Hi, we’re CalibrationModel.
Our aim is to transform your NIR data to superior calibration models.
We do this by using knowledge driven software
applying good practices
and rules from literature, publications, regulatory guidelines and more.
Our service is used by NIR specialists to deliver
a valuable model
for their NIR analysis measurements.
With CalibrationModel services, NIR specialists can find out how
their NIR Data can be robust and optimally modeled
by which data preprocessing and wavelength selection, etc.
You can implement CalibrationModel in a matter of minutes
using our contact form
and send your NIR data
to receive optimized model settings as a blueprint
NIR specialists (Spectroscopist, Chemometricians)
love perfect models. They’re curious about how to
improve their models even further, because all
NIR models need continuous maintenance and updates.
Using CalibrationModel services, NIR Specialists
can deliver real value to their measurement results
through powerful model optimization capabilities.
We make NIR Chemometrics easy.
When you send your quantitative NIR spectra data to our NIR Calibration Model Service, you get a detailed calibration report
(calibration protocol) of the found optimal calibration settings, so you are able to see all insights and easily re-build the model in your NIR/Chemometric software.
Here is a part of our calibration report, that exactly describes the data used in the calibration set (CSet), the validation set (VSet) and the test set (TSet). The numbers are the number ids of the spectra in your delivered NIR data file.
The calibration method settings and parameters are
: the variable selection or wavenumber selection or wavelength selection
: the spectral data pre-processing
: the number of Principal Components (PC) or Latent Variables (LV)
: the modeling method algorithm used, e.g. PLS
Then the statistical analysis of the PLS model by the different sets (CSet, VSet, Tset).
Statistical analysis of calibration, validation and test results : 1 Name, 2 Unit, 3 N : number of spectra, 4 N : number of samples, 5 Average spectra count per sample, 6 Reference values, 7 Min, 8 Mean, 9 Median, 10 Max, 11 Standard deviation, 12 Skewness : left (-) or right (+) lack of symmetry, 13 Kurtosis : flat (-) or peaked (+) shape, 14 Model statistics, 15 RPD, 16 R², 17 RMSEC, RMSEP, RMSET : root mean square of prediction errors, 18 SEC, SEP, SET : standard error (bias corrected), 19 Bias, 20 Skewness of prediction errors, 21 Kurtosis of prediction errors, 22 Intercept, 23 Slope, 24 Intercept (reverse), 25 Slope (reverse), 26 Sample Prediction Repeatability Error, 27 Sample Prediction Repeatability Error (of Missing data MSet)
This shows how we deliver the optimal settings. With the statistical values, the NIR model predicted values
of all spectra and additional plots
you are able to compare with your re-built model to verify that the models perform nearly equally.
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.
Contact us for trial
Your NIR data is modeled by thousands of different useful calibration models and you get the best of them! That was not possible before in such a easy and fast way! See How it works
Ci spiace, ma questo articolo è disponibile soltanto in Deutsch e English.
( to part 1 )
All the below categories are implemented by using multiple different algorithms and formulas which leads to many different calibrations.
Steps in modeling
- Data Cleaning – (bad data, missing values, duplicate elimination, spectral quality / intensity / noise, input value typing errors, …)
- Initial Calibration set up – selection of calibration, validation and test samples
- Wavelengths selection
- Data preprocessing, pretreatments
- Method calculation
- Choosing the number of Principal Components / Latent Variables
- Validation of calibration model / Statistics of performance – (accuracy, precision, linearity, repeatability, range, distribution, robustness / stability, sensitivity, simplicity, etc.)
- Outlier examination and removal
of choosing the optimal number of factors
to find the optimum between underfitting
by having multiple methods and protocols implemented leading to multiple calibrations.
and the selection of the best calibration
is based on many individual statistical values including the most popular RMSEP, SEP, Bias, SEC, R2 and PCs
Results and Reporting
A detailed calibration report
is provided detailing the best available calibration containing all calibration parameter settings and statistics
of prediction performance of the calibration
set, the validation
set and the test
set. A visual expression
of the calibration is provided with the most importance
works with any quantitative NIR spectra
data set in the standard JCAMP-DX format
and uses mainly PLS
to be compatible with other
chemometric calibration software.
Meet us at the NIR 2013 – 16th International Conference on Near Infrared Spectroscopy (ICNIRS 2013) in Montpeiller, France , 2-7 June 2013.
If you are interested in analysis and optimization of your data during the conference, please take a JCAMP export of your data on a USB-memory stick with you.
We will also present a poster P129 :
‘A novel intelligent knowledge-based Chemometric Software Framework for quantitative NIR Calibration Modeling‘ by Roman Bossart
We develop the NIR calibration models with a manufacturer independent chemometrics software mainly with the widely used and proven methods of PLS and PCR, and supports all common data pretreatments. So with every manufacturer specific chemometric software the model can be used.
This are lists of compatible chemometric software packages:
Chemometric software as a service
Chemometric software bundled with NIR-Spectrometers *
Standalone Chemometric software packages *
*) Disclaimer: We have no affiliation with any of these sites or their companies.