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

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.