Spectroscopy and Chemometrics News Weekly #11, 2019

This week’s NIR news Weekly is sponsored by YourCompanyNameHere – BestNIRinstruments. Check out their product page … link

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Spectroscopy and Chemometrics News Weekly 10, 2019 | NIRS NIR Spectroscopy Chemometrics analysis Spectral Spectrometer Spectrometric Analytical Sensors LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 10, 2019 | NIRS NIR Spektroskopie Chemometrie Proben Analyse Spektrometer Spektral Sensor Nahinfrarot Analysengeräte Analysentechnik Analysemethode Analyzer Nahinfrarotspektroskopie LINK

Spettroscopia e Chemiometria Weekly News 10, 2019 | NIRS Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Sensore Attrezzatura analitica LINK


” First-Principles and Empirical Approaches to Predicting In Vitro Dissolution for Pharmaceutical Formulation and Process Development and for Product Release Testing” LINK

“Near-Infrared Spectroscopy Analytical Model Using Ensemble Partial Least Squares Regression” LINK

Near Infrared

“Consumer perception of d’Anjou pear classified by dry matter at harvest using near-infrared spectroscopy” LINK

“Small interferometer with wide wavelength coverage” NIREOS LINK

“Application of NIRS to the Direct Measurement of Carbonization in Torrefied Wheat Straw Chars” LINK

“New Near-Infrared Imaging and Spectroscopy of NGC 2071-IR [GA]” 400 Parsec away 😉 LINK

“Medicine Discrimination of NIRS Based on Regularized Collaborative Representation Classification with Gabor Optimizer” LINK


“Real-Time Release Testing of Herbal Extract Powder by Near-Infrared Spectroscopy considering the Uncertainty around Specification Limits” LINK

” On-line glucose monitoring by near infrared spectroscopy during the scale up steps of mammalian cell cultivation process development” LINK

“Ex Vivo Assessment of Various Histological Differentiation in Human Carotid Plaque with Near-infrared Spectroscopy Using Multiple Wavelengths.” LINK

“Application of near-infrared spectroscopy for screening the potato flour content in Chinese steamed bread” LINK

“Understanding the role of water in the aggregation of poly(N,N-dimethylaminoethyl methacrylate) in aqueous solution using temperature-dependent near-infrared spectroscopy.” LINK

“Ranking the solubility of ammonia in ionic liquids using near infrared spectroscopy and multivariate curve resolution” LINK

“Breakthrough Potential in Near-Infrared Spectroscopy: Spectra Simulation. A Review of Recent Developments.” LINK

“Screening of maize haploid kernels based on near infrared spectroscopy quantitative analysis” LINK

“An optimized non-invasive glucose sensing based on scattering and absorption separating using near-infrared spectroscopy” LINK


“Improving InSitu Estimation of Soil Profile Properties Using a Multi-Sensor Probe.” LINK

Process Control



“Laboratory Visible and Near-Infrared Spectroscopy with Genetic Algorithm-Based Partial Least Squares Regression for Assessing the Soil Phosphorus Content of Upland and Lowland Rice Fields in Madagascar” RemoteSensing LINK

“Water molecular structure underpins extreme desiccation tolerance of the resurrection plant Haberlea rhodopensis.” LINK

“The Relation between Soil Water Repellency and Water Content can be Predicted by Vis-NIR Spectroscopy” LINK


“Development of a Low-Cost Multi-Waveband LED Illumination Imaging Technique for Rapid Evaluation of Fresh Meat Quality” LINK

“Distinguishing between bread wheat and spelt grains using molecular markers and spectroscopy.” LINK


“FT-IR Spectroscopy Applied for Identification of a Mineral Drug Substance in Drug Products: Application to Bentonite” LINK


“On feasibility of near-infrared spectroscopy for noninvasive blood glucose measurements” LINK

Spectroscopy and Chemometrics News Weekly #50, 2018


Near Infrared (NIR) Analysis Software, work smart with all NIR Spectrometers for quantitative sensing & detection. | AnalyticalChemistry LabManger Chemical Analysis Equipment ChemicalAnalysis Analytical Instruments Laboratory LabEquipment LINK

Spectroscopy and Chemometrics News Weekly 49, 2018 | NIRS Spectroscopy Chemometrics analysis Spectral Spectrometer Sensors Spectrometry LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 49, 2018 | NIRS Spektroskopie Chemometrie analyse Spektral Spektrometer Sensor LINK

Spettroscopia e Chemiometria Weekly News 49, 2018 | NIRS Spettroscopia Chemiometria analisi Spettrale Spettrometro Sensore LINK

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


“Prediction of Sugar and Acidity Contents in Pineapple using Near Infrared Spectroscopy” LINK

“Authenticity Analysis of Almond Powder using FT-NIR Spectroscopy and Chemometrics” LINK

“Chemometrics in China – Discover a special issue highlighting the best of Chinese chemometrics research” LINK

“Cetane number prediction of waste cooking oil-derived biodiesel prior to transesterification reaction using near infrared spectroscopy” LINK

“Tea types classification with data fusion of UV-Vis, synchronous fluorescence and NIR spectroscopies and chemometric analysis” LINK

“Qualitative and quantitative analysis of counterfeit Fluconazole capsules: A non-invasive approach using NIR spectroscopy and chemometrics” LINK

“Comparison of Multivariable Techniques for Brand Classification of Turmeric Powders by Near-infrared (NIR) Spectroscopy” LINK


“Research on Near Infrared Spectroscopy Application for Aflatoxin Testing on Rice” LINK

“Antioxidant capacity of Camellia japonica cultivars assessed by near- and mid-infrared spectroscopy” LINK

“Evaluation of Carbohydrate Concentrations in Phalaenopsis Using Near-infrared Spectroscopy” LINK

“Nondestructive egg freshness assessment from the equatorial and blunt region based on visible near infrared spectroscopy” LINK

“Detection of parameters in solid state fermentation of Monascus by near infrared spectroscopy” LINK


“Hyperspectral imaging under low illumination with a single photon camera” LINK

Spectral Imaging

“Vegetation monitoring using multispectral sensors – best practices and lessons learned from high latitudes” NDVI UAV LINK


“Non-destructive Determination of Moisture Content of Livestock Feed” LINK

“Application of Spectral Analysis and Wavelenghth Selection Techniques to Agricultural Products” LINK


“Non Invasive Blood Glucose Detection along with an Assistive Diabetes Monitoring App” LINK


“High-performance and scalable on-chip digital Fourier transform spectroscopy” LINK


“A review of optical methods for continuous glucose monitoring” LINK

“Preiswerte Chips revolutionieren optische Spektrometrie” LINK

Spectroscopy and Chemometrics News Weekly #34, 2018


New : FREE NIR Predictor Software, drag&drop spectral data to plug&play calibrations, for any NIRS Analysis sensor type. | Chemometrics Prediction LINK

Spectroscopy and Chemometrics News Weekly 33, 2018 | NIRS Spectroscopy Chemometrics analysis Spectral Spectrometer Sensors LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 33, 2018 | NIRS Spektroskopie Chemometrie analyse Spektral Spektrometer Sensor LINK

Spettroscopia e Chemiometria Weekly News 33, 2018 | NIRS Spettroscopia Chemiometria analisi Spettrale Spettrometro Sensore LINK

We updated the Near Infrared (NIR) Spectrometer Directory of Suppliers / Manufacturers / Vendors. The list includes now also mobile miniature NIR spectrometer sensors. | NIRS NIR FTNIR NIT NearInfrared MEMS Spectral Sensor IoT LINK


“Enhancing near infrared spectroscopy models to identify omega-3 fish oils used in the nutraceutical industry by means of calibration range extension” omega3 LINK

“Near infrared spectroscopy coupled with chemometric algorithms for predicting chemical components in black goji berries (Lycium ruthenicum Murr.)” LINK

“Towards innovation in paper dating: a MicroNIR analytical platform and chemometrics” nondestructive diagnostic forensic LINK

“Least-squares support vector machine and successive projection algorithm for quantitative analysis of cotton-polyester textile by near infrared spectroscopy” LINK

“Direct calibration transfer to principal components via canonical correlation analysis” NIRS corn tobacco LINK

“Collaborative representation based classifier with partial least squares regression for the classification of spectral data” LINK

Near Infrared

“Rapid and non-destructive discrimination of special-grade flat green tea using Near-infrared spectroscopy.” LINK

“HOW DID SCIENTISTS DISCOVER WATER ON THE SURFACE OF THE MOON? …. used near-infrared reflectance spectroscopy (NIRS) to find surface water at the moon’s polar regions. …. electromagnetic spectrum, from about 700 to 2,500 manometers.” LINK


“DSC, FTIR and Raman Spectroscopy Coupled with Multivariate Analysis in a Study of Co-Crystals of Pharmaceutical Interest” LINK


“Calibration transfer of near infrared spectrometers for the assessment of plasma ethanol precipitation process” LINK


“ILS: An R package for statistical analysis in Interlaboratory Studies” | outliers ANOVA LINK


“Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination” LINK

Spectroscopy and Chemometrics News Weekly #33, 2018

Near Infrared

“Accurate and rapid detection of soil and fertilizer properties based on visible/near-infrared spectroscopy.” LINK

“Authenticity Detection of Black Rice by Near-Infrared Spectroscopy and Support Vector Data Description.” NIRS LINK

“MicroNIR™ PAT-W for Blend Endpoint Analysis in a High Dosage Product” LINK


“Which regression method to use? Making informed decisions in “data-rich/knowledge poor” scenarios – The Predictive Analytics Comparison framework (PAC)” LINK

“Determination of salvianolic acid B and borneol in compound Danshen tablet by near-infrared spectroscopy and establishment of dependency model” LINK

“Error propagation of partial least squares for parameters optimization in NIR modeling.” LINK

“Rapid quantification of the adulteration of fresh coconut water by dilution and sugars using Raman spectroscopy and chemometrics” LINK

“Predicting pork freshness using multi-index statistical information fusion method based on near infrared spectroscopy.” LINK

“Validation of short wave near infrared calibration models for the quality and ripening of ‘Newhall’ orange on tree across years and orchards” fruits SWNIRS LINK

“Fault detection based on time series modeling and multivariate statistical process control.” LINK


Near Infrared (NIR) Analysis Software, work smart with all NIR Spectrometers for quantitative sensing & detection. | AnalyticalChemistry LabManger Chemical Analysis Equipment ChemicalAnalysis Analytical Instruments Laboratory LabEquipment LINK

New : FREE NIR Predictor Software, drag&drop spectral data to plug&play calibrations, for any NIRS Analysis sensor type. | Chemometrics Prediction LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 32, 2018 | NIRS Spektroskopie Chemometrie analyse Spektral Spektrometer Sensor LINK

Spettroscopia e Chemiometria Weekly News 32, 2018 | NIRS Spettroscopia Chemiometria analisi Spettrale Spettrometro Sensore LINK

The non-destructive technique such as Near Infrared Spectroscopy NIRS along with Chemometrics can predict quality parameters of measurements by using the free NIR-Predictor Software. QualityControl QualityAssurance foodsafety productinspection LINK


“Estimating soil heavy metals concentration at large scale using visible and near-infrared reflectance spectroscopy.” LINK

“Overall uncertainty measurement for near infrared analysis of cryptotanshinone in tanshinone extract.” LINK


“Hyperspectral imaging reveals wound problems” LINK


“Sensoren machen guten Wein – Mit Hilfe von Sensoren können Winzer Informationen zu Reife, Qualität, Ertragsaussichten und Krankheitsrisiken ihrer Reben erhalten.” LINK


“Fourier transform infrared spectrometer based on an electrothermal MEMS mirror.” LINK


“Discrimination of Milks with a Multisensor System Based on Layer-by-Layer Films” LINK


“Watch out, birders: Artificial intelligence has learned to spot birds from their songs” LINK

Spectroscopy and Chemometrics News Weekly #32, 2018

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


“Quantification of active ingredients in Potentilla tormentilla by Raman and infrared spectroscopy.” LINK

“Selecting the Best Machine Learning Algorithm for Your Regression Problem” LINK

New Release via OSA_Optica: Machine Learning Technique Reconstructs Images Passing through a Multimode Fiber. “The new method showed remarkable robustness against environmental instabilities even over long fibers.” Read More: LINK

“Building Reliable Machine Learning Models with Cross-validation” LINK

“Assessing the capability of Fourier transform infrared spectroscopy in tandem with chemometric analysis for predicting poultry meat spoilage” LINK

“Rapid identification and quantification of Panax notoginseng with its adulterants by near infrared spectroscopy combined with chemometrics.” Adulteration LINK

“Quantitative analysis modeling of infrared spectroscopy based on ensemble convolutional neural networks” LINK

“Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification” LINK

“Rapid detection of three quality parameters and classification of wine based on Vis-NIR spectroscopy with wavelength selection by ACO and CARS algorithms.” LINK

Near Infrared

“Single-trial NIRS data classification for brain-computer interfaces using graph signal processing.” LINK

“Non-destructive prediction of protein content in wheat using NIRS.” Nondestructive LINK

“Fast authentication of tea tree oil through spectroscopy.” NIRS AnalyticalChemistry LINK

“Real time release testing of tablet content and content uniformity.” | Pharma dosage QualityByDesign QbD NIRS LINK

“Physical Barrier Type Abuse-Deterrent Formulations: Monitoring Sintering-Induced Microstructural Changes in Polyethylene Oxide Placebo Tablets by Near Infrared Spectroscopy (NIRS).” LINK

“Rapid Authentication and Quality Evaluation of Cinnamomum verum Powder Using Near-Infrared Spectroscopy and Multivariate Analyses.” LINK


“New study first to demonstrate a chip-scale broadband optical system that can sense molecules in the mid-infrared” LINK


“An improved method based on a new wavelet transform for overlapped peak detection on spectrum obtained by portable Raman system” LINK


“A small team of student AI coders beats Google’s machine-learning code” LINK

“Machine Learning Technique Reconstructs Images Passing through a Multimode Fiber” LINK


“Are your spectroscopic data FAIR?” – FAIR stands for Findable, Accessible, Interoperable, Reusable. – IUPAC JCAMP-DX 6.0 is coming…. – spectroscopy LINK

Process Control

The key concept of sampling errors – the Theory of Sampling (TOS) – is applied in key industrial sectors (mining, minerals, cement and metals processing). LINK


“Canopy Hyperspectral Sensing of Paddy Fields at the Booting Stage and PLS Regression can Assess Grain Yield” LINK

“Agricultural Applications of Spectroscopy – The basic principle of spectroscopy involves dissecting the light of a specific object into various wavelengths that represent different physical properties of the object, some of which include temperature..” LINK


“ILS: An R package for statistical analysis in Interlaboratory Studies” LINK


Near Infrared (NIR) Analysis Software, work smart with all NIR Spectrometers for quantitative sensing & detection. | AnalyticalChemistry LabManger Chemical Analysis Equipment ChemicalAnalysis Analytical Instruments Laboratory LabEquipment LINK

New : FREE NIR Predictor Software, drag&drop spectral data to plug&play calibrations, for any NIRS Analysis sensor type. | Chemometrics Prediction LINK

Spectroscopy and Chemometrics News Weekly #31, 2018


How to Configure the Number of Layers and Nodes in NeuralNetworks: BigData DataScience AI MachineLearning DeepLearning Algorithms by Source for graphic: | abdsc (2018.08.02) LINK

“Visible-Near-Infrared Spectroscopy can predict Mass Transport of Dissolved Chemicals through Intact Soil.” (2018.08.02) LINK

“Classification and compositional characterization of different varieties of cocoa beans by near infrared spectroscopy and multivariate statistical analyses.” (2018.08.02) LINK

“Classification of Chicken Parts Using a Portable Near-Infrared (NIR) Spectrophotometer and Machine Learning.” (2018.08.02) LINK

“Rapid Prediction of Low (<1%) trans Fat Content in Edible Oils and Fast Food Lipid Extracts by Infrared Spectroscopy and Partial Least Squares Regression” (2018.07.31) LINK

“Evaluating the performance of a consumer scale SCiO™ molecular sensor to predict quality of horticultural products” (2018.07.30) LINK

“Estimation of Total Phenols, Flavanols and Extractability of Phenolic Compounds in Grape Seeds Using Vibrational Spectroscopy and Chemometric Tools” (2018.07.26) LINK

Near Infrared

“FT-NIR, MicroNIR and LED-MicroNIR for Detection of Adulteration in Palm Oil via PLS and LDA” FTNIR NIRS (2018.08.03) LINK

“Long-Length Fiber Optic Near-Infrared (NIR) Spectroscopy Probes for On-Line Quality Control of Processed Land Animal Proteins” (2018.08.03) LINK

“Near-infrared spectroscopy for rapid and simultaneous determination of five main active components in rhubarb of different geographical origins and processing.” (2018.08.02) LINK

“Marktech Optoelectronics Introduces Silicon Avalanche Photodiodes for Low-Level Light and Short Pulse Detection” UV NIR NIRS SWIR (2018.08.02) LINK

“Innovative Technology Promises Fast, Cost-Efficient Age Data for Fisheries Management” FTNIR (2018.07.31) LINK

“Rapid qualitative and quantitative analysis of methamphetamine, ketamine, heroin, and cocaine by near-infrared spectroscopy.” (2018.07.31) LINK

We (led by ) have been independently assessing thew value of consumer scale NIR devices for horticultural quality assessment. Here is our published work assessing (2018.07.30) LINK


“Fault Detection Based on Near-Infrared Spectra for the Oil Desalting Process” (2018.08.05) LINK

“Common Infrared Optical Materials and Coatings: A Guide to Properties, Performance and Applications” (2018.08.04) LINK


SpectraBase – FREE, fast text access to hundreds of thousands of NMR, IR, Raman, UV-Vis, and mass spectra! spectroscopy (2018.08.02) LINK


“Three-Dimensional Reconstruction of Soybean Canopies Using Multisource Imaging for Phenotyping Analysis” (2018.08.03) LINK

“Smartphone Spectroscopy Promises a Data-Rich Future – An upsurge of portable, consumer-facing devices at the intersection of smartphone computing and spectroscopy is now leveraging integration. ” (2018.08.02) LINK

Innovative Technology Promises Fast, Cost-Efficient Age Data for Fisheries Management (2018.07.31) LINK

“Smartphone-Based Food Diagnostic Technologies: A Review” (2018.07.30) LINK


“Detection, Purity Analysis, and Quality Assurance of Adulterated Peanut (Arachis Hypogaea) Oils” (2018.08.02) LINK


“Potential of near infrared spectroscopy and pattern recognition for rapid discrimination and quantification of Gleditsia sinensis thorn powder with adulterants.” (2018.08.02) LINK


A micro-spectrometer fit for a smartphone: Could the power to measure things like CO2, food freshness, and blood sugar levels soon be in the palm of our hands? |rld/magazine/article/323/micro-spectrometer-opens-door-to-a-wealth-of-new-smartphone-functions?utm_source=twitter.com/CalibModel health safety medicine spectroscopy (2018.02.25) LINK

“Near-infrared spectroscopy detects age-related differences in skeletal muscle oxidative function: promising implications for geroscience.” (2018.02.08) LINK


69% of decision makers say industrial analytics will be crucial for business in 2020. | IoT IIoT MT LINK


Free Chemometric NIR Predictor Software! Simple plug&play calibrations, drag&drop spectral data, for any NIRS sensor device. Easy to use software for off-line and real-time prediction without limits. offline realtime (2018.08.04) LINK

Automated NIRS spectroscopy chemometrics method development with MachineLearning for spectrometer Spectral IoT sensor SmartSensor SmartSensors (2018.07.25) LINK

Automatic NIR Spectroscopy Calibration-Development as a Service. Applicable with free NIR-Predictor software or via OEM API. | NIRSpectroscopy NearInfrared NIRanalysis spectrometers DataAnalytics Regression Spectral Sensors QualityControl Lab (2018.07.26) LINK

Increase Your Profit with optimized NIRS Accuracy with Calibration as a Service (CaaS) and the new free NIR-Predictor software. | foodsafety Feed Lab QC QA testing (2018.08.03) LINK

New : FREE NIR Predictor Software, drag&drop spectral data to plug&play calibrations, for any NIRS Analysis sensor type. | Chemometrics Prediction (2018.07.24) LINK

Spectroscopy and Chemometrics News (KW 11-30 2018) | NIRS Spectroscopy Chemometrics analysis Spectral Spectrometer Sensors (2018.07.25) LINK

Spektroskopie und Chemometrie Neuigkeiten (KW 11-30 2018) | NIRS Spektroskopie Chemometrie analyse Spektral Spektrometer Sensor (2018.07.25) LINK

Spettroscopia e Chemiometria Weekly (KW 11-30 2018) | NIRS Spettroscopia Chemiometria analisi Spettrale Spettrometro Sensore (2018.07.25) LINK

光谱学和化学计量学新闻(KW#11-#30 2018) | 近红外光谱化学计量学分析光谱仪传感器 (2018.07.26) LINK

分光法とケモメトリックスニュース(KW#11-#30 2018) | 赤外分光法・ケモメトリックスの分光センサーの近く (2018.07.26) 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.

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. 


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, …).


*) 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.  


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.

Recent advanced chemometric methods

You are searching for recent advanced chemometric methods to get better calibration models for NIR? Methods and algorithms like:
  • Artificial Neural Networks (ANN)
  • General Regression Neural Networks (GR-NN)
  • RBF Neural Networks (RBF-NN)
  • Support Vector Machines (SVM)
  • Multiway Partial Least Squares (MPLS),
  • Orthogonal PLS (OPLS), (O-PLS), OPLS-AA, OPLS-ANN
  • Hierarchical Kernel Partial Least Squares (HKPLS)
  • Random Forest (RF)
  • etc.
and data pre-processing methods like
  • Extended Multiplicative Signal Correction (EMSC)
  • Orthogonal Signal Correction (OSC)
  • Dynamic Orthogonal Projection (DOP)
  • Error Removal by Orthogonal Subtraction (EROS)
  • External Parameter Orthogonalization (EPO)
  • etc.
that are partly available as modules for software packages like Matlab, Octave, R-Project, etc. Why invest a lot of time and money with new tools? Have you tried it really hard to optimize your calibrations with standard chemometrics methods like Partial Least Squares (PLS), Principal Component Regression (PCR) and Multiple Linear Regression (MLR) which are available in all chemometric software packages? Are you sure you have tried all the good rules and optimization possibilities? Get it done right with the compatible standard methods, we are specialized in optimization and development of NIR calibrations, let us help you, give us a try!