Unscrambler analytics suite: Unscrambler
Modeling, prediction and optimisation.
Unscrambler is the industry leading tool for modeling, prediction and optimisation using powerful analytics and interactive visualisations for developing products faster, improving product quality and optimising processes. It is the preferred tool for 25000 scientists, researchers and engineers who need to analyse large and complex data sets quickly, easily and accurately.
Ways to get started
Get a free 10-day trial of Unscrambler to experience the ease of use, powerful analytics and spectral features that makes your analysis faster, easier and more reliable.
Unscrambler is available as subscription with single- and multi-user plans. Please contact us for a quote and we’ll help you find the best solution.
Try the most powerful tool for multivariate analysis
Get a free 10-day trial of the industry leading tool for multivariate analysis and spectroscopy, including support for Python, to experience the ease of use, powerful analytics and interactive visualisations.
Please note, that download is not supported on mobile devices.
Your data will love Unscrambler.
You will love it as well.
Current release highlights:
- Python scripting support
- Improved usability with updated user interface
- Increased speed with more automated tasks and faster calculations
Resources: Get started with Python
To help you get started we have made a collection of scripts and links to relevant Python pages. Use the Community to ask questions and share resources and scripts.
- 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
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
Preferred by more than 25000 scientists, researchers and engineers.
The only tools you need.
Explore more possibilities.
Bring together data, tools and people to optimise all steps from design to production
to develop products faster, improve product quality and optimise processes.
Powerful analytics, interactive graphics and visualisations, with unique capabilities for spectroscopy and chemometrics.
Unscrambler is built to solve complex problems using powerful multivariate analysis, with unique capabilities for spectroscopy and chemometrics. Choose from more than 20 different methods to analyse data, including exploratory data analysis, Partial Least Squares Regression (PLSR), Principal Component Analysis (PCA) and Soft Independent Modelling of Class Analogies (SIMCA). In addition Python gives access to thousands of free scripts for use in Unscrambler with additional methods for import, preprocessing and machine learning. Easily explore and validate models through interactive graphics and visualisations to optimise product development, improve product quality and process efficiency.
Easy import and preparation of large and complex data sets.
Quickly prepare data for analysis using Unscrambler, with easy import of all types of data like material, sensor, process and spectral data, from more than 30 different formats. Use a wide range of capabilities for data exploration, transformation, scatter correction and other spectral transformations that ensures data quality and more accurate analysis results.
Batch Modeling: Assumption-free modeling of time-dependent processes.
Batch modeling is a procedure that models processes changing over time, from an initial starting point to an end point, where the chemistry or biology is changing during the process. The best possible end product quality is achieved by adapting batch operations according to any detectable changes during processing, thus providing a control mechanism to drive a product towards its desired state. Camo has developed a new batch modeling approach using Principal Component Analysis (PCA) accommodating uneven batch lengths and different chemical or biological starting points. The method models the data in relative time and is also independent of the actual sampling rate between the batches. The Batch Modeling product is available as a plug-in to Unscrambler and can also be utilised in a process monitoring solution with using Unscrambler Process Pulse.
- Explore, analyse and interpret batch processes or other systems with time-dependent behavior
- Generate a trajectory model with confidence limits
- Follow a batch over time, detect out-of-spec situations, find cause and take action
- Visualize how a process evolves over time, independent of sampling rate
The batch modelling approach is useful in many different application areas like fermentation, drying, mixing, chemical reactions and industrial production.
The batch trajectory from Camo: A model is developed to calculate the confidence limits shown by the dotted lines in the figure. This batch tunnel corresponds to the underlying transition in the process, and new batches can be monitored and deviations from the trajectory can be fed back to the control system.
Run Unscrambler models to monitor, predict and optimise process and product quality in real-time.
Get real-time process monitoring and control with Unscrambler Process Pulse for full process visibility, early fault detection, process deviation warnings and continuous improvement. Process Pulse enables a single view of your processes by combining and presenting all process data in interactive control charts on a single dashboard. This real-time process visibility and analysis help operators to identify and handle process deviations immediately with early fault detection and process deviation warnings. Process parameters contributing to deviations can be investigated through interactive plots and advanced process analytics using Unscrambler for process optimisation. Process Pulse is a scalable and flexible process monitoring tool that uses powerful analytics to monitor at-line, on-line and in-line processes, for improved processes, better quality and significant cost reductions.
Closes the gap between
R&D and production.
Unscrambler and Process Pulse can be utilised in many different parts and levels of the organisation to optimise across the entire value chain with easy to act on insights for data scientists, process engineers, process operators and quality managers. Together Unscrambler and Process Pulse closes the gap between R&D and production with all tools needed for Quality by Design, Process Analytical Technology (PAT) and real-time process monitoring, control and optimisation.
The world is multivariate. Your analysis should be as well.
There is always more than one side to the problem you are trying to solve. It’s the same in your data. So, to disover all sides of your data, we use the latest multivariate and machine learning techniques to uncover insights in your data that can lead to big discoveries – and better process and product quality. We’ve been applying multivariate analysis to process and product quality problems for decades. With Camo, you get powerful analytics tools built on our outstanding analytical pedigree, proprietary software and commercial success.
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The most easy to use, powerful and complete analytical tool with unique capabilities for spectroscopy and chemometrics, suitable for all types of data.
Get started with Unscrambler.
Maximize your analytical skills and accelerate your organisations success using Unscrambler with our flexible training options to suit different learning preferences, skill levels and user roles.
- May 19-20, 2021 | Online, instructor led
- 25 au 27 Mai 2021 | Online, instructor led
- May 26-27, 2021 | Online, instructor led
- June 10-11, 2021 | Berlin, Germany
- 15 au 17 juin 2021 | Online, instructor led
Book: An introduction to Multivariate Analysis.
All updated 6th edition of the best selling book on chemometrics and multivariate techniques, covering PLS, PCA, TOS, DOE and much more.
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