Spectroscopy and Chemometrics News Weekly #21-24, 2017

Chemometrics

DD-SIMCA A MATLAB GUI tool for data driven SIMCA approach LINK

Fourier transform infrared spectroscopy coupled with chemometrics for determining geographical origin of kudzu root LINK

Chemical Variability & Calibration Algorithms on Prediction of Solid Fraction of Compacted Ribbons Using NIR LINK

Calibration transfer of flour NIR spectra between benchtop and portable instruments LINK

Rapid Prediction of Moisture Content in Intact Green Coffee Beans Using Near Infrared Spectroscopy LINK


Calibration transfer of flour NIR spectra between benchtop and portable instruments LINK

Simultaneous Quantification of Paracetamol and Caffeine in Powder Blends for Tableting by NIR-Chemometry LINK


Model evaluation, model selection, and algorithm selection in machine learning. MachineLearning LINK

Development & Validation of a New Near-Infrared Sensor to Measure Polyethylene Glycol (PEG) Concentration in Water LINK



Near Infrared

In-situ & real-time monitoring of ultrasonic-assisted enzymatic hydrolysis process of corn gluten meal by NIRS LINK

Near-Infrared Spectroscopic Evaluation of Water Content of Molded Polylactide under the Effect of Crystallization LINK


Repetition Suppression in Aging: A Near-Infrared Spectroscopy Study on the Size-Congruity Effect LINK

Broadband Light Source and Its Application to Near-Infrared Spectroscopy | sensor via LINK

Industrial applications using NIR chemical imaging LINK

Analysis of oilseeds for protein, oil, fiber and moisture by near-infrared reflectance spectroscopy LINK

Global Near Infrared Spectroscopy Market to Grow at a CAGR of Over 9% Through 2021, Reports Technavio -Business Wire LINK

Optical Sensors Advancing Precision in Agricultural Production – near-infrared spectroscopy (NIRS) LINK


“Could NIRS be useful to digital agriculture?”, high quality keynote by Veronique Bellon-Maurel from & … LINK!

Near Infrared (NIR) Spectroscopy for plant health monitoring! electronics engineering optics LINK



Raman

Raman Spectroscopy of Blood and Blood Components LINK



Hyperspectral

Short Wave Infrared and its use in Hyperspectral Imaging – SWIR HSI LINK



Equipment

Make or buy your spectrometer – OEM Spectrometer LINK



Laboratory

Video: How near-infra red technology measures grass quality … LINK



Agriculture

Leveraging IoT to Improve Data Collection for Agriculture LINK



Food & Feed

‘Infrared spectrometers: NIR and MIR compared’ from the course ‘Identifying FoodFraud’. LINK



Other

“Spectroscopy for the Masses” | Spectroscopy via LINK

Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency. 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.