Automatic Development of NIR-Spectroscopic Chemometric Models as a Service

For all NIR Spectrometers.
You don’t need a Chemometric Software.

Starting at €393.-
per Chemometric Model
See detailed Price List

Start Calibrate

It Enables

  • Quantitative Analysis of Concentrations and Constituents Using NIR Spectroscopy (NIRS is fast, harmless, non-destructive and it’s miniaturized)
  • Chemometric Analysis for NIR-Spectroscopy Made Easy
  • NIR-Calibration Optimization and Running Prediction Models
  • Using NIR-Spectrometer with Calibration Curves/Equations

Our Knowhow

Why you can Trust us

Spectroscopy and Chemometrics News Weekly #1, 2019

Develop customized NIRS applications and freeing up hours of spectroscopy analysts time | spectroscopist chemist laboratory LINK

Increase Your Profit with optimized NIR Accuracy Beverage Processing Dairy LINK

Neue Möglichkeiten in der Entwicklung von Applikationen für die NIR-Analytik | Labor NIRS Analytik LaborAnalytik LINK

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

Service für professionelle Entwicklung von Nah-Infrarot Spektroskopie Kalibrations Methoden | NIRS Qualität Testen LINK

Spectroscopy and Chemometrics News Weekly 52, 2018 | NIRS Spectroscopy Chemometrics analysis Spectral Spectrometer Sensors Spectrometry LINK


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


“Nocturnal Hypoglycemic Alarm Based on Near-Infrared Spectroscopy: In Vivo Studies with a Rat Animal Model.” LINK

“Hydrolysis kinetics of silane coupling agents studied by near-infrared spectroscopy plus partial least squares model” LINK

“Utilizing visible and near infrared spectroscopy based on multi-class support vector machines classification to characterize olive oil adulteration” LINK

“Analysis of Near-Infrared (NIR) Spectroscopy for Chlorophyll Prediction in Oil Palm Leaves” LINK

“基于卷积神经网络的烟叶近红外光谱分类建模方法研究” “The Study of Classification Modeling Method for Near Infrared Spectroscopy of Tobacco Leaves Based on Convolution Neural Network” LINK

Near Infrared

“Determination of the geographical origin of green coffee beans using NIR spectroscopy and multivariate data analysis” LINK

“Acton Optics & Coatings Develops New UV-NIR Neutral Density Filters That Offer Unmatched Broadband Performance” LINK

“The Influence of Packaging on Cosmetic Emulsion during Storage Assessed by FT-NIR Spectroscopy and Color Measurements” – Society of Cosmetic Chemists LINK

The Influence of Packaging on Cosmetic Emulsion during Storage Assessed by FT-NIR Spectroscopy and Color Measurements – Society of Cosmetic Chemists LINK

“Measurement of pesticide residues in peppers by near-infrared reflectance spectroscopy” NIRS LINK

“Near infrared reflectance spectroscopy of pasticceria foodstuff as protein content predicting method” NIRS NIR LINK

“Application of portable micro near infrared spectroscopy to the screening of extractable polyphenols in grape skins: A complex challenge.” vineyard NIR LINK

“Qualitative Identification of Pesticide Residues in Pakchoi Based on Near Infrared Spectroscopy” NIRS LINK

“稻谷有害霉菌侵染的近红外光谱快速检测” “Rapid Detection of Harmful Mold Infection in Rice by Near Infrared Spectroscopy” LINK

“Quantitative Characterization of Arnicae flos by RP-HPLC-UV and NIR Spectroscopy.” LINK


“Fast detection of cocoa shell in cocoa powders by Near Infrared Spectroscopy and multivariate analysis” LINK

“SDAE-BP Based Octane Number Soft Sensor Using Near-infrared Spectroscopy in Gasoline Blending Process” LINK

“Differentiating between bottled water from different sources using near-infrared spectroscopy” LINK

“Nondestructive Detection of Pesticide Residue on Banana Surface Based on Near Infrared Spectroscopy” LINK

“Raman spectroscopy of a near infrared absorbing proteorhodopsin: Similarities to the bacteriorhodopsin O photointermediate.” LINK

“Determination of Total Polysaccharides and Total Flavonoids in Chrysanthemum morifolium Using Near-Infrared Hyperspectral Imaging and Multivariate Analysis.” LINK


“Surface Chemistry of Oil-Filled Organic Nanoparticle Coated Papers Analyzed Using Micro-Raman Mapping” LINK


“Spectroscopy and Spectral Imaging Techniques for Non-destructive Food Microbial Assessment” LINK

“Evaluating Soybean Cultivars for Low- and High-Temperature Tolerance During the Seedling Growth Stage” Agronomy NIRS LINK


to acquire Celgene to create a leading innovative biopharma company LINK


“The nutritive value of hay from the family farms of northwestern Croatia” LINK

“A comparison study of five different methods to measure carotenoids in biofortified yellow cassava (Manihot esculenta)” LINK

“大気中光電子収量分光分析による有機薄膜半導体のエネルギー準位の測定” LINK

Spectroscopy and Chemometrics News Weekly #48, 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 45-47, 2018 | NIRS Spectroscopy Chemometrics analysis Spectral Spectrometer Sensors LINK


“Category identification of textile fibers based on near-infrared spectroscopy combined with data description algorithms” LINK

“Combination of fractional order derivative and memory-based learning algorithm to improve the estimation accuracy of soil organic matter by visible and near-infrared spectroscopy” LINK

“Modelling and optimization of physical characteristics based on UV-VIS/NIR spectra of aqueous extracts of lavender, mint and melissa” LINK

“Natural durability, ethanol-toluene extractives and phenol content prediction of eight wood species from Madagascar using NIRS multispecific models” LINK

Near Infrared

“What are you drinking? Wine labelling is not as accurate as you might think” NIRS alcohol LINK

“MIMOS, a Malaysian national applied research and development agency, has unveiled a new GlucoSenz device for blood glucose screening.” glucometer monitoring NIRS infrared sensor LINK

Read this article in our PossibilityHub to learn more about how a group of undergraduates at have developed a low-cost NIR imaging solution for venipuncture: LINK

“Monitoring texture and other quality parameters in spinach plants using NIR spectroscopy” LINK

“Feasibility of Using NIR Spectroscopy with SVM to Identify Kinds of Oil in Character Components” EdibleOil LINK

“A novel use of infra-red spectroscopy (NIRS and ATR-FTIR) coupled with variable selection algorithms for the identification of insect species (Diptera: Sarcophagidae) of medico-legal relevance.” LINK

“Fruit ripening evolution in diverse commercial apricots by conventional and non-destructive methods: preliminary results” cultivar grading maturity NIRS infrared analyzer LINK

“Using Vis-Nir Spectroscopy to Estimate Soil Organic Content” LINK


“Evaluation of wood variation based on the eigenvalue distribution of near infrared spectral matrix” LINK

“Detection of Adulteration in Camellia Oil Using Near-Infrared Spectroscopy” LINK

“Interpretation and rapid detection of secondary structure modification of actomyosin during frozen storage by near-infrared hyperspectral imaging” LINK

“The Use of Mobile Near-Infrared Spectroscopy for Real-Time Pasture Management” LINK

“Accurate Identification of the Sex and Species of Silkworm Pupae Using Near Infrared Spectroscopy” LINK

“Quantitative and qualitative assessment of pork meatball containing borax using near infrared spectroscopy” LINK

“Journal Highlight: Effects of hypnotics on prefrontal cortex activity during a verbal fluency task in healthy male subjects: A nearinfrared spectroscopy study” LINK

“Near infrared spectroscopy as an easy and precise method to estimate soil texture” LINK


“Characterization of building materials by means of spectral remote sensing: The example of Carcassonne’s defensive wall (Aude, France)” LINK

Process Control

“Advances in Vision Inspection Systems – Food Engineering Magazine” LINK

“2D spectroscopy offers new insight into molecular processes” LINK


“China sets out for the far side of the moon” “The impact may have brought material from the moon’s upper mantle to the surface, a scenario that data from a visible and near-infrared imaging spectrometer might be able to verify.” soil LINK

Food & Feed

“Global wine adulteration: wine forgery is on the rise” LINK


“Optical Functions of Methanol and Ethanol in Wide Spectral Range” LINK


“Biocellulose Masks as Delivery Systems: A Novel Methodological Approach to Assure Quality and Safety” Cosmetics LINK


“La virtualizacion y analisis en tiempo real de productos farmaceuticos y alimenticios por espectroscopia de infrarrojo cercano” LINK

“Coherent multidimensional spectroscopy of dilute gas-phase nanosystems” LINK

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

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 #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? 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

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

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.

NIRS Calibration Model Equation – Optimal Predictive Model Selection

To give you an insight what we do to find the optimal model, imagine a NIR data set, where a NIR specialist works hard for 4 hours in his chemometric software to try what he can with his chemometric-, NIR spectroscopic- and his product-knowledge to get a good model. During the 4 hours he finds 3 final candidate models for his application. With the RMSEP of 0.49 , 0.51 and 0.6. Now he has to choose one or to test all his three models on new measured NIR spectra.

That is common practice. But is this good practice?

And nobody asks, how long, how hard have you tried, how many trial have you done, if this really the best model that is possible from the data?
And imagine the cost of the data collection including the lab analytics!
And behind this costs, have you really tried hard enough to get the best out of your data? Was the calibration done quick and dirty on a Friday afternoon? Yes, time is limited and manually clicking around and wait in such kind of software is not really fun, so what are the consequences?

Now I come to the most important core point ever, if you own expensive NIR spectrometer system, or even many of them, and your company has collected a lot of NIR spectra and expensive Lab-reference data over years, do you spend just a few hours to develop and build that model, that will define the whole system’s measurement performance for the future? And ask yourself again (and your boss will ask you later), have you really tried hard enough, to get the best out of your data? really?

What else is possible? What does your competition do?

There is no measure (yet) what can be reached with a specific NIR data set.
And this is very interesting, because there are different beliefs if a secondary method like NIR or Raman can be more precise and accurate, as the primary method.

What we do different is, that our highly specialized software is capable of creating large amounts of useful calibrations to investigate this limits – what is possible. It’s done by permutation and combination of spectra-selection, wave-selection, pre-processing sequences and PC selections. If you are common with this, then you know that the possibilities are huge.

For a pre-screening, we create e.g. 42’000 useful calibrations for the mentioned data set. With useful we mean that the model is usable, e.g. R² is higher than 0.8, which shows a good correlation between the spectra and the constituent and it is well fitted (neither over-fitted nor under-fitted) because the PC selection for the calibration-set is estimated by the validation-set and the predictive performance of the test-set is used for model comparisons.

Here the sorted RMSEP values of the Test Set is shown for 42’000 calibrations.
You can immediately see that the manually found performance of 0.49 is just in the starting phase of our optimization. Interesting is the steep fall from 1.0 to 0.5 where manually optimization found it’s solutions. A range where ca. 2500 different useful calibrations exist. The following less steep fall from 0.5 to 0.2 contains a lot more useful models and between 0.2 to 0.08 the obvious high accurate models are around 2500 different ones. So the golden needle is not in the first 2500 models, it must be somewhere in the last 2500 models in the haystack.

Sorted RMSEP plot of 42'000 NIR Calibration Model Candidates

That allows us to pick the best calibration out of 42’000 models, depending on multiple statistical evaluation criteria, that is not just the R² or RPD, SEC, SEP or RMSEP, (or Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Multivariate AIC  (MAIC) etc.) we do the model selection based on multiple statistical parameters.

Dengrogram plot of similar  NIR Calibration Models

To compare the calibration models by similarity it is best viewed with dendrogram plots like this (zoomed in), where the settings are shown versus the models overall performance similarity. In the settings you can see a lot of different permutations of pre-processings combined with different wave-selections.