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?
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
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.
- 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.