Spectroscopy and Chemometrics/Machine-Learning News Weekly #25, 2022

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 24, 2022 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Near-Infrared Spectroscopy (NIRS)

“Nanoarchitectonics of Glass Coatings for Near-Infrared Shielding: From Solid-State Cluster-Based Niobium Chlorides to the Shaping of Nanocomposite Films” LINK

“Differentiation of Gelsemium elegans-containing toxic honeys and non-toxic honeys by near infrared spectroscopy combine with aquaphotomics” LINK

“Identification of geographical origin and different parts of Wolfiporia cocos from Yunnan in China using PLS-DA and ResNet based on FT-NIR” LINK

“Rapid Detection of Goat Milk Mixed with Bovine Milk and Infant Goat Milk Formulas Mixed with Bovine Whey Powder by NIRS Fingerprints LINK

“Nondestructive Characterization of Citrus Fruit by near-Infrared Diffuse Reflectance Spectroscopy (NIRDRS) with Principal Component Analysis (PCA) and Fisher …” LINK

“Near-Infrared Spectroscopy to Predict Provitamin A Carotenoids Content in Maize” LINK

“… in Cytochrome C Oxidase Redox State and Hemoglobin Concentration in Rat Brain During 810 nm Irradiation Measured by Broadband Near-Infrared Spectroscopy” LINK

“A stacked regression ensemble approach for the quantitative determination of biomass feedstock compositions using near infrared spectroscopy” LINK

“Prediction of maize flour adulteration in chickpea flour (besan) using near infrared spectroscopy” | LINK

“Country of origin label monitoring of musky and common octopuses (Eledone spp. and Octopus vulgaris) by means of a portable near-infrared spectroscopic device” LINK

“A Long Short-Term Memory Neural Network Based Simultaneous Quantitative Analysis of Multiple Tobacco Chemical Components by Near-Infrared” LINK

“Identification of human hair wigs and animal hair wigs by the method of near infrared spectroscopy modeling” LINK

“Real-time recognition of different imagined actions on the same side of a single limb based on the fNIRS correlation coefficient” | LINK


“Agronomy : Potential of NIRS Technology for the Determination of Cannabinoid Content in Industrial Hemp (Cannabis sativa L.)” LINK

“Polymers : Near-Infrared Light-Remote Localized Drug Delivery Systems Based on Zwitterionic Polymer Nanofibers for Combination Therapy” LINK

“Identification of geographical origin and different parts of Wolfiporia cocos from Yunnan in China using PLSDA and ResNet based on FTNIR” LINK

“Highly Efficient and Stable Near-Infrared Broadband Garnet Phosphor for Multifunctional Phosphor-Converted Light-Emitting Diodes” LINK

“Comparative Determination of Phenolic Compounds in Arabidopsis thaliana Leaf Powder under Distinct Stress Conditions Using FT-IR and FT-NIR Spectroscopy” LINK

“Application of near-infrared spectroscopy/artificial neural network to quantify glycosylated norisoprenoids in Tannat grapes” LINK

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

“Excessive Increase in the Optical Band Gap of NearInfrared Semiconductor Lead (II) Sulfide via the Incorporation of Amino Acids” LINK

“Analyzing the Water Confined in Hydrogel Using Near-Infrared Spectroscopy” LINK

“Rapid identification of the geographic origin of Taiping Houkui green tea using near‐infrared spectroscopy combined with a variable selection method” LINK

“Near-Infrared Spectroscopy to Predict Provitamin A Carotenoids Content in Maize” LINK

Hyperspectral Imaging (HSI)

“High-Quality Self-Supervised Snapshot Hyperspectral Imaging” LINK

“Non-destructive age estimation of biological fluid stains: An integrated analytical strategy based on near-infrared hyperspectral imaging and multivariate regression” LINK

“Foods : Identification of Moldy Peanuts under Different Varieties and Moisture Content Using Hyperspectral Imaging and Data Augmentation Technologies” LINK

“Remote Sensing : Wood Decay Detection in Norway Spruce Forests Based on Airborne Hyperspectral and ALS Data” LINK

“Hyperspectral imaging coupled with CNN: A powerful approach for quantitative identification of feather meal and fish by-product meal adulterated in marine fishmeal” LINK

Chemometrics and Machine Learning

“Angle Prediction of Lipid Rich and Calcified Plaque in Computed Tomography Angiography Images” LINK

“Classification of Waste Wood Categories According to the Best Reuse Using Ft-Nir Spectroscopy and Chemometrics” LINK

“Developing a generalized vis-NIR prediction model of soil moisture content using external parameter orthogonalization to reduce the effect of soil type” LINK

“Applied Sciences : A New CO2-EOR Methods Screening Model Based on Interdependency Parameters” LINK

“Insights on the role of chemometrics and vibrational spectroscopy in fruit metabolite analysis” | LINK

“Modeling method and miniaturized wavelength strategy for near-infrared spectroscopic discriminant analysis of soy sauce brand identification” LINK

Optics for Spectroscopy

“Chemical Interface Damping in Nonstoichiometric Semiconductor Plasmonic Nanocrystals: An Effect of the Surrounding Environment” LINK

Research on Spectroscopy

“Alternative Supervised Methods” LINK

“Supervised Methods” LINK

Equipment for Spectroscopy

“As the number falls, alternatives to the Hagberg-Perten falling number method: A review” LINK

Process Control and NIR Sensors

“Cognitive and linguistic dysfunction after thalamic stroke and recovery process: possible mechanism” | LINK

“Sensors : Applications of Online UV-Vis Spectrophotometer for Drinking Water Quality Monitoring and Process Control: A Review” LINK

Agriculture NIR-Spectroscopy Usage

“Foods : Analysis of Key Chemical Components in Aqueous Extract Sediments of Panax Ginseng at Different Ages” LINK

” AA′-Stacked Trilayer Hexagonal Boron Nitride” LINK

“Biomass and Plastic Co-Pyrolysis for Syngas Production: Characterisation of Celtis Mildbraedii Sawdust as a Potential Feedstock” LINK

“Estimation of forage quality by near infrared reflectance spectroscopy in dallisgrass, Paspalum dilatatum, poir” LINK

“Detection of Stored Grain Insect” LINK

“Estimation of crude protein and amino acid contents in whole, ground and defatted ground soybeans by different types of near-infrared (NIR) reflectance spectroscopy” LINK

“Molecules : The Potential Use of Herbal Fingerprints by Means of HPLC and TLC for Characterization and Identification of Herbal Extracts and the Distinction of Latvian Native Medicinal Plants” LINK

ZEUTEC presents a new generation of the SpectraAlyzer GRAIN – the SpectraAlyzer GRAIN NEO with new features, state-of-the-art design, better performance and with the aim of bringing a new perspective to grain testing. LINK

“Genetic variation for seed storage protein composition in rapeseed (Brassica napus) and development of near‐infrared reflectance spectroscopy calibration …” LINK

Food & Feed Industry NIR Usage

“Effect of microbial community structures and metabolite profile on greenhouse gas emissions in rice varieties” LINK

“Quantitative assessment of wheat quality using near‐infrared spectroscopy: A comprehensive review” LINK

Pharma Industry NIR Usage

“Rapid Pentosan Polysulfate Sodium (PPS) Maculopathy Progression.” LINK

Laboratory and NIR-Spectroscopy

“Multispectral Smartphone Camera Reveals Paintings’ Inner Secrets” LINK


“Preparation of One-Dimensional Polyaniline Nanotubes as Anticorrosion Coatings” LINK

“Complex Block Structures; with Focus on LShape Relations” LINK

“D–A type conjugated indandione derivatives: ultrafast broadband nonlinear absorption responses and transient dynamics” | LINK

“Spectroscopic and optical investigations on Er3+ ions doped alkali cadmium phosphate glasses for laser applications” LINK

“Carbazole Isomerism in Helical Radical Cations: Spin Delocalization and SOMO-HOMO Level Inversion in the Diradical State” LINK

Spectroscopy and Chemometrics/Machine-Learning News Weekly #10, 2022

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 9, 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 9, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 9, 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)

“Near-infrared solar reflectance and chromaticity properties of novel green ceramic pigment Cr-doped Y3Al5O12” LINK

“Near-Infrared Spectroscopy (NIRS) as a Method for Biological Sex Discrimination in the Endangered Houston Toad (Anaxyrus houstonensis)” LINK

“Kernel Flow: a high channel count scalable time-domain functional near-infrared spectroscopy system” LINK

“Model Development of Non-Destructive Coffee Beans Moisture Content Determination Using Modified Near Infrared Spectroscopy Instrument” LINK

“Machine Learning Approach Using a Handheld Near-Infrared (NIR) Device to Predict the Effect of Storage Conditions on Tomato Biomarkers” LINK

“Adaptively Optimized Gas Analysis Model with Deep Learning for Near-Infrared Methane Sensors” LINK

“Development of non-invasive blood glucose regression based on near-infrared spectroscopy combined with a deep learning method” LINK

“Assessing the Spectral Characteristics of Dye-and Pigment-Based Inkjet Prints by VNIR Hyperspectral Imaging” LINK

“Fusion of a Low-cost Electronic Nose and Near Infrared Spectroscopy for Qualitative and Quantitative Detection of Beef Adulterated with Duck” LINK


” A Nondestructive Identification Method of Producing Regions of Citrus Based on Near Infrared Spectroscopy” LINK

“Detecting the content of the bright blue pigment in cream based on deep learning and near-infrared spectroscopy” LINK

“A NIR Study on Hydrogen Bonds of Bamboo-Based Cellulose Ⅱ” LINK

“Vis-NIR Spectra Discriminant of Pesticide Residues on the Hami Melon Surface by GADF and Multi-Scale CNN” LINK

“Pedometric tools for classification of southwestern Amazonian soils: A quali-quantitative interpretation incorporating visible-near infrared spectroscopy” LINK

“… Frozen-Thawed Tuna with Non-Destructive Methods and Classification Models: Bioelectrical Impedance Analysis (BIA), Near-Infrared Spectroscopy (NIR) and Time …” LINK

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

“Effect of Soil Particle Size on Prediction of Soil Total Nitrogen Using Discrete Wavelength NIR Spectral Data” LINK

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

“A Nondestructive Identification Method of Producing Regions of Citrus Based on Near Infrared Spectroscopy” LINK

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

“Accessing High-Power Near-Infrared Spectroscopy Using Cr3+-Substituted Metal Phosphate Phosphors” LINK

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

“System Design and Preliminary Analysis of the UQ Near Infrared Spectroscopy Data of the Hayabusa2 Re-entry” LINK

“A fast method to measure the degree of oxidation of dialdehyde celluloses using multivariate calibration and infrared spectroscopy” LINK

” In-Line Identification of Different Grades of GPPS Based on Near-Infrared Spectroscopy” LINK

“Eu2+Doped Layered Double Borate Phosphor with Ultrawide NearInfrared Spectral Distribution in Response to UltravioletBlue Light Excitation” LINK

“Near-Infrared Spectroscopy Applied to the Detection of Multiple Adulterants in Roasted and Ground Arabica Coffee” LINK

Raman Spectroscopy

“RAMANMETRIX: a delightful way to analyze Raman spectra. (arXiv:2201.07586v1 [physics.data-an])” LINK

Hyperspectral Imaging (HSI)

“Rapid and nondestructive determination of sorghum purity combined with deep forest and near-infrared hyperspectral imaging” LINK

“Intraoperative hyperspectral imaging (HSI) as a new diagnostic tool for the detection of cartilage degeneration” | LINK

“Application of Visible/Near-Infrared Hyperspectral Imaging with Convolutional Neural Networks to Phenotype Aboveground Parts to Detect Cabbage Plasmodiophora …” LINK

“Detection of nutshells in cumin powder using NIR Hyperspectral Imaging and chemometrics tools” LINK

“Gastric Cancer Detection by Two-step Learning in Near-Infrared Hyperspectral Imaging” LINK

“A Spectral and Spatial Attention Network for Change Detection in Hyperspectral Images” LINK

Chemometrics and Machine Learning

“Analysis and classification of peanuts with fungal diseases based on real-time spectral processing” LINK

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

“PLS-R Calibration Models for Wine Spirit Volatile Phenols Prediction by Near-Infrared Spectroscopy” LINK

“Development and assessment of spectroscopy methodologies and chemometrics strategies to detect pharmaceuticals blend endpoint in a pharmaceutical …” LINK

“Quick Measurement Method of Condensation Point of Diesel Based on Temperature-Compensation Model” LINK

“Prediction Model of TVB-N Concentration in Mutton Based on Near Infrared Characteristic Spectra” LINK

“Evaluation of a robust regression method (RoBoost-PLSR) to predict biochemical variables for agronomic applications: Case study of grape berry maturity monitoring” LINK


“Nondestructive internal quality evaluation of pears using X-ray imaging and Machine Learning” LINK

Research on Spectroscopy

“Mechanical-based and Optical-based Methods for Nondestructive Evaluation of Fruit Firmness” | LINK

Equipment for Spectroscopy

“Application of a portable near-infrared spectrometer for rapid, non-destructive evaluation of moisture content in Para rubber timber” | LINK

“The Feasibility of Two Handheld Spectrometers for Meat Speciation Combined with Chemometric Methods and Its Application for Halal Certification” LINK

“Rapid identification of the storage age of dried tangerine peel using a hand-held near infrared spectrometer and machine learning” LINK

“pType NearInfrared Transparent Delafossite Thin Films with Ultrahigh Conductivity” LINK

Agriculture NIR-Spectroscopy Usage

“Agricultural practices of perennial energy crops affect nitrogen cycling microbial communities” LINK

“The Simultaneous Prediction of Soil Properties and Vegetation Coverage from Vis-NIR Hyperspectral Data with a One-Dimensional Convolutional Neural Network: A …” LINK

“Alginate-enabled green synthesis of S/Ag1. 93S nanoparticles, their photothermal property and in-vitro assessment of their anti-skin-cancer effects augmented by a …” LINK

“The Relative Performance of a Benchtop Scanning Monochromator and Handheld Fourier Transform Near-Infrared Reflectance Spectrometer in Predicting Forage …” LINK

“Compact meta-spectral image sensor for mobile applications” | LINK

“Detection of insect damaged rice grains using visible and near infrared hyperspectral imaging technique” LINK

“Terrain analysis, erosion simulations, and sediment fingerprinting: a case study assessing the erosion sensitivity of agricultural catchments in the border of the …” | LINK


Horticulture NIR-Spectroscopy Applications

“Interactions of Linearly Polarized and Unpolarized Light on Kiwifruit Using Aquaphotomics” LINK

Beverage and Drink Industry NIR Usage

“Beyond Beers Law: Quasi-Ideal Binary Liquid Mixtures” LINK


” From Polymorph Screening to Dissolution Testing” LINK

“Charge and Spin Delocalization in Mixed-Valent Vinylruthenium-Triarylamine-Conjugates with Planarized Triarylamines” LINK


“Bioelectrochemical Partial-Denitrification Coupled with Anammox for Autotrophic Nitrogen Removal” LINK

“Broadband Optical Phase Modulation by Colloidal CdSe Quantum Wells” LINK


Spectroscopy and Chemometrics/Machine-Learning News Weekly #2, 2022

NIR Calibration-Model Services

NIR Spectrometry Custom Applications for chemical analysis | laboratory analyzer analyser QA QC Testing QAQC LINK

Protip: For NIR Spectroscopy Data Analysis use a Data Analytics Service that is NIR Domain related LINK

Spectroscopy and Chemometrics News Weekly 1, 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 1, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 1, 2022 | 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

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Near-Infrared Spectroscopy (NIRS)

“Minerals : VIS-NIR/SWIR Spectral Properties of H2O Ice Depending on Particle Size and Surface Temperature” LINK


“Prediksi Kehilangan Hara Pada Tanah Tererosi Menggunakan Near Infrared Reflectance Spectroscopy (NIRS)” LINK

“Aplikasi Teknologi Near Infrared Reflectance Spectroscopy Dengan Metode Partial Least Square Untuk Prediksi Kadar Patchouli Alkohol Minyak Nilam” LINK

“Near infrared spectroscopy and aquaphotomics evaluation of the efficiency of solar dehydration processes in pineapple slices” LINK

“Comparisons of commercially available NIRS-based analyte predictions of haylage quality for equid nutrition” LINK

“Rapid discrimination of wood species from native forest and plantations using near infrared spectroscopy” LINK

“Identification of cocoa bean quality by near infrared spectroscopy and multivariate modeling” LINK

“The Influence of Ingredients, Corn Particle Size, and Sample Preparation on the Predictability of the Near Infrared Reflectance Spectroscopy” LINK

“Machine Learning Approach Using a Handheld Near-Infrared (NIR) Device to Predict the Effect of Storage Conditions on Tomato Biomarkers” LINK

“Solid Physical State Transformation in Hot Melt Extrusion Revealed by Inline Near-Infrared (NIR) Spectroscopy and Offline Terahertz (THz) Raman Imaging” LINK

“Thermal Insulation Performance of Novel Coated Fabrics Based on Fe-Doped BaSnO3 Near-Infrared Reflectance Pigments” LINK

“Non-destructive method for discrimination of weedy rice using near infrared spectroscopy and modified self-organizing maps (SOMs)” LINK

“Potential of VIS/NIR spectroscopy to detect and predict bitter pit in ‘Golden Smoothee’apples” LINK

“Litterbag-NIRS to Forecast Yield: a Horticultural Case with Biofertilizer Effectors” | LINK

“Determination of SSC and TA content of pear by Vis-NIR spectroscopy combined CARS and RF algorithm” LINK

“Evaluation of the robustness of a novel NIR-based technique to measure the residual moisture in freeze-dried products” LINK

“Comparison of Partial Least Square, Artificial Neural Network and Support Vector Regressions for real time monitoring of CHO cell culture processes using in situ Near Infrared spectroscopy” LINK

“Evaluating the impact of NIR pre-processing methods via multiblock partial least-squares” LINK

“Differentiation of Organic Cocoa Beans and Conventional Ones by Using Handheld NIR Spectroscopy and Multivariate Classification Techniques” | LINK

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

“NearInfrared II Plasmonic Phototheranostics with Glutathione Depletion for Multimodal ImagingGuided HypoxiaTolerant ChemodynamicPhotocatalyticPhotothermal Cancer Therapy Triggered by a Single Laser” LINK

“Nearinfrared spectroscopy aids ecological restoration by classifying variation of taxonomy and phenology of a native shrub” LINK

“Optimization of sweet basil harvest time and cultivar characterization using nearinfrared spectroscopy, liquid and gas chromatography, and chemometric statistical methods” LINK

Hyperspectral Imaging (HSI)

“Prediction and Distribution Visualization of Salmon Quality Based on Hyperspectral Imaging Technology” LINK

“A data fusion method of electronic nose and hyperspectral to identify the origin of rice” LINK

“Plants : Hyperspectral Reflectance Response of Wild Rocket (Diplotaxis tenuifolia) Baby-Leaf to Bio-Based Disease Resistance Inducers Using a Linear Mixed Effect Model” LINK

“Prediction of moisture content in steamed and dried purple sweet potato using hyperspectral imaging analysis” | LINK

“Integration of textural and spectral features of Raman hyperspectral imaging for quantitative determination of a single maize kernel mildew coupled with chemometrics” LINK

Chemometrics and Machine Learning

“Near infrared spectroscopy combined with chemometrics for quantitative analysis of corn oil in edible blend oil” LINK

“Applications of NIR spectroscopy and chemometrics to illicit drug analysis: an example from inhalant drug screening tests” LINK

“Remote Sensing : Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF Algorithm” LINK

“Monitoring Molecular Weight Changes during Technical Lignin Depolymerization by Operando Attenuated Total Reflectance Infrared Spectroscopy and Chemometrics” LINK

“Modification of the effect of maturity variation on nondestructive detection of apple quality based on the compensation model” LINK

“Foods : Rapid Detection of Thermal Treatment of Honey by Chemometrics-Assisted FTIR Spectroscopy” LINK

“Applied Sciences : Dual Image-Based CNN Ensemble Model for Waste Classification in Reverse Vending Machine” LINK

Optics for Spectroscopy

Five highly cited papers in the fields of biosensors materials sensors (a thread) LINK


“Remote Sensing : Towards a Deep-Learning-Based Framework of Sentinel-2 Imagery for Automated Active Fire Detection” LINK

Research on Spectroscopy

“Residence Time Distribution as a Traceability Method for Lot Changes in A Pharmaceutical Continuous Manufacturing System” LINK

Environment NIR-Spectroscopy Application

“Remote Sensing : Inversion Evaluation of Rare Earth Elements in Soil by Visible-Shortwave Infrared Spectroscopy” LINK

“Heavy rainfall in peak growing season had larger effects on soil nitrogen flux and pool than in the late season in a semiarid grassland” LINK

“Rachis browning and water loss description during postharvest storage of ‘Krissy’and ‘Thompson Seedless’ table grapes” LINK

“Retrieving zinc concentrations in topsoil with reflectance spectroscopy at Opencast Coal Mine sites” | LINK

“Remote Sensing : Soil Organic Carbon Content Prediction Using Soil-Reflected Spectra: A Comparison of Two Regression Methods” LINK

Agriculture NIR-Spectroscopy Usage

“The Promise of Hyperspectral Imaging for the Early Detection of Crown Rot in Wheat” LINK

“Ecological effects on the nutritional value of bromeliads, and its influence on Andean bears’ diet selection” LINK

“Agronomy : Complex Spectroscopic Study for Fusarium Genus Fungi Infection Diagnostics of “Zalp” Cultivar Oat” LINK

“Predicting Protein Content in Grain Using Hyperspectral Deep Learning” LINK

“Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods” | LINK

Horticulture NIR-Spectroscopy Applications

“Determination of Sugar Adulteration in Honey Using Conductivity Meter and pH Meter” LINK

“A Comparative Analysis of Hybrid SVM and LS-SVM Classification Algorithms to Identify Dried Wolfberry Fruits Quality Based on Hyperspectral Imaging Technology” LINK

Chemical Industry NIR Usage

“Polymers : Hybrid Proton-Exchange Membrane Based on Perfluorosulfonated Polymers and Resorcinol-Formaldehyde Hydrogel” LINK

Laboratory and NIR-Spectroscopy

“Improving Quality Inspection of Textiles by an Augmented RGB-IR-HS-AI Approach” LINK


“External beam irradiation angle measurement using Cerenkov emission I: Signal dependencies consideration” LINK

“Applications of Sensing for Disease Detection” LINK

“Functionalized Tris (anilido) triazacyclononanes as Hexadentate Ligands for the Encapsulation of U (III), U (IV) And La (III) Cations” LINK

“การ ประยุกต์ ใช้ เทคนิค สเปก โทร ส โค ปี อินฟราเรด ย่าน ใกล้ สำหรับ ทำนาย ปริมาณ แค โร ที น อย ด์ ใน เชื้อ พันธุกรรม ข้าวโพด หวาน” LINK

“抑郁症的近红外光谱研究进展” LINK

“高光谱成像技术在医药领域中的应用进展研究” LINK

Spectroscopy and Chemometrics/Machine-Learning News Weekly #45, 2021

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Spectroscopy and Chemometrics News Weekly 44, 2021 | 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

Near-Infrared Spectroscopy (NIRS)

“Study on Characteristic Wavelength Extraction Method for Near Infrared Spectroscopy Identification Based on Genetic Algorithm” LINK

“Uji Karakteristik Biochar dengan Pendekatan Near Infrared Spectroscopy (NIRS)” LINK

“Establishment of online quantitative model for moisture content determination of hydroxychloroquine sulfate particles by near infrared spectroscopy” LINK

“Nondestructive detection model of soluble solids content of an apple using visible/near-infrared spectroscopy combined with CARS and MPGA” LINK

“Detecting cadmium contamination in loessal soils using near-infrared spectroscopy in the Xiaoqinling gold area” LINK

“Egg Freshness Evaluation Using Transmission and Reflection of NIR Spectroscopy Coupled Multivariate Analysis” LINK

“Determination of Alcohol Content in Beers of Different Styles Based on Portable Near-Infrared Spectroscopy and Multivariate Calibration” | LINK

“Analysing the Water Spectral Pattern by Near-Infrared Spectroscopy and Chemometrics as a Dynamic Multidimensional Biomarker in Preservation: Rice Germ …” LINK

“Remote Sensing : The Application of NIRS to Determine Animal Physiological Traits for Wildlife Management and Conservation” LINK

“Wavelength selection method for near-infrared spectroscopy based on standard-sample calibration transfer of mango and apple” LINK

“A portable NIR-system for mixture powdery food analysis using deep learning” LINK

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

“Compositional and sensory quality of beef and its determination by near infrared” LINK

“Butyrylcholinesterase responsive supramolecular prodrug with targeted nearinfrared cellular imaging property” LINK

Raman Spectroscopy

“Quantitative analysis of binary and ternary organo-mineral solid dispersions by Raman spectroscopy for robotic planetary exploration missions on Mars” | OpenAccess LINK

Hyperspectral Imaging (HSI)

“A shallow network for hyperspectral image classification using an autoencoder with convolutional neural network” | LINK

“Hyperspectral camera development on an unmanned aerial vehicle” LINK

“Direct reflectance transformation methodology for drone-based hyperspectral imaging” LINK

Spectral Imaging

“Remote Sensing : Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves” LINK

Chemometrics and Machine Learning

“Sensors : Adaboost-Based Machine Learning Improved the Modeling Robust and Estimation Accuracy of Pear Leaf Nitrogen Concentration by In-Field VIS-NIR Spectroscopy” LINK

“Discrimination of Manufacturers Origin of Oxytetracycline Using Terahertz Time-Domain Spectroscopy with Chemometric Methods” LINK


“Sensors : Non-Invasive Monitoring of Ethanol and Methanol Levels in Grape-Derived Pisco Distillate by Vibrational Spectroscopy” LINK

Equipment for Spectroscopy

“Improving the thermoelectric performances of polymer via synchronously realizing of chemical doping and side-chain cleavage” LINK

Environment NIR-Spectroscopy Application

“Determining physical and mechanical volcanic rock properties via reflectance spectroscopy” LINK

“Unauthorized landfills of solid household and industrial wastes detection in the Arctic and Subarctic territories using remote sensing technologies” LINK

“Evaluating the effects of distinct water saturation states on the light penetration depths of sand-textured soils” LINK

Agriculture NIR-Spectroscopy Usage

“Sorghum Grains Grading for Food, Feed, and Fuel Using NIR Spectroscopy” LINK

“Estimation of leaf area index at the late growth stage of crops using unmanned aerial vehicle hyperspectral images” LINK

“Analysis of Spectral Reflectance Characteristics Using Hyperspectral Sensor at Diverse Phenological Stages of Soybeans” LINK

Horticulture NIR-Spectroscopy Applications

“Data fusion for fruit quality authentication: combining non-destructive sensing techniques to predict quality parameters of citrus cultivars” | LINK

Food & Feed Industry NIR Usage

“Buckwheat Identification by Combined UV-VIS-NIR Spectroscopy and Multivariate Analysis” LINK


“Effect of the annealing temperature on the growth of the silver nanoparticles synthesized by physical route” LINK

Spectroscopy and Chemometrics/Machine-Learning News Weekly #40, 2021

NIR Calibration-Model Services

Improve Accuracy of fast Nondestructive NIR Analytics by Optimal Calibration | Food Feed FoodSafety ag Lab LINK

Increase Your Profit with optimized NIR Accuracy Process Protein Oil plastic colors paints milk soy Soybean LINK

Spectroscopy and Chemometrics News Weekly 39, 2021 | 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 39, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 39, 2021 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

Near-Infrared Spectroscopy (NIRS)

“Convenient use of near-infrared spectroscopy to indirectly predict the antioxidant activitiy of edible rose (Rose chinensis Jacq “Crimsin Glory” HT) petals during …” LINK

“Near-infrared emission from spatially indirect excitons in type II ZnTe/CdSe/(Zn,Mg)Te core/double-shell nanowires” LINK

“Rapid Detection of Fatty Acids in Edible Oils Using Vis-NIR Reflectance Spectroscopy with Multivariate Methods” LINK

“An Improved Residual Network for Pork Freshness Detection using Near-Infrared Spectroscopy” LINK

“Methylglyoxal Adducts Levels in Blood Measured on Dried Spot by Portable Near-Infrared Spectroscopy” LINK

“Application of Near-Infrared Spectroscopy to statistical control in freeze-drying processes” LINK

“TeaNet: Deep learning on Near-Infrared Spectroscopy (NIR) data for the assurance of tea quality” LINK

“Detecting Residual Awareness in Patients With Prolonged Disorders of Consciousness: An fNIRS Study” | LINK

“Dry Matter Estimation of Standing Corn with Near-infrared Reflectance Spectroscopy” LINK

“Conditional-GAN Based Data Augmentation for Deep Learning Task Classifier Improvement Using fNIRS Data” | LINK

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

“Vis-NIR hyperspectral imaging along with Gaussian process regression to monitor quality attributes of apple slices during drying” LINK

“Potential of Near Infrared Spectroscopy as a Rapid Method to Discriminate OTA and Non-OTA-Producing Mould Species in a Dry-Cured Ham Model System” LINK

“Pendugaan Tingkat Fermentasi Kakao Secara Non-Destruktif dengan NIRS” LINK

“Penentuan Tingkat Kekerasan dan Kemanisan Buah Naga Merah (Hylocereus polyrhizus) Secara Nondestruktif Menggunakan Near Infrared Spectroscopy (NIRS)” LINK

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


“A novel strategy of “pick the best of the best” for the nondestructive identification of Poria cocos based on near-infrared spectroscopy” LINK

“The Neural Processing of Vocal Emotion After Hearing Reconstruction in Prelingual Deaf Children: A Functional Near-Infrared Spectroscopy Brain Imaging Study” LINK

“Antinutrient to mineral molar ratios of raw common beans and their rapid prediction using near-infrared spectroscopy” LINK

“Effect of spectral pretreatment on qualitative identification of adulterated bovine colostrum by near-infrared spectroscopy” LINK

“A Rotational-Linear Sample Probing Device to Improve the Performance of Compact Near-Infrared Spectrophotometers” LINK

“Machine Learning Calibration for Near TS Infrared Spectroscopy Data: A Visual kkS Programming Approach” LINK

Hyperspectral Imaging (HSI)

“Selecting informative bands for partial least squares regressions improves their goodness-of-fits to estimate leaf photosynthetic parameters from hyperspectral data” LINK

“Hyperspectral detection of salted sea cucumber adulteration using different spectral preprocessing techniques and SVM method” LINK

“An automated approach for fringe frequency estimation and removal in infrared spectroscopy and hyperspectral imaging of biological samples” LINK

“New evidence from hyperspectral imaging analysis on the effect of photobiomodulation therapy on normal skin oxygenation” LINK

Chemometrics and Machine Learning

“The Use of Chemometrics for Classification of Sidaguri (<i>Sida rhombifolia</i>) Based on FTIR Spectra and Antiradical Activities” LINK

“Massive spectral data analysis for plant breeding using parSketch-PLSDA method: Discrimination of sunflower genotypes” LINK

“Htype indices with applications in chemometrics I: hmultiple similarity index” LINK

“Near-Infrared Spectroscopy and Machine Learning-Based Classification and Calibration Methods in Detection and Measurement of Anionic Surfactant in Milk” LINK

“Remote Sensing : Validation of FY-3D MERSI-2 Precipitable Water Vapor (PWV) Datasets Using Ground-Based PWV Data from AERONET” LINK

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

“Foods : Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality” LINK

“Estimating the Forage Neutral Detergent Fiber Content of Alpine Grassland in the Tibetan Plateau Using Hyperspectral Data and Machine Learning Algorithms” LINK

“Optical spectroscopy methods for the characterization of sol-gel materials” LINK

“Sensors : Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors” LINK

“Agronomy : How Different Cooking Methods Affect the Phenolic Composition of Sweet Potato for Human Consumption (Ipomea batata (L.) Lam)” LINK

Equipment for Spectroscopy

“Miniaturized VIS-NIR Spectrometers Based on Narrowband and Tunable Transmission Cavity Organic Photodetectors with Ultrahigh Specific Detectivity above 10(14) Jones” LINK

In the market for a palm spectrometer, bandpass filter, or microscopy stage? Those and more are featured in the October Product Showcase out today. | photonics optics LINK


Process Control and NIR Sensors

“Multi-modal diffuse optical spectroscopy for high-speed monitoring and wide-area mapping of tissue optical properties and hemodynamics” LINK

“Process analytical technique (PAT) miniaturization for monoclonal antibody aggregate detection in continuous downstream processing” LINK

“Development of a Robust Control Strategy for Fixed-Dose Combination Bilayer Tablets with Integrated Quality by Design, Statistical, and Process Analytical …” LINK

Environment NIR-Spectroscopy Application

“Recent Advances in Plasmonic Photocatalysis Based on TiO2 and Noble Metal Nanoparticles for Energy Conversion, Environmental Remediation, and Organic Synthesis” LINK

“Mapping liquid water content in snow: An intercomparison of mixed-phase optical property models using hyperspectral imaging and in situ measurements” LINK

“Estimating Atterberg limits of soils from reflectance spectroscopy and pedotransfer functions” LINK

Agriculture NIR-Spectroscopy Usage

“Seeing the wood for the trees: hyperspectral imaging for high throughput QTL detection in raspberry, a perennial crop species” LINK

“Agronomy : Effect of Different Edaphic Crop Conditions on the Free Amino Acid Profile of PH-16 Dry Cacao Beans” LINK

Horticulture NIR-Spectroscopy Applications

“Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches” LINK

“Predicting soluble solids content in “Fuji” apples of different ripening stages based on multiple information fusion” LINK

Food & Feed Industry NIR Usage

“Effects of cultivars and fertilization levels on the quality of brown and polished rice” LINK

“Foods : Physicochemical and Functional Properties of Snack Bars Enriched with Tilapia (Oreochromis niloticus) by-Product Powders” LINK

“Using data science to combat poverty” | BASE foodwaste LINK

“Foods : HPLC Fingerprints for the Characterization of Walnuts and the Detection of Fraudulent Incidents” LINK

“A novel approach to identify the spectral bands that predict moisture content in canola and wheat” LINK

Medicinal Spectroscopy

“Investigating spectroscopic measurement of sublingual veins and tissue to estimate central venous oxygen saturation” LINK

“Detection of the Communication Site by Indocyanine Green Adsorbed to Human Serum Albumin Fluorescence During Surgery for a Pleuroperitoneal …” LINK



“Efecto del ambiente ruminal y la fuente de fibra sobre la dinámica de desaparición de la materia orgánica y sus componentes en bovinos en confinamiento.” LINK

“催熟对采后菠萝品质的影响与光谱识别” 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

NIR Method Development Service for Labs and NIR-Vendors (OEM)

CalibrationModel.com ia a perfect match for
    – NIR Vendors    , selling NIR            , with limited capacity for NIR method development
    – Labs                , using NIR            , with limited capacity for NIR method development
    – small Labs        , starting with NIR , with no or less Chemometric knowledge

The Triple to success :
faster better analytics
    LAB Reference Analytics + NIR Spectroscopy + ChemoMetrics
    LAB + NIR +
    => use CM as a Service : CalibrationModel

NIR Method Development : Before / After
    – The
need of a chemometric software ($$)
    – The
need of expert training courses (time,$$)
    – The
need of manual expert work (time,$$$)
    – The
freedom without a chemometric software
    – The
freedom without being an expert
    – The
freedom of using a Service ($)
work smart, not hard
See Cost Comparision

    Cloud Service
        DATA ->
CalibrationModel -> CALIB
                    fix cost, pay per CALIB development and usage

    Local Usage (no internet connection)
        DATA -> CALIB +
Predictor -> RESULT
                                included, no extra cost

    DATA = exported
Spectra and (Lab-)reference values as JCAMP-DX or other data formats
    CALIB = single quantitative property

Sending DATA
    DATA is sent by email, 2-3 days later, receive email with link to
      WebShop to purchase CALIB with PayPal/CreditCard
    DATA is
deleted after processing (Terms of Service TOS)
    optional: JCAMP
Anonymizer (removes sensitive information) before sending DATA

As Middleman you can
hide/cover the Service (white-label)
    Customer <————————> CalibrationModel
    Customer <–>
Middleman <–> CalibrationModel
                        NIR Company
                        NIR Sales, Consultancy

Riskless Predictor OEM integration (white label) (in NIR-Vendors Instrument Software)
    Predictor as a
hidden second engine (second Heart)
    Windows .NET, easy programming interface (API)

DATA owner -> CALIB owner ==> use as your Pre-CALIB
    CALIB is licensed to owner and so copy protected
    The owner can Re-License a CALIB to others
    owner can
re-sell CALIBs in its own WebShop with own prices

    DATA + DATA -> CALIB    same easy workflow as    DATA -> CALIB
    optimize from scratch, benefit from complete optimization possibilities
learn more

NIR-Predictor Software
Contact us for predictor integration