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 #48, 2020

NIR Calibration-Model Services

Do you use Molecular Spectroscopy with Multivariate Regression Models? That will save you development time LINK

Using cost saving NIR-Spectroscopy Analysis? You can Save even more Costs and Time! How? Read here | VIS NIR NIRS Spectroscopy LabManager Analysis Labs QualityControl CostSaving foodindustry foodproduct Spectrometer Sensor Analytics IoT LINK

Spectroscopy and Chemometrics News Weekly 47, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

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

Near-Infrared Spectroscopy (NIRS)

“Influence of forage particle size and residual moisture on near infrared reflectance spectroscopy (NIRS) calibration accuracy for macro-mineral determination” LINK

“Rapid detection of adulteration in Dendrobium Huoshanense using NIR spectroscopy coupled with chemometric methods” LINK

“Using near-infrared spectroscopy to discriminate closely related species: A case study of neotropical ferns” LINK

“Temperature-dependent, VIS-NIR reflectance spectroscopy of sodium sulfates” LINK

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

“Using near-infrared spectroscopy to determine intramuscular fat and fatty acids of beef applying different prediction approach.” LINK

“Estimating the sensory qualities of tomatoes using visible and near-infrared spectroscopy and interpretation based on gas chromatography–mass …” LINK

“Near Infrared Spectroscopy-Based Evaluation of Patellar Tendon and Knee Ligaments” LINK

“Predictive capacity of some wood properties by near-infrared spectroscopy” LINK

“Predicting Marian Plum Fruit Quality without Environmental Condition Impact by Handheld Visible–Near-Infrared Spectroscopy” LINK

“Near infrared reflectance spectroscopy to quantify Perkinsus marinus infecting Crassostrea virginica” LINK

“Application of genetic algorithm and multivariate methods in detection and measurement of milk‐surfactant adulteration by attenuated total reflection and near‐infrared spectroscopy” LINK

“Classifying Thermal Degradation of Polylactic Acid by Using Machine Learning Algorithms Trained on Fourier Transform Infrared Spectroscopy Data” LINK

“Classification Option for Korean Traditional Paper Based on Type of Raw Materials, Using Near-infrared Spectroscopy and Multivariate Statistical Methods” LINK

“Rapid and simultaneous quality analysis of the three active components in Lonicerae Japonicae Flos by near-infrared spectroscopy” LINK

“Determination of Adenosine and Cordycepin Concentrations in Cordyceps militaris Fruiting Bodies Using Near-Infrared Spectroscopy” LINK

“Application of near-infrared hyperspectral imaging for variety identification of coated maize kernels with deep learning” LINK

Hyperspectral Imaging (HSI)

Konica Minolta to acquire Specim, the leading global supplier of hyperspectral imaging.   “Konica Minolta shares our vision and values and will greatly support our business through improved sell-through,” said Tapio Kallonen, CEO of Specim LINK

“Research on Citrus grandis Granulation Determination Based on Hyperspectral Imaging through Deep Learning” LINK

Wavelength Selection Method Based on Partial Least Square from Hyperspectral Unmanned Aerial Vehicle Orthomosaic of Irrigated Olive Orchards” LINK

Chemometrics and Machine Learning

“Fractional order modeling and recognition of nitrogen content level of rubber tree foliage” LINK

“Development of multi-product calibration models of various root and tuber powders by fourier transform near infra-red (FT-NIR) spectroscopy for the quantification of …” LINK

“Detection and Quantification of Tomato Paste Adulteration Using Conventional and Rapid Analytical Methods” LINK

“Prediction of Acidity Level of Avomango (Gadung Klonal 21) Using Local Polynomial Estimator” LINK

“A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra” LINK

“Spectrometric Classification of Bamboo Shoot Species by Comparison of Different Machine Learning Methods” LINK

Supporting the Sensory Panel to Grade Virgin Olive Oils: An In-House-Validated Screening Tool by Volatile Fingerprinting and Chemometrics” LINK

Process Control and NIR Sensors

“A Process Analytical Concept for In-Line FTIR Monitoring of Polysiloxane Formation” Polymers LINK

Agriculture NIR-Spectroscopy Usage

“Vis–NIR spectroscopy: from leaf dry mass production estimate to the prediction of macro-and micronutrients in soybean crops” LINK

“Comparison of benchtop and handheld near‐infrared spectroscopy devices to determine forage nutritive value” LINK

Food & Feed Industry NIR Usage

“Rapid Authentication of 100% Italian Durum Wheat Pasta by FT-NIR Spectroscopy Combined with Chemometric Tools” LINK

“Identification of Corn Seeds with Different Freezing Damage Degree Based on Hyperspectral Reflectance Imaging and Deep Learning Method” LINK

“Near-and mid-infrared determination of some quality parameters of cheese manufactured from the mixture of different milk species” LINK

“Portable NIR spectrometer for quick identification of fat bloom in chocolates.” LINK

Laboratory and NIR-Spectroscopy

“A Novel Spectral Matching Approach for Pigment: Spectral Subsection Identification Considering Ion Absorption Characteristics” LINK


“Development of a novel quantitative function between spectral value and metmyoglobin content in Tan mutton” LINK

” 基于稀疏网络的可见光/近红外反射光谱土壤有机质含量估算” LINK

“基于可见-近红外光谱的茄子叶绿素荧光参数 Fv/Fm 预测方法” LINK

“A Miniaturized and Fast System for Thin Film Thickness Measurement” LINK

“Molecular spectroscopy with optical frequency combs” LINK