Spectroscopy and Chemometrics News Weekly #10, 2017

Near Infrared

ウィスキーの分光とかめっちゃ面白い論文 Near infrared spectroscopic analysis of single malt Scotch whisky on an optofluidic LINK

Applications of near-infrared spectroscopy (NIRS) in biomass energy conversion processes: A review LINK

โครงการวิจัยเรื่อง การวิเคราะห์คุณภาพมะละกอเพื่อการบริโภคด้วย Near Infrared (NIR) Spectroscopy, … LINK

Simultaneous quantitative analysis of 3 components in mixture samples based on NIR spectra with temperature effect LINK

Non-Destructive Evaluation of the Leaf Nitrogen Concentration by In-Field Visible/Near-Infrared Spectroscopy in Pear LINK

Combination of near infrared spectroscopy & EEG lead to best brain computer interface. Another step in neuro restoratio… LINK


Meta-Lenses Bring Benchtop Performance to Small, Hand-Held Spectrometer | Spectrometer LINK


IBM adds new API to quantum computing cloud service | Via LINK


How to improve near-infrared Spectroscopy analysis? Get the free White Paper | Laboratory ExaminingFood petfood LINK

Rapid development of robust quantitative methods for near-infrared spectroscopy Instruments NIRS mills oilandgas LINK

Service for Professional development of NIR calibrations | NIRS NearInfrared Spectroscopy Pittcon2017 Lab LINK

Spectroscopy and Chemometrics News Weekly 7-9, 2017 | NearInfrared Spectroscopy NIRS Chemometrics Raman LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 7-9, 2017 | NIRS NahInfraRot Spektroskopie Chemometrie LINK

Spettroscopia e Chemiometria Weekly News 7-9, 2017 | NIRS vicinoinfrarosso Spettroscopia Chemiometria LINK

Timesaving NIRS Calibration for nearinfrared spectroscopy | protein fat LINK

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

Spectroscopy and Chemometrics News Weekly #33, 2016


Quality assessment of refined oil blends during repeated deep frying monitored by SPME–GC–EIMS, GC and chemometrics LINK

Near Infrared

The use of portable near infrared spectroscopy in elite sport. PhD thesis, 2012 | Rio2016 spectroscopy LINK

Saphenous vein graft near-infrared spectroscopy imaging insights from the lipid core plaque association with… LINK

Corn Quality Variation Demonstrates Benefits of NIRS Analysis LINK


Raman-Test für Photovoltaik: Raman-Spektroskopie erlaubt kontaktfreie Analyse von Siliziumwafern. LINK

The new themed issue dedicated to SERS is now published! LINK

Spectral Imaging

Nondestructive inspection of insects in chocolate using near infrared multispectral imaging LINK


Machine Intelligence 2.0 in Charts and Graphs ArtificialIntelligence machineintelligence MachineLearning Swiss LINK


Raman spectroscopy could help identify life in Martian rocks | fossil LINK

Food & Feed

IR Spectroscopy Market Worth 1.26 Billion USD by 2022 – Biological, Pharmaceuticals, Chemicals, Food & Beverages LINK


XRF and Raman spectroscopy to identify pigments in early Irish manuscripts LINK

Laboratory Spectroscopy Assessments of Rainfed Paddy Soil Samples on Visible and Near-Infrared Spectroscopy LINK

Benefits of latest NIR developments – laboratory-based feed analysis LINK


“Companies don’t have ideas. Only people do.” LINK

Chemometrics and Spectroscopy News Weekly #9, 2015

Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us.


Improvement of NIR model by Fractional order Savitzky-Golay derivation (FOSGD) coupled with wavelength selection LINK

Near Infrared

Near-infrared nanolaser : GaAs–AlGaAs nano laser featured in LINK
Near‐Infrared (NIR) Analysis Provides Efficient Evaluation of Biomass Samples | bioenergy LINK
A new alcohol and extract meter for beer : Anton Paar Alex 500 | NIR spectroscopy LINK
Combining NIR Spectroscopy & Machine Vision for Rapid Grain Inspection | NIRS LINK
Near-Infrared Spectroscopy (NIR): In-depth focus 2015 – European Pharmaceutical Review LINK
NIR measuring Asphaltenes in in crude oil. | NIRS LINK
CytoViva Enhanced Darkfield Hyperspectral Microscope Webinar | Nanoparticles VNIR LINK
Tellspec Food Scanner | Handheld Spectrometer TedxZowlle | NIRS LINK
Rapid phosphorous test with NIR helps to hit a moving target in feed formulation: LINK
The future of waste: five things to look for by 2025 | NIRS near-infrared spectroscopy LINK


Using Raman Spectroscopy in Forensic Science LINK
Raman Spectroscopy in-depth focus 2014 LINK
Surprising behavior benzoic acid raman LINK


New compact NIR spectrometer – Avaspec-NIR 256-HSC – Avantes LINK
Bottle Analyzer Performs Within Seconds – industrial FT/NIR spectrometer – LINK
A simple way of making optical spectrometers – TI DLP® technology for spectroscopy – CES2015 LINK


Multivariate Exploratory Data Analysis (MEDA) Toolbox for Matlab LINK
TacticID handheld spectral analysis instrument for non-contact forensic analysis LINK


Explore the spectrometry market that is set to surpass USD 19.6 billion LINK


Develop & Optimize NIR chemometric methods for Chemical Analysis with ease LINK
Develope analytical methods for FT-NIR spectroscopy and optimize for accurate prediction model | NIRS NIR FTNIR LINK
Development of quantitative Multivariate Prediction Models for Near Infrared Analyzers | NIRS NIR NIT SWIR LINK
Efficient development of new quantitative prediction equations for multivariate NIR spectra data NIRS NIR NIT LINK
Improve your NIR Analysis Results for ProcessControl with optimized Models | PAT Pharma pauto LINK
Increase Your Profit with optimized NIRS Accuracy QA QC Food Feed Lab PetCare vitamins LINK
News: Chemiometria e Spettroscopia Weekly News 8, 2015 | NIRS Spettroscopia Chemiometria news LINK
News: Chemometrics and Spectroscopy News Weekly 8, 2015 | NIRS Spectroscopy Chemometrics news LINK
News: Chemometrie und Spektroskopie Neuigkeiten Wöchentlich 8, 2015 | NIRS Spektroskopie Chemometrie news LINK
Nine Reasons why near-Infrared Spectroscopy Applications need periodic Calibration Maintenance | NIR NIRS Infrared LINK
Quantitative Analytical NIR Method Development for Quality Control Laboratory & Analytical Laboratories | pauto QAQC LINK
Reduce Workload and Response Time of NIRS Analytical Laboratory Method Development | NIRS NIR NIT LINK
Sie verwenden Nah-Infrarot Spektrometer mit Chemometrischen Methoden? Sparen Sie Zeit | pharma food feed NIR LINK
Stop Paying Too Much Time for NIRS Chemometrics Calibration Method development | accuracy measure NIR LINK

We make NIR Chemometrics easy

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.
CalibrationModel We make NIR Chemometrics easy. Near-Infrared Data Modeling Calibration Service

Procedures for NIR calibration – Creation of NIRS spectroscopy calibration curves

Do you know the effect that you prefer to try out their favorite data pretreatments in combination and often try the same wavelength selections based spectra of the visualized?

You try as six to ten combinations until one of them selects his favorite calibration model, to then continue to optimize. Since then suddenly fall to outliers, because it goes in depth, so is familiar with the data, we know now the spectra of numbers of outliers and is familiar with the extreme values.

Now, the focus is on the major components (principal components, Latent Variables, factors) and makes sure not to over-fit and under-fit not to. The whole takes a few hours and finally one is content with the model found.

So what would happen if you all in the beginning tried variants found outliers removed and re-evaluated and compared? The results would be better than that of the previous model choice? One does not try out? Because it is cumbersome and takes hours again?

We have developed a software which simplifies this so that also the number of model variations can be increased as desired. The variants generation is automated with an intelligent control system, as well as the optimization and comparing the models and finally the final selection of the best calibration model.

Our software includes all the usual known data pretreatment methods (data pre-processing) and can combine them useful. Since many Preteatments are directly dependent on the wavelength selection, such as the normalization the determined within a wavelength range of the scaling factors to normalize the spectra so that pretreatments with the wavelength ranges may be combined. So a variety of settings sensible model comes together that are all calculated and optimized. For the automatic selection of the relevant wavelength ranges, different methods are used, which are based on the spectral intensities. Thus, for example, regions with total absorption is not used, and often interfering water bands removed or retained.

Over all the calculated model variations as a summary outlier analysis can be made. Are there any new outliers (hidden outlier) discovered, all previous models can be automatically recalculated, optimized and compared without these outliers.

From this great number of calculated models with the statistical quality reviews (prediction performance) the optimum calibration can now be selected. For this purpose, not simply sorting by the prediction error (prediction error, SEP RMSEP) or the coefficient of determination (coefficient of determination r2), but by several statistical and test values are used jointly toward the final assessment of optimal calibration.

Thus we have created a platform that allows the highly automated work what a man can never do with a commercial software.

We therefore offer the largest number of matched to your application problem modeling calculations and choose the best calibration for you!

This means that our results are faster, more accurate, robust and objective basis (person independent) and quite easy for you to apply.

You have the full control of the models supplied by us, because we provide a clearly structured and detailed blueprint of the complete calibration, with all settings and parameters, with all necessary statistical characteristics and graphics.

Using this blueprint, you can adjust the quantitative calibration model itself in the software you use, understand and compare. You have everything under control form model creation, model validation and model refinement.

Your privacy is very important to us. The NIR data that you briefly provide us for the custom calibration development will remain of course your property. Your NIR data will be deleted after the job with us.

Interested, then do not hesitate to contact us.


It is easy
1. send your NIR data (we do not collect, share or sell your data)
2. receive your optimal model blueprint
3. build it, validate it, use it

We have a Chemometric software not to do chemometrics,
we have the solution to build an optimal model for your data
so you can get better NIR measurement results.

So we don’t name it a “Chemometric Software”.
It’s a service named, as that what it delivers, a Calibration Model.
It gives you an optimal chemometric model for your NIR data.
That is what you want to achieve.

So don’t bother about Chemometrics and endless helpless possibilities
and spend your time with clicking and waiting for a chemometric software,
when you can get an optimal model for your data as a service!

There is no lock-in.
Because there is no software to install.

There is no black-box.
Because the model is delivered as a detailed and complete blueprint in human readable form.

You stay independent.
Because you can always choose:
- You can still do it as you have done it before.
- You will experience that with the service you will get the better models faster and is inexpensive.

Let’s have a try
please contact us, so we can help you!

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

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. 


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, …).


*) 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.  


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.