Spectroscopy and Chemometrics Machine-Learning News Weekly #18, 2022

NIR Calibration-Model Services

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

Spectroscopy and Chemometrics News Weekly 17, 2022 | 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 17, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 17, 2022 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

Near-Infrared Spectroscopy (NIRS)

“Towards a NIR Spectroscopy ensemble learning technique competing with the standard ASTM-CFR: An optimal boosting and bagging extreme learning machine …” LINK

“Near-infrared spectroscopy and HPLC combined with chemometrics for comprehensive evaluation of six organic acids in Ginkgo biloba leaf extract” | LINK

“Application of near infrared spectroscopy and real time release testing combined with statistical process control charts for on-line quality control of industrial …” LINK

“Agronomy : Quality Assessment of Red Wine Grapes through NIR Spectroscopy” LINK

“An authenticity method for determining hybrid rice varieties using fusion of LIBS and NIRS” LINK

“Agriculture : A Standard-Free Calibration Transfer Strategy for a Discrimination Model of Apple Origins Based on Near-Infrared Spectroscopy” LINK

“Quantitative Analysis of Agricultural Compost Indicator Factors Based on Different Nir Feature Variable Selection Methods” LINK

“Near-infrared spectroscopy for the inline classification and characterization of fruit juices for a product-customized flash pasteurization” LINK

“Foods : Determination of Cultivation Regions and Quality Parameters of Poria cocos by Near-Infrared Spectroscopy and Chemometrics” LINK

“Prediction of formaldehyde and residual methanol concentration in formalin using near infrared spectroscopy” LINK

“Feasibility of Near-Infrared Spectroscopy for Rapid Detection of Available Nitrogen in Vermiculite Substrates in Desert Facility Agriculture” LINK

“Near-infrared spectroscopy and HPLC combined with chemometrics for comprehensive evaluation of six organic acids in Ginkgo biloba leaf extract” LINK

“A Review of Visible and Near-Infrared (Vis-NIR) Spectroscopy Application in Plant Stress Detection” LINK

“Plants : Comparative Determination of Phenolic Compounds in Arabidopsis thaliana Leaf Powder under Distinct Stress Conditions Using Fourier-Transform Infrared (FT-IR) and Near-Infrared (FT-NIR) Spectroscopy” LINK

“A feasibility study on improving the non-invasive detection accuracy of bottled Shuanghuanglian oral liquid using near infrared spectroscopy” LINK

“Application of portable visible and near-infrared spectroscopy for rapid detection of cooking loss rate in pork: Comparing spectra from frozen and thawed pork” LINK

“Development of a calibration model for near infrared spectroscopy using a convolutional neural network” LINK

“Feasibility of near infrared spectroscopy to classify lamb hamburgers according to the presence and percentage of cherry as a natural ingredient” LINK

“Gaming behavior and brain activation using functional near-infrared spectroscopy, Iowa gambling task, and machine learning techniques” | LINK

“A non-motorized spectro-goniometric system to measure the bi-directional reflectance spectra of particulate surfaces in the visible and near-infrared” LINK

“Sand fractions micromorphometry detected by VisNIRMIR and its impact on water retention” LINK

“Sensors : Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks” LINK

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

“Infrared Organic Photodetectors Employing Ultralow Bandgap Polymer and Non-Fullerene Acceptors for Biometric Monitoring” | LINK

“Infrared Organic Photodetectors Employing Ultralow Bandgap Polymer and NonFullerene Acceptors for Biometric Monitoring” LINK

“Polymers : Infrared Linear Dichroism for the Analysis of Molecular Orientation in Polymers and in Polymer Composites” LINK

“Nearinfrared chemiluminescent carbon nanogels for oncology imaging and therapy” LINK

“Applied Sciences : Identification of Biochemical Differences in White and Brown Adipocytes Using FTIR Spectroscopy” LINK

Raman Spectroscopy

“Surface-Enhanced Raman Scattering Spectroscopy Combined With Chemical Imaging Analysis for Detecting Apple Valsa Canker at an Early Stage” | LINK

“Recent advances in background-free Raman scattering for bioanalysis” | LINK

Hyperspectral Imaging (HSI)

“Sensors : Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview” LINK

“Remote Sensing : Classification of Tree Species in Different Seasons and Regions Based on Leaf Hyperspectral Images” LINK

“Visible and Near-Infrared Hyperspectral Imaging (HSI) can reliably quantify CD3 and CD45 positive inflammatory cells in myocarditis: Pilot study on formalin-fixed …” LINK

Chemometrics and Machine Learning

“Molecules : Application of Transmission Raman Spectroscopy in Combination with Partial Least-Squares (PLS) for the Fast Quantification of Paracetamol” LINK

“Digital Assessment and Classification of Wine Faults Using a Low-Cost Electronic Nose, Near-Infrared Spectroscopy and Machine Learning Modelling” LINK

“Interpersonal Neural Synchronization Predicting Learning Outcomes From Teaching-Learning Interaction: A Meta-Analysis” | LINK

“Non-destructive near infrared spectroscopy externally validated using large number sets for creation of robust calibration models enabling prediction of apple firmness” LINK

“Sensors : Behavioural Classification of Cattle Using Neck-Mounted Accelerometer-Equipped Collars” LINK

Optics for Spectroscopy

“Physically Detachable and Operationally Stable Cs2SnI6 Photodetector Arrays Integrated with LEDs for Broadband Flexible Optical System” LINK


“A fast multi-source information fusion strategy based on deep learning for species identification of boletes” LINK

Research on Spectroscopy

“Reactivity between late first-row transition metal halides and the ligand bis (2-pyridylmethyl) disulfide: vibrantly-colored compounds with variable molecular …” LINK

Equipment for Spectroscopy

“Foods : Discrimination of the Red Jujube Varieties Using a Portable NIR Spectrometer and Fuzzy Improved Linear Discriminant Analysis” LINK

Environment NIR-Spectroscopy Application

“Remote Sensing : Comparing Sentinel-2 and WorldView-3 Imagery for Coastal Bottom Habitat Mapping in Atlantic Canada” LINK

“Soil water repellency prediction in high‐organic agricultural soils from Greenland: Comparing vis-NIRS to pedotransfer functions” LINK

“Remote Sensing : Simulation of Soil Organic Carbon Content Based on Laboratory Spectrum in the Three-Rivers Source Region of China” LINK

“Remote Sensing : Unmanned Aerial Vehicle (UAV)-Based Remote Sensing for Early-Stage Detection of Ganoderma” LINK

“Soil water repellency prediction in highorganic agricultural soils from Greenland: Comparing visNIRS to pedotransfer functions” LINK

Agriculture NIR-Spectroscopy Usage

“Assessing the nutritional quality of stored grain legume fodders: Correlations among farmers’ perceptions, sheep preferences, leaf-stem ratios and laboratory …” LINK

“Use of spectroscopic sensors in meat and livestock industries” LINK

“IJMS : Fourier-Transform Infra-Red Microspectroscopy Can Accurately Diagnose Colitis and Assess Severity of Inflammation” LINK

“Agronomy : Combining Variable Selection and Multiple Linear Regression for Soil Organic Matter and Total Nitrogen Estimation by DRIFT-MIR Spectroscopy” LINK

“Development of a Low-Cost Method for Quantifying Microplastics in Soils and Compost Using Near-Infrared Spectroscopy” LINK

“Applied Sciences : Optimization of Soybean Protein Extraction Using By-Products from NaCl Electrolysis as an Application of the Industrial Symbiosis Concept” LINK

Horticulture NIR-Spectroscopy Applications

“Nondestructive prediction of total soluble solids in strawberry using near infrared spectroscopy” LINK

Food & Feed Industry NIR Usage

“Effects of Irrigation Strategy and Plastic Film Mulching on Soil N2O Emissions and Fruit Yields of Greenhouse Tomato” LINK

“Making Cocoa Origin Traceable” LINK

Pharma Industry NIR Usage

“Microfluidic Synthesis of Block Copolymer Micelles: Application as Drug Nanocarriers and as Photothermal Transductors When Loading Pd Nanosheets” LINK


“腔内影像学方法评价降脂治疗对冠状动脉粥样硬化斑块影响的研究进展” LINK

Electric scooter battery swap in Taiwan. LINK

“Resonance Couplings in Si@MoS<sub>2</sub> Core-Shell Architectures” LINK

“Relationship between the distribution of vegetation and the environment in the coastal embryo dunes of Jalisco, México” | LINK

“Consistency of children’s hemodynamic responses during spontaneous speech” | LINK

Spectroscopy and Chemometrics/Machine-Learning News Weekly #8, 2022

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 7, 2022 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

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

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

Near-Infrared Spectroscopy (NIRS)

“Nirs Technology (Near Infrared Reflectance Spectroscopy) for Detecting Soil Fertility Case Study in Aceh Province” LINK

“Fourier transform and near infrared dataset of dialdehyde celluloses used to determine the degree of oxidation with chemometric analysis” LINK

“NIRS and Aquaphotomics Trace Robusta-to-Arabica Ratio in Liquid Coffee Blends” LINK

“Development of a generic NIRS calibration pipeline using deep learning and model ensembling: application to some reference datasets” LINK

“Foods : Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time Domain Reflectometry (TDR)” LINK

“Rapid and accurate determination of prohibited components in pesticides based on near infrared spectroscopy” LINK

“NIR spectroscopic methods for monitoring blend potency in a feed frame – calibration transfer between offline and inline using a continuum regression filter” LINK

“A FT-NIR Process Analytical Technology Approach for Milk Renneting Control” LINK

“Construction and Verification of a Mathematical Model for Near-Infrared Spectroscopy Analysis of Gel Consistency in Southern Indica Rice” LINK

“Research on Construction of Visible-Near Infrared Spectroscopy Analysis Model for Soluble Solid Content in Different Colors of Jujube” LINK

“Quantitative Detection of Agaricus Bisporus Freshness Based on VIS-NIR Spectroscopy” LINK

“Foods : Rapid Determination of β-Glucan Content of Hulled and Naked Oats Using near Infrared Spectroscopy Combined with Chemometrics” LINK

“A ready-to-use portable VIS-NIR spectroscopy device to assess superior EVOO quality” | LINK

“Challenges, Opportunities and Recent Advances in Near Infrared Spectroscopy Applications for Monitoring Blend Uniformity in the Continuous Manufacturing of Solid …” LINK

“The use of near-infrared reflectance spectroscopy (NIRS) to predict dairy fibre feeds in vitro digestibility” LINK

“Near-Infrared Reflectance Spectroscopy (NIRS) detection to differentiate morning and afternoon milk based on nutrient contents and fatty acid profiles” LINK

“Application of Various Algorithms for Spectral Variable Selection in NIRS Modeling of Red Ginseng Extraction” LINK

“Near infrared spectroscopy to predict plaque progression in plaque-free artery regions” LINK

“The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation” LINK

“Quantitative Detection of Agaricus Bisporus Freshness Based on VIS-NIR Spectroscopy” LINK

“Application of SG-MSC-MC-UVE-PLS Algorithm in Whole Blood Hemoglobin Concentration Detection Based on Near Infrared Spectroscopy” LINK

“Near infrared reflectance spectroscopy as a tool to predict non-starch polysaccharide composition and starch digestibility profiles in common monogastric …” LINK

“Development of SOP for NIRS spectral measurement on fresh grounded yam tubers and cassava roots” LINK

“Development of calibration models within a closed feed frame to determine drug concentration using near infrared spectroscopy” LINK

“Amazonian cacao-clone nibs discrimination using NIR spectroscopy coupled to naïve Bayes classifier and a new waveband selection approach” LINK

“Foods : A FT-NIR Process Analytical Technology Approach for Milk Renneting Control” LINK

“Study on Soil Salinity Estimation Method of “Moisture Resistance” Using Visible-Near Infrared Spectroscopy in Coastal Region” LINK

“Can Grassland Chemical Quality Be Quantified Using Transform Near-Infrared Spectroscopy?” LINK

“High Near-Infrared Reflective Zn 1-x A x WO 4 Pigments with Various Hues Facilely Fabricated by Tuning Doped Transition Metal Ions (A= Co, Mn, and Fe)” LINK

“Rapid Differentiation of Unfrozen and Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time Domain Reflectometry (TDR)” LINK

“The Relationship Between Genetic Variations and NIRs Differences of Eucalyptus Pellita Provenances” LINK

“Prediction of quality of total mixed ration for dairy cows by near infrared reflectance spectroscopy and empirical equations” LINK

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

“Agricultural products quality determination by means of near infrared spectroscopy” LINK

“A Near-Infrared TDLAS Online Detection Device for Dissolved Gas in Transformer Oil” LINK

“Near-Infrared Spectroscopy Detection of Cotton/Polyester Content Based on Dropout Deep Belief Network” LINK

“Study on Near-Infrared Spectrum Acquisition Method of Non-Uniform Solid Particles” LINK

“Scanning interferometric near-infrared spectroscopy” LINK

“A broadband near‐infrared Sc1‐x(PO3)3:xCr3+ phosphor with enhanced thermal stability and quantum yield by Yb3+ codoping” LINK

“Minerals : Geometallurgical Characterisation with Portable FTIR: Application to Sediment-Hosted Cu-Co Ores” LINK

Hyperspectral Imaging (HSI)

“Developing deep learning based regression approaches for prediction of firmness and pH in Kyoho grape using Vis/NIR hyperspectral imaging” LINK

“Robustness and accuracy evaluation of moisture prediction model for black tea withering process using hyperspectral imaging” LINK

Spectral Imaging

“Remote Sensing : Improved Method to Detect the Tailings Ponds from Multispectral Remote Sensing Images Based on Faster R-CNN and Transfer Learning” LINK

Chemometrics and Machine Learning

“Nir Spectral Techniques and Chemometrics Applied to Food Processing” | LINK

“Consistent Value Creation from Bioprocess Data with Customized Algorithms: Opportunities Beyond Multivariate Analysis” LINK

“Effect of variable selection algorithms on model performance for predicting moisture content in biological materials using spectral data” LINK

“Chemometrics: An Excavator in Temperature-Dependent Near-Infrared Spectroscopy” LINK

“Development of Calibration-Free/Minimal Calibration Wavelength Selection for Iterative Optimization Technology Algorithms toward Process Analytical Technology Application” LINK

Equipment for Spectroscopy

“The utility of a near-infrared spectrometer to predict the maturity of green peas (Pisum sativum)” LINK

“Effect of lanthanum content on the thermophysical properties and near-infrared reflection properties of lanthanum-cerium oxides” LINK

Process Control and NIR Sensors

“Emerging non-destructive imaging techniques for fruit damage detection: Image processing and analysis” LINK

Environment NIR-Spectroscopy Application

“Fusion of visible nearinfrared and midinfrared data for modelling key soilforming processes in loess soils” LINK

Agriculture NIR-Spectroscopy Usage

“Preharvest phenotypic prediction of grain quality and yield of durum wheat using multispectral imaging” LINK

“Remote Sensing : A Random Forest Algorithm for Retrieving Canopy Chlorophyll Content of Wheat and Soybean Trained with PROSAIL Simulations Using Adjusted Average Leaf Angle” LINK

“Agriculture : Application of Fourier Transform Infrared Spectroscopy and Multivariate Analysis Methods for the Non-Destructive Evaluation of Phenolics Compounds in Moringa Powder” LINK

Horticulture NIR-Spectroscopy Applications

“Nondestructive detection of total soluble solids in grapes using VMDRC and hyperspectral imaging” LINK

Forestry and Wood Industry NIR Usage

“Influence of growth parameters on wood density of Acacia auriculiformis” | LINK

Food & Feed Industry NIR Usage

“Data fusion of near-infrared diffuse reflectance spectra and transmittance spectra for the accurate determination of rice flour constituents” LINK

Medicinal Spectroscopy

“Carbon Dots with Intrinsic Bioactivities for Photothermal Optical Coherence Tomography, tumorspecific Therapy and Postoperative Wound Management” LINK


“Potential denitrification activity response to long-term nitrogen fertilization-A global meta-analysis” LINK

“Annealing induced phase transformation from amorphous to polycrystalline SnSe2 thin film photo detector with enhanced light-matter interaction” LINK

“MoS2/PVA Hybrid Hydrogel with Excellent LightResponsive Antibacterial Activity and Enhanced Mechanical Properties for Wound Dressing” LINK

“Influence of Substrate Temperature on Structural and Optical Properties of Co-Evaporated Cu2SnS3/ITO Thin Films” | LINK


Spectroscopy and Chemometrics/Machine-Learning News Weekly #42, 2021

NIR Calibration-Model Services

Spectroscopy and Chemometrics/Machine-Learning News Weekly 41, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensor QA QC Testing Quality LINK

Spektroskopie und Chemometrie/Machine-Learning Neuigkeiten Wöchentlich 41, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT sensors Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria/Machine-Learning Weekly News 41, 2021 | 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 Spectroscopy and Chemometrics News Weekly in real time on Twitter @ CalibModel and follow us.

Near-Infrared Spectroscopy (NIRS)

“Development and Submission of Near Infrared Analytical Procedures Guidance for Industry” FDA LINK

“Identification prediction moisture content of Thai coconut sugar (Cocos nucifera L.) using FT-NIR spectroscopy” LINK

“Blood identification of NIR spectroscopy based on BP neural network combined with particle swarm optimization” LINK

“A preliminary study on the utilisation of near infrared spectroscopy to predict age and in vivo human metabolism” LINK

“Near-infrared guidance finalized for small molecule testing, with biologics to come” RAPS LINK

“Wavelength Selection Method for Near Infrared Spectroscopy Based on Iteratively Retains Informative Variables and Successive Projections Algorithm” LINK

“Near-infrared spectroscopy calibration strategies to predict multiple nutritional parameters of pasture species from different” LINK

” Comparison of metabolites and variety authentication of Amomum tsao-ko and Amomum paratsao-ko using GC-MS and NIR spectroscopy” LINK

“Hyperfine-Resolved Near-Infrared Spectra of H(2)(17)O” LINK

“Geographical Differentiation of Hom Mali Rice Cultivated in Different Regions of Thailand Using FTIR-ATR and NIR Spectroscopy” LINK

“Seasonal Snowpack Classification Based on Physical Properties Using Near-Infrared Proximal Hyperspectral Data” | LINK

“NIR-based sensing system for non-Invasive detection of Hemoglobin for point-of-care applications” LINK

“A promising inorganic YFeO3 pigments with high near-infrared reflectance and infrared emission” LINK

“Classification of Softwoods using Wood Extract Information and Near Infrared Spectroscopy.” LINK

“Rapid determination and origin identification of total polysaccharides contents in Schisandra chinensis by near-infrared spectroscopy” LINK

“Near-Infrared Spectroscopy Technology in Food” | LINK

“Postharvest ripeness assessment of ‘Hass’ avocado based on development of a new ripening index and Vis-NIR spectroscopy” LINK

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

“Utility of near‐infrared spectroscopy to detect the extent of lipid core plaque leading to periprocedural myocardial infarction” LINK

“Scaling up Sagebrush Chemistry with Near-Infrared Spectroscopy and Uas-Acquired Hyperspectral Imagery” LINK

Hyperspectral Imaging (HSI)

“Spatially Resolved Spectroscopic Characterization of Nanostructured Films by Hyperspectral Dark-Field Microscopy” LINK

“Visual attention-driven framework to incorporate spatial-spectral features for hyperspectral anomaly detection” LINK

Spectral Imaging

“Spectral Super-Resolution of Multispectral Images Using Spatial-Spectral Residual Attention Network” LINK

Chemometrics and Machine Learning

“Feasibility of a chromameter and chemometric techniques to discriminate pure and mixed organic and conventional red pepper powders: A pilot study” LINK

“Cyanobacteria cell prediction using interpretable deep learning model with observed, numerical, and sensing data assemblage” LINK

“Applied Sciences : A Novel Principal Component Analysis Integrating Long Short-Term Memory Network and Its Application in Productivity Prediction of Cutter Suction Dredgers” LINK

“Plants : Morpho-Physiological Classification of Italian Tomato Cultivars (Solanum lycopersicum L.) According to Drought Tolerance during Vegetative and Reproductive Growth” LINK

“Automatic food and beverage authentication and adulteration detection by classification hybrid fusion” LINK

“Remote Sensing : Improving Accuracy of Herbage Yield Predictions in Perennial Ryegrass with UAV-Based Structural and Spectral Data Fusion and Machine Learning” LINK

“Estimating Fat Components of Potato Chips Using Visible and Near-Infrared Spectroscopy and a Compositional Calibration Model” LINK

“Wavelengths selection based on genetic algorithm (GA) and successive projections algorithms (SPA) combine with PLS regression for determination the soluble …” LINK

“Comparison of PLS and SVM models for soil organic matter and particle size using vis-NIR spectral libraries” LINK

“Verified the rapid evaluation of the edible safety of wild porcini mushrooms, using deep learning and PLS‐DA” LINK

Optics for Spectroscopy

“Scientists Teach AI Cameras to See Depth in Photos Better” AI Camera Depth LINK


“Deep learning accelerates super-resolution microscopy by up to ten times” | DeepLearning microscopy LINK

“Statistical Learning to Operationalize a Domain Agnostic Data Quality Scoring. (arXiv:2108.08905v1 [cs.LG])” LINK

Research on Spectroscopy

“Foods : Instrumentation for Routine Analysis of Acrylamide in French Fries: Assessing Limitations for Adoption” LINK

Process Control and NIR Sensors

“NIR spectroscopy for monitoring of the critical manufacturing steps and quality attributes of paliperidone prolonged release tablets” LINK

Environment NIR-Spectroscopy Application

“Spatial Differentiation Analysis of Water Quality in Dianchi Lake Based on GF-5 NDVI Characteristic Optimization” LINK

“Remote Sensing : Using Sentinel-2 for Simplifying Soil Sampling and Mapping: Two Case Studies in Umbria, Italy” LINK

“Sensors : Ground-Based Hyperspectral Remote Sensing for Estimating Water Stress in Tomato Growth in Sandy Loam and Silty Loam Soils” LINK

“Potential of high-spectral resolution for field phenotyping in plant breeding: Application to maize under water stress” LINK

Agriculture NIR-Spectroscopy Usage

“Study the Genetic Diversity in Protein, Zinc and Iron in Germplasm Pools of Desi Type Chickpeas as Implicated in Quality Breeding” LINK

“Additives and soy detection in powder rice beverage by vibrational spectroscopy as an alternative method for quality and safety control” LINK

“Remote Sensing : Generating Up-to-Date Crop Maps Optimized for Sentinel-2 Imagery in Israel” LINK

“Fodder biomass, nutritive value, and grain yield of dual‐purpose Pearl Millet, Sorghum and Maize cultivars across different agro‐ecologies in Burkina Faso” LINK

“Agronomy : Effects of the Foliar Application of Potassium Fertilizer on the Grain Protein and Dough Quality of Wheat” LINK

“Remote Sensing : Sentinel-2 and Landsat-8 Multi-Temporal Series to Estimate Topsoil Properties on Croplands” LINK

“Sensors : Assessment of Grain Harvest Moisture Content Using Machine Learning on Smartphone Images for Optimal Harvest Timing” LINK

“Using UAV image data to monitor the effects of different nitrogen application rates on tea quality” LINK

“A single calibration of near-infrared spectroscopy to determine the quality of forage for multiple species” LINK

Forestry and Wood Industry NIR Usage

“Teakwood Chemistry and Natural Durability” LINK

Food & Feed Industry NIR Usage

“Foods : Temporal Sensory Perceptions of Sugar-Reduced 3D Printed Chocolates” LINK

“Foods : Rapid Nondestructive Simultaneous Detection for Physicochemical Properties of Different Types of Sheep Meat Cut Using Portable Vis/NIR Reflectance Spectroscopy System” LINK

“Foods : Real-Time Gauging of the Gelling Maturity of Duck Eggs Pickled in Strong Alkaline Solutions” LINK

Chemical Industry NIR Usage

“Polymers : Drug Amorphous Solid Dispersions Based on Poly(vinyl Alcohol): Evaluating the Effect of Poly(propylene Succinate) as Plasticizer” LINK

Pharma Industry NIR Usage

” Effects of acetazolamide and furosemide on ventilation and cerebral blood volume in normocapnic and hypercapnic COPD patients” LINK


“Advances, challenges and perspectives of quantum chemical approaches in molecular spectroscopy of the condensed phase” LINK

“Calidad composicional y sensorial de la carne bovina y su determinación mediante infrarrojo cercano” LINK

“Bioinspired StimuliResponsive Hydrogel with Reversible Switching and Fluorescence Behavior Served as LightControlled Soft Actuators” LINK

“Neural Efficiency in Athletes: A Systematic Review” LINK

“Tailored Chiral Copper Selenide Nanochannels for Ultrasensitive Enantioselective Recognition and Detection” LINK

“In vivo diffuse reflectance spectroscopic analysis of fatty liver with inflammation in mice” LINK

“Métodos de análise da composição química e valor nutricional de alimentos para ruminantes” LINK

“Dissociation between exercise intensity thresholds: mechanistic insights from supine exercise” LINK

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

NIR-Predictor – Manual

NIR-Predictor – Manual

Predicting Spectra

It’s easy to use with NIR-Predictor,
just drag & drop your data for getting the prediction results.

It supports an automatic file format detection.
So you don’t need to specify the instrument type and settings! See the list of supported formats and NIR Vendors: NIR-Predictor supported Spectral Data File Formats

Use the included data to checkout how it feels:

  1. Open the demo Spectra folder by using the Menu > Open Demo Spectra or press F8.
    There are files with spectra from different Vendors.

  2. Drag & drop a spectra file onto the NIR-Predictor window (or press Ctrl+O as for ’Open some files).

  3. The spectra will be

    • loaded
    • pre-processed
    • predicted and
    • reported

All the steps are fully automatic.
All calibrations that are compatible with the spectra, will produce prediction results in one go.
To select specific calibrations choose the Application. Where the ” ” empty means use all the calibrations.
To define a Application read more in chapter “Applications”

To get access to Statistics of Predictions and Reports use the Menu > Show more/less (Ctrl+M) or you can simply resize the window. Here you can also re-do the Analyze step manually with changed inputs (e.g. Result Ordering).

Creating your own Calibrations

How it works – step by step

  1. You have measured your samples with you NIR-Instrument Software.
    And got the Lab-values of these samples.

    -> measured NIR-spectra
    -> Lab-references analytics

  2. Now you need to combine these data.

    NIR-spectra + Lab-references
    -> PropertiesBySamples

    Note: If you combined these data already in your NIR software used,
    and you can export it as a JCAMP-DX file then use
    Menu > Create Request File .req ... (F2)
    and read the “Help.html” and NIR-Predictor JCAMP.
    Else proceed as below.

    The NIR-Predictor provides tooling for that:

    Menu > Create Properties File... (F6)

    Select the folder with your NIR spectra measured for an application.
    NIR-Predictor creates a customized Properties file template for that data to enter the Lab values.

    Note: You don’t need to specify your instrument or vendor or an application. It’s all done automatically. And also the sample spectra are detected and grouped automatically!

  3. Use your favorite editor or spreadsheet program to enter and copy&paste
    the Lab-references Values into the columns “Prop1”, “Prop2” etc. and save the file.

  4. A final check of your entered data is done by NIR-Predictor,
    to make sure your data ist complete and all is fine.

    Menu > Create Calibration Request... (F7)

    Select the folder with the filled file.
    A CalibrationRequest.zip is created with the necessary data
    if enougth diverse Lab values are entered.

  5. Email the CalibrationRequest.zip file
    to info@CalibrationModel.com to develop the calibrations.

  6. When your calibrations are ready, you will receive an email with a link
    to the CalibrationModel WebShop where
    you can purchase and download the calibration files,
    that work with our free NIR-Predictor software without internet access.

    Note: Your sent NIR data is deleted after processing.
    We do not collect your NIR data!

Note: Further details can be found under “Create Properties File” and “Create Calibration Request”.

Configure the Calibrations for prediction usage


  1. in NIR-Predictor : Menu > Open Calibrations (F9)

  2. an explorer window is opened where the calibrations are located

  3. create a folder for your application, choose a name

  4. copy the calibration file(s) (*.cm) into that folder

  5. in NIR-Predictor : Menu > Search and load Applications (F4)


  1. in NIR-Predictor : open the Application drop down list, and select your application by name

  2. if all is fine, the calibration file is valid and not expired, it shows : Calibration “1 valid calibation”

  3. the NIR-Predictor is now ready to predict

  4. to switch the application, goto 6.


The Application concept allows to group multiple Calibrations together for an Application. By selecting an Application before prediction, only the Calibrations belonging to the Application will be used for Prediction. In the Demo Data this is used to have multiple spectrometer as Application. This can be used easily as e.g. as Application “Meat Products” containing Fat and Moisture Calibration.

To create an Application, create a folder with the Application’s name inside the Calibrations folder, and move/copy all the Calibrations files to this Application folder. To remove a Calibration from the Application, remove the Calibration file from the Application folder.

After creating an new Application folder, press menu Search and load Applications (F4) to update the NIR-Predictor dialog where the Application can be selected via the dropdown list. You don’t need to close the NIR-Predictor.

After moving Calibration files around, press menu Search and load Calibrations (F5) to update the NIR-Predictor dialog.

The use-all case

In the NIR-Predictor dialog where the Application can be selected via the dropdown list, the empty "" name means that all (yes all) valid Calibrations will be used for prediction.

Note: The Prediction Report will contain only results from spectral compatible Calibrations with the given spectra. That allows to automatically handle the multi vendor NIR instrument usage.

Prediction Result Report

Histograms of Prediction Values per Property

Shows the distribution of the predicted results per calibration. The histogram range contains the range of the calibrated property and includes the predicted results.

The histogram bar (bin) color is defined as follow:

  • blue : all predictions inside calibration range.
  • red : all predictions outside calibration range.
  • orange : some overlaps with calibration range.
    So not all spectra in a orange bin are outside calibration range.

Note: Predicted values are always shown in Histogram table and Prediction Value List table, even if the spectrum does not fit into model (spectrum different to model, aka Residual Outlier) shown as Out = X.

Note: Old browsers like Microsoft Internet Explorer 11 don’t support the grafics for Histogram charts. Use an current browser like Firefox or Chrome or Edge.

Note: If your browser opens the report too slow, try to deactivate some browser plugins, because they can filter what you look at and some add-ons are really slow.

Spectra Plot Thumbnail on the Prediction Report

Visualizes the min,median,max spectrum of the spectra dropped as files on the NIR-Predictor. This gives a minimal and good spectral overview of the predicted property results.

  • Spectra Plot color legend: min,median,max spectrum by predicted property or if no calibration is available by spectral intensity.

  • The min,median,max is determined from the predicted properties or if not available from the intensity of the spectra.

  • Beside the histogram of the predicted properties, where the distribution can be seen, the spectra shown are the ones from min,median,max predicted property.

  • This gives a minimal and good spectral overview of the predicted property results.

  • The “Spectral Range” and number of datapoints is shown in the Prediction Report Header below the listed spectra files.

  • To zoom the spectra plot a little, zoom the report in the browser (hold ctrl + mouse wheel, or pinch on touch screen).

  • The spectra plots and histograms are stored with the report and can be archived.


  • Note that the spectra are shown in the raw values that are loaded, they are not shown pre-processed as the calibration model uses them to make the prediction.

  • Note that the median property spectrum is the median from the predicted property pobulation and not the “median” of the calibration property range.

  • Note that in the multi calibration prediction case, the spectra are selected for each property based on the related predicted property values and so the spectra plots shows typical different spectra.

Spectra Plot

Outlier Detection

To safeguard the prediction results, outliers are automatically checked for each individual prediction. This is based on limits that are determined when creating the calibration with the base data. Thus, a strange spectral measurement can be detected and signaled as an outlier even without base data only by means of the calibration and the NIR predictor. A prediction result with outlier warning is to be distrusted. How the various outlier tests are interpreted and how to avoid them in practice is described here.

The spectrum is an outlier to the model, if the spectrum is not similar with the spectra and lab-values the model is built with.

This legend is shown on each NIR-Predictor prediction report below the results:

Outlier (Out) Symbol Description

  • “X” : spectrum does not fit into model (spectrum different to model)
  • “O” : spectrum is wide outside model center (spectrum similar to model but far away)
  • “=” : prediction is outside upper or lower range of model (property outside model range)
  • “-” : spectrum is incompatible to calibration

Note: A prediction result with outlier warning is to be distrusted.

There are 3 outlier cases (X, O, =) and the incompatible data case “-”.

  • The bad case is “X”
  • the medium case is “O”
  • and the soft case is “=”.

The technical names in literature correspond to:

  • “X” : Spectral Residual Outlier
  • “O” : Leverage Outlier
  • “=” : Property Range Outlier

These 3 outlier cases can appear in combinations, like “XO=” or “XO” or “O=” or “X=”. The more outlier marker are shown the more likely the spectrum is an Outlier.

The default setting in NIR-Predictor Menu > “Report with Simplified Outlier Symbols”

  • is ON, that will show only the worst case instead of all combinations to have a simplified minimal information.
  • if OFF, that will show the combinations (e.g. “XO=” or “XO” or “O=” or “X=”), which is more informative for analyzing problem cases.

Some hints to avoid these Outliers:

  • “X” : spectrum does not fit into model (spectrum different to model)
    Check if the spectrum is noise only, or has no proper signal. That can happen when measured past the sample or measured into the air or at a different substance. If you have multiple NIR instruments of the same type, use spectra measured with different instruments for the calibration.

  • “O” : spectrum is wide outside model center (spectrum similar to model but far away) Sample temperature has an effect on NIR spectra shape, use spectra measured at different (typical use) temperatures (sample temperature, instrument temperature).

  • “=” : prediction is outside upper or lower range of model (property outside model range)
    Use more spectra for the calibration in the Lab value region where your special interest is. If the predicted value is only a little bit out of the calibration range, it can be Ok. Add these spectra to the calibration spectra (with the Lab values), to extend the prediction range of the calibration.

  • “-” : spectrum is incompatible to calibration
    The spectra (from the NIR instrument) has a different wavelength range or a different resolution than the spectra used for calibration. Check Instrument settings (wavelength range, resolution)

Result Ordering

To change the ordering, a drop-down-box is located below the Analyze button. If there is an analysis from the current session, and the Result Ordering is changed, the data is re-Analyzed and reported with the new Result Ordering setting. That allows to compare the different orderings. The Result Ordering is listed in the Prediction Report above the Prediction Value List and stored in the settings.

The order/sorting of the prediction results of the spectra can be defined:

  • GivenOrder (default) the given order of the spectra from file select dialog or drag&drop

*) sorted : ascending sort

  • Date_Name sorted by Date (if any) and then by Name
  • Name_Date sorted by Name and then by Date
  • Date_NamesWithNumbers sorted by Date (if any) and then by Name with number logic
  • NamesWithNumbers_Date sorted by Name with number logic (e.g. “ABC1” is before “ABC002” ) and then by Date

*) as above but sorted Rev : reverse sort = descending sort

  • Rev_Date_Name
  • Rev_Name_Date
  • Rev_Date_NamesWithNumbers
  • Rev_NamesWithNumbers_Date

E.g. with reverse sort by Rev_Date_Name, the newest spectra appear on top.

Depending on how many calibrations are used the result table is getting broader. To print the report (e.g. to Adobe PDF, FreePDF or Microsoft XPS), sometimes the landscape format is shorter in number of pages or in portrait a scale of 80% fits nicely. Or try another internet browser (Mozilla Firefox, Google Chrome, Microsoft Edge, …) to print the report and set the browser as your default browser so it will be opened by default.

Archiving Reports

Each report is contained in one file only, including the grafics. To save storage space the report file folder can be compressed to a zip file (.zip, .7z).

Enter lab values to NIR spectra

Entering the laboratory reference values for NIR calibrations

We have developed specialized tools into NIR-Predictor to combine the NIR and Lab data is a sample-based safe manner.

The main target is to improve Data Quality during the step of combining of the Lab data and the NIR data, because to model a good reliable calibration the data that build the base needs to be of high quality.

It also simplifies to enter the lab values manually to the corresponding NIR data, because of automatically grouping repeated NIR measurements of the same sample, so the lab values can be entered sample based and not by spectrum.

It helps to avoid false reference data, because of the broken relation of NIR spectra and reference values, data entry on the wrong position in the table.

And Helps to detect errors of duplicated or multiple copies of spectra files, and checks for inconsistencies in Date-Time and Sample-Naming. It also checks for missing values.

That all increases the Data Quality for the next step of Calibration Development, and makes data entry a less time consuming and less risky work.

How it works

  1. Menu > Create Properties File... (F6) select the folder with your NIR spectra measured for an application. NIR-Predictor creates a Properties file template for that data : PropertiesBySamples.csv.txt

  2. Use your favorite editor or spreadsheet program to enter and copy&paste the Lab Values into the columns and save the file.

  3. Menu > Create Calibration Request... (F7) select the folder with the filled file for a last check and a Calibration Request file is created with the needed data as a single zip file.

  4. Email the Calibration Request file to info@CalibrationModel.com to develop the calibrations.

Ok that is it, the NIR-Predictor guides you through the steps needed. And if you need to know more details, the Chapter “Create Properties File” is for you.

Create Properties File


  • If you have (exported) JCAMP-DX files containing the Lab-Values, you don’t need to do this step.
    You can send the JCAMP file with your Request (.req) file directly to the calibration service at info@CalibrationModel.com.
  • If your JCAMP-DX files does NOT contain Lab-Values, this is a way to go.

For calibrating the spectra to the lab-values you need to assign the lab-values to the spectra. The easiest way is to have a table where each spectrum (row) is linked to multiple lab-values (columns). This function Create Properties File build such a table for the selected spectra folder automatically!

This table is stored in the file PropertiesBySamples.csv.txt. This can be created for any spectra folder you like. The file extension is .csv.txt to make it easy to edit in a text editor and also in a spreadsheet (excel). The columns are standard TAB separated.

The file header line contains :

Sample Replicates Names Prop1 Prop2 Prop3 DateFirst DateLast Hashes

Where Name and Date describes the spectrum.

Prop1, Prop2, Prop3 are the place to enter the Lab Reference Concentrations properties corresponding to each spectrum. It can be extended to Prop4, Prop5, … etc. Of course you can enter real word names like “Fat (%)” instead of “Prop1”. It’s recommended to put the measurement unit beside the name.

Replicates is the number of replicated or repeated spectra of a sample that is grouped together in the Sample based property file. Sample name and the DateFirst / DateLast between the sample spectra are measured.

Date format is ISO-8601. Missing Dates are 0002-02-02T00:00:00.0000000.

If the file PropertiesBySamples.csv.txt already exist in the selected folder, the user will be notified (it will not be overwritten, because the file may contain user entered Lab-values). The Lab Reference Concentrations values are initialized to 0 (zero) and needed to be changed.

Note: 0 is not interpreted as missing value! If you have a 0 concentration value, put in 0 or 0.0 .

The entry of properties is as easy as possible, because it’s organized by Sample (and not by Spectra), so it’s like your Lab-Value Table that is sample based. The sample rows are sorted in a special way by Sample name. Sorting by Date or alphabetically by Sample can done easily in a spreadsheet program.

Note: when coping lab values to the samples make sure they correspond, so that there are no gaps and the sorting is the same.

The Spectra (rows) are initially sorted by name (and date) to have the replicates/repeats together. You can sort for your convenience in a spreadsheet program.

Enter the Lab Reference Concentrations to the spectra/sample.

Enter the Lab-Values in spreadsheet (e.g. Excel) or a text editor (e.g. Notepad++). If done, use the next menu Create Calibration Request.

Hints: Data handling:

  • The NIR-Predictor creates the PropertiesBySamples.csv.txt once, after that the user is responsible for its content. That means NIR-Predictor does not change this file anymore.

  • You can remove entire rows (spectra) in the property file. You don’t need to remove the spectra files. The NIR-Predictor is aware of the relation, the PropertiesBySamples.csv.txt defines what will be calibrated.

  • How to add more spectra files?

    The additional spectra can be handled in a separate folder, create the property file and copy the spectra to the other folder and copy/merge the property files together in your editor or spreadsheet.


    Copy the spectra into the folder, rename the PropertiesBySamples.csv.txt to e.g. “PropertiesBySamples-Part1.csv.txt” and use Create Properties File to create a new PropertiesBySamples.csv.txt with all the spectra. You can copy/merge the content of the Properties files together in your editor or spreadsheet.

  • What happens with possible duplicate rows? It does no harm to the Calibration because we do an exact checking and data cleaning in the calibration process.

  • What happens to duplicate spectra names? The spectra names are not relevant for the calibration process. The spectra names are helpful to assign the lab-values to the corresponding spectrum entry. That’s why the table is initially sorted by name. The spectra names can be edited by the user.

Create Calibration Request

The menu function Create Calibration Request packs a created Properties file (see “Create Properties File”) and it’s linked spectra files in a compressed ZIP file for sending to the CalibrationModel.com Service.

Please note that the number of measured quantitative samples need to be at least 60 . That means you need at least 60 different spectra (not counting the replicate/repeated measurements).

It shows additional property information about the data you have entered, like – the property type (Quantitative) – it’s range (min – max) and – the number of unique values and – if the Lab-values are enough diverse to get calibrated.

First select the folder with the PropertiesBySamples.csv.txt and measured spectra files of samples you have Lab-values. The data is checked and you get notified what is missing or might be wrong. If something needs to be changed, edit the PropertiesBySamples.csv.txt and do Create Calibration Request again. Your last selected folder is remembered, so you can press return in the folder selection dialog.

Hint: The keyboard shortcuts for redoing it after you edited some entries is : F7 Return – that allows you to get the property information quickly.

Hint: If you open the PropertiesBySamples.csv.txt in a spreadsheet program, you can create Histogram plots of the entered Lab-values, to see in which range are to less samples measurements.

When all is fine

When all is fine the “CalibrationRequest.zip” file is created for that data.

The ZIP file contains:

  • your PropertiesBySamples.csv.txt
  • your personal REQuest file for your computer system, that looks like
    e.g. “337dcdc06b2d6dfb0b5c4bba578642312edf2ae84d909281624d7e26283e8b07 WIN-GB0PB48GSK4.req”
  • the spectra data files

Note: If the CalibrationRequest.zip file is already created and you change the PropertiesBySamples.csv.txt make sure to delete the old CalibrationRequest.zip file first! In the dialog it states if it was successfully created or NOT because it already exist. So you are always on the safe side.

Note: CalibrationRequest.zip file name contains the property names to know what would be calibrated and at the end an identification number for referencing the file. E.g. “CalibrationRequest ‘Prop1’ – ‘Prop2’ h31T3wOH.zip”

Program Settings

  • The users program settings are stored in UserSettings.json
  • The program counters are stored in GlobalCounters.json

Further References

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

In a nutshell (TL;DR) : spectroscopy calibration service and for which users is it suitable?

From your NIR + Lab data, we develop optimal NIR-Calibrations for you.
  • For any NIR spectrometer.
  • You don’t need a Chemometric or Math software!
It’s Your Data and Your Calibration.
  • You can anonymize your NIR + Lab data before sending.
  • We delete your NIR + Lab data after processing.
  • Only you get download access to your Calibrations.
Download the Calibrations.
  • You can see the Calibrations performance statistics.
  • You can try the Calibrations before you buy.
  • Fix cost per Calibration development and use. Paid on download.
Use the free NIR-Predictor Software tooling to
  • Check which of your NIR Spectral Data Formats is supported.
  • Combine your NIR + Lab data and create your Calibration Request.
  • Use your Calibrations to create Analysis Reports from new NIR data of measured samples.

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!

With the NIR-Predictor software,
you can use your NIRS calibration files locally and offline.

That means you can predict as much NIR data as you want,
at full speed without waiting at no extra cost
(it’s NOT a cloud prediction where you pay per result).

The NIR-Predictor shows which samples should be sent to the laboratory for reference analysis in order to complete the data for the next calibration.
This minimizes the laboratory effort and further costs. This is based on the fact that sample spectra that are foreign to the calibration are marked as outliers in the prediction report generated by the NIR-Predictor. This way, these samples can be analyzed in the laboratory and used to enhance the NIR + Lab data.

You don’t need a Chemometric Software.

NIR Calibration Service explained

See detailed Price List

Start Calibrate


It Enables





Our Knowhow

Why you can Trust us

  • Try before you buy with : free NIR-Predictor software included
  • Off-line predictions with NIR-Predictor, your data will not go into the cloud.
  • Data Privacy :
    General Data Protection Regulation (GDPR)
    Datenschutz-Grundverordnung (DSGVO)
  • We delete your data after processing : Terms of Service
  • Optionally data can be anonymized : Anonymizer Software
  • Swiss Quality Service and Software made in Switzerland
What our service provides is also known as:
  • NIR chemometric analytical method development
  • NIR chemometric analysis method development
  • NIR Spectrometric analytical method development
  • NIR Spectroscopic analytical method development
  • NIR spectrometry analytical method development
  • NIR spectrometry analysis method development
  • NIR Spectroscopic Analysis Methods Development
  • NIR spectral analysis methods development
  • NIR Spectrometry Analysis Methods Development
  • NIR Spectroscopy Analysis Methods Creation
  • Development of chemometric analysis methods in the NIR range
  • Development of chemometric analysis methods in the NIRS range
  • NIR Spectrometric analytical method development
  • NIR Spectrometric Analysis Method Development
  • NIRS Spectroscopic Analysis Method Development
  • NIR Development of spectroscopic analysis methods
  • Development of analytical methods of NIR spectrometry
  • NIR spectrometry analysis method development