The Unscrambler 9.8
The Unscrambler is the complete Multivariate Analysis and Experimental Design software, equipped with powerful methods including Multivariate Curve Resolution (MCR), PLS-DA Classification, PLS regression, Clustering (K-Means), SIMCA, 3-Way PLS regression and PCA etc.
The Unscrambler is the complete Multivariate Analysis and Experimental Design software, equipped with powerful methods including Multivariate Curve Resolution (MCR), PLS-DA Classification, PLS regression, Clustering (K-Means), SIMCA, 3-Way PLS regression and PCA etc.
This market-leading software is extensively used across a wide range of research and industrial applications, including advanced Chemometrics, Spectroscopy and cutting-edge Sensometrics. The results are demand-driven formulations, process optimization, cost-savings and increased ROI in product development, process control, quality control and R&D.
The Unscrambler features
Analysis Methods
Descriptive statistics (Mean, Standard Deviation, Box-Plot, skewness, kurtosis, correlation matrix)
Principal Component Analysis (PCA)
Multivariate Curve Resolution (MCR)
Regression (MLR, PCR, PLS-R, 3-way PLS-R)
Prediction from PCR, PLS-R and 3-way PLSR models
Clustering (K-Means)
Classification (SIMCA, PLS-DA)
ANOVA and Response Surface ANOVA
Validation options: Leverage Correction, Cross-Validation (freely choose number of samples per segment), Test Set
Variable Scaling options: Scaling is free on each variable. Suggested options: Auto-scaling, Constant, Passify
Interaction and Square terms can be included in PCA, MLR, PCR and PLS-R models
Smart Tools for Analysis
Automatic detection of significant X-variables in PCR and PLS-R
Model Stability in PCA, PCR and PLS-R
Automatic outlier detection in PCA, MLR, PCR, PLS-R and Prediction
Message list of recommendations in MCR modeling
Interactive analysis:
Mark samples and/or variables on plots
Recalculate With or Without Marked samples or variables
Recalculate With Passified Marked or Unmarked variables
Extract Data from Marked or Unmarked
Automatic Pretreatments in Prediction and Classification
Interactive help
Tutorial exercises guiding you through the use of all The Unscrambler® modeling techniques in application examples
Design of Experiments
Design Wizard: Takes you through the stages of building a design
Fractional and Full Factorial designs
Plackett-Burman designs
Central Composite designs
Box Behnken designs
Mixture designs (Simplex-Lattice, Axial, Simplex-Centroid)
D-Optimal designs of mixtures and non-mixtures
Effects plots of main effects and interaction effects; response surface plots
Data Pretreatments
Smoothing: Moving Average, Savitzky Golay, Median filter, Gaussian filter
Normalize: Area, Unit Vector, Mean, Maximum, Range, Peak
Spectroscopic conversions: absorbance/reflectance, reflectance/ Kubelka-Munk, wave number / nanometers
Multiplicative Signal Correction (MSC) & Extended MSC (EMSC)
Noise
Derivatives: Norris-Gap, Gap-Segment and Savitzky Golay
Baseline offset and Linear Baseline correction
Standard Normal Variate (SNV)
Mean centering , standard deviation scaling
User-Defined pretreatments (programmed for e.g. in Matlab or C++ and utilized in The Unscrambler® as a dll)
The Result Viewer
Over 100 pre-defined plots (Scores, Loadings, Loading weights, Correlation Loadings, Stability Plot, Bi-plot, Leverages, Variances, RMSE, Regression coefficients, Predicted vs. Measured, Residuals, Response Surface…)
Many plot options: Sample grouping with colors or symbols; Insert text; Remove grid; Display plot as Line/Bars/Accumulated bars…
Live rotation of 3D plots
PC Navigation tool: navigate along model components in a single click
Spreadsheet Editor
No size limit
2D and 3D tables supported
Continuous, discrete and mixture variables supported
Sample and variable names of up to 49 characters
Raw data plots (Line, 2D scatter, 3D scatter, Normal probability, Histogram, Matrix, Matrix 3D)
Set Editor: allows to work freely either on the whole table or on a selection of samples and/or variables
Sort samples / Sort Variables
Transpose
Mathematical array manipulation
Fill missing values
Undo/Redo functionality
Interactions with Other Software
Drag 'n' drop values, sample names and variable names from Excel to The Unscrambler
Copy/Paste data or plots to Microsoft Office applications
Data import of 2D and 3D data tables with over 20 file formats supported: ASCII, spreadsheet software (Excel, Lotus), statistical software (NSAS, Matlab) and instrument software: JCAMP-DX, Tracker (Foss), APC, Grams (Thermo Electron), MVACDF, Indico (Analytical Spectral Devices), CLASS-PA & SpectrOn (Guided-Wave), F3D (Hitachi), BFF3 (Brimrose)
User-Defined Import: allows to import any file format using a preprogrammed DLL
Data export in various formats (ASCII, Matlab, JCAMP-DX…)
Model export in various formats (ASCII, NSAS, Vision, Tracker)
21 CFR Part 11 compliant
Password protected Login, with p assword expiration after 70 days
Secure electronic signatures
The user name, date, time and software version used to generate data and model files are registered
Details from the instrument source file are passed on into the Unscrambler data file under importation
All formatting operations on the data set are recorded in the Log trail
All computations performed on the data set are recorded in the Log trail
All transformations performed on the data set are recorded in the Log trail
Locked data sets cannot be edited
The source data set’s name and path are recorded in the resulting model file
The source data set’s transformations are recorded in the resulting model file
The complete and detailed model settings are recorded in the resulting model file
Printed plots contain date, time, file name, software name and user initials
Limitations
30-Day Trial
tags
are recorded the unscrambler data set set are pcr and the log log trail and pls pcr pls the data model file resulting model the resulting
Download The Unscrambler 9.8
Download The Unscrambler 9.8
Authors software
The Unscrambler 9.8
CAMO Inc
The Unscrambler is the complete Multivariate Analysis and Experimental Design software, equipped with powerful methods including Multivariate Curve Resolution (MCR), PLS-DA Classification, PLS regression, Clustering (K-Means), SIMCA, 3-Way PLS regression and PCA etc.
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