Digitization in the field of NIR spectroscopy (smart sensors)

Digitalization is advancing, also in NIR spectroscopy, which enables trainable miniature smart sensors e.g. for analyses in the food&feed, chemical and pharmaceutical sectors.

The calibration is the core of a NIR spectroscopy sensor, it enables the numerous applications and should therefore not be the weakest link in the measurement chain.

The development of calibrations that turn NIR spectrometers into smart sensors is done manually by experts (NIR specialist, chemometrician, data scientist) with so-called chemometrics software.

This is very time-consuming (time to market) and the result is person-dependent and thus suboptimal, because each expert has his own preferred way of proceeding. In addition, the calibrations have to be maintained, as new data has been collected in the meantime, which can be used to extend and improve the calibrations.

This is where our automated service comes in, combining the knowledge and good practices of NIR spectroscopy and chemometrics collected in one software and using machine learning to generate optimal calibrations.

Based on this, we have developed a complete technology platform (Time to Market) that covers the entire process from sending NIR + Lab data, to NIR Calibration as a Service, from online purchase of calibrations, to NIR Predictor software that directly evaluates newly measured NIR data locally and generates result reports.

Besides the free desktop version with user interface, the NIR Predictor can also be integrated (OEM). This can be integrated in parallel as a complement to your current Predictor, allowing the user to choose how they want to calibrate. And give them the advantage in NIR feasibility studies and NIR spectrometer evaluations to quickly provide the customer with a solid and accurate calibration that will make their NIR system deliver better results.

Advantages for your NIR users (internal or external)
  • no initial costs (no chemometrics software license required),
  • calculable operating costs (fixed amount instead of time and hourly rate) (calibration development, calibration maintenance)
  • easy to use (no chemometrics and software training),
  • quicker to use (no calibration development work) and
  • better calibrations (precision, accuracy, robustness, …)


Our chargeable service is based on the calibration development and the annual calibration use. Calibration development and calibration use can also be carried out separately (manufacturer / user).

For you as a spectrometer manufacturer, this means that you can deliver your system pre-calibrated for certain applications without incurring software license costs. And without your application specialists having to provide additional calibration services.

The unique advantages of our calibration service together with the free NIR Predictor are:
  • no software license costs (chemometrics software, predictor software, OEM integration)
  • no chemometrics know-how necessary
  • no time needed to develop optimal NIR calibrations.


If interested in using/evaluating the service :

About CalibrationModel.com : Time and knowledge intensive creation and optimization of chemometric evaluation methods for spectrometers as a service to enable more accurate analysis and measurement results.



see also

Paradigm Change in NIR

Five Mistakes to avoid on Digitalization in NIR

NIR – Total cost of ownership (TCO)

OEM / White Label Software

White Paper



Spectroscopy and Chemometrics News Weekly #33, 2020

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 32, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 32, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 32, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

This week’s NIR news Weekly is sponsored by Your-Company-Name-Here – NIR-spectrometers. Check out their product page … link

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




Near-Infrared Spectroscopy (NIRS)

“Integrated soluble solid and nitrate content assessment of spinach plants using portable NIRS sensors along the supply chain” LINK

“Evaluation of Near Infrared Spectroscopy (NIRS) and Remote Sensing (RS) for Estimating Pasture Quality in Mediterranean Montado Ecosystem” LINK

“Evaluation of Homogeneity in Drug Seizures Using Near-Infrared (NIR) Hyperspectral Imaging and Principal Component Analysis (PCA)”LINK

“FT-NIRS Coupled with PLS Regression as a Complement to HPLC Routine Analysis of Caffeine in Tea Samples” Foods LINK




Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)

“Model based optimization of transflection near infrared spectroscopy as a process analytical tool in a continuous flash pasteurizer” LINK

“EXPRESS: Monitoring Polyurethane Foaming Reactions Using Near-Infrared Hyperspectral Imaging” LINK

“Near-infrared spectroscopy for monitoring free jejunal flap.” LINK

“Real-Time and Online Inspection of Multiple Pork Quality Parameters Using Dual-Band Visible/N ear-Infrared Spectroscopy” LINK

“An approach to quantify natural durability of Eucalyptus bosistoana by near infrared spectroscopy for genetic selection” LINK

“Rapid detection of green pea adulteration in ground pistachio nuts using near and mid-infrared spectroscopy” LINK

“Non‐invasive quality analysis of thawed tuna using near infrared spectroscopy with baseline correction” LINK




Raman Spectroscopy

“Low-Content Quantitation in Entecavir Tablets Using 1064 nm Raman Spectroscopy” LINK




Hyperspectral Imaging (HSI)

“Detecting defects on cheese using hyperspectral image analysisLINK

“Non-destructive discrimination of the variety of sweet maize seeds based on hyperspectral image coupled with wavelength selection algorithm” LINK

“Hyperspectral Imaging from a Multipurpose Floating Platform to Estimate Chlorophyll-a Concentrations in Irrigation Pond Water” Remote Sensing LINK

“Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning” LINK




Chemometrics and Machine Learning

“Maintaining the predictive abilities of near-infrared spectroscopy models for the determination of multi-parameters in White Paeony Root” LINK

“Machine Learning Modeling of Wine Sensory Profiles and Color of Vertical Vintages of Pinot Noir Based on Chemical Fingerprinting, Weather and Management Data” Sensors LINK

“Authentication of the Geographical Origin of “Vallerano” Chestnut by Near Infrared Spectroscopy Coupled with Chemometrics” LINK




Agriculture NIR-Spectroscopy Usage

“Relationship between chemical composition and standardized ileal digestible amino acid contents of corn grain in broiler chickens” LINK

“NIR spectroscopy and management of bioactive components, antioxidant activity, and macronutrients in fruits” LINK

“Determination of mechanical properties of whey protein films during accelerated aging: Application of FTIR profiles and chemometric tools” LINK

“A portable IoT NIR spectroscopic system to analyze the quality of dairy farm forage” LINK

“Exploring Relevant Wavelength Regions for Estimating Soil Total Carbon Contents of Rice Fields in Madagascar from Vis-NIR Spectra with Sequential Application of …” LINK

“Applied Sciences, Vol. 10, Pages 4345: Observation of Potential Contaminants in Processed Biomass Using Fourier Transform Infrared Spectroscopy” LINK

“Animals, Vol. 10, Pages 1095: Comparison of Fatty Acid Proportions Determined by Mid-Infrared Spectroscopy and Gas Chromatography in Bulk and Individual Milk Samples” LINK

“Manipulation of Fruit Dry Matter via Seasonal Pruning and Its Relationship to dAnjou Pear Yield and Fruit Quality” Agronomy LINK




Forestry and Wood Industry NIR Usage

“The Effect of Construction and Demolition Waste Plastic Fractions on Wood-Polymer Composite Properties” LINK




Food & Feed Industry NIR Usage

“Analysis of sorghum content in corn–sorghum flour bioethanol feedstock by near infrared spectroscopy” LINK

“Quantitative detection of fatty acid value during storage of wheat flour based on a portable near-infrared (NIR) spectroscopy system” LINK

“Integration of spectra and image features of Vis/NIR hyperspectral imaging for prediction of deoxynivalenol contamination in whole wheat flour” LINK

“Ongoing Research on Microgreens: Nutritional Properties, Shelf-Life, Sustainable Production, Innovative Growing and Processing Approaches” Foods LINK




Pharma Industry NIR Usage

“Direct Catalytic Fuel Cell Device Coupled to Chemometric Methods to Detect Organic Compounds of Pharmaceutical and Biomedical Interest” Sensors LINK




Laboratory and NIR-Spectroscopy

“Application of infrared microscopy and alternating least squares to the forensic analysis of automotive paint chips” LINK




Other

“Phenotypic plasticity and nonstructural carbohydrates in annual growth rings of the australian red cedar clones in contrasting enviroments” LINK





NIR-Predictor Download

The free NIR-Predictor software
  • comes with demo data, so you can predict sample spectra with demo calibrations.
  • has no functional limitations, no nagging, no ads and needs no license-key.
  • you need no account and no registration to download and use.
  • runs on Microsoft Windows 10/8/7 (Starter, Basic, Professional) (32 bit / 64 bit).
  • no data is ever transmitted from your local machine. We don’t even collect usage data.
See more Videos



Beside the free NIR-Predictor software with Windows user interface,
the real-time Predictor Engine is also available
  • for embedded integration in application, cloud and instrument-software (ICT).
  • As a light-weigt single library file (DLL)
    with application programming interface (API),
    documentation and software development kit (SDK)
    including sample source code (C#).
  • Easy integration and deployment,
    no software license protection (no serial key, no dongle).
  • Put your spectrum as an array into the multivariate predictor,
    no specific file format needed.
  • Fast prediction speed and low latency
    because of compiled code library (direct call, no cloud API).
  • Protected prediction results with outlier detection information.
See NIR Method Development Service for Labs and NIR-Vendors (OEM, White-Label)



Software Size Date Comment
NIR-Predictor V2.6.0.2 (download)

What’s new, see Release Notes

By downloading and/or using the software
you accept the Software License Agreement (EULA)
3.7 MB 18.08.2021 public release

Minimal System Requirements
Windows 7 Starter 32Bit, 1.6 GHz, 2 GB RAM, non-Administrator account

Installation
There are no administrator rights required, unpack the zip file to a folder “NIR-Predictor” in your documents or on your desktop.
Read the ReadMe.txt and double click the NIR-Predictor.exe file.

Upgrade
If you have installed an older version of NIR-Predictor then unpack into a different folder named e.g. “NIR-PredictorVx.y”. All versions can run side-by-side. Copy the Calibrations in use to the new version into the “Calibration” folder. That’s all.

Uninstall
Make sure to backup your reports and calibrations inside your “NIR-Predictor” folder. Delete the “NIR-Predictor” folder.


Start Calibrate

See also: