Spettroscopia e Chemiometria Weekly News #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



Spettroscopia e Chemiometria Weekly News #48+49, 2016

Ci spiace, ma questo articolo è disponibile soltanto in English.

Le procedure di calibrazione NIR – Realizzazione di curve di calibrazione NIRS spettroscopia

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.

(English) 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.

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.

(English) Summary of the NIR Chemometric survey polls

(English) NIR Calibration Modeling

The majority of NIR calibrations are generated using a small number of different parameter settings and all too often are restricted to the time a user has available, their spectroscopic and chemometric knowledge and their ability (tedious use of the software) to choose and combine all the possible parameter settings required for good calibrations.

There are many published standards and guidelines (protocols) available for developing NIR calibrations from Standards Consortium such as ASTM, EMEA, ICH, IUPAC, ISO, USP, PASG etc. as well as many good recommendations and guidelines found in various textbooks and papers.

The difficulty with so many ‘Protocols’ for the NIR user is to have them all available and in their thought processes during calibration work and in addition to execute, check and challenge all calibrations generated manually. This is time consuming and sometimes boring repetitive work.

To simplify this for the person generating the NIR Calibrations, we have collected the good practices protocols and integrated them into our service that automates the calibration building and evaluation procedures.

to part 2

Benefici

Il servizio di calibrazione NIR offre i seguenti vantaggi: Risparmiare soldi
  • Migliorando la precisione e l’affidabilità di modelli di calibrazione NIR pre-esistenti si possono aumentare le potenzialità in vari processi di produzione, nonché la garanzia di qualità.
  • Maggiore accuratezza dell’analisi assicura un miglior controllo del processo produttivo, flusso di processo ottimale e meno scarti di produzione.
  • Velocità e poco costo per creare modelli di calibrazione professionali.
  • Sollievo del personale.
Risparmiare tempo
  • Ripulendo i dati e aumentandone la qualità – dati mancanti, ricerca di valori anomali, dati errati (informazioni contraddittorie), rimozione di valori anomali.
  • Ricercando impostazioni ottimali dei parametri del modello NIR (set di calibrazione, selezione lunghezza d’onda, pre-trattamentidi dati, selezione fattore).
  • Calcolando diverse varianti del modello.
  • Facendo valutazioni di convalida e selezione del modello ottimale (errore, SEP, RMSEP, RMSEC, RPD, fit, R2, bias, slope, ).
  • Per il calcolo dei modelli di calibrazioni enormi.
Precisione analitica NIR
  • Maggiore affidabilità grazie alla precisione e robustezza dei modelli di calibrazione NIR.
  • Possibilità di confronto del metodo creato e quello eventualmente da acquistare.
  • Aumento della performance di accuratezza analitica quanto possibile.
  • Miglioramento della robustezza cambiando matrice e possibile derivata.
Modelli di calibrazione NIR professionali
  • Decenni di esperienza nella chemiometria per la spettroscopia NIR.
  • Su base teorica e applicata di buone tecniche e know-how.
  • Applicazione di linee guida.
  • Presenza di venditori indipendenti di software chemiometrici NIR.
  • Manutenzione equazione di taratura.
  • Migliorare la solidità del modello di predizione NIR.
  • Evitare trappole e trabocchetti della complicata chemiometria.
Risultati nel dettaglio
  • Il servizio fornisce le impostazioni di calibrazione ottimali per i vostri dati NIR.
  • È possibile avere una piena conoscenza della calibrazione NIR, in quanto vengono forniti in modo dettagliato valori statistici come indice di performance assistita e relativi grafici.
Un ulteriore aspetto dei servizi di modellazione chemiometrica.