Spectroscopy and Chemometrics News Weekly #44+45, 2016

Chemometrics (Data Analysis)

NIRS wavelength range & calibration algorithms on prediction of crushing strength of pharmaceutical tablets LINK

“The chemometrics revolution re‐examined” | DataAnalysis PredictiveAnalytics LINK

Chemical Variability and Calibration Algorithms on Prediction of Solid Fraction of RCR Using NIR Spectroscopy LINK

Near Infrared (NIR, NIRS)

Exploring process dynamics by near infrared spectroscopy in lactic fermentations | fermentation via LINK

Modular open hardware for Near Infrared Spectroscopy fNIRS via LINK

NIR spectroscopy is a new accurate & fast method of solid waste characterization anaerobicdigestion LINK

Specim announces the world’s smallest and fastest NIR hyperspectral camera for industry hyperspectralcamera | NIRS LINK

Assessing wine sensory attributes using Vis/NIR | NIRS via LINK

Osram presents first broadband infrared LED – Compound Semiconductor | NIRspectroscopy NIRS via LINK

“Advancements in Feed Analysis by NIR Set to Deliver Greater Benefits to Feed Formulation” | NIRS FeedAnalysis ag LINK

“Real time monitoring under harsh conditions” – New Food (blog) | sensor VisNIR sugarcontent contamination LINK

SCiO: Instant Animal Feed Analysis | IoT tech NIRS sensor via LINK


Classification of individual cotton seeds with respect to variety using near-infrared hyperspectral imaging LINK


Molecular Spectroscopy Market Size, Analysis, and Forecast Report 2015-2025 | Spectroscopy via LINK


THE FUTURE OF DATA ANALYSIS – Eduard Tufte | DataAnalysis MachineLearning DataScience LINK


Advertise Your NIR Spectrometer With Us! Reach your target audience We offer Banner advertising | … LINK

Spectroscopy and Chemometrics News Weekly 43, 2016 | NIRS Raman Spectroscopy LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 43, 2016 | NIRS Spektroskopie Chemometrie news LINK

Spettroscopia e Chemiometria Weekly News 43, 2016 | NIRS Spettroscopia Chemiometria news LINK

How to develop near-infrared spectroscopy calibrations in the 21st Century?

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