NIR Spectroscopy Calibration Report for quantitative predictive models

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 (ISO 12099) and easily capable to 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).

Calibration Report

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.

Your Calibration Report including all calibration-, validation- and test statistics and plots can be downloaded from our Web-Shop after processing of your Calibration Request.

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

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! Start Calibrate See How it works

Proof of Concept and WorkProof of Concept and WorkProof of Concept and Work

In 2021 we automatically calibrated Mango DM with RMSEP = 0.7247 with our software
that performs better than the one in an research paper that states :
  • “Readers are encouraged to use this big data set and produce innovative ideas and algorithms to achieve RMSEP better than 0.79%.”
See Mango DM – dry matter prediction in mango fruit with near-infrared (NIR) spectroscopy




Chemometric software competitions (aka shootouts) are a good way to check algorithms, software and knowledge against all other experts in the field.

Imagine that the prediction results can be produced with any kind of software and newest algorithms.

And we just use PLS right to generate models that can be used on all NIR software systems, because PLS is a quasi standard, supported in all major chemometrics software.

Our software framework reached very good results, got gold (rank #1) and silver (rank #2) during well known international NIR Chemometric software shootouts* so far, the competitions are held bi-annual.

Rank / competitors  Competition / Conference  Year
 #1 / 1  **  Kaji / ANSIG  2014
 #1 / 150  Kaji / ANSIG  2012
 #2 / ???  IDRC / IDRC  2012
The Kaji Competition

A set of NIR spectral data will be available for downloading from the ANISG website and contestants will be asked to find and explain a "best" chemometric model to robustly predict samples of the same type.
A panel will select the three "best" entries based on the predicted results and spectroscopic explanation of the products and attributes of interest. 

http://www.anisg.com.au/the-kaji-competition


The IDRC Competition

The Software Shootout has been a staple of the IDRC. It is a competition amongst participants of the conference that aims at determining the person who developed the best model and obtained the lowest prediction error for a particular problem.
Every IDRC, a new challenge is proposed to participants. The challenge consists of a data set with calibration, test and a validation set.
Participants are given target values for the calibration and test sets but must do their best to develop a model that will predict the validation set as accurately and precisely as possible. Challenges from all sorts of fields of NIRS have been used (agriculture, biomedical, pharmaceutical, soil, …).

IDRC


*) The author was unable to present the results at the conferences, so this ranking was not official but confirmed by the shootout organizers. Thanks go to Benoit Igne, IDRC 2012 shootout organizer and Steve Holroyd, Kaji Competition organizer at ANISG Conference 2012.  

Conclusion

Our chemometric software framework can significantly reduce the time spent for NIR method development and fine optimization. The time saving can be achieved through highly automated experiments and the usage of cloud computing. Calibrations are built and evaluated using automated good practices protocols resulting in useful, precise and robust Calibrations. The high number of experiments enables a deep screening of the solution domain to find the optimum calibration settings, something currently unavailable in standard chemometric software.

**) We were the only participator that got the 4 competition tasks (4-times more than usual) completed in that short time and submitted the fully documented results. After the competition, the information was given, that the data was originated from forages and the constituents were dry matter, organic matter digestibility, protein and ash. Thanks go to Daniel Cozzolino, Kaji 2014 Competition organizer.

In 2021 we automatically calibrated Mango DM with RMSEP = 0.7247 with our software
that performs better than the one in an research paper that states :
  • “Readers are encouraged to use this big data set and produce innovative ideas and algorithms to achieve RMSEP better than 0.79%.”
See Mango DM – dry matter prediction in mango fruit with near-infrared (NIR) spectroscopy




Chemometric software competitions (aka shootouts) are a good way to check algorithms, software and knowledge against all other experts in the field.

Imagine that the prediction results can be produced with any kind of software and newest algorithms.

And we just use PLS right to generate models that can be used on all NIR software systems, because PLS is a quasi standard, supported in all major chemometrics software.

Our software framework reached very good results, got gold (rank #1) and silver (rank #2) during well known international NIR Chemometric software shootouts* so far, the competitions are held bi-annual.

Rank / competitors Competition / Conference Year
#1 / 1 ** Kaji / ANSIG 2014
#1 / 150 Kaji / ANSIG 2012
#2 / ??? IDRC / IDRC 2012
The Kaji Competition

A set of NIR spectral data will be available for downloading from the ANISG website and contestants will be asked to find and explain a "best" chemometric model to robustly predict samples of the same type.
A panel will select the three "best" entries based on the predicted results and spectroscopic explanation of the products and attributes of interest.

http://www.anisg.com.au/the-kaji-competition


The IDRC Competition

The Software Shootout has been a staple of the IDRC. It is a competition amongst participants of the conference that aims at determining the person who developed the best model and obtained the lowest prediction error for a particular problem.
Every IDRC, a new challenge is proposed to participants. The challenge consists of a data set with calibration, test and a validation set.
Participants are given target values for the calibration and test sets but must do their best to develop a model that will predict the validation set as accurately and precisely as possible. Challenges from all sorts of fields of NIRS have been used (agriculture, biomedical, pharmaceutical, soil, …).

IDRC


*) The author was unable to present the results at the conferences, so this ranking was not official but confirmed by the shootout organizers. Thanks go to Benoit Igne, IDRC 2012 shootout organizer and Steve Holroyd, Kaji Competition organizer at ANISG Conference 2012.

Conclusion

Our chemometric software framework can significantly reduce the time spent for NIR method development and fine optimization. The time saving can be achieved through highly automated experiments and the usage of cloud computing. Calibrations are built and evaluated using automated good practices protocols resulting in useful, precise and robust Calibrations. The high number of experiments enables a deep screening of the solution domain to find the optimum calibration settings, something currently unavailable in standard chemometric software.

**) We were the only participator that got the 4 competition tasks (4-times more than usual) completed in that short time and submitted the fully documented results. After the competition, the information was given, that the data was originated from forages and the constituents were dry matter, organic matter digestibility, protein and ash. Thanks go to Daniel Cozzolino, Kaji 2014 Competition organizer.

In 2021 we automatically calibrated Mango DM with RMSEP = 0.7247 with our software
that performs better than the one in an research paper that states :
  • “Readers are encouraged to use this big data set and produce innovative ideas and algorithms to achieve RMSEP better than 0.79%.”
See Mango DM – dry matter prediction in mango fruit with near-infrared (NIR) spectroscopy




Chemometric software competitions (aka shootouts) are a good way to check algorithms, software and knowledge against all other experts in the field.

Imagine that the prediction results can be produced with any kind of software and newest algorithms.

And we just use PLS right to generate models that can be used on all NIR software systems, because PLS is a quasi standard, supported in all major chemometrics software.

Our software framework reached very good results, got gold (rank #1) and silver (rank #2) during well known international NIR Chemometric software shootouts* so far, the competitions are held bi-annual.

Rank / competitors Competition / Conference Year
#1 / 1 ** Kaji / ANSIG 2014
#1 / 150 Kaji / ANSIG 2012
#2 / ??? IDRC / IDRC 2012
The Kaji Competition

A set of NIR spectral data will be available for downloading from the ANISG website and contestants will be asked to find and explain a "best" chemometric model to robustly predict samples of the same type.
A panel will select the three "best" entries based on the predicted results and spectroscopic explanation of the products and attributes of interest.

http://www.anisg.com.au/the-kaji-competition


The IDRC Competition

The Software Shootout has been a staple of the IDRC. It is a competition amongst participants of the conference that aims at determining the person who developed the best model and obtained the lowest prediction error for a particular problem.
Every IDRC, a new challenge is proposed to participants. The challenge consists of a data set with calibration, test and a validation set.
Participants are given target values for the calibration and test sets but must do their best to develop a model that will predict the validation set as accurately and precisely as possible. Challenges from all sorts of fields of NIRS have been used (agriculture, biomedical, pharmaceutical, soil, …).

IDRC


*) The author was unable to present the results at the conferences, so this ranking was not official but confirmed by the shootout organizers. Thanks go to Benoit Igne, IDRC 2012 shootout organizer and Steve Holroyd, Kaji Competition organizer at ANISG Conference 2012.

Conclusion

Our chemometric software framework can significantly reduce the time spent for NIR method development and fine optimization. The time saving can be achieved through highly automated experiments and the usage of cloud computing. Calibrations are built and evaluated using automated good practices protocols resulting in useful, precise and robust Calibrations. The high number of experiments enables a deep screening of the solution domain to find the optimum calibration settings, something currently unavailable in standard chemometric software.

**) We were the only participator that got the 4 competition tasks (4-times more than usual) completed in that short time and submitted the fully documented results. After the competition, the information was given, that the data was originated from forages and the constituents were dry matter, organic matter digestibility, protein and ash. Thanks go to Daniel Cozzolino, Kaji 2014 Competition organizer.

NIR Calibration Modeling (Part 2)NIR Kalibrationsentwicklung (Teil 2)NIR calibrazione Modeling (parte 2)

( 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


The problem of choosing the optimal number of factors to find the optimum between underfitting and overfitting is solved by 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 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 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.

( zu Teil 1 )

Alle folgenden Kategorien werden durch die Verwendung mehrerer verschiedener Algorithmen und Formeln umgesetzt, was zu vielen unterschiedlichen Kalibrierungen führt.

Arbeitsschritte bei der Modell Erstellung
  • Daten Bereinigung - (schlechte Daten, fehlende Werte, Duplikateliminierung, spektrale Qualität / Intensität / Rauschen, Eingabewert Tippfehler, ...)
  • Initial Kalibrierung einrichten - Auswahl der Kalibrierungs-, Validierungs- und Test-Sets
  • Wellenlängen Auswahl
  • Datenvorverarbeitung, Datenvorbehandlungen
  • Method Berechnung
  • Die Wahl der Anzahl der Hauptkomponenten / Latente Variablen / Faktoren
  • Validierung des Kalibrierungs Modell / Performance Statistiken - (Genauigkeit, Präzision, Linearität, Wiederholbarkeit, Reichweite, Verteilung, Robustheit / Stabilität, Empfindlichkeit, Einfachheit, etc.)
  • Ausreißer Untersuchung und Beseitigung


Das Problem der Wahl der optimalen Anzahl von Faktoren, um das Optimum zwischen Unterfittung und Überfittung zu finden, wird gelöst durch mehrere implementierte Methoden und Protokollen, was zu mehreren Kalibrierungen führt.

Die Auswertung und die Auswahl der besten Kalibrierung basiert auf vielen einzelnen statistischen Werten, einschließlich der beliebtesten RMSEP, SEP, Bias, SEC, R2 und PCs usw.

Ergebnisse und Berichte

Eine detailliertes Kalibrierprotokoll wird bereitgestellt, das die beste verfügbare Kalibrierung detailliert mit allen Kalibrierparameter Einstellungen und Statistiken der Vorhersage Leistung des Kalibrier-Sets, des Validierungs-Sets und des Test-Sets beinhaltet. Eine visuelle Betrachtung der Kalibrierung wird mit den wichtigsten Grafiken zur Verfügung gestellt.

Unser Service funktioniert mit jedem quantitative NIR-Spektren Daten Satz im Standardformat JCAMP-DX-Format und verwendet hauptsächlich PLS und PCR um kompatibel zu sein mit anderen chemometrischen Kalibrierungssoftwaren.

( 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


The problem of choosing the optimal number of factors to find the optimum between underfitting and overfitting is solved by 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 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 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.

Chemometrics / Chemometrik

ChemoMetric

Definitions

  • Chemometrics is the science of extracting information from chemical systems by data-driven means. It is a highly interfacial discipline, using methods frequently employed in core data-analytic disciplines such as multivariate statistics, applied mathematics, and computer science, in order to address problems in chemistry, biochemistry, medicine, biology and chemical engineering. In this way, it mirrors several other interfacial ‘-metrics’ such as psychometrics and econometrics.

    Wikipedia (2012)
  •  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.

Links


ChemoMetric

Definition

  • Unter Chemometrik oder auch Chemometrie versteht man die chemische Teildisziplin, die sich mit der Anwendung mathematischer und statistischer Methoden beschäftigt, um zum einen in optimaler Weise chemische Verfahren und Experimente zu planen, zu entwickeln oder auszuwählen. Zum anderen kann mit chemometrischen Methoden ein Maximum an chemischen Informationen aus experimentellen Messdaten extrahiert werden. Beispielsweise sind Spektren der Nahinfrarotspektroskopie nur mittels der Chemometrie auswertbar. – Wikipedia (2012)
  • Chemometrik ist das, was Chemometriker tun. – Anonym
  • Chemometrie ist die Anwendung von mathematischen und statistischen Techniken in der Chemie.

Links

Chemometrie Programme / Software