How to develop near-infrared spectroscopy calibrations in the 21st Century? / Wie werden Nahinfrarotspektroskopie Kalibrierungen im 21. Jahrhundert entwickelt?

The Problem

Calibration modeling is a complex and very important part of NIR spectroscopy, especially for quantitative analysis. If the model is badly designed the best instrument precision and highest data quality does not help getting good and robust measurement results. And NIR Spectroscopy requires periodically recalibration and validation.

How are NIR models built today?

In a typical usage in industry, a single person is responsible to develop the models (see survey). He or she uses a Chemometric software that has a click-and-wait working process to adjust all the possible settings for the used algorithms in dialogs and wait for calculations and graphics and then to think about the next modeling steps and the time is limited to do so. Do we expect to find the best use-able or optimal model that way? How to develop near-infrared spectroscopy calibrations in the 21st Century?

Our Solution

Why not put all the knowledge a good model builder is using into software and let the machines do the possibilities of calculations and presenting the result? Designing the software that way, that the domain knowledge is built-in, not just only the algorithms for machine learning and make it possible to scale the calculations to multi-core computers and up to cloud servers. Extend the Chemometric Software with the Domain Knowledge and make as much computer power available as needed.

As it was since the beginning

User  → Chemometric Software → one Computer → some results to choose from

==> User's time needed to click-and-wait for creating results

Our Solution

User → (Domain Knowledge → automatized Chemometric Software) → many Computers → the best models

==> User's time used to study the best models and reasoning about his product / process

Note that the “Domain Knowledge” here does perfectly support the User's product and process knowledge to get the things done right and efficient.

Scaling at three layers

  • Knowledge : use the domain knowledge to drive the Chemometric Software
  • Chemometric Software : support many machine learning algorithms and data pre-processings and make it automatic
  • Computer : support multi-core calculations and scale it to the cloud
The hard part in doing this, is of course the aggregation of the needed domain knowledge and transform it into software. The Domain Knowledge for building Chemometric NIR Spectroscopic models is well known and it's huge and spreads multiple disciplines. Knowledge-driven software for computing helps to find the gold needle in the haystacks. It's all about scaling that makes it possible. See Proof of Concept.

New possibilities

  • NIR users can get help working more efficient and getting better models.
  • New types of applications for NIR can be discovered.
  • Evaluation of NIR Applications to replace conventional analytical methods.
  • Hopeless calibrations development efforts can be re-started.
  • Higher model accuracy and robustness can be delivered.
  • Automate the experimental data part of your application study.
  • Person independent optimization will show new solutions, because it's not limited by a single mindset => combining all the aggregated knowledge and its combinations.
  • Software independent optimization will show new solutions, because none of vendor specific limitations and missing algorithms are present => combining all open available algorithms and there permutations.
  • Computing service is included.

Contact us for trial

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Recent advanced chemometric methodsNeueste weiterentwickelte chemometrische Methoden

You are searching for recent advanced chemometric methods to get better calibration models for NIR? Methods and algorithms like:
  • Artificial Neural Networks (ANN)
  • General Regression Neural Networks (GR-NN)
  • RBF Neural Networks (RBF-NN)
  • Support Vector Machines (SVM)
  • Multiway Partial Least Squares (MPLS),
  • Orthogonal PLS (OPLS), (O-PLS), OPLS-AA, OPLS-ANN
  • R-PLS, UVE-PLS, RUVE-PLS, LOCAL PLS
  • Hierarchical Kernel Partial Least Squares (HKPLS)
  • Random Forest (RF)
  • etc.
and data pre-processing methods like
  • Extended Multiplicative Signal Correction (EMSC)
  • Orthogonal Signal Correction (OSC)
  • Dynamic Orthogonal Projection (DOP)
  • Error Removal by Orthogonal Subtraction (EROS)
  • External Parameter Orthogonalization (EPO)
  • etc.
that are partly available as modules for software packages like Matlab, Octave, R-Project, etc. Why invest a lot of time and money with new tools? Have you tried it really hard to optimize your calibrations with standard chemometrics methods like Partial Least Squares (PLS), Principal Component Regression (PCR) and Multiple Linear Regression (MLR) which are available in all chemometric software packages? Are you sure you have tried all the good rules and optimization possibilities? Get it done right with the compatible standard methods, we are specialized in optimization and development of NIR calibrations, let us help you, give us a try!Sie suchen nach den neusten weiterentwickelten chemometrischen Methoden, um bessere NIR-Kalibrierungs Modelle zu erstellen? Methoden und Algorithmen wie:
  • Künstliche Neuronale Netze (KNN, ANN)
  • Allgemeine Regression Neural Networks (GR-NN)
  • RBF Neuronale Netze (RBF-NN)
  • Support Vector Machines (SVM)
  • Multiway Partial Least Squares (MPLS)
  • Orthogonale PLS (OPLS), (O-PLS), OPLS-AA, OPLS-ANN
  • R-PLS, UVE-PLS, Rüve-PLS, PLS LOCAL
  • Hierarchische Kernel Partial Least Squares (HKPLS)
  • Random Forest (RF)
  • usw.
und Daten-Vorverarbeitungs Methoden wie
  • Erweiterte Multiplikative Signal Correction (EMSC)
  • Orthogonale Signal Correction (OSC)
  • Dynamische orthogonale Projektion (DOP)
  • Fehlerbeseitigung durch orthogonale Subtraktion (EROS)
  • Externe Parameter Orthogonalisierung (EPA)
  • usw.
die z.T. erhältlich sind als Module für Software-Pakete wie Matlab, Octave, R-Project, usw. Warum viel Zeit und Geld investieren in neue Werkzeuge? Haben Sie es wirklich ausgiebig versucht, Ihre Kalibrierungen mit Standard-Methoden zu optimieren? Zu den chemometrischen Standard-Methoden gehören Partial Least Squares (PLS), Principal Component Regression (PCR) und multiple lineare Regression (MLR), diese sind verfügbar in nahezu allen chemometrischen Software-Paketen. Sind Sie sicher, dass Sie alle guten Regeln und Optimierungsmöglichkeiten versucht haben? Verwenden Sie einfach die üblichen kompatiblen Standard-Methoden, wir sind auf die Optimierung und Erstellung von NIR-Kalibrierungen spezialisiert, lassen Sie uns helfen, kontaktieren Sie uns!