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Copia de Explain Any Topic

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Aitziber Miguel Oyarbide

on 14 October 2016

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Transcript of Copia de Explain Any Topic

AUNIR's Library

FEED FEED INGREDIENTS
Prediction Engine
Aim
: create an engine which can predict, from a spectra file, which group this spectra belongs to.

-First, we need a classification for each group.

-Based on the classification, models are built for each step when removing groups.

-The engine is built placing these models one after another; they act like filters when a spectra file is entered.

-Engine works in a hierarchical (step-wise) manner.

-Depending on the characteristics of each spectra, the models will guide the files to a final solution, which will be then displayed.



An NIRS Prediction Engine for Discrimination of Animal Feed Ingredients
Calibration / Validation samples
100 representative samples were taken for calibration and the rest for validation in each of the groups. Kennard-Stone algorithm was used, based on spectral data.

Reason: We want to have a similar number of samples of all the groups and we want them to be representative of their whole group.


Aitziber Miguel Oyarbide
Universidad Autónoma de Madrid

Who is AUNIR?
The leading company in NIR calibration
development and delivery.

AUNIR provides NIR solutions and calibration requirements for the animal feed industry all over the world.
De-Cluttering
-Pre-processing method that is applied to the spectra before performing PCA.

-Covariance filter: identifies patterns in the variables which should be removed.

-Down-weighing of features that make the data differ.

-Mean centers each class, removes between-class variations and orthogonalises to the clutter subspace.

-It reduces the number of factors required for discriminant modelling.


Poultry
Ruminant
Swine
Equine
Concentrates
Aqua Feed
Pet

Cereals
High Protein High Oil
High Protein Low Oil
Legume
Low Protein High Oil
Low Protein Low Oil
Animal Protein
Milk Powders



Future Work

-Classification and building of a prediction engine for more groups of our library.

-Improvement of the accuracy of prediction engines.

-Expansion of the application of this project to all groups in our database.
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