Easy import of your spectral data
Importing your spectral data into Unscrambler is easy. Simply select the spectral data format you want from the menu, and the import wizard will guide you through the import. Unscrambler can read more than 30 different data formats including generic spectral and chromatograpic formats and instrument spectral formats. New data formats are easily added using Python.
Effective preprocessing of your data
The most important step before analysing your spectral data is to remove noise or irrelevant variation. Unscrambler has an extensive set of transformations and preprocessing tools, such as correction for baseline offset and scatter, that is easily performed on your data. Use the preview function to assess the transformation effect and tune parameters. Preprocessing is saved so that it can be applied automatically on new data. This optimises the analysis workflow and facilitates real-time implementation.
Solving the mixture analysis problem
Decompose mixture spectra into pure (chemical) components and their respective estimated concentrations using multivariate curve resolution (MCR). Flexible options are available to optimise the procedure. These inlcude options for adding constraints and information about available pure spectra or concentration profiles. In addition to concentration and pure component profiles, outputs include diagnostics to evaluate the model fit.
Flexibility without complexity
Easily run thousands of free Python scripts in Unscrambler for additional instrument formats, preprocessing tools and machine learning methods.
Easily examine, compare and prepare your data through interactive visualisations, and enjoy the explorative model bulding in Unscrambler.
Secure and compliant
Unscrambler has compliance mode, electronic signatures, user authentication and audit trails for compliance with 21 CFR Part 11 and EU Annex 11.
- Generic import formats such as ASCII (text), MS Excel, Matlab, JCAMP-DX, NetCDF, JEOL, as well as generic database import
- Vendor specific formats from Thermo Fisher Scientific (GRAMS, OMNIC), Bruker (OPUS), Perten, rap-ID, Brimrose, ASD (Indico), Varian, Guided Wave (SpectrOn, Class-PA, NIRO JSON), FOSS (NSAS), PerkinElmer, DeltaNu, VisioTec and Viavi (MicroNIR™ Pro)
- Data and models from Design-Expert® and previous versions of Unscrambler can also be imported
- Some formats and database connections that are not listed above may be available as plugins. New formats easily added
Combining or reducing data
- Reduce (Average) along samples or variables
- Reshape using Row/Column major, Sequence wise or Level wise
- Augment or Append two or more matrices with matching dimensions
- Append two or more matrices based on column header names
- Flexible Sample Alignment by Polling, Event, Sample ID, Event within Sample ID
- Dimension Reduction for individual blocks of variables using PCA, PCR, PLSR
Scatter correction and other spectral transforms
- Smoothing with Moving average, Gaussian filter, Median filter, Savitzky-Golay
- Normalization to common Mean, Max, Range, Area under the curve, Unit vector normalization, Peak normalization
- Baseline correction using Offset or Straight line
- Derivatives using Gap, Gap-Segment, Savitzky-Golay up to 4th order
- Standard Normal Variate (SNV)
- Multiplicative Scatter Correction (MSC)
- Extended Multiplicative Signal Correction (EMSC)
- Orthogonal Signal Correction (OSC)
- Correlation Optimization Warping (COW)
- Missing values
- Level (Mean, Max, Min, Median, Quartiles)
- Range (Max-Min, Std., Variance, RMS)
- Distribution (Skewness, Kurtosis)
- Cross correlations
- Scatter effects
- Equality of means (Paired t-test, Equal variance Student’s t-test, Unequal variance Student’s t-test)
- Equality of variances (F-test, Levene’s test, Bartlett’s test)
- Normality (Kolmogorov-Smirnov test, Mardia’s test of multivariate normality)
- Contingency analysis
- K-means, K-medians
- Hierarchical Cluster Analysis (HCA), including Single linkage, Complete linkage, Average linkage, Median linkage and Ward’s method
- Principal Component Analysis (PCA)
- Rotated PCA (Varimax, Equimax, Quartimax, Parsimax)
- Multivariate Curve Resolution (MCR)
- Multiple Linear Regression (MLR)
- Principal Component Regression (PCR)
- Partial Least Squares Regression (PLSR)
- Support Vector Machines Regression (SVR)
- L-PLS Regression, incorporating three data tables
- Projection using PCA, PCR or PLSR models
- Soft Independent Modelling of Class Analogy (SIMCA)
- Linear Discriminant Analysis (LDA) with Linear, Quadratic, Mahalonobis options
- PCA-LDA, for classification of correlated data by LDA
- Support Vector Machines Classification (SVC)
- Bias and Slope correction
- Piecewise Direct Standardization (PDS)
- Absorbance to Reflectance/Transmittance
- Reflectance/Transmittance to Absorbance
- Reflectance to Kubelka-Munk
- Attenuated Total Reflectance (ATR) Correction
General and variance transforms
- Various Centre and Scale options
- Interaction and Square effects
- Compute General, with operations such as log(x), 1/x, etc
- Quantile Normalize
- Fill missing
- Additive and Proportional Noise
- Statistical Process Control (SPC) with Capability analysis
- Moving Block methods (Mean, Std., Relative std., F-test)
- Variable Limits filtering
Design of Experiments
- Two-level factorial screening designs
- General factorial studies
- Response surface methods (RSM)
- Mixture design techniques
- Combinations of process factors, mixture components, and categorical factors
- Design and analysis of split plots
Batch modeling (plug-in – sold seperately)
- Modeling batch progression in relative time
- Prediction of new batch trajectories
- Any pretreatment of the data e.g. for spectra are stored within the model and applied for new batches
- The method is independent of sampling time, sampling period, batch progression and unequal batch lengths
- Dynamic limits for scores for individual components and the overall model
- Dynamic limits for the residual distance to the model (F-residual statistics)
- Contribution plot for drill-down functionality
- No missing value problem during prediction
Ways to get started
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