The Unscrambler 9.8

The Unscrambler 9.8 offers you a complete multivariate analysis and experimental design software, equipped with powerful methods including Principal Component Analysis (PCA), Multivariate Curve Resolution (MCR), Partial Least Squares Regression (PLS-R), 3-Way PLS Regression, K-Means Clustering and SIMCA Classification.

Extensively used across a wide range of research and industrial applications including advanced Chemometrics, Spectroscopy and cutting-edge Sensometrics, this market-leading software yields demand-driven formulations, process optimization, cost- savings and increased ROI in product development, process control, quality control and R&D.

Application Areas:

It is used in the Chemical, Pharmaceutical, Food Processing, Consumer Products and Agricultural industries.

• Pharmaceutical & Biotechnology
• • Improved understanding of drug manufacturing processes. Provides data analysis tools for implementing Process Analytical Technologies(PAT) & Quality by Design (QbD) initiatives
  • Creation of classification and prediction models that can be used with third party software and Instrumentation. Applications include, raw material identification and quantification of active drug ingredients
  • Analysis of data, isolating those process variables that impact on drug product quality
• Chemical
• • Allows the implementation of MSPC in conjunction with the Unscrambler Online
• Food & Beverage
• • Efficiently formulate new products & determine their consumer preference attributes
• Energy
• • Facilitates fuel classification according to type and correlates physical parameters such as Density, Flash Point, Naphthalene’s, Aromatics etc
  • Enables determining of the proportion of Carbon Monoxide, Nitric Oxide and water vapor concentrations for different engine conditions and at different temperatures

Major Benefits:

1. Combines ease-of- use with comprehensive DoE and MVA methods
2. Ensures greater ROI by reducing the number of experiments required
3. Generates models that can be used for on-line prediction and classification in CAMO Software's Online Unscrambler Predictor (OLUP) & Online Unscrambler Classifier (OLUC)
4. Generates models that can be used for faster product and process optimization in CAMO Software's Unscrambler Optimizer

Major Features:

1. Exploratory Data Analysis
   • Descriptive Statistics (
   • Mean, Standard Deviation, Box-Plot,
   • Skewness, Kurtosis, Cross Correlation (Matrix Plot), Histo
   • gram, Probability Plot)
   • Principal Component Analysis (PCA),
   • PCA Projection
   • Multivariate Curve Resolution (MCR)
   • Message list of recommendations in MCR modeling
   • Clustering (K-Means)
   • Classification (SIMCA, PLS-DA)
   • ANOVA and Response
   • Surface ANOVA
   • 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
   • Varimax Rotation
2. Regression and Classification
   • Automatic detection of
   • significant X-variables in PCR, PLS-R and PCA (Marten’s Uncertainty Test on Cross Validation, Stability Plots)
   • Automatic outlier detection in PCA, MLR, PCR, PLS-R and
   • Prediction
   • Regression (MLR, PCR,
   • PLS-R, 3-way PLS-R)
   • MLR Prediction
   • Prediction with Y-values and deviations (= uncertainty limits)
   • 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
   • Prediction from PCR, PLS-R and 3-way PLSR models
3. Design of Experiments
   • Design Wizard: Takes you through the stages of building a design
   • Factorial(F) and Fractional Factorial (FF) designs
   • Plackett - Burman (PB) designs
   • Central Composite Designs (CCD)
   • Box Behnken (BB) designs
   • Mixture designs (Simplex- Lattice, Axial, Simplex-Centroid)
   • D-Optimal designs of mixtures and non-mixtures
   • Plots of main and interaction effects; Response Surface
   • Modeling (RSM)
   • Third order variable interactions
4. 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 Insertion
   • 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 Transformations (UDT), programmed for e.g. in
   • Matlab or C++ and utilized in The Unscrambler® as a DLL
   • Easy registration of pretreatment steps
   • De-trending (1st to 4th polynomial order)

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