Transcript Tutorials Layout

Chemomatrics.xla
&
various excercises
Oxana Rodionova
Semenov Institute of Chemical Physics
[email protected]
18.07.2015
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Chemometric
calculations in
Excel
Input
Provides user with all
possibilities of Excel
interface, worksheet
calculations, worksheet
functions, charts, etc.
R esults
VBA helps to simplify routine
work
VBA
Functions
D ATA
Excel User
Interface
С++
DLL
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Calculations
All calculations are made "on
the fly“ and very fast
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User Interface
XcalC
XtstC
nPC
={ScoresPCA(XcalC,5,0,XtstC)}
centering and
weighting
character
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User Interface
XcalC
Excel worksheet function
nPC
=TRANSPOSE(LoadingsPCA(XcalC,5,0))
centering and
weighting
character
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Only Excel worksheet function
List of user-defined functions
PCA Decomposition
Options:
ScoresPCA <for calibration or test samples>
LoadingsPCA
•Centering
•Weighting
•number of PCs
PLS Decomposition
ScoresPLS <X-scores for calibration or test samples>
UScoresPLS <Y-scores for calibration or test samples>
LoadingsPLS <P-loadings>
WLoadingsPLS
QLoadingsPLS
PLS2 Decomposition
ScoresPLS2 <X-scores for calibration or test samples>
UScoresPLS2 <Y-scores for calibration or test samples>
LoadingsPLS2 <P-loadings>
WLoadingsPLS2
QLoadingsPLS2
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Course Layout
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Tutorials
Exercises
Basics
Basics
Chemometrics
Chemometrics
Advanced
Advanced
Projects
Tests
Chemometrics
Chemometrics
Advanced
Advanced
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Exercises
Specially prepared Excel workbooks with
tasks and solutions
Mainly formal examples
Short description
Ex.1 - Questions
Ex.1 - Solutions
...
Ex.n - Questions
Ex.n - Solutions
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Exercises – Matrices (1)
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Show solution
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Exercises – Matrices (2)
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Show solution
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Tests
Specially prepared Excel workbooks with
tasks (no solutions!) + theoretical questions
Formal and simplified real examples
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Test <PCA>. Excel workbook
Data set description: results of DSC experiment
Calibration: 11 samples
Test:
4 samples
Variables:
5 temperature rates
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Projects
Big tasks for working without assistance
Goal : to find a solution for a real examples
Analyzing raw data
Data pre-processing
Data processing by
projection methods
Results analysis
Conclusions
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Project <Classification>
Problem description
Objects: pills of 3 lots
(30 samples)
Lot 1: Genuine pills
Batch “G1” : 5 pills ;
Batch “G2”: 5 pills
Lot 2: Generic pills
Batch ”V”: 10 pills
Lot 3: Fakes
Batch “F”: 10 pills
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Variables:
NIR measurements in
1100 -2400 nm range
(234 wave lengths)
Initial data
X(30234)
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Initial Data
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After variable selection and SNV correction
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Data overview
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G-model
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Influence plot. Outlier detection
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V-model
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,etc.
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What is done and what is waiting to
be down?
Main projection
functions - DONE
Chemomatrics.xla
Additional service
functions (leverages,
residuals, preprocessing, etc)
Should be added
Exercises
Test
Projects
Now only idea how
to do
Should be
prepared
Should be verified
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