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.
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
Waveselection : the variable selection or wavenumber selection or wavelength selection
Pretreatments : the spectral data pre-processing
PCs : the number of Principal Components (PC) or Latent Variables (LV)
Method : 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’stime 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 modelsandreasoning 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
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
Data preprocessing, pretreatments
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
The problem of choosing the optimal number of factors to find the optimum betweenunderfitting and overfitting is solvedby having multiple methods and protocols implemented leading to multiple calibrations.
The evaluation 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 etc.
Results and Reporting
A detailedcalibration 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 plots.
Our service works with any quantitative NIR spectra data set in the standard JCAMP-DX format and uses mainly PLS and PCR to be compatible with other chemometric calibration software.
Summary of the NIR Chemometric survey polls (as of end of Sept. 2013)
The interesting finding is that most of the answers fit the following pattern. The most companies that use NIR have one NIR Instrument and only one employee that is able to develop NIR calibrations. For that the most common off-the-shelf chemometrics program is used and spent 2 hours or over a month and therefore gets no calibration training about the complex topics like Chemometrics and NIR Spectroscopy or only once (introduction). The calibration maintenance ranges from never to 3 times a year. Interestingly, there was no one who uses portable NIR instruments. We continue our surveys, for the discovery of new trends. Conclusion Seeing this picture, we think that there is huge potential to improve the calibrations. Advanced knowledge can help individuals to build the calibrations with best practices and improve their models accuracy and reliability. Once the decision and investment in NIR technology is done, you should get the best out of your data, because this extra NIR performance can be given by calibration optimization. We offer this as an easy to use and independent service.
Chemometrics is the science of relating measurements made on a chemical system or process to the state of the system via application of mathematical or statistical methods. Chemometric research spans a wide area of different methods which can be applied in chemistry. There are techniques for collecting good data (optimization of experimental parameters, design of experiments, calibration, signal processing) and for getting information from these data (statistics, pattern recognition, modeling, structure-property-relationship estimations). Chemometrics tries to build a bridge between the methods and their application in chemistry.
– The International Chemometrics Society (ICS)
Chemometrics is what chemometricians do. - Anonymous
Chemometrics is the application of mathematical and statistical techniques in chemistry.
Chemometrics is the application of mathematical or statistical methods to chemical data.
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: