Spektroskopie und Chemometrie Neuigkeiten Wöchentlich #13-15, 2017

Chemometrics

NIR spectroscopy and cellulose content predicted coating build-up on drug layered pellets AAPSPT | h… LINK

Prediction of Soil Physical & Chemical Properties by Visible & Near-Infrared Diffuse Reflectance Spectroscopy in … LINK

How does multivariate calibration work for Raman monitoring? Bioprocess | LINK

Characterization of Mammalian Cell Culture Raw Materials by Combining Spectroscopy and Chemometrics. LINK

Near Infrared Spectroscopy Predicts Compositional & Mechanical Properties of Hyaluronic Acid-Based Engineered Cart. LINK

PAT for Continuous API Manufacturing Progresses – Chemometrics are applied to collected spectra to maximize the … LINK

Quantification of Lycopene,Carotene,Soluble Solids in Red-Flesh Watermelon Using On-Line Near-Infrared Spectroscopy LINK



Near Infrared

PAT-Based Control of Fluid Bed Coating Process Using NIR Spectroscopy to Monitor the Cellulose Coating on Pellets LINK

RISI Pulp: Fitnir Analyzers to supply FT-NIR online analyzer system to Harmac Pacific’s NBSK pulp mill in Nanaimo,… ht… LINK!

Paperindex Times: Harmac Pacific Selects Fitnir Analyzers To Supply Online Ft-Nir Analyzer LINK

Using Spectroscopy to Grade and Sort Fruit – choosing appropriate wavelengths – monitoring the entire NIR spectra LINK

Global Near Infrared Spectroscopy Market 2017 – Market Research News by | (press release) LINK



Infrared

Active Mode Remote Infrared Spectroscopy Detection of TNT and PETN on Aluminum Substrates LINK



Food & Feed

Congratulations to the winners of the foodscanner HorizonPrize! ScioScan cebit17 LINK!



Agriculture

From Crop Science to Space Exploration, Optical Sensing on the Rise | OpticalSensing LINK



Laboratory

Der Laborausrüster Sartorius kauft den Datenspezialisten Umetrics. Datenanalyse LINK



Petro

Visible and Near-IR Sensing: Plastic-optical-fiber-based ethanol sensor is simple, low-cost | NearIR LINK



Raman

Fructose and Pectin Detection in Fruit-Based Food Products by Surface-Enhanced Raman Spectroscopy (SERS) LINK



Other

A Retiree Discovers an Elusive Math Proof-And Nobody Notices – WIRED LINK



Spektroskopie und Chemometrie Neuigkeiten Wöchentlich #48+49, 2016

Leider ist der Eintrag nur auf English verfügbar.

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 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).

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.

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! See How it works

Proof of Concept

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.

Summary of the NIR Chemometric survey polls

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.

Angepasste NIR Kalibrationen

Steigern Sie Ihren Gewinn mit optimierter NIR Genauigkeit


Wir helfen Ihnen, die optimalen Einstellungen für eine höhere NIR Genauigkeit und Zuverlässigkeit zu finden.

Sie können Ihre eigenen NIR-Kalibrierungs Modelle mit diesen optimierten Einstellungen erzeugen.

Wir bieten einen quantitative NIR-Kalibrierung und Optimierungs Service.

Neu: White Paper (English) über die Details, was dahinter steckt.


NIR Messgenauigkeit Verbessern

  • näher an Ihre Produkt Spezifikationsgrenzwerte gehen und Rentabilität maximieren
  • Optimierung Ihrer Modelle ergeben eine Prozessoptimierung und Optimierung der Produktivität
  • Wettbewerb gegen andere NIR-Anbieter in einer Machbarkeitsstudie

Einfach anzuwenden

  • kompatibel mit jedem NIR Anbieter
  • keine Installation, kein Lernen
  • quantitative NIR Calibration Development as a Service

Sicherheit

  • hilft häufige Fehler bei der Methodenentwicklung zu vermeiden
  • bevor Sie Ihre Lösung validieren und freigeben für den Einsatz in der Produktion:
    • überprüfen Sie ob eine bessere Kalibrierung gefunden werden kann
    • vergleichen Sie Ihre Kalibrierung mit Lösungen anderer Experten

Geschwindigkeit

  • keine umständliche Versuch-und-Irrtum Modellierungs Schritte
  • Rechenzeit auf unseren Hochleistungs-Infrastruktur auslagern
  • schnelle Ergebnisse, Kalibrierungen innerhalb weniger Tage entwickelt

Festpreisangebote

  • Fixkosten, hängt nur von Datengröße ab (nicht Stundensatz für Service)
  • enorme Einsparung bei den Methodenentwicklungs Kosten
  • einfach zu planen
Mehr Vorteile, für wen und wo, erfahren Sie mehr, Kontakt

NIR Spektroskopie und Chemometrie Umfragen, Erhebungen und Wertungen (Teil 3)

10. NIR in Supply Chain Where in the supply chain are your NIR instruments located? 11. NIR Usage How long has your company used NIR spectroscopy? 12. NIR instruments How many NIR instrument units are in use in your company? 13. NIR Mobile How much is the mobile hand-held percentage of total NIR devices in your company? 14. Calibration Source How do you get the NIR Calibrations developed? 15. Calibration Training How often do the operators get training about NIR Spectroscopy and Chemometrics? 16. NIR PreCalibrations How many NIR Pre-Calibration, NIR factory calibrations or NIR starter calibrations have you in use? 17. Calibration Spectra How many Spectra does your quantitative Calibration have in average? Please vote and see the assessments below. Part 1, Part 2
NIR in Supply Chain
Where in the supply chain are your NIR instruments located?
NIR Usage
How long has your company used NIR spectroscopy?
NIR instruments
How many NIR instrument units are in use in your company?
NIR Mobile
How much is the mobile hand-held percentage of total NIR devices in your company?
Calibration Source
How do you get the NIR Calibrations developed?
Calibration Training
How often do the operators get training about NIR Spectroscopy and Chemometrics?
NIR PreCalibrations
How many NIR Pre-Calibration, NIR factory calibrations or NIR starter calibrations have you in use?
Calibration Spectra
How many Spectra does your quantitative Calibration have in average?
Part 1, Part 2

NIR Kalibrationsentwicklung

Die Mehrheit der NIR Kalibrierungen werden unter Verwendung einer kleinen Anzahl von verschiedenen Parametereinstellungen erzeugt und allzu oft eingeschränkt durch die zu Verfügung stehende Zeit die ein Benutzer hat, deren spektroskopisches und chemometrisches Fachwissen und die Fähigkeit (mühsames bedienen der Software) alle möglichen Parametereinstellungen zu wählen und zu kombinieren, die für gute Kalibrierungen erforderlich sind.

Es gibt viele veröffentlichte Normen und Richtlinien (Protokolle) für die Entwicklung von NIR-Kalibrierungen von Normierungsbehörden wie ASTM, EMEA, ICH, IUPAC, ISO, USP, PASG etc. sowie viele gute Empfehlungen und Richtlinien die in verschiedenen Lehrbüchern und Fachbeiträgen gefunden werden können.

Die Schwierigkeit mit so vielen ‘Protokollen’ für den NIR Benutzer besteht darin, dass sie alle verfügbar und in ihren Denkprozessen präsent sind während der Kalibrierungs Arbeit und zusätzlich beim Ausführen, Überprüfen und Bewerten aller manuell erzeugten Kalibrierungen. Dies ist zeitaufwendig und manchmal langweilig wiederholende Arbeit.

Um dies für die Person die NIR-Kalibrierung entwickeln zu vereinfachen, haben wir die guten Praktiken Protokolle gesammelt und sie in unseren Service integriert, der die Kalibrierungs Erstellung und das Evaluierungsverfahren automatisiert.

zu Teil 2

NIR Spektroskopie und Chemometrie Umfragen, Erhebungen und Wertungen (Teil 2)

5. Calibration Precision

Was glauben Sie, können NIR-Kalibrierung Modelle genauer sein als die Referenzwerte?

6. Calibration Maintenance

Wie oft aktualisieren Sie Ihre quantitative Kalibrierungen pro Jahr?

7. Quantitative Calibrations

Wie viele quantitative (%) Kalibrierungen haben Sie im Einsatz?

8. Quantitative Parameters

In allen Ihrer quantitative Kalibrierungen, wie viele Parameter (Eigenschaften) haben Sie insgesamt?

9. Qualitative Calibrations

Wie viele qualitative (Identifikation) Kalibrierungen haben Sie im Einsatz?

Bitte stimmen Sie und sehen Sie die Einschätzungen unten.

Calibration Precision
What do you believe, can NIR calibration models be more accurate than reference method?
Calibration Maintenance
How often do you update your quantitative calibrations per year?
Quantitative Calibrations
How many quantitative (%) calibrations do you have in use?
Quantitative Parameters
In all your quantitative calibrations, how many parameters (properties) you have in total?
Qualitative Calibrations
How many qualitative (identification) calibrations do you have in use?

Teil 1