详细的UnscramblerX教程（图文结合）

发表于 2018-8-29 16:34:40

详细的UnscramblerX教程（图文结合）详细的UnscramblerX教程（图文结合）- 一起下吧

Tutorial A: A simple example of calibration ..................................1
Description .........................................................................................................................................................1
Opening the project file......................................................................................................................................2
Define ranges......................................................................................................................................................4
Univariate regression..........................................................................................................................................7
Calibration..........................................................................................................................................................9
Interpretation of the results...............................................................................................................................11
Prediction..........................................................................................................................................................17
Evaluation of the predicted results ...................................................................................................................18
Tutorial B: Quality analysis with PCA and PLS..........................22
Description .......................................................................................................................................................22
Preparing the data.............................................................................................................................................26
Objective 1: Find the main sensory qualities....................................................................................................32
Objective 2: Explore the relationships between instrumental/chemical data (X) and sensory data (Y)...........43
Objective 3: Predict user preference from sensory measurements ...................................................................51
Tutorial C: Spectroscopy and interference problems ...................62
Description .......................................................................................................................................................62
Get to know the data.........................................................................................................................................64
Univariate regression........................................................................................................................................68
Calibration........................................................................................................................................................71
Multiplicative Scatter Correction (MSC) .........................................................................................................77
Check the error in original units: RMSE..........................................................................................................84
Predict new MSCorrected samples...................................................................................................................85
Guidelines for calibration of spectroscopic data ..............................................................................................87
Tutorial D: Screening and optimization designs ..........................89
Description .......................................................................................................................................................89
Build a screening design...................................................................................................................................90
Estimate the effects...........................................................................................................................................98
Draw a conclusion from the screening design................................................................................................104
Build an optimization design..........................................................................................................................104
Compute the response surface........................................................................................................................110
Draw a conclusion from the optimization design...........................................................................................117
Tutorial E: SIMCA classification...............................................118
Description .....................................................................................................................................................118
Reformat the data table...................................................................................................................................119
Graphical clustering........................................................................................................................................120
Make class models..........................................................................................................................................124
Classify unknown samples .............................................................................................................................126
Interpretation of classification results.............................................................................................................128
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Diagnosing the classification model...............................................................................................................132
Tutorial F: Interacting with other programs ...............................136
Description .....................................................................................................................................................136
Import spectra from an ASCII file..................................................................................................................137
Import responses from Excel..........................................................................................................................138
Create a categorical variable...........................................................................................................................140
Append a variable to the data set....................................................................................................................143
Organizing the data.........................................................................................................................................143
Study the data before modeling......................................................................................................................145
Make a PLS Model.........................................................................................................................................148
Save PLS model file.......................................................................................................................................152
Export ASCII-MOD file.................................................................................................................................152
Export data to ASCII file................................................................................................................................154
Tutorial G: Mixture design ........................................................155
Description .....................................................................................................................................................155
Design variables and responses ......................................................................................................................156
Build a simplex centroid design .....................................................................................................................157
Import response values from Excel ................................................................................................................164
Check response variations with statistics .......................................................................................................166
Model the mixture response surface...............................................................................................................169
Conclusions ....................................................................................................................................................174
Tutorial H: PLS Discriminant Analysis (PLS-DA) ....................177
Description .....................................................................................................................................................177
Build PLS regression model...........................................................................................................................179
Classify unknown samples .............................................................................................................................186
Some general comments on classification......................................................................................................190
Tutorial I: Multivariate curve resolution (MCR) of dye mixtures
...................................................................................................192
Description .....................................................................................................................................................192
Data plotting...................................................................................................................................................195
Run MCR with default options.......................................................................................................................197
Plot MCR results ............................................................................................................................................199
Interpret MCR results .....................................................................................................................................200
Run MCR with initial guess ...........................................................................................................................201
Validate the estimated results with reference information..............................................................................202
View an MCR result matrix............................................................................................................................203
Tutorial J: MCR constraint settings ...........................................207
Description .....................................................................................................................................................207
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Data plotting...................................................................................................................................................208
Estimate the number of pure components and detect outliers with PCA........................................................209
Run MCR with default settings ......................................................................................................................213
Tune the model’s sensitivity to pure components...........................................................................................214
Run MCR with a constraint of closure ...........................................................................................................216
Remove outliers and noisy wavelengths with recalculate ..............................................................................218
Tutorial K: Clustering................................................................221
Description .....................................................................................................................................................221
Transform the raw spectra ..............................................................................................................................223
Application of K-Means clustering ................................................................................................................226
Application of Hierarchical Cluster Analysis (HCA) .....................................................................................229
Repeat the HCA using a correlation-based measure.......................................................................................230
Using the results of HCA to confirm the results of PCA................................................................................233
Tutorial L: L-PLS ......................................................................237
Description .....................................................................................................................................................237
Open and study the data .................................................................................................................................241
Build a L-PLS model......................................................................................................................................241
Interpret the results .........................................................................................................................................244
Verify the results.............................................................................................................................................250
Bibliography...................................................................................................................................................254
Tutorial M: Variable selection and model stability.....................255
Description .....................................................................................................................................................255
Create a PLS model ........................................................................................................................................256
Interpret a PLS model.....................................................................................................................................257
Conclusions ....................................................................................................................................................265

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