Batchmaturity and Release Notes Version 8 5: Difference between pages

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===Purpose===


Batch process model and monitoring, identifying outliers.
==Version 8.5==
Version 8.5 of PLS_Toolbox and Solo was released in September, 2017.


===Synopsis===
For general product information, see [http://www.eigenvector.com/software/pls_toolbox.htm PLS_Toolbox Product Page]. For information on Solo, see [http://www.eigenvector.com/software/solo.htm Solo Product Page].
: model = batchmaturity(x,ncomp_pca,options);
: model = batchmaturity(x,y,ncomp_pca,options);
: model = batchmaturity(x,y,ncomp_pca,ncomp_reg,options);
: pred  = batchmaturity(x,model,options);
: pred  = batchmaturity(x,model);


===Description===
(back to [[Release Notes PLS Toolbox and Solo]])
Analyzes multivariate batch process data to quantify the acceptable
variability of the process variables during normal processing conditions as a function of the percent of batch completion.
The resulting model can be used on new batch process data to identify
measurements which indicate abnormal processing behavior (See the
pred.inlimits field for this indicator.)


The progression through a batch is described in terms of the "Batch Maturity", BM, which is often defined in terms of percentage of completion of the process. In the BM analysis method a PCA model is built on the calibration batch dataset and the samples' PCA scores are binned according to the samples' BM values. The resulting model contains confidence limits on PCA scores as a function of batch maturity and reflect the normal range of variability of the data at any stage of progression through the batch. If one of the measured variable represents BM, or if BM is a known function of the measured variables, then a sample from a new batch can be tested against the model's score limits at its known BM value to see if it is normal sample or not. However, BM is often not measurable or known as a function of the measured variables in new batches. Instead, this relationship is estimated by building a PLS model using the calibration dataset where BM is provided. The PLS model is then used to predict the BM value for any sample.
==Overview==


For assistance in preparing batch data for use in Batchmaturity please see [[bspcgui]].
* The primary focus of version 8.5 has been in Calibration Transfer and additional importers.


====Methodology:====
* PLS_Toolbox/Solo 8.5 has been tested extensively with Matlab 2017b prerelease and should be compatible with the coming release.
Given multivariate X data and a Y variable which represents the
corresponding state of batch maturity (BM) build a model by:
# Build a PLS model on X and Y using specified preprocessing. Use its self-prediction of Y, ypred, as the indicator of BM.
# Simplify the X data by performing PCA analysis (with specified preprocessing). We now have PC scores and a measure of BM (ypred) for each sample.
# Sort the samples to be in order of increasing BM. Calculate a running-mean of each PC's ordered scores ("smoothed score means"). Calculate deviations of scores from the smoothed means for each PC.
# Form a set of equally-spaced BM values over the range (BMstart, BMend). For each BM point, find the ''n'' samples which have BM closest to that value.
# For each BM point, calculate low and high score limit values corresponding to the cl/2 and 1-cl/2 percentiles of the ''n'' sample score deviations just selected (repeat for each PC). Add the smoothed scores to these limits to get the actual limits for each PC at each BM point. These BM points and corresponding low/high score limits constitute a lookup table for score limits for each PC in terms of BM value.
# The score limits lookup table contains upper and lower score limits for each PC, for every equally-spaced BM point over the BM range.
# The batch maturity model contains the PLS and PCA sub-models and the score limits lookup table. It is applied to a new batch processing dataset, X1, by applying the PLS sub-model to get BM (ypred), then applying the PCA sub-model to get scores. The upper and lower score limits (for each PC) for each sample are obtained by using the sample's BM value and querying the score limits lookup table. A sample is considered to be an inlier if its score values are within the score limits for each PC.


Fig. 1 shows an example of the Batch Maturity Scores Plot (obtained from the BATCHMATURITY Analysis window's Scores Plot). This shows the second PC scores upper and lower limit as a function of BM as calculated form the "Dupont_BSPC" demo dataset using cl = 0.9 and step 2 only from batches 1 to 36. These batches had normal processing conditions so the shaded zone enclosed by the limit lines indicates the range where a measured sample's PC=2 scores should occur if processing is evolving "normally". Similar plots result for the other modeled PCs. The data points shown are the PCA model scores, which are accessible from the batchmaturity model or pred's <tt>t</tt> field.
* Simplified interface design changes. Various controls have been moved to simplify interfaces. Seldom used settings have been moved to options in several cases (e.g., simplified [[Svm|SVM]] panel).


<gallery caption="Fig. 1. Batchmaturity Scores Plot." widths="400px" heights="300px" perrow="1">
* The Analysis window's confusion matrix and table toolbar icon now returns classification results for both [http://wiki.eigenvector.com/index.php?title=Sample_Classification_Predictions#Class_Pred_Most_Probable 'most probable'] and [http://wiki.eigenvector.com/index.php?title=Sample_Classification_Predictions#Class_Pred_Strict  'strict'] classifications. In the 'strict' case it also lists the 'strictthreshold' value used.
File:BMScoreScoresPlot.png|Plot showing Scores for PC 2 as a function of batch maturity (Ypred from the PLS model).
</gallery>


====Inputs====
==Calibration Transfer==
* '''x''' = X-block (2-way array class "double" or "dataset").
* [[MCCTTool | New Model-centric Calibration Transfer]] - Interface to develop instrument specific models with calibration transfer.
* '''y''' = Y-block (vector class "double" or "dataset").
* '''ncomp_pca''' = Number of components to to be calculated  in PCA model (positive integer scalar).
* '''ncomp_reg''' = Number of latent variables for regression method.


====Outputs====
* [[nlstd| NLSTD]] - Create or apply non-linear instrument transfer models (PLS_Toolbox only).
* '''model''' = standard model structure containing the PCA and Regression model (See MODELSTRUCT).
* '''pred''' = prediction structure contains the scores from PCA model for the input test data as pred.t.


Model and pred contain the following fields which relate to score limits and
* [[sstcal| SST]] - Spectral Subspace Transformation calibration transfer.
whether samples are within normal ranges or not:
:'''limits''' : struct with fields:
::'''cl''': value used for cl option
::'''bm''': (1 x bmlookuppts) bm values for score limits
::'''low''': (nPC x bmlookuppts) lower score limit of inliers
::'''high''': (nPC x bmlookuppts) upper score limit of inliers
::'''median''': (nPC x bmlookuppts) median trace of scores
:'''inlimits''' : (nsample x nPC) logical indicating if samples are inliers.
:'''t''' : (nsample x nPC) scores from the PCA submodel.
:'''t_reduced''' : (nsample x nPC) scores scaled by limits, with limits -> +/- 1 at upper/lower limit, -> 0 at the median score.
:'''submodelreg''' : regression model built to predict bm. Only PLS currently.
:'''submodelpca''' : PCA model used to calculate X-block scores.


===Options===
* [[Demonstration_Datasets|Corn DSO]] - New calibration transfer demonstration data set added. 80 samples of corn measured on 3 different NIR spectrometers with moisture, oil, protein and starch values for each of the samples is also included.


options = a structure array with the following fields:
==Importers==


* '''regression_method''' : [ {'pls'} ] A string indicating type of regression method to use. Currently, only 'pls' is supported.
* [[shimadzueemreadr]] - Imports Shimadzu EEM formatted text files.
* '''preprocessing''' : { [] } preprocessing structure goes to both PCA and PLS. PLS Y-block preprocessing will always be autoscale.
* [[visionairxmlreadr]] - Imports Vision Air formatted XML files (X- & Y-Blocks).
* '''zerooffsety''' : [ 0 | {1}] transform y resetting to zero per batch
* [[pltreadr]] - Imports Vision Air model files (.plt).
* '''stretchy''' : [ 0 | {1}] transform y to have range=100 per batch
* '''cl''' : [ 0.90 ] Confidence limit (2-sided) for moving limits (defined as 1 - Expected fraction of outliers.)
* '''nearestpts''' : [{25}] number nearby scores used in getting limits
* '''smoothing''' : [{0.05}] smoothing of limit lines. Width of window used in Savgol smoothing as a fraction of BM range.
* '''bmlookuppts''' : [{1001}] number of equally-spaced points in BM lookup table mentioned in Methodology Step 4 above. Default gives lookup values spaced every 0.1% over the BM range.
* '''plots''' : [ 'none' | 'detailed' | {'final'} ] governs production of plots when model is built. 'final' shows standard scores and loadings plots. 'detailed' gives individual scores plots with limits for all PCs.
* '''waitbar''' : [ 'off' | {'auto'} ] governs display of waitbar when calculating confidence limits ('auto' shows waitbar only when the calculation will take longer than 15 seconds)


===See Also===
* [[aqualogreadr]] - Improved capability of loading multiple files containing samples of varying sizes (PLS_Toolbox only).


[[batchfold]], [[batchdigester]], [[bspcgui]].
* [[jascoeemreadr]] - Improved capability of loading multiple files containing samples of varying sizes (PLS_Toolbox only).
 
* [[hitachieemreadr]] - Improved capability of loading multiple files containing samples of varying sizes (PLS_Toolbox only).
 
* [[rawread|RAWREAD]] - Added support for new version format (3 & 4) (See MIA_Toolbox).
 
* [[spgreadr|SPGREADR]] Added new feature, options.spectrumindex now can be an integer, an array of integers (indices, & order doesn't matter), or 'all'. When loading multiple files, options.spectrumindex must be either a single value or 'all'.
 
==Exporters==
 
* [[writeplt]] - Exports EVRI Model structures to Vision Air PLT files.
 
==Other Features and Improvements==
* [[Cooksd| Cook's Distance]] - Calculates Cooks Distance for samples in a regression model.
 
* [[rpls| RPLS]] - A recursive PLS and PCR variable selection algorithm.
 
* [[manhattandist| MANHATTANDIST]] - Calculate Manhattan Distance between rows of a matrix.
 
* [[Confusionmatrix]] and [[Confusiontable]] - Classification results formatting options added. Can now specify use of mostprobable or strict classification rule.
* [[calcdifference]] - Calculate difference between two datasets.
* Added GLS Weighting to model optimizer.

Revision as of 09:35, 7 February 2019

Version 8.5

Version 8.5 of PLS_Toolbox and Solo was released in September, 2017.

For general product information, see PLS_Toolbox Product Page. For information on Solo, see Solo Product Page.

(back to Release Notes PLS Toolbox and Solo)

Overview

  • The primary focus of version 8.5 has been in Calibration Transfer and additional importers.
  • PLS_Toolbox/Solo 8.5 has been tested extensively with Matlab 2017b prerelease and should be compatible with the coming release.
  • Simplified interface design changes. Various controls have been moved to simplify interfaces. Seldom used settings have been moved to options in several cases (e.g., simplified SVM panel).
  • The Analysis window's confusion matrix and table toolbar icon now returns classification results for both 'most probable' and 'strict' classifications. In the 'strict' case it also lists the 'strictthreshold' value used.

Calibration Transfer

  • NLSTD - Create or apply non-linear instrument transfer models (PLS_Toolbox only).
  • SST - Spectral Subspace Transformation calibration transfer.
  • Corn DSO - New calibration transfer demonstration data set added. 80 samples of corn measured on 3 different NIR spectrometers with moisture, oil, protein and starch values for each of the samples is also included.

Importers

  • aqualogreadr - Improved capability of loading multiple files containing samples of varying sizes (PLS_Toolbox only).
  • jascoeemreadr - Improved capability of loading multiple files containing samples of varying sizes (PLS_Toolbox only).
  • hitachieemreadr - Improved capability of loading multiple files containing samples of varying sizes (PLS_Toolbox only).
  • RAWREAD - Added support for new version format (3 & 4) (See MIA_Toolbox).
  • SPGREADR Added new feature, options.spectrumindex now can be an integer, an array of integers (indices, & order doesn't matter), or 'all'. When loading multiple files, options.spectrumindex must be either a single value or 'all'.

Exporters

  • writeplt - Exports EVRI Model structures to Vision Air PLT files.

Other Features and Improvements

  • RPLS - A recursive PLS and PCR variable selection algorithm.
  • MANHATTANDIST - Calculate Manhattan Distance between rows of a matrix.
  • Confusionmatrix and Confusiontable - Classification results formatting options added. Can now specify use of mostprobable or strict classification rule.
  • calcdifference - Calculate difference between two datasets.
  • Added GLS Weighting to model optimizer.
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