Spettroscopia e Chemiometria Weekly News #3+4, 2017


Chemometrics

Fast sampling, analyses and chemometrics for plantbreeding: Bitter acids, xanthohumol and terpenes in lupulin … LINK


Near Infrared

Measurement of Soy Contents in Ground Beef Using Near-Infrared Spectroscopy LINK


Near infrared spectroscopy for body fat sensing in neonates: quantitative analysis by GAMOS simulations | Bodyfat LINK



Fatty acids and fat-soluble vitamins in ewe’s milk predicted by near infrared reflectance spectroscopy. | NIRS LINK


NeoSpectra Micro, a small, chip-scale, near infra-red (NIR) spectral sensor – LINK


Hyperspectral

Drones on the Farm: Agribotix Partners with senseFly, and Sentera Announces Real-Time NDVI Streaming LINK


Early Detection of Aspergillus parasiticus Infection in Maize Kernels Using NIR Hyperspectral Imaging and MDA LINK



Nondestructive Estimation of Moisture Content, pH and Soluble Solid Contents in Intact Tomatoes Using HSI LINK


Equipment

Spectrometers: Excitation source parameters dictate Raman spectroscopy outcomes LINK


Future

Is molecular scanning the next killer smartphone app? killerapp futuretrends sensorik sensor LINK


Other

It took 50 years for the world to install the first million industrial robots. The next million will take only eight … LINK!


“How Statistics lost their power – and why we should fear what comes next” | BigDataAnalytics bigdata LINK



Spettroscopia e Chemiometria Weekly News #48+49, 2016

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

Spettroscopia e Chemiometria Weekly News #43, 2016

Chemometrics

SWIR region contains chemical spectral info. Chemometrics differentiate 4 sugars. Realtime spectral processing LINK


Near Infrared

Using advanced NIR sensors, our hygenic TS line measures fluid absorption for FoodandBeverage applications: LINK!

“NIR penetrates much further into samples and, unlike Raman, is unaffected by fluorescence.” | Env… LINK

Qualitätskontrolle während der Extrusion – Folie Fremdpolymeren NIRAnalyse Inspektionssystem LINK

Pre-grazing significantly boosts first cut silage quality | NIRanalycer NIRmachine via LINK


Infrared

Selective Weighted Least Squares Method for Fourier Transform Infrared Quantitative Analysis LINK

Multivariate Analysis of Hemicelluloses in Bleached Kraft Pulp Using Infrared Spectroscopy LINK


Hyperspectral

Combining hyperspectral and lidar is a great approach to identify & monitor invasive plants species… LINK


Environment

DETECTION OF CANNABIS PLANTS BY HYPER-SPECTRAL REMOTE SENSING MEANS LINK


Pharma

US FDA Purchases Transmission Raman for Quantitative Analysis of Tablets & Capsules – European Pharmaceutical Review LINK


Laboratory

“Washington State University (WSU) portable smartphone spectrometer laboratory detects cancer” LINK

Spectroscopy Outside the Lab: LINK


CalibrationModel.com

Spectroscopy and Chemometrics News 42, 2016 | NIRS Spectroscopic Chemometric Software LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 42, 2016 | NIRS Spektroskopie Chemometrie Kalibration LINK

Spettroscopia e Chemiometria Weekly News 42, 2016 | NIRS Spettroscopia Chemiometria news LINK


(English) 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

Teoria del campionamento (TOS)

What is Theory of Sampling (TOS)? TOS is a theory about the sampling of materials. Here are some recommended links. Links Gy’s sampling theory TOS forum edited by Professor Kim H. Esbensen Presentations Representative Sampling – a critical success factor for all analytical endeavors (incl. NIR), Kim Esbensen, IDRC 2014 Papers Theory of sampling (TOS) versus measurement uncertainty (MU) – A call for integration, Kim H. Esbensen, Claas Wagner Books Process Analytical Technology: Spectroscopic Tools and Implementation Strategies for the Chemical and Pharmaceutical Industries, Second Edition, Chapter 3. Process Sampling: Theory of Sampling – the Missing Link in Process Analytical Technologies (PAT), Katherine A. Bakeev, Kim H. Esbensen and Peter Paasch-Mortensen Related: Why NIR Method Maintenance?

(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.

Come sviluppare calibrazioni spettroscopia nel vicino infrarosso nel 21 ° secolo?


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.

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