Spectroscopy and Chemometrics News Weekly #14, 2019

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Spectroscopy and Chemometrics News Weekly 13, 2019 | NIRS NIR Spectroscopy Chemometrics analysis Spectral Spectrometer Spectrometric Analytical Sensors QC Lab Labs Laboratories Laboratory LabServices Software Testing Quality Checking LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 13, 2019 | NIRS NIR Spektroskopie Chemometrie Spektrometer Spektral Sensor Nahinfrarot Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse Qualitätslabor LINK

Spettroscopia e Chemiometria Weekly News 13, 2019 | NIRS Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Sensore Attrezzatura analitica Laboratorio analisi prova qualità LINK




Chemometrics

“Fraud detection in hen housing system declared on the eggs’ label: An accuracy method based on UV-VIS-NIR spectroscopy and chemometrics.” LINK

“Use of near infrared reflectance spectroscopy to predict chemical composition of some pastoral species appeased by the dromedaryin southern Tunisia” LINK

“On-line vis-NIR spectroscopy prediction of soil organic carbon using machine learning” LINK

” Kısmi En Küçük Kareler Regresyonu (KEKKR) ve Yapay Sinir Ağı (YSA) Modelleri Kullanarak, Kanopi Kızılötesi Spektroskopisi (KS) ile Kış Buğdayında …” LINK

“Application of invasive weed optimization and least square support vector machine for prediction of beef adulteration with spoiled beef based on visible near-infrared (Vis-NIR) hyperspectral imaging.” LINK

“Comparison of multivariate models and variable selection algorithms for rapid analysis of the chemical composition of field crops” NIRS NIR LINK




Near Infrared

Near-infrared spectroscopy could improve flu vaccine manufacturing NIR virus cell near infrared spectroscopy |rld/magazine/article/707/near-infrared-spectroscopy-could-improve-flu-vaccine-manufacturin.XKcA_t2OuwY.twitter LINK

“Calibration development for nutritional evaluation of Yam (Dioscorea sp.) using Near-Infrared Reflectance Spectrophotometry (NIRS)” LINK

“DETERMINATION OF CAROTENOIDS AND DOBI CONTENT IN CRUDE PALM OIL BY SPECTROSCOPY TECHNIQUES: COMPARISON OF RAMAN AND FT-NIR SPECTROSCOPY” FTNIR NIRS LINK

“Feedforward and Feedback Control of a Pharmaceutical Coating Process.” NIRS LINK

“Development of Portable Non-Invasive Blood Glucose Measuring Device Using NIR Spectroscopy” LINK

“Akurasi Metode NIRS dalam Prediksi Kandungan Kimia Bubuk Green Coffee Bondowoso dengan Model Kubelka-Munk” LINK

“Identification of fiber added to semolina by near infrared (NIR) spectral techniques” LINK




Infrared

“Comparing the Potential of Near-and Mid-Infrared Spectroscopy in Determining the Freshness of Strawberry Powder from Freshly Available and Stored Strawberry” LINK

“Near-Infrared Imaging of Artificial Enamel Caries Lesions with a Scanning Fiber Endoscope” Sensors LINK

“Near-infrared imaging of diseases: A nanocarrier approach.” LINK

“N-Linked Glycosylation and Near-Infrared Spectroscopy in the Diagnosis of Prostate Cancer.” LINK

“Identification of Coal Geographical Origin Using Near Infrared Sensor Based on Broad Learning” LINK

“Online determination of chemical and physical properties of Poly(ethylene vinyl acetate) pellets using a novel method of Near-Infrared spectroscopy combined with angle transform” LINK

“Study on the Processing Technology of Calamine Calcination by Near-Infrared Spectroscopy” LINK

“A new approach to vibrational sum frequency generation spectroscopy using near infrared pulse shaping.” LINK

“Fourier transform near-infrared spectroscopy coupled to a long fibre optic head for the quality control of IBERIAN pork loins: Intact versus minced” LINK




Raman

“Recent Advancement in the Surface-Enhanced Raman Spectroscopy-Based Biosensors for Infectious Disease Diagnosis” LINK




Facts

“A Machine Learning Approach for Biomass Characterization” LINK




Equipment

“Classification of Food Powders with Open Set using Portable VIS-NIR Spectrometer” LINK




Future

“The 2019 Spectroscopy Salary Survey: A Surprising Downtrend” TL;DR : “The 2019 Spectroscopy salary survey results showed a 14% decrease in average base salaries compared to 2018. ” LINK




Environment

” Predicting bioavailability change of complex chemical mixtures in contaminated soils using visible and near-infrared spectroscopy and random forest regression” LINK

“Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review” LINK




Agriculture

1000ogs project on regulated deficit irrigation in orchards of apple blueberry cherry considering the actual fruit developmental stage based on its size & brightening of ground colour (healthy chlorophyll, pheophytin_a) to save water. LINK

“Influence of moisture and grain sizes on the analysis of nutrients in German agricultural soils using DP-LIBS” LINK

“Change in the Microviscosity of Erythrocyte Membranes and Proteins in Blood Plasma after Graphene Oxide Addition: The ESR Spectroscopy Study” LINK




Pharma

“Spectral interval combination optimization (ICO) on rapid quality assessment of Solanaceae plant: a validation study” LINK




Laboratory

“On-farm instant quality analysis” by Cherney Jerry and Cherney Debbie (Progressive Dairyman ) forage laboratory NIR NIRS handheld LINK





Spectroscopy and Chemometrics News Weekly #11, 2019

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


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


CalibrationModel.com

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




Chemometrics

” 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




Infrared

“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




Equipment

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




Process Control

” DESIGN AND SIMULATION OF AN AUTOMATED POULTRY FEED MIXING MACHINE USING PROCESSCONTROLLER” LINK




Environment

“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




Agriculture

“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




Pharma

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




Medicinal

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





Spectroscopy and Chemometrics News Weekly #7, 2019

CalibrationModel.com

Automatic Development of NIR-Spectroscopic Chemometric Models as a Service LINK

Develop near infrared spectroscopy applications & freeing up hours of analysts time QAQC QualityManager QualityAssurance LINK

Optimized NIR-Spectroscopy Accuracy Increases Your Profit manufacturing QA QC Food Feed Lab PetCare vitamins LINK

Rapid development of robust quantitative methods by near-infrared spectroscopy for NIR NIRS LINK

Spectroscopy and Chemometrics News Weekly 6, 2019 | NIRS NIR Spectroscopy Chemometrics analysis Spectral Spectrometer Spectrometric Analytical Sensors LINK






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


Chemometrics

“DATA FUSION APPROACHES IN SPECTROSCOPIC CHARACTERIZATION AND CLASSIFICATION OF PDO WINE VINEGARS” LINK

“Time-Space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series” LINK

“Prediction of Dissolution Profiles from Process Parameters, Formulation and Spectroscopic Measurements.” LINK

“Near-infrared spectroscopy coupled with chemometrics tools: a rapid and non-destructive alternative on soil evaluation” LINK

“Near infrared hyperspectral imaging for the prediction of gaseous and particulate matter emissions from pine wood pellets” LINK

“Development of a general calibration model and long-term performance evaluation of lowcost sensors for air pollutant gas monitoring” LINK

“Volatiles Profiling, Allelopathic Activity, and Antioxidant Potentiality of Xanthium Strumarium Leaves Essential Oil from Egypt: Evidence from Chemometrics Analysis” LINK




Near Infrared

“Application of NIR spectroscopy and image analysis for the characterisation of grated Parmigiano-Reggiano cheese” LINK

“A method for differentiating between exogenous and naturally embedded ash in bio-based feedstock by combining ED-XRF and NIR spectroscopy” LINK

“Use of Visible-Near-Infrared (Vis-NIR) Spectroscopy to Detect Aflatoxin B1 on Peanut Kernels.” LINK

Planning your trip to the 19th International NIR conference also plan to attend pre-conf workshops. Imaging (2 days), R for NIR (2 days), Aquaphotomics (1 day), Sampling (1 day) and PAT (1 day). Limited spots. Register ASAP. LINK

“Comparing vis-NIRS, LIBS, and Combined vis-NIRS-LIBS for Intact Soil Core Soil Carbon Measurement” LINK

“Propiolic Acid in Solid Nitrogen: NIR- and UV-Induced CisTrans Isomerization and Matrix-Site Dependent TransCis Tunneling.” LINK

“Chemical composition of melamine-urea-formaldehyde (MUF) resins assessed by near-infrared (NIR) spectroscopy” LINK




Infrared

“Detection of malaria in insectary-reared Anopheles gambiae using near-infrared spectroscopy” LINK

“Recognition of wood surface defects with near infrared spectroscopy and machine vision” LINK

“Wearable-band Type Visible-Near Infrared Optical Biosensor for Non-invasive Blood Glucose Monitoring” LINK

“Near-infrared spectroscopy reveals neural perception of vocal emotions in human neonates.” LINK

“Characterization of green, roasted beans, and ground coffee using near infrared spectroscopy: A comparison of two devices” LINK

“Near infrared spectroscopic analysis of total alkaloids as nicotine, total nitrogen and total ash in Cuban cigar tobacco” LINK




Equipment

“Portable NIR Spectrometer for Prediction of Palm Oil Acidity.” LINK




Process Control

“Manufacturing fit-for-purpose paper packaging containers with controlled biodegradation rate by optimizing addition of natural fillers” LINK




Environment

“Quantitatively estimating main soil water-soluble salt ions content based on Visible-near infrared wavelength selected using GC, SR and VIP.” LINK




Agriculture

“Effect of Cereal Grain Source and Cereal Silage Source on Nutrient Utilization and Performance of Finishing Beef” LINK




Food & Feed

“Measurements of lycopene contents in fruit: A review of recent developments in conventional and novel techniques” LINK




Other

“Freshness Evaluation of Three Kinds of Meats Based on the Electronic Nose” Sensors LINK

New post: Smallest optical frequency comb to-date | frequencycomb spectroscopy LINK





Professional Development of NIR‑Spectroscopic Chemometric Calibration Models as a Service

For all NIR Spectrometers.

Use our included free NIR-Predictor software to create results!
Now new with native File Format support of mobile NIR instruments!

Starting at € 299.-
per Chemometric Model (NIRS Calibration Equation)
You don’t need a Chemometric Software.
See detailed Price List


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Benefit

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Spectroscopy and Chemometrics News Weekly #31, 2018

Chemometrics

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



Infrared

“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



Raman

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



Agriculture

“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



Petro

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



Pharma

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



Medicinal

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




Other

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





CalibrationModel.com

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.


Start Calibrate


Interested, then do not hesitate to contact us.

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 (ISO 12099) and easily capable to 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!

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See How it works

Summary of the NIR Chemometric survey polls

Summary of the NIR Chemometric survey polls (as of end of Sept. 2013)

The interesting finding is that most of the answers fit the following pattern. The most companies that use NIR have one NIR Instrument and only one employee that is able to develop NIR calibrations. For that the most common off-the-shelf chemometrics program is used and spent 2 hours or over a month and therefore gets no calibration training about the complex topics like Chemometrics and NIR Spectroscopy or only once (introduction). The calibration maintenance ranges from never to 3 times a year. Interestingly, there was no one who uses portable NIR instruments. We continue our surveys, for the discovery of new trends. Conclusion Seeing this picture, we think that there is huge potential to improve the calibrations. Advanced knowledge can help individuals to build the calibrations with best practices and improve their models accuracy and reliability. Once the decision and investment in NIR technology is done, you should get the best out of your data, because this extra NIR performance can be given by calibration optimization. We offer this as an easy to use and independent service.