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• Users
• Matlab
• Excel
• VBA using Excel

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• Rapid / cheap analysis. Obtain information
  rapidly and cheaply as an alternative to
  chromatography. Miniaturisation.
• Importance, e.g. on-line monitoring
• NIR
• MIR
• Uv/vis
• FIA

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• Can we exploit ever sophisticated forms of
  information, with trend to coupled chromatography
  e.g. DAD-LC-NMR-MSMS?
• Can we replace slow chromatography with rapid
  methods and use chemometrics to obtain
  information? Examples process control, reaction
  monitoring?

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• APPLICATIONS
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• Wide range. Examples.
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• Pharmaceutical industry.
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• Impurity monitoring in process control.
• Rapid reaction monitoring.
• Chromatographic column evaluation.
• Combinatorial chemistry.
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• Environmental monitoring.
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• Workplace pollutants by MIR.
• PAHs by uv/vis.
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• Forensic work
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• Horse racing forensic lab.
• Customs and drugs.
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• Food chemistry
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• Determining protein in wheat.
• Process control of drinks.
• Link taste to chemical composition.