A powerful decision-tree approach to multivariate modeling of complex data

Hierarchical modeling solutions

Make modeling of non-linear data more efficient and robust

Join Classification and Prediction models using multi-level modeling

Use Hierarchical Models on-line with instrument integration

Bring data to life

Hierarchical ModelDevelopment Module

Hierarchical ModelEngine

02 CAMO Hierarchical Model Development Module & Engine

Unscrambler® X Hierarchical Model Development Module & Hierarchical Model Engine

When analyzing process or other complex data, it is often necessary

to refine models based on the output of initial investigations. In most

cases, this is done manually and in many steps, becoming a laborious

and time consuming process which is prone to error.

Hierarchical Modeling (HM) is the joining of a number of multivariate

models using logic statements in order to arrive at a single, unique

result. This is the classic logic tree or decision tree approach to

systematically solve problems, where the analysis in one step is guided

by the previous step.

With most data, it is very difficult to make a global prediction or

classification model that predicts well in every area. In many real-world

applications non-linear models are needed to understand the data.

Hierarchical models address these issues by focusing, or ‘zooming in’,

on smaller areas of a data set more precisely.

Hierarchical models are ideal for applications such as:

Fermentation analysis of batches in biotech, food, brewing, wine

Managing products with hard to control or unwanted additives

Gasoline blending in petroleum refining

Reaction monitoring in chemical manufacturing

Classifying and characterizing pharmaceutical raw materials for

better campaigning into batches

Early stage development of drug formulations and manufacturing

systems used to develop them

Manually developing models with 3 or 4 levels is very time consuming, but with hierarchical models you only need to set it up once and validate once, then you can use the model as much as required in run time applications.

Once set up, validated models can be applied across different sites and processes for use by non-experts, enabling efficient process management and technology transfer.

Hierarchical models are ideal for at-line process analysis. They allow non-normal situations to be identified and alarmed so that third-party control systems can make quality decisions.

In the example above, the material is first classified by the model, and then the correct prediction meth-ods are assigned automatically. Alarms are shown by the red cells, where the model has detected that the samples are new or unknown.

We offer a range of tools so you can use the power of hierarchical

models on your data in the way that suits you.

Projection methods (PCA, PCR, PLSR)

Classification methods (SIMCA, LDA, SVM)

Regression methods (MLR, PCR, PLSR)

Accepts data in wide range of formats

Auto-pretreatment options for data

Alarms and warnings can be set

Output in tabular format with cell colours according to

alarm state

Can be intergated with 3rd party systems including OPC UA

Hierarchical Model Development Module & Engine 03 CAMO

Hierarchical models are very useful for complex classification problems such as raw material analysis. If a global classification model cannot uniquely identify all the groups, a hierarchical model can be developed to do the classification in a stepwise manner. This is a convenient way to join multivariate models into one versatile tool.

The example above shows a global model of different raw material spectra developed and plotted in The Unscrambler® X software. Hierarchical modeling is performed by joining together up to 10 levels of models in The Unscrambler® X.

Hierarchical models are useful if dealing with non-linear regression problems over large concentration ranges. The first predictor lets you define where you are, then the second model gives a more precise analysis of the area in question. In this manner a set of robust and locally validated models can replace highly non-linear models that are prone to over-fitting.

The example above shows how a non-linear response can be modeled by two linear models, each applied to different concentration ranges.

Hierarchical Modeling tools Key features

Hierarchical Model Development Module: Plug-in module for The Unscrambler® X software for off-line data

analysis or to develop hierarchical models for on-line use.

Hierarchical Model Engine: Apply hierarchical models in real-time by integrating them into

scientific instruments or process equipment.

Full customization options: Unscrambler® X Hierarchical Models can be integrated into process

monitoring or control systems with customization as required.

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Apply 1st level(Global)

classification model

Class 1Report Class 1ID + PredictedValues and/orDiagnostics

LEVEL 1

Example workflow of a Hierarchical Model

LEVEL 2 LEVEL 3

Report Class 2ID + PredictedValues and/orDiagnostics

Report Class 3ID + PredictedValues and/orDiagnostics

Report Class 4ID + PredictedValues and/orDiagnostics

PLS Model 1

PLS Model 2

PLS Model 3

PCR Model 1

PLS Model 4

MLR Model 1

PCR Model 2

Class 2

Class 3

Class 4

Hierarchical models allow a number of multivariate models to be joined using logic statements in order to arrive at a single, unique result.

Most of the powerful classification, prediction or projection methods in The Unscrambler® X can be used as the building blocks.

The hierarchical model is built in a top-down manner, with a global model in level 1

Conditional statements about the outcome at one level define the actions to be taken on the next level

Outcomes such as classification, prediction, deviation, leverage, etc. can be tested

Up to 10 levels can be defined

Events and alarms are set up to highlight suspect samples or stop the hierarchy