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



Spectroscopy and Chemometrics News Weekly #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



Spectroscopy and Chemometrics News Weekly #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



Spectroscopy and Chemometrics News Weekly #48+49, 2016

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



Near Infrared

Cannabis Analysis – On-Site Determination of Cannabis Strength using FT-IR Spectrosocopy FTNIR Ingredients LINK

Near Infrared NIRS, GC and HPLC Applications in Cannabis Testing THC CBD LINK

Raman

What happens when you use Raman spectroscopy to discriminate between brands of extra-virgin olive oil LINK

Raman spectroscopy of chocolate bloom LINK


Hyperspectral

Hyperspectral photoluminescence imaging of defects in solar cells | solar cells via LINK


Agriculture

Soy meal Protein bands LINK

Vitamin C distribution in acerola fruit by near infrared hyperspectral imaging HSI LINK


Equipment

Spectroscopists need freedom to analyse their spectral data, uncoupled from spectrometer hardware! LINK!


Chemometrics

Quality parameters in Castanhola fruit by NIRS to development of prediction models using PLS … in laboratory scale LINK

Monitoring Process-Water Quality Using NIRS and PLSR with Prediction Uncertainty Estimation LINK


Food & Feed

NIR diffuse reflection analysis of fruit – Food Science & Technology LINK


Agriculture

Innovation für die Obstwirtschaft: Neue Ansätze zur Messung und Vorhersage der Apfelqualität MONALISA LINK


Other

Hackers beware! Faking 3D-printed products just got harder. Full-spectrum spectroscopy for the win! LINK!

3D NDVI, using a low cost multi spectral camera. LINK

On the Generation of Random Multivariate Data | Multivariate Data LINK


CalibrationModel.com

Spectroscopy and Chemometrics News Weekly 46+47, 2016 | Spectroscopy NIRS Multivariate DataAnalysis Software LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 46+47, 2016 | NIRS Spektroskopie Multivariate DatenAnalyse LINK

Spettroscopia e Chemiometria Weekly News 46+47, 2016 | NIRS Spettroscopia Chemiometria LINK


Spectroscopy and Chemometrics News Weekly #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


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

Theory of Sampling (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?

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.

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. 

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.