Custom NIR Calibration Models development for a large list of NIR
spectrometers NIRS spectroscopy spectrometer chemometrics
MachineLearning DigitalTransformation miniaturization mobileDevices
MobileSpectrometers NIRanalysis Laboratoires LINK
How to improve your NIRS analysis, get the free White Paper | NIR infrared ag lab QAQC QC quality measurement LINK
Spectroscopy and Chemometrics News Weekly 16, 2022 | NIRS NIR
Spectroscopy MachineLearning Spectrometer Spectrometric Analytical
Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software
IoT Sensors QA QC Testing Quality LINK
“Penilaian Sejawat: Fast, simultaneous and contactless assessment of
intact mango fruit by means of near infrared spectroscopy. AIMS Press.”
| Agriculture (Gabungan Nilai).PDF LINK
“Relevance of Near infrared (NIR) spectroscopy in the determination of
intrinsic Rheological properties of crude oil components from Asabor
Platform, Nigeria.” LINK
“Feasibility of Near-Infrared Spectroscopy for Rapid Detection of
Available Nitrogen in Vermiculite Substrates in Desert Facility
Agriculture” LINK
“Establishment of a Nondestructive Analysis Method for Lignan Content in Sesame using Near Infrared Reflectance Spectroscopy” LINK
“Near Infrared Spectroscopy: A useful technique for inline monitoring of
the enzyme catalyzed biosynthesis of third-generation biodiesel from
waste cooking oil” LINK
“A Study on Nitrogen Concentration Detection Model of Rubber Leaf Based
on Spatial-Spectral Information with NIR Hyperspectral Data” LINK
“Design and Performance of a Near-Infrared Spectroscopy Measurement System for In-Field Alfalfa Moisture Measurement” LINK
“Estimating Forest Soil Properties for Humus Assessment—Is Vis-NIR the Way to Go?” LINK
“Association and solubility of chlorophenols in CCl4: MIR/NIR spectroscopic and DFT study” LINK
“Prediction of rhodinol content in Java citronella oil using NIR
spectroscopy in the initial stage developing a spectral smart sensor
system” | LINK
“Classification of Cocoa Beans Based on Fermentation Level Using PLS-DA
Combined with Visible Near-Infrared (VIS-NIR) Spectroscopy” LINK
“Carbon Footprint Reduction by Utilizing Real Time NIR Spectroscopy for RVP Measurement in Natural Gas Condensate Stabilizers” LINK
“Determination of Total Saccharide Content in Auricularia auricula Based on Near-Infrared Spectroscopy” | LINK
“Prediction of crosslink density of prevulcanised latex using NIR
Spectroscopy based on combination of fractional order derivative (FOD)
and variable selection …” LINK
“Determination of Acid Level (pH) and Moisture Content of Cocoa Beans at
Various Fermentation Level Using Visible Near-Infrared (Vis-NIR)
Spectroscopy” LINK
“Near infrared spectroscopy and machine learning classifier of crosslink density level of prevulcanized natural rubber latex” LINK
“Development of a calibration model for near infrared spectroscopy using a convolutional neural network” LINK
“Novel fNIRS study on homogeneous symmetric feature-based transfer learning for brain-computer interface” | LINK
“Hybrid motion artifact detection and correction approach for functional near-infrared spectroscopy measurements” | LINK
“Predicting Soil Organic Carbon Mineralization Rates Using δ13C,
Assessed by Near-Infrared Spectroscopy, in Depth Profiles Under
Permanent Grassland Along a …” | LINK
“Sensors : A Multiblock Approach to Fuse Process and Near-Infrared Sensors for On-Line Prediction of Polymer Properties” LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
“Determination of Storage Period of Harvested Plums by NearInfrared Spectroscopy and Quality attributes” LINK
“Performance improvement in a supercontinuum fiber-coupled system for near infrared absorption spectroscopy” LINK
Hyperspectral Imaging (HSI)
“New Zealand Honey Botanical Origin Classification with Hyperspectral Imaging” LINK
“Scanning Hyperspectral Imaging for In Situ Biogeochemical Analysis of Lake Sediment Cores: Review of Recent Developments” LINK
Chemometrics and Machine Learning
“Non-destructive near infrared spectroscopy externally validated using
large number sets for creation of robust calibration models enabling
prediction of apple firmness” LINK
“Foods : Predicting Satiety from the Analysis of Human Saliva Using Mid-Infrared Spectroscopy Combined with Chemometrics” LINK
“A NIR,1H-NMR, LC-MS and chemometrics pilot study on the origin of
Carvedilol drug substance: a tool for discovering falsified active
pharmaceutical ingredients” LINK
“Algorithm of Stability-Analysis-Based Feature Selection for NIR Calibration Transfer” LINK
“Improving TVB-N prediction in pork using portable spectroscopy with just-in-time learning model updating method” LINK
“Method Development and Validation of a Near-infrared spectroscopic
method for In-line API Quantification during Fluidized Bed Granulation” LINK
“Non-destructive follow-up of ‘Jintao’kiwifruit ripening through VIS-NIR
spectroscopy-individual vs. average calibration model’s predictions” LINK
“Machine Learning-Powered Models for Near-Infrared Spectrometers: Prediction of Protein in Multiple Grain Cereals” LINK
“A hybrid variable selection and modeling strategy for the determination of target compounds in different spectral datasets” LINK
“Sensors : A Data-Driven Model with Feedback Calibration Embedded Blood
Pressure Estimator Using Reflective Photoplethysmography” LINK
“Non-Destructive Genotyping of Cultivars and Strains of Sesame through NIR Spectroscopy and Chemometrics” | LINK
Research on Spectroscopy
“Quadruple Functionalization of a Tetraphenylethylene Aromatic Scaffold
with Ynamides or Tetracyanobutadienes: Synthesis and Optical Properties”
LINK
Environment NIR-Spectroscopy Application
“Marriage of Heterobuckybowls with Triptycene: Molecular Waterwheels for Separating C60 and C70” LINK
“Performance of hyperspectral data in predicting and mapping zinc concentration in soil” LINK
Agriculture NIR-Spectroscopy Usage
“Study of neurophysiological responses associated with the application of magnetic fields to the brain” LINK
“PLANT DISEASE DETECTION USING IMAGE SENSORS: ASTEP TOWARDS PRECISION AGRICULTURE” LINK
“Linking long-term soil phosphorus management to microbial communities involved in nitrogen reactions” | LINK
“Bovine fecal chemistry changes with progression of Southern Cattle
Tick, Rhipicephalus (Boophilus) microplus (Acari: Ixodidae) infestation”
LINK
“Remote Sensing : The Accuracy of Winter Wheat Identification at Different Growth Stages Using Remote Sensing” LINK
Horticulture NIR-Spectroscopy Applications
“Accurate nondestructive prediction of soluble solids content in citrus
by nearinfrared diffuse reflectance spectroscopy with characteristic
variable selection” LINK
Food & Feed Industry NIR Usage
“Verifying the Geographical Origin and Authenticity of Greek Olive Oils
by Means of Optical Spectroscopy and Multivariate Analysis” LINK
“Analysis of wheat flour-insect powder mixtures based on their near infrared spectra” LINK
Laboratory and NIR-Spectroscopy
“Utilization of Pollution Indices, Hyperspectral Reflectance Indices,
and Data-Driven Multivariate Modelling to Assess the Bottom Sediment
Quality of Lake Qaroun …” LINK
“Oxygen vacancies and defects tailored microstructural, optical and
electrochemical properties of Gd doped CeO2 nanocrystalline thin films” LINK
Other
“Simultaneous removal of aromatic pollutants and nitrate at high
concentrations by hypersaline denitrification: Long-term continuous
experiments investigation” LINK
“Study of sub-band state formation in the optical band gap of CuGaS2 thin films by electronic excitations” LINK
“A sensor combination based automatic sorting system for waste washing machine parts” LINK
“Synaptosomal-Associated Protein 25 Gene Polymorphisms Affect Treatment
Efficiency of Methylphenidate in Children With Attention-Deficit
Hyperactivity Disorder: An fNIRS Study” | LINK
“Applied Sciences : Numerical Study of Near-Infrared Light Propagation
in Aqueous Alumina Suspensions Using the Steady-State Radiative Transfer
Equation and Dependent Scattering Theory” LINK
“Reducing false discoveries in resting-state functional connectivity using short channel correction: an fNIRS study” | LINK
“Diffuse Optical Tomography Using fNIRS Signals Measured from the Skull Surface of the Macaque Monkey” LINK
“Prediction of cellulose nanofibril (CNF) amount of CNF/polypropylene composite using near infrared spectroscopy” LINK
“Near infraredbased process analytical technology module for estimating gelatinization optimal point” LINK
“A Novel NIR-Based Strategy for Rapid Freshness Assessment of Preserved Eggs” | LINK
“Machine Learning-Based Assessment of Cognitive Impairment Using
Time-Resolved Near-Infrared Spectroscopy and Basic Blood Test” | LINK
“Rapid Detection Method of Pleurotus Eryngii Mycelium based on Near Infrared Spectral Characteristics” LINK
“Research on moisture content detection method during green tea
processing based on machine vision and near-infrared spectroscopy
technology” LINK
“Use of Artificial Neural Networks and NIR Spectroscopy for Non-Destructive Grape Texture Prediction” LINK
“NIRS for vicine and convicine content of faba bean seed allowed GWAS to
prepare for marker-assisted adjustment of seed quality of German winter
faba beans” LINK
“Evidence that large vessels do affect near infrared spectroscopy” | LINK
“Use of LC-Orbitrap MS and FT-NIRS with multivariate analysis to determine geographic origin of Boston butt pork” LINK
“Prediction of the intramuscular fat and protein content of freeze dried
ground meat from cattle and sheep using Near‐Infrared Spectroscopy
(NIRS)” LINK
“Prediction of quality of total mixed ration for dairy cows by near infrared reflectance spectroscopy and empirical equations” LINK
“Analysis and Model Comparison of Carbon and Nitrogen Concentrations in
Sediments of the Yellow Sea and Bohai Sea by Visible-Near Infrared
Spectroscopy” | LINK
“Non-destructive measurement and real-time monitoring of apple hardness
during ultrasonic contact drying via portable NIR spectroscopy and
machine learning” LINK
“Polymers : A NIR-Light-Driven Twisted and Coiled Polymer Actuator with a
PEDOT-Tos/Nylon-6 Composite for Durable and Remotely Controllable
Artificial Muscle” LINK
“Invited review: A comprehensive review of visible and near-infrared
spectroscopy for predicting the chemical composition of cheese” LINK
“Chemometrics: An Excavator in Temperature-Dependent Near-Infrared Spectroscopy” LINK
“Are NIRS tools useful to assess digestible energy of pig feedstuffs?” | LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
“Biosensors : Haemoprocessor: A Portable Platform Using Rapid
Acoustically Driven Plasma Separation Validated by Infrared Spectroscopy
for Point-of-Care Diagnostics” LINK
“Estimating texture and organic carbon of an Oxisol by near infrared spectroscopy” LINK
“PhotomultiplicationType Organic Photodetectors for NearInfrared Sensing with High and BiasIndependent Specific Detectivity” LINK
Hyperspectral Imaging (HSI)
“Detection and discrimination of disease and insect stress of tea plants
using hyperspectral imaging combined with wavelet analysis” LINK
“Evaluation of the benefits of combined reflection and transmission
hyperspectral imaging data through disease detection and quantification
in plant-pathogen …” | LINK
“Multi-layer Cascade Screening Strategy for Semi-supervised Change Detection in Hyperspectral Images” LINK
“Comparison of near-infrared, mid-infrared, Raman spectroscopy and
near-infrared hyperspectral imaging to determine chemical, structural
and rheological properties …” LINK
“Developing an affordable hyperspectral imaging system for rapid
identification of Escherichia coli O157:H7 and Listeria monocytogenes in
dairy products” LINK
Chemometrics and Machine Learning
“Simultaneous Monitoring of the Evolution of Chemical Parameters in the
Fermentation Process of Pineapple Fruit Wine Using the Liquid Probe for
Near-Infrared Coupled with Chemometrics” LINK
“Agronomy : Decision Support System (DSS) for Managing a Beef Herd and
Its Grazing Habitat’s Sustainability: Biological/Agricultural Basis of
the Technology and Its Validation” LINK
“Prediction of Entire Tablet Formulations From Pure Powder Components
Spectra via a Two-Step Non-Linear Optimization Methodology” LINK
Optics for Spectroscopy
“Polymers : Development and Characterization of Plantain (Musa
paradisiaca) Flour-Based Biopolymer Films Reinforced with Plantain
Fibers” LINK
Research on Spectroscopy
“Understanding water dynamics in different subcellular environments by
combining multiplexing imaging and dimethylamino naphthalene fluorescent
derivatives”LINK
Environment NIR-Spectroscopy Application
“Performance of hyperspectral data in predicting and mapping zinc concentration in soil” LINK
“Prediction of soil carbon and nitrogen contents using visible and near
infrared diffuse reflectance spectroscopy in varying salt-affected soils
in Sine Saloum (Senegal)” LINK
“Machine Learning Framework for Intelligent Detection of Wastewater Pollution by IoT-Based Spectral Technology” | LINK
Agriculture NIR-Spectroscopy Usage
“Applied Sciences : Combining Hyperspectral Reflectance and Multivariate
Regression Models to Estimate Plant Biomass of Advanced Spring Wheat
Lines in Diverse Phenological Stages under Salinity Conditions” LINK
“Applied Sciences : Mineral Content Estimation of Lunar Soil of Lunar
Highland and Lunar Mare Based on Diagnostic Spectral Characteristic and
Partial Least Squares Method” LINK
“Extraction of rock and alteration geons by FODPSO segmentation and GP
regression on the HyMap imagery: a case study of SW Birjand, eastern
Iran” LINK
“The Relative Performance of a Benchtop Scanning Monochromator and
Handheld Fourier Transform Near-Infrared Reflectance Spectrometer in
Predicting Forage …” LINK
“Non-destructive assessment of amylose content in rice using a quality inspection system at grain elevators” LINK
“Remote Sensing : Predicting the Chlorophyll Content of Maize over
Phenotyping as a Proxy for Crop Health in Smallholder Farming Systems” LINK
Horticulture NIR-Spectroscopy Applications
“Correction of Temperature Variation with Independent Water Samples to
Predict Soluble Solids Content of Kiwifruit Juice Using NIR
Spectroscopy” LINK
Forestry and Wood Industry NIR Usage
“Study of Variability of Waste Wood Samples Collected in a Panel Board Industry” | LINK
“PREDICTION FOR TOTAL MOISTURE CONTENT IN WOOD PELLETS BY NEAR INFRARED SPECTROSCOPY NIRS” LINK
Chemical Industry NIR Usage
“Enhancement of Photostability through Side Chain Tuning in Dioxythiophene-Based Conjugated Polymers” LINK
Petro Industry NIR Usage
“Towards energy discretization for muon scattering tomography in GEANT4
simulations: A discrete probabilistic approach. (arXiv:2201.08804v1
[physics.comp-ph])” LINK
Pharma Industry NIR Usage
“Revealing the Effect of Heat Treatment on the Spectral Pattern of Unifloral Honeys Using Aquaphotomics” LINK
“Spectroscopic Characteristics of Xeloda Chemodrug” LINK
“Bilateral acute macular neuroretinopathy in a young woman after the first dose of Oxford-AstraZeneca COVID-19 vaccine” LINK
“Spatial particle size distribution at intact sample surfaces of a Dystric Cambisol under forest use” LINK
Other
“Chemosensors : Development of Cyanine 813-Based Doped Supported Devices for Divalent Metal Ions Detection” LINK
“Spectral actinometry at SMEAR-Estonia. (arXiv:2202.06132v1 [physics.ao-ph])” LINK
“Experimental and theoretical investigation on the nonlinear optical properties of LDS 821 dye in different solvents and DNA” LINK
“Domain invariant covariate selection (Di-CovSel) for selecting generalized features across domains” LINK
“Structural, optical analysis, and Poole-Frenkel emission in NiO/CMC-PVP: Bio-nanocomposites for optoelectronic applications” LINK
“Research of Parameter Optimization of Preprocessing and Feature Extraction for NIRS Qualitative Analysis Based on PSO Method” LINK
“Fusion of a low-cost electronic nose and Fourier transform
near-infrared spectroscopy for qualitative and quantitative detection of
beef adulterated with duck.” LINK
“Assessment oil composition and species discrimination of Brassicas
seeds based on hyperspectral imaging and portable near infrared (NIR)
spectroscopy tools and …” LINK
“Prediksi Kualitas Buah Jambu Biji “Kristal” Secara NonDestruktif Menggunakan Portable Near Infrared Spectrometer (Nirs)” LINK
“Quantitative Analysis of Blend Uniformity within a Three-Chamber Feed
Frame using Simultaneously Raman and Near-Infrared Spectroscopy” LINK
“Rapid Identification of Peucedanum Praeruptorum Dunn and its Adulterants by Hand-Held near-Infrared Spectroscopy” | LINK
“Methods of Detecting Multiple Chemical Substances Based on
Near-Infrared Colloidal Quantum Dot Array and Spectral Reconstruction
Algorithm” LINK
“Near-Infrared Spectroscopy Detection of Cotton/Polyester Content Based on Dropout Deep Belief Network” LINK
“Non-destructive prediction of the hotness of fresh pepper with a single
scan using portable near infrared spectroscopy and a variable selection
strategy.” LINK
“NIR Calibration Transfer Method Based on Minimizing Mean Distribution Discrepancy” LINK
“Near infrared spectroscopy reveals instability in retinal mitochondrial
metabolism and haemodynamics with blue light exposure at environmental
levels” LINK
“Study on Characteristic Wavelength Extraction Method for Near Infrared Spectroscopy Identification Based on Genetic Algorithm” LINK
“Discrimination of Transgenic Canola (Brassica napus L.) and their
Hybrids with B. rapa using Vis-NIR Spectroscopy and Machine Learning
Methods” LINK
“Recent Advances in Application of Near-Infrared Spectroscopy for Quality Detections of Grapes and Grape Products” LINK
“Detection of calcium chloride salts in rubber cublum using Near-Infrared Spectroscopy Technique” LINK
“PLS-DA and Vis-NIR spectroscopy based discrimination of abdominal tissues of female rabbits” LINK
“Sparse Reconstruction using Block Sparse Bayesian Learning with Fast
Marginalized Likelihood Maximization for Near-Infrared Spectroscopy” LINK
“Study on Online Detection Method of “Yali” Pear Black Heart Disease
Based on Vis-Near Infrared Spectroscopy and AdaBoost Integrated Model” LINK
“Detection of Umami Substances and Umami Intensity Based on Near-Infrared Spectroscopy” LINK
“Farklı ana materyal üzerinde oluşmuş toprakların adli bilim için VNIRS
tekniği ile spektral karakterizasyonu ve özelliklerinin tahmin edilmesi”
LINK
“Prediction of soil hydraulic properties using VIS-NIR spectral data in semi-arid region of Northern Karnataka Plateau” LINK
“Rapid Assessment of Fresh Beef Spoilage Using Portable Near-Infrared Spectroscopy” LINK
“The Relationship Between Genetic Variations and NIRs Differences of Eucalyptus Pellita Provenances” LINK
“Geographical Origin Discrimination of Taiping Houkui Tea Using Convolutional Neural Network and Near-Infrared Spectroscopy” LINK
“Assessment of soil quality using VIS-NIR spectra in invaded coastal wetlands” | LINK
“Relationship Between Visible/Near Infrared Spectral Data and
Fertilization Information at Different Positions of Hatching Eggs” LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
“Near Infrared Spectral Wavelength Selection Based on Improved Team Progress Algorithm” LINK
“Coupling ATR-FTIR Spectroscopy and Chemometric Analysis for Rapid and
Non-Destructive Ink Discrimination of Forensic Documents” LINK
“Model-based mid-infrared spectroscopy for on-line monitoring of volatile fatty acids in the anaerobic digester” LINK
“Depthdependent hydration dynamics in human skin: Vehiclecontrolled
efficacy assessment of a functional 10% urea plus NMF moisturizer by
nearinfrared confocal spectroscopic imaging (KOSIM IR) and capacitance
method complemented by volunteer perception” LINK
“Detection of Umami Substances and Umami Intensity Based on Near-Infrared Spectroscopy” LINK
“Early Detection of Cauliflower Gray Mold Based on Near-Infrared Spectrum Feature Extraction” LINK
Hyperspectral Imaging (HSI)
“Hyperspectral imaging for non-destructive detection of honey adulteration” LINK
“Study on the Spectral Characteristics of Ground Objects in Land-Based Hyperspectral Imaging” LINK
“Combine Hyperspectral Imaging and Machine Learning to Identify the Age of Cotton Seeds” LINK
“Nondestructive detection of total soluble solids in grapes using VMD‐RC and hyperspectral imaging” LINK
“Nondestructive Detection of Codling Moth Infestation in Apples Using
Pixel-Based NIR Hyperspectral Imaging with Machine Learning and Feature
Selection” LINK
“Research on Rich Borer Detection Methods Based on Hyperspectral Imaging Technology” LINK
Chemometrics and Machine Learning
“A Data Fusion Model to Merge the Spectra Data of Intact and Powdered
Cayenne Pepper for the Fast Inspection of Antioxidant Properties” LINK
“Rapid Determination of β-Glucan Content of Hulled and Naked Oats Using near Infrared Spectroscopy Combined with Chemometrics” LINK
“Prediction Model of TVB-N Concentration in Mutton Based on Near Infrared Characteristic Spectra” LINK
“Optimization of Fruit Pose and Modeling Method for Online Spectral Detection of Apple Moldy Core” LINK
Research on Spectroscopy
“Local Preserving Projection Similarity Measure Method Based on Kernel Mapping and Rank-Order Distance” LINK
“A Method for Detecting Sucrose in Living Sugarcane With Visible-NIR Transmittance Spectroscopy” LINK
Process Control and NIR Sensors
“Cognitive and linguistic dysfunction after thalamic stroke and recovery process: possible mechanism” LINK
Environment NIR-Spectroscopy Application
“Empower Innovations in Routine Soil Testing” LINK
Agriculture NIR-Spectroscopy Usage
“Preharvest phenotypic prediction of grain quality and yield of durum wheat using multispectral imaging” LINK
“Cove-Edged Graphene Nanoribbons with Incorporation of Periodic Zigzag-Edge Segments” LINK
“Utilizing near infra-red spectroscopy to identify physiologic variations during digital retinal imaging in preterm infants” | LINK
Forestry and Wood Industry NIR Usage
“Spectrometric prediction of nitrogen content in different tissue types of trees 2” LINK
“Spectrometric Prediction of Nitrogen Content in Different Tissues of Slash Pine Trees” LINK
Food & Feed Industry NIR Usage
“Optical techniques in non-destructive detection of wheat quality: A review” LINK
Chemical Industry NIR Usage
“Quantifying Polaron Mole Fractions and Interpreting Spectral Changes in Molecularly Doped Conjugated Polymers” LINK
Pharma Industry NIR Usage
“Quantitative Changes in Muscular and Capillary Oxygen Desaturation
Measured by Optical Sensors during Continuous Positive Airway Pressure
Titration for …” LINK
Medicinal Spectroscopy
“406: PERIOPERATIVE NONINVASIVE NEUROMONITORING IN INFANTS WITH CONGENITAL HEART DISEASE” LINK
Laboratory and NIR-Spectroscopy
“Available on line at Directory of Open Access Journals” LINK
Other
“A Spectroscopic Study of Dysprosium-Doped TlPb2Br5 for Development of Novel Mid-IR Gain Media” LINK
“No differences in splenic emptying during on-transient supine cycling
between aerobically trained and untrained participants” | LINK
“Influence of Substrate Temperature on Structural and Optical Properties of Co-Evaporated Cu2SnS3/ITO Thin Films” LINK
“Synthesis and characterization of new multinary selenides Sn4In5Sb9Se25 and Sn6. 13Pb1. 87In5. 00Sb10. 12Bi2. 88Se35” 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)
“Qualitative Discrimination of Intact Tobacco Leaves Based on Near-Infrared Technology” | LINK
“Compositionally complex (Ca, Sr, Ba) ZrO3 fibrous membrane with excellent structure stability and NIR reflectance” LINK
“Applicability of Near Infrared Reflectance Spectroscopy to Predict Amylose Contents of Single-Grain Maize” LINK
“Potential of Near-Infrared (NIR) spectroscopy technique for early detection of Insidious Fruit Rot (IFR) disease in Harumanis mango” LINK
“Nondestructive prediction of fresh pepper hotness with a single scan using portable near infrared spectroscopy and variable selection strategy” LINK
“Interferometric near Infrared Spectroscopy (iNIRS): From Conception to Human Brain Imaging” LINK
“Heat impact control in flash pasteurization by estimation of applied pasteurization units using near infrared spectroscopy” LINK
“… Square, Artificial Neural Network and Support Vector Regressions for real time monitoring of CHO cell culture processes using in situ Near Infrared spectroscopy” LINK
“Phonetic versus spatial processes during motor-oriented imitations of visuo-labial and visuo-lingual speech: a functional near-infrared spectroscopy study” LINK
” The Relationship Between Genetic Variations and NIRs Differences of Eucalyptus Pellita Provenances” LINK
“Sensors : Field Detection of Rhizoctonia Root Rot in Sugar Beet by Near Infrared Spectrometry” LINK
“Use of visible-near infrared spectroscopy to predict nutrient composition of poultry excreta” LINK
“Use of Functional Near-Infrared Spectroscopy to Predict and Measure Cochlear Implant Outcomes: A Scoping Review” LINK
“Applied Sciences : Potencial Use of Near Infrared Spectroscopy (NIRS) to Categorise Chorizo Sausages from Iberian Pigs According to Several Quality Standards” LINK
Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR)
“NearInfrared Light Responsive TiO2 for Efficient Solar Energy Utilization” LINK
“Hydration of LiOH and LiCl─ Near-Infrared Spectroscopic Analysis” LINK
“NEAR INFRARED-HYPER SPECTRAL IMAGING APPLICATIONS IN FISHERIES” LINK
“Optimization of sweet basil harvest time and cultivar characterization using near‐infrared spectroscopy, liquid and gas chromatography, and chemometric statistical …” LINK
“Fiber-based source of 500 kW mid-infrared solitons” LINK
Hyperspectral Imaging (HSI)
“Hyperspectral Image Classification Using Factor Analysis and Convolutional Neural Networks” | LINK
“Pre-Launch Calibration of the HYPSO-1 Cubesat Hyperspectral Imager” LINK
“Potential of in-field Vis/NIR hyperspectral imaging to monitor quality parameters of alfalfa” LINK
“A comparison of machine learning algorithms for mapping soil iron parameters indicative of pedogenic processes by hyperspectral imaging of intact soil profiles” LINK
“Inversion modeling of rice canopy nitrogen content based on MPA-GA-ELM UAV hyperspectral remote sensing” LINK
“Sensors : Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging” LINK
“Interleaved attention convolutional compression network: An effective data mining method for the fusion system of gas sensor and hyperspectral” LINK
Chemometrics and Machine Learning
“Remote Sensing : Machine Learning Classification of Endangered Tree Species in a Tropical Submontane Forest Using WorldView-2 Multispectral Satellite Imagery and Imbalanced Dataset” LINK
“Sensors : Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine” LINK
“Image analysis for predicting phenolics in Arabidopsis” LINK
“Ultravioletvisual spectroscopy estimation of nitrate concentrations in surface waters via machine learning” LINK
“Near-infrared calibration models for estimating volatile fatty acids and methane production from in vitro rumen fermentation of different total mixed rations” LINK
“Foods : Tea and Chicory Extract Characterization, Classification and Authentication by Non-Targeted HPLC-UV-FLD Fingerprinting and Chemometrics” LINK
Optics for Spectroscopy
“Perovskites Enabled Highly Sensitive and Fast Photodetectors” LINK
Facts
“Tracing the aqueous alteration history between Isidis and Hellas Planitiae on Mars” LINK
“Remote Sensing : Monitoring Forest Health Using Hyperspectral Imagery: Does Feature Selection Improve the Performance of Machine-Learning Techniques?” | LINK
Research on Spectroscopy
“Sustainability : Finger Millet Production in Ethiopia: Opportunities, Problem Diagnosis, Key Challenges and Recommendations for Breeding” LINK
“Foods : Online Detection of Watercore Apples by Vis/NIR Full-Transmittance Spectroscopy Coupled with ANOVA Method” LINK
“Biosensors : Reflectance Spectroscopy with Multivariate Methods for NondestructiveDiscrimination of Edible Oil Adulteration” LINK
Environment NIR-Spectroscopy Application
“Environmental assessment of soil quality indices using near infrared reflectance spectroscopy” LINK
“Remote Sensing : Ground Observations and Environmental Covariates Integration for Mapping of Soil Salinity: A Machine Learning-Based Approach” LINK
Agriculture NIR-Spectroscopy Usage
“Animals : Optimizing Near Infrared Reflectance Spectroscopy to Predict Nutritional Quality of Chickpea Straw for Livestock Feeding” LINK
“Foods : Development of Nano Soy Milk through Sensory Attributes and Consumer Acceptability” LINK
“Progression of macular atrophy in patients receiving long-term anti-VEGF therapy for age-related macular degeneration; Real Life Data” LINK
“Complexity Assessment of Chronic Pain in Elderly Knee Osteoarthritis Based on Neuroimaging Recognition Techniques” | LINK
“Integrating Genomic and Phenomic Breeding Selection Tools with Field Practices to Improve Seed Composition Quality Traits in Soybean” LINK
“Impact of Forage Diversity on Forage Productivity, Nutritive Value, Beef Cattle Performance and Enteric Methane Emissions.” LINK
“Ecological effects on the nutritional value of bromeliads, and its influence on Andean bears’ diet selection” LINK
Horticulture NIR-Spectroscopy Applications
“Correction of Temperature Variation with Independent Water Samples to Predict Soluble Solids Content of Kiwifruit Juice Using NIR Spectroscopy” Kiwi LINK
Food & Feed Industry NIR Usage
“Real-Time Detection of Rice Growth Phase Transition for Panicle Nitrogen Application Timing Assessment” LINK
“Prediction of pork meat quality parameters with existing hyperspectral devices” LINK
Pharma Industry NIR Usage
“First report of rapid, non-invasive, and reagent-free detection of malaria through the skin of patients with a beam of infrared light” LINK
Laboratory and NIR-Spectroscopy
“Yield and Quality Response of Alfalfa Varieties to High Salinity Irrigation” LINK
“Physical and Chemical Characterisation of the Pigments of a 17th-Century Mural Painting in the Spanish Caribbean” LINK
Other
“A Learned SVD approach for Inverse Problem Regularization in Diffuse Optical Tomography. (arXiv:2111.13401v1 [math.NA])” LINK
We create custom calibrations out of your NIR + Lab measurements of XY.
We do not sell off-the-shelf calibrations.
I have downloaded the software but can’t see it?
Please note, that the download time will be very short, because of the small file size.
Check your browser’s download folder. The download is the file “NIR-PredictorVx.y.zip”
Why a .zip and no installer (Setup.exe) ?
Because a .zip deploy keeps it simple for all:
easy : no Administrator rights needed to install, delete it to uninstall
harmless : no system changes during setup
transparency : you see what you get
Is there a command line (CLI) version of NIR-Predictor ?
If you want to customize it in all details, our OEM API for NIR-instrument-software (White-Label) integration gives you full access. If you are an NIR-Vendor (or similar) please contact us via email info@CalibrationModel.com
The free NIR-Predictor does not create a model, what is wrong ?
As the name says, the NIR-Predictor just predicts NIR data with a model. To create a model you need to send your data to the CalibrationModel service, after development process you get an email with a link to the calibration where it can be purchased and downloaded.
Why does the creation of the PropertyFile.txt take so long with hundrets of spectra files?
It normally takes only 1-10 seconds not minutes.
Make sure that the spectra data files are stored locally on your main drive
and not on a cloud-drive or network storage or slow USB thumb drive or SD-Card.
Is there a way to use converted ASCII spectrum data to be used in NIR-Predictor?
Can you convert old calibration data from vendor A to be integrated to our new vendor B NIRS calibration data in our instrument?
No, we don’t do model or spectra conversion / transformation (aka model transfer).
We build optimized models with wavelength compatible data.
Does NIR-Predictor contain any malware, spyware or adware?
No, NIR-Predictor does not contain any malware, spyware or adware.
How to copy the prediction results from the table in the browser?
Copy selected columns from the table.
By holding down the Ctrl key, rectangular areas in the table can be selected with the mouse and copied to the clipboard with Ctrl+C and then copied to a spreadsheet program with Ctrl+V.
Will an expired calibration still work?
No. Until you extend the usage time.
The expired calibration file will be moved to the CalibrationExpired folder on the next start of NIR-Predictor or “Search and load Calibrations” menu function.
Selected Calibration files from the folder CalibrationExpired can be send to info@CalibrationModel.com with your Request file (.req) files for extending their usage time.
There is the possibility to get a perpetual usage, which means their is no expiration (valid until 2050).
That way you can get the time extended calibration back, that behaves exactly as the one before with extended usage time.
What does the calibration Expiration date mean exactly on the Prediction Report?
The Expiration date, it is the final day when the calibration will be valid (similar to credit cards).
What does the (number) in brackets after the [range] mean?
During the creation of a Calibration Request the NIR-Predictor shows a message containing
” ‘Prop1’ / “Quantitative [1.40 – 2.90] (154) ”
here 154 is the number of unique values in the property range.
How long does it take to create of the property file and calibration request file from 500 spectra files?
Use a local folder on your computer (not a network drive) for your spectra files then the property file and the calibration request file is created in around 1-3 seconds. (measured on a system with SSD drive, Intel i7, 2.4 GHz)
I tried to create the calibration request and got the error message: The number of Property Values of all spectra are different?
Use the generated PropertiesBySpectra (Note: Spectra not Sample) template file.
Do not reformat the generated template file, just fill in the Property values and save as Text CSV (*.csv) file (not as Excel file “.xlsx”).
Are you able to create the calibration if we only have 20 spectra?
To create a reliable quantitative calibration you need measured spectra of at least 48..60 (more is better) different samples with different Lab-values in the required measuring range!
The NIR-Predictor will check for that automatically.
Same sample measured multiple times (replicate measurements), how to enter the Lab-value only once in Property file?
Use the created PropertiesBySample template (not the PropertiesBySpectra).
Different samples with exact the same Lab-value, how to enter the Lab-Values?
Entering the Lab-values into the generated PropertiesBySpectra template the NIR-Predictor detects them as the same Sample and as a result we have too less different values to build a calibration.
But in my case these are different samples with exact the same Lab-value, how to do?
You have to cheat a little bit to make NIR-Predictor do not detect same sample as measured multiple times.
This is because most NIR users do replicate measurements on the same sample and NIR-Predictor looks for that.
In PropertiesBySpectra modify the values a little bit to make them different, e.g.
0.18
0.18001
0.18002
Is the free NIR-Predictor software you provide for anyone to download and use?
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.
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.
See also manual chapter Outliers.
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:
Open the demo Spectra folder by using the Menu > Open Demo Spectra or press F8.
There are files with spectra from different Vendors.
Drag & drop a spectra file onto the NIR-Predictor window (or press Ctrl+O as for ’Open some files).
The spectra will be
loaded
pre-processed
predicted and
reported
Note:
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”
Hint:
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
You have measured your samples with you NIR-Instrument Software.
And got the Lab-values of these samples.
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!
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.
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.
Email the CalibrationRequest.zip file
to info@CalibrationModel.com to develop the calibrations.
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
Configuration:
in NIR-Predictor : Menu > Open Calibrations (F9)
an explorer window is opened where the calibrations are located
create a folder for your application, choose a name
copy the calibration file(s) (*.cm) into that folder
in NIR-Predictor : Menu > Search and load Applications (F4)
Usage:
in NIR-Predictor : open the Application drop down list, and select your application by name
if all is fine, the calibration file is valid and not expired, it shows : Calibration “1 valid calibation”
the NIR-Predictor is now ready to predict
to switch the application, goto 6.
Applications
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.
Histograms
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
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.
Print to PDF
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
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
Use your favorite editor or spreadsheet program to enter and copy&paste the Lab Values into the columns and save the file.
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.
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
Note:
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.
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.
Or
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
We use cookies to ensure that we give you the best experience on our website. If you continue to use this site we will assume that you are happy with it.Ok