Spectroscopy and Chemometrics News Weekly #9, 2021

NIR Calibration-Model Services

How to Develop Chemometric Near-Infrared Spectroscopy Calibrations in the 21st Century? LINK

Spectroscopy and Chemometrics News Weekly 8, 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 8, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 8, 2021 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

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

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




Near-Infrared Spectroscopy (NIRS)

” Profiling Mannheimia haemolytica infection in dairy calves using near infrared spectroscopy (NIRS) and multivariate analysis (MVA)” LINK

“Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes” LINK

“Prediction of Physicochemical Properties in Honeys with Portable Near-Infrared (microNIR) Spectroscopy Combined with Multivariate Data Processing” LINK

“Monitoring the Bacterial Response to Antibiotic and Time Growth Using Near-infrared Spectroscopy Combined with MachineLearning” LINK

“Reducing the Moisture Effect and Improving the Prediction of Soil Organic Matter With VIS-NIR Spectroscopy in Black Soil Area” LINK

“Nirs tools for prediction of main extractives compounds of teak (Tectona grandis L.) heartwood” LINK




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

“Using near-infrared spectroscopy to determine moisture content, gel strength, and viscosity of gelatin” LINK

“Forensic potentiality of near-infrared in the geographical discrimination of Astrocaryum aculeatum Oil” LINK

“Anomaly detection during milk processing by autoencoder neural network based on near-infrared spectroscopy” LINK

“Infrared Spectroscopy of Blood” LINK

“Using a Portable Near-infrared Spectroscopy Device to Estimate The Second Ventilatory Threshold.” LINK

“Prediction of Specialty Coffee Flavors Based on NearInfrared Spectra Using Machine and Deep Learning Methods” LINK

“Applications of near infrared spectroscopy for fish and fish products quality: a review” LINK

“Prediction of Diet Quality in Mongolian Livestock with Portable near Infrared Spectroscopy Offeces” LINK

“Application of Fourier Transform Near-infrared Spectroscopy combined with Gas Chromatograpy in rapid and simultaneous determination of essential components in …” LINK

“Evaluation of swelling properties and drug release from mechanochemical pre-gelatinized glutinous rice starch matrix tablets by near infrared spectroscopy” LINK

“Rapid Screening of Mentha spicata Essential Oil and l-Menthol in Mentha piperita Essential Oil by ATR-FTIR Spectroscopy Coupled with Multivariate Analyses” LINK

“Application of Fourier transform near-infrared spectroscopy combined with GC in rapid and simultaneous determination of essential components in Amomum villosum.” LINK

“A Hybrid Variable Selection Strategy of Near Infrared Spectroscopy for Detection the Ratio of Tea Polyphenols to Amino Acids in Green Tea Infusion” LINK




Hyperspectral Imaging (HSI)

“Identification of Weeds Based on Hyperspectral Imaging and Machine Learning” | LINK

“AI-based hyperspectral and VOCs assessment approach to identify adulterated extra virgin olive oil” | LINK




Chemometrics and Machine Learning

“Trends in artificial intelligence, machine learning, and chemometrics applied to chemical data” LINK

“Detection of camellia oil adulteration using chemometrics based on fatty acids GC fingerprints and phytosterols GC-MS fingerprints” LINK

“Exploring two-trace two-dimensional (2T2D) correlation spectroscopy as an effective approach to improve accuracy of discriminant analysis by highlighting asynchronous features in two separate spectra of a sample” LINK

“Self-Cleaning-Mediated SERS Chip Coupled Chemometric Algorithms for Detection and Photocatalytic Degradation of Pesticides in Food” LINK

“Chemometrics and Experimental Design for the Quantification of Nitrate Salts in Nitric Acid: Near-Infrared Spectroscopy Absorption Analysis” LINK




Agriculture NIR-Spectroscopy Usage

“Biochemical methane potential prediction for mixed feedstocks of straw and manure in anaerobic co-digestion” LINK

” Effective Quantification of Tannin Content in Sorghum Grains Using Near-infrared Spectroscopy” LINK

“Continuous blending monitored and feedback controlled by machine vision-based PAT tool.” LINK

“Near-Infrared Hyperspectral Imaging Spectroscopy to Detect Microplastics and Pieces of Plastic in Almond Flour” | in-almond-flour LINK




Horticulture NIR-Spectroscopy Applications

“Fast, simultaneous and contactless assessment of intact mango fruit by means of near infrared spectroscopy [J]” LINK




Food & Feed Industry NIR Usage

“Predictive Ability of Four SmallScale Quality Tests for Dough Rheological Properties and Baking Quality in Hard Red Spring Wheat” LINK

“Improved prediction of minced pork meat chemical properties with near-infrared spectroscopy by a fusion of scatter- correction techniques” LINK




Laboratory and NIR-Spectroscopy

“Improving performance: a collaborative strategy for the multi-data fusion of electronic nose and hyperspectral to track the quality difference of rice” LINK




Other

“Effect of different levels of fertilization on bean biochemical composition in Coffea arabica cv. Rubi” LINK





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Spectroscopy and Chemometrics News Weekly #6, 2021

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 5, 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 5, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 5, 2021 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

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

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




Near-Infrared Spectroscopy (NIRS)

“An innovative non-targeted control system based on NIR spectral information for detecting non-compliant batches of sweet almonds” LINK

“Quali-quantitative monitoring of chemocatalytic cellulose conversion into lactic acid by FT-NIR spectroscopy” LINK

“Prediction of soil calcium carbonate with soil visible-near-infrared reflection (Vis-NIR) spectral in Shaanxi province, China: soil groups vs. spectral groups” LINK

“Prediction of some quality properties of rice and its flour by nearinfrared spectroscopy (NIRS) analysis” LINK

“On the discrimination of soil samples by derivative diffuse reflectance UV-vis-NIR spectroscopy and chemometric methods.” LINK

“Authentication of Six Indonesian Ground Roasted Specialty Coffees According to Variety and Geographical Origin using NIR Spectroscopy with Integrating Sphere” LINK

“Prediction of some quality properties of rice and its flour by near‐infrared spectroscopy (NIRS) analysis” LINK

“Using vis-NIRS and Machine Learning methods to diagnose sugarcane soil chemical properties” LINK

“Mahalanobis Distance Based Similarity Regression Learning of NIRS for Quality Assurance of Tobacco Product with Different Variable Selection Methods” LINK

“The manifestation of VIS-NIRS spectroscopy data to predict and mapping soil texture in the Triffa plain (Morocco)” LINK




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

“Penerapan Teknologi Visible-Near Infrared Spectroscopy untuk Prediksi Cepat dan Simultan Kadar Air Buah Melon (Cucumis melo L.) Golden” LINK

“Comparison of mutton freshness grade discrimination based on hyperspectral imaging, near infrared spectroscopy and their fusion information” LINK

“On-site identification of counterfeit drugs based on near-infrared spectroscopy Siamese-network modeling” LINK

“Method utilizing in-situ, subsurface, near-infrared spectroscopy to detect buried human remains” LINK

“Sensing of epigallocatechin gallate and tannic acid based on near infrared optical spectroscopy of DNA-wrapped single-walled carbon nanotube hybrids” LINK

“PREDICTION OF PROXIMATE ANALYSIS AND PROCESS TEMPERATURE OF TORREFIED AND PYROLYZED WOOD PELLETS BY NEAR-INFRARED SPECTROSCOPY COUPLED WITH MACHINE …” LINK

“NON-DESTRUCTIVE DETERMINATION OF ETHANOL LEVELS IN BRANDY USING PARTIAL LEAST SQUARES-NEAR INFRARED SPECTROSCOPY” LINK

“Machine learning augmented near-infrared spectroscopy: In vivo follow-up of cartilage defects.” LINK

” Determination of the quality of metronidazole formulations by near-infrared spectrophotometric analysis” LINK

“Rapid Detection of Total Viable Count in Whole Chicken Breast by An Online Near-infrared Spectroscopy System” LINK

“Optimization of in-line near-infrared measurement for practical real time monitoring of coating weight gain using design of experiments” LINK

“Method of Monitoring the Number of Amide Bonds in Peptides Using Near-Infrared Spectroscopy” LINK

“Mapping metamorphic hydration fronts with field-based near‑infrared spectroscopy: Teakettle Junction contact aureole, Death Valley National Park (California, USA)” LINK

“Near-infrared spectroscopy: a non-invasive tool for quality evaluation of seafood” LINK




Chemometrics and Machine Learning

“Chemometrics and Experimental Design for the Quantification of Nitrate Salts in Nitric Acid: Near-Infrared Spectroscopy Absorption Analysis.” LINK

“Monitoring the extraction process of acidic polysaccharides in Poria cocos by near infrared spectroscopy combined with chemometrics” LINK

“Authentication of Rice (Oryza sativa L.) using Near Infrared Spectroscopy Combined with Different Chemometric Classification Strategies” LINK

“Rapid detection and quantification of adulteration in Chinese hawthorn fruits powder by near-infrared spectroscopy combined with chemometrics” LINK




Facts

“3D-printed spectrometer: how small can you go?” | 3Dprinting Femtosecond microoptics miniaturisation LINK




Equipment for Spectroscopy

“3D-printed miniature spectrometer for the visible range with a 100 × 100 μm2 footprint” LINK




Process Control and NIR Sensors

“Real-Time Monitoring of Yogurt Fermentation Process by Aquaphotomics Near-Infrared Spectroscopy” LINK




Agriculture NIR-Spectroscopy Usage

“Non-invasive spectroscopic and imaging systems for prediction of beef quality in a meat processing pilot plant.” LINK

“Faecal-NIRS for predicting animal-to-animal variation in feed organic matter digestibility in cattle” LINK

“Estimation of relative feed value, relative forage quality and net energy lactation values of some roughage samples by using near infrared reflectance spectroscopy” LINK




Pharma Industry NIR Usage

“Rapid Authentication of Potato Chip Oil by Vibrational Spectroscopy Combined with Pattern Recognition Analysis” LINK




Other

“Detection of browning of freshcut potato chips based on machine vision and electronic nose” 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



Spectroscopy and Chemometrics News Weekly #37, 2020

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 36, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 36, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 36, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

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

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




Near-Infrared Spectroscopy (NIRS)

“NIR Spectroscopic Techniques for Quality and Process Control in the Meat Industry” LINK

“Estimating coefficient of linear extensibility using Vis–NIR reflectance spectral data: Comparison of model validation approaches” LINK

“NIR spectroscopy and chemometric tools to identify high content of deoxynivalenol in barley” LINK

“Combining multivariate method and spectral variable selection for soil total nitrogen estimation by Vis–NIR spectroscopy” LINK

“Multi-task deep learning of near infrared spectra for improved grain quality trait predictions” LINK

“Multi-factor Fusion Models for Soluble Solid Content Detection in Pear (Pyrus bretschneideri ‘Ya’) Using Vis/NIR Online Half-transmittance Technique” LINK

“Determining regression equations for predicting the metabolic energy values of barley-producing cultivars in Iran and comparing the results with the results of NIRS method and cultivars …” LINK

“Using Near-Infrared Reflectance Spectroscopy (NIRS) to Predict Glucobrassicin Concentrations in Cabbage and Brussels Sprout Leaf Tissue” LINK

“Near-Infrared Spectroscopy for Analyzing Changes of Pulp Color of Kiwifruit in Different Depths” LINK

“Novel NIR modeling design and assignment in process quality control of Honeysuckle flower by QbD” LINK




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

“Near-infrared spectroscopy to determine cold-flow improver concentrations in diesel fuel” LINK

“Improving spatial synchronization between X-ray and near-infrared spectra information to predict wood density profiles” LINK

“Functional principal component analysis for near-infrared spectral data: a case study on Tricholoma matsutakeis” LINK

“Midinfrared spectroscopy as a tool for realtime monitoring of ethanol absorption in glycols” LINK

“Inline characterization of dispersion uniformity evolution during a twinscrew blending extrusion based on nearinfrared spectroscopy” LINK

“Development of Fourier Transform near Infrared Spectroscopy Methods for the Rapid Quantification of Starch and Cellulose in Mozzarella and Other Italian-Type CHEESES” LINK

“Prediction of Anthocyanin Content in Three Types of Blueberry Pomace by Near-Infrared Spectroscopy” LINK

“Sweetness Detection and Grading of Peaches and Nectarines by Combining Short-and Long-Wave Fourier-Transform Near-Infrared Spectroscopy” LINK




Spectral Imaging

“Use of UAS Multispectral Imagery at Different Physiological Stages for Yield Prediction and Input Resource Optimization in Corn” Remote Sensing LINK




Chemometrics and Machine Learning

“Combination of visible reflectance and acoustic response to improve nondestructive assessment of maturity and indirect prediction of internal quality of redfleshed pomelo” LINK

“Green Analytical Methods of Antimalarial Artemether-Lumefantrine Analysis for Falsification Detection Using a LowCost Handled NIR Spectrometer with DD-SIMCA and Drug Quantification by HPLC” LINK

“Data fusion of UPLC data, NIR spectra and physicochemical parameters with chemometrics as an alternative to evaluating kombucha fermentation” LINK

“Effect of physicochemical factors and use of milk powder on milk rennet-coagulation: Process understanding by near infrared spectroscopy and chemometrics” LINK

“A Digital Approach to Model Quality and Sensory Traits of Beers Fermented under Sonication Based on Chemical Fingerprinting” LINK

“Latent Variable Graphical Modeling for High Dimensional Data Analysis” LINK




Equipment for Spectroscopy

“Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer” Sensors LINK

“Developing a soil spectral library using a low-cost NIR spectrometer for precision fertilization in Indonesia” LINK

“Compact Solid Etalon Computational Spectrometer: FY19 Optical Systems Technology Line-Supported Program” LINK




Agriculture NIR-Spectroscopy Usage

“Detection of Melamine Adulteration in Milk Powder by Using Optical Spectroscopy Technologies in the Last Decade—a Review” LINK




Horticulture NIR-Spectroscopy Applications

“Accurate non-destructive prediction of peach fruit internal quality and physiological maturity with a single scan using near infrared spectroscopy” LINK

“Recent Advances in Spectral Analysis Techniques for Non-Destructive Detection of Internal Quality in Watermelon and Muskmelon: A Review” “光谱分析在西甜瓜内部品质无损检测中的研究进展” LINK




Food & Feed Industry NIR Usage

“Detection of fraud in highquality rice by nearinfrared spectroscopy” LINK

“Detecting food fraud in extra virgin olive oil using a prototype portable hyphenated photonics sensor” LINK

“Nondestructive detection of sunset yellow in cream based on near-infrared spectroscopy and interval random forest” LINK




Other

“The Detection of Substitution Adulteration of Paprika with Spent Paprika by the Application of Molecular Spectroscopy Tools.” LINK

“The Effect of Monomers on the Recognition Properties of Molecularly Imprinted Beads for Proto-hypericin and Proto-pseudohypericin” | FLOREA GAVRILA 1 20.pdf LINK





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Spectroscopy and Chemometrics News Weekly #24, 2020

NIR Calibration-Model Services

Machine Learning for NIR Spectroscopy as a Service, a Game Changer for Productivity and Accuracy/Precision! Use the free NIR-Predcitor software to combine NIRS + Lab data and send your Calibration Request. LabManager Analysis MachineLearning LINK

“Food quality digitized at the “speed of light” ” : Food Sample -> measured with a NIRS spectrometer -> spectral data -> ⚖️ predicted with a NIRPredictor & CalibrationModel -> % quantitative results -> quality decision -> LINK

Spectroscopy and Chemometrics News Weekly 23, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 23, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 23, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK




Near-Infrared Spectroscopy (NIRS)

“Fiber Content Determination of Linen/Viscose Blends Using NIR Spectroscopy” LINK

“Characterization of a high power time-domain NIRS device: towards faster and deeper investigation of biological tissues” LINK

“… chamosite from an hydrothermalized oolitic ironstone (Saint-Aubin-des-Châteaux, Armorican Massif, France): crystal chemistry, Vis-NIR spectroscopy (red variety) and …” LINK

“Study on evolution methods for the optimization of machine learning models based on FT-NIR spectroscopy” LINK

“Vibrational coupling to hydration shell – Mechanism to performance enhancement of qualitative analysis in NIR spectroscopy of carbohydrates in aqueous environment.” LINK

” RAPID EVALUATION OF DRY WHITE KIDNEY BEANS COOKING CHARACTERISTICS BY NEAR-INFRARED (NIR) SPECTROSCOPY” LINK

For food analysts, how to choose between a ‘classic’ method and a ‘modern’ technique such as FT-NIR or RMN? Our recently available paper tries to answer that question based on error evaluation: LINK

“FT-NIR combined with chemometrics versus classic chemical methods as accredited analytical support for decision-making: application to chemical compositional compliance of feedingstuffs” LINK




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

“Functional Classification of Feed Items in Pampa Grassland, Based on Their Near-Infrared Spectrum” LINK

“A Rapid and Nondestructive Approach for the Classification of Different-Age Citri Reticulatae Pericarpium Using Portable Near Infrared Spectroscopy” LINK

“Near-infrared spectroscopy as a new method for post-harvest monitoring of white truffles” LINK

“Rapid Prediction of Apparent Amylose, Total Starch, and Crude Protein by Near‐Infrared Reflectance Spectroscopy for Foxtail Millet (Setaria italica)” LINK

“New Induced Mutation Genetic Algorithm for Spectral Variables Selection in Near Infrared Spectroscopy” LINK

“Quantification of Plant Root Species Composition in Peatlands Using FTIR Spectroscopy” LINK

“Functional classification of feed items in pampa grassland, based on their near-infrared spectrum” LINK

“A feasibility of nondestructive rapid detection of total volatile basic nitrogen content in frozen pork based on portable near-infrared spectroscopy” LINK

” Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy” LINK

“Has the time come to use near-infrared spectroscopy in your science classroom?” LINK

“Feasibility of using near-infrared measurements to detect changes in water quality” LINK

“A novel CC-tSNE-SVR model for rapid determination of diesel fuel quality by near infrared spectroscopy” LINK

“Optimizing analysis of coal property using laser-induced breakdown and near-infrared reflectance spectroscopies” LINK

“Probing Active Sites and Reaction Intermediates of Electrocatalysis Through Confocal Near-Infrared Photoluminescence Spectroscopy: A Perspective.” LINK

“Determination of in situ ruminal degradation of phytate phosphorus from single and compound feeds in dairy cows using chemical analysis and near-infrared spectroscopy” LINK

“Non-destructive assessment of moisture content and modulus of rupture of sawn timber Hevea wood using near infrared spectroscopy technique” LINK

“Accurate prediction of glucose concentration and identification of major contributing features from hardly distinguishable near-infrared spectroscopy” LINK

” Multiblock PLS-DA on fecal and plasma visible-near-infrared spectra for discriminating young bulls according to their efficiency. Preliminary results” LINK

“Examining the Utility of Visible Near-Infrared and Optical Remote Sensing for the Early Detection of Rapid ‘Ōhi‘a Death” LINK

“Developing deep learning based regression approaches for determination of chemical compositions in dry black goji berries (Lycium ruthenicum Murr.) using near-infrared hyperspectral …” LINK

” RAPID, NONDESTRUCTIVE AND SIMULTANEOUS PREDICTIONS OF SOIL CONTENT IN WULING MOUNTAIN AREA USING NEAR INFRARED …” LINK




Raman Spectroscopy

“Differentiating cancer cells using Raman spectroscopy (Conference Presentation)” LINK

“Applied Sciences, Vol. 10, Pages 3545: Raman Spectral Analysis for Quality Determination of Grignard Reagent” LINK

“Surfaceenhanced Raman spectroscopy for onsite analysis: A review of recent developments” LINK




Hyperspectral Imaging (HSI)

“Estimating leaf mercury content in Phragmites australis based on leaf hyperspectral reflectance” LINK

“A hyperspectral microscope based on a birefringent ultrastable common-path interferometer (Conference Presentation)” LINK

“Hyperspectral imaging of beet seed germination prediction” LINK

“Hyperspectral imaging for discrimination of Keemun black tea quality categories: Multivariate calibration analysis and data fusion” LINK

“Potential of deep learning and snapshot hyperspectral imaging for classification of species in meat” LINK

“Performance of Fluorescence and Diffuse Reflectance Hyperspectral Imaging for Characterization of Lutefisk: A Traditional Norwegian Fish Dish” LINK




Spectral Imaging

“Identify the ripening stage of avocado by multispectral camera using semi-supervised learning on small dataset” LINK

“Multispectral imaging for predicting the water status in mushroom during hotair dehydration” LINK




Chemometrics and Machine Learning

“Sample selection, calibration and validation of models developed from a large dataset of near infrared spectra of tree leaves” Eucalyptus forage quality LINK

“Determination of Loline Alkaloids and Mycelial Biomass in Endophyte-Infected Schedonorus Pratensis by Near-Infrared Spectroscopy and Chemometrics” LINK

“Detection and Assessment of Nitrogen Effect on Cold Tolerance for Tea by Hyperspectral Reflectance with PLSR, PCR, and LM Models” LINK

“Application of vibrationnal spectroscopy and chemometrics to access the quality of Locally produced antimalarial medicines in the Democratic Republic of Congo.” LINK

“Predicting total petroleum hydrocarbons in field soils with VisNIR models developed on laboratoryconstructed samples” LINK

“National spectral data and learning algorithms for potentially toxic elements modelling in forest soil horizons” LINK

“Rapid determination of the textural properties of silver carp (Hypophthalmichthys molitrix) using near-infrared reflectance spectroscopy and chemometrics” LINK

“Vibrational spectroscopy and chemometrics for quantifying key bioactive components of various plum cultivars grown in New Zealand” LINK




Equipment for Spectroscopy

“NearInfrared Multipurpose LanthanideImaging Nanoprobes” LINK




Process Control and NIR Sensors

“Non-invasive measurement of quality attributes of processed pomegranate products” LINK




Environment NIR-Spectroscopy Application

“Spectral Feature Selection Optimization for Water Quality Estimation.” LINK

“Remote Sensing, Vol. 12, Pages 931: Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters” LINK

“Estimation of total nitrogen and organic carbon contents in mine soils with NIR reflectance spectroscopy and various chemometric methods” LINK




Agriculture NIR-Spectroscopy Usage

“Development of a compact multimodal imaging system for rapid characterisation of intrinsic optical properties of freshly excised tissue (Conference Presentation)” LINK

“Agriculture, Vol. 10, Pages 181: Grafting and ShadingThe Influence on Postharvest Tomato Quality” LINK

“Remote Sensing, Vol. 12, Pages 940: Editorial for the Special Issue Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery”” LINK

“Predicting grain protein content of field-grown winter wheat with satellite images and partial least square algorithm.” LINK

“The development of models to predict the nutritional value of feedstuffs and feed mixture using NIRS” LINK

“Permafrost soil complexity evaluated by laboratory imaging Vis‐NIR spectroscopy” LINK




Horticulture NIR-Spectroscopy Applications

“Recent advances in imaging techniques for bruise detection in fruits and vegetables” LINK




Forestry and Wood Industry NIR Usage

“Nutritional characterization of trees from the Amazonian piedmont, Putumayo department, Colombia” LINK




Food & Feed Industry NIR Usage

“Approaches, applications, and future directions for hyperspectral vegetation studies: An emphasis on yieldlimiting factors in wheat” LINK

“Beef Nutritional Quality Testing and Food Packaging” LINK




Laboratory and NIR-Spectroscopy

“UV Irradiation and Near Infrared Characterization of Laboratory Mars Soil Analog Samples: the case of Phthalic Acid, Adenosine 5′-Monophosphate, L-Glutamic Acid …” molecular biosignatures; spectroscopy; lifedetection LINK




Other

LINK

“Effect of substrate temperature on the microstructural and optical properties of RF sputtered grown ZnO thin films” LINK

Using near-infrared light to 3-D print an ear inside the body LINK

“Eco-friendly dye sensitized solar cell using natural dye with solid polymer electrolyte as hole transport material” solarcell LINK





Spectroscopy and Chemometrics News Weekly #19, 2020

NIR Calibration-Model Services

“Simultaneously multi quantitative value determination from a bunch of NIR spectra by drag and drop of multiple spectral files.” | NIRS Spectroscopy – Image is Preview of V2.6 LINK

Calibration Model’s free NIR-Predictor V2.5 Software Update is available today | Download here | NIRS NIR Spectroscopy Sensor Application Chemometric Prediction Report QualityControl Lab Laboratory Analysis Production LINK

Spectroscopy and Chemometrics News Weekly 18, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 18, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 18, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

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




Near-Infrared Spectroscopy (NIRS)

“Estimation of Harumanis (Mangifera indica L.) Sweetness using Near-Infrared (NIR) Spectroscopy” LINK

“Near-Infrared (NIR) Spectroscopy to Differentiate Longissimus thoracis et lumborum (LTL) Muscles of Game Species” LINK

“Fault detection with moving window PCA using NIRS spectra for the monitoring of anaerobic digestion process” LINK

“New applications of visnir spectroscopy for the prediction of soil properties” LINK

“Simultaneous determination of quality parameters in yerba mate (Ilex paraguariensis) samples by application of near-infrared (NIR) spectroscopy and partial least …” LINK

“Control of ascorbic acid in fortified powdered soft drinks using near-infrared spectroscopy (NIRS) and multivariate analysis.” LINK




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

“Determination of nutritional parameters of bee pollen by Raman and infrared spectroscopy.” LINK

“Rapid quantitative detection of mineral oil contamination in vegetable oil by near-infrared spectroscopy” LINK

“Scientists demonstrate the ability of infrared ion spectroscopy to identify and distinguish the molecular structure of three isomers of fluoroamphetamine and two ring-isomers of both MDA and MDMA.” LINK

“Application of near-infrared hyperspectral imaging to identify a variety of silage maize seeds and common maize seeds” LINK

“Protein, weight, and oil prediction by single-seed near-infrared spectroscopy for selection of seed quality and yield traits in pea (Pisum sativum).” phenotyping LINK

“Investigating the Quality of Antimalarial Generic Medicines Using Portable Near-Infrared Spectroscopy” LINK

“THE DETERMINATION OF FATTY ACIDS IN CHEESES OF VARIABLE COMPOSITION (COW, EWE’S, AND GOAT) BY MEANS OF NEAR INFRARED SPECTROSCOPY” LINK

“Modeling bending strength of oil-heat-treated wood by near-infrared spectroscopy” LINK

“Non-Invasive Blood Glucose Monitoring using Near-Infrared Spectroscopy based on Internet of Things using Machine Learning” LINK

“Protein, weight, and oil prediction by singleseed nearinfrared spectroscopy for selection of seed quality and yield traits in pea (Pisum sativum)” LINK

“Estimating δ15N and δ13C in Barley and Pea Mixtures Using Near-Infrared Spectroscopy with Genetic Algorithm Based Partial Least Squares Regression” LINK

“ripening stages monitoring of Lamuyo pepper using a new‐generation near‐infrared spectroscopy sensor” LINK

“Performance Improvement of Near-Infrared Spectroscopy-Based Brain-Computer Interface Using Regularized Linear Discriminant Analysis Ensemble Classifier Based on Bootstrap Aggregating.” LINK

“Continuously measurement of the dry matter content using near-infrared spectroscopy” LINK

“Rapid identification of Lilium species and polysaccharide contents based on near infrared spectroscopy and weighted partial least square method.” LINK

“A Rapid and Nondestructive Approach for the Classification of Different-Age Citri Reticulatae Pericarpium Using Portable Near Infrared Spectroscopy.” LINK




Raman Spectroscopy

“Quantitative models for detecting the presence of lead in turmeric using Raman spectroscopy” LINK

“Diagnosis of Citrus Greening using Raman Spectroscopy-Based Pattern Recognition” LINK




Hyperspectral Imaging (HSI)

“Rapid detection of quality index of postharvest fresh tea leaves using hyperspectral imaging.” LINK

“Diagnosis of Late Blight of Potato Leaves Based on Deep Learning Hyperspectral Images” LINK

“Applied Sciences, Vol. 10, Pages 2259: Hyperspectral Inversion Model of Chlorophyll Content in Peanut Leaves” LINK

“Rapid detection of quality index of postharvest fresh tea leaves using hyperspectral imaging” LINK

“Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms” LINK

“A deep learning based regression method on hyperspectral data for rapid prediction of cadmium residue in lettuce leaves” LINK

“Deep learning applied to hyperspectral endoscopy for online spectral classification” DOI:10.1038/s41598-020-60574-6 LINK

“Detection of fish fillet substitution and mislabeling using multimode hyperspectral imaging techniques” LINK




Chemometrics and Machine Learning

“Incorporation of two-dimensional correlation analysis into discriminant analysis as a potential tool for improving discrimination accuracy: Near-infrared spectroscopic discrimination of adulterated olive oils.” LINK

“Building kinetic models for apple crispness to determine the optimal freshness preservation time during shelf life based on spectroscopy” LINK

“Molecules, Vol. 25, Pages 1453: Characterization, Quantification and Quality Assessment of Avocado (Persea americana Mill.) Oils” LINK

“Comparison of CNN Algorithms on Hyperspectral Image Classification in Agricultural Lands” LINK




Research on Spectroscopy

“Automatisierte und digitale Dokumentation der Applikation organischer Düngemittel” LINK

“Plenary Lecture Methods and Tools for Sensors Information Processing” LINK




Equipment for Spectroscopy

“Monitoring wine fermentation deviations using an ATR-MIR spectrometer and MSPC charts” LINK

“Determination of soluble solids content in Prunus avium by Vis/NIR equipment using linear and non-linear regression methods” LINK

“Characterization of Deep Green Infection in Tobacco Leaves Using a Hand-Held Digital Light Projection Based Near-Infrared Spectrometer and an Extreme Learning …” LINK




Agriculture NIR-Spectroscopy Usage

“Portable IoT NIR Spectrometer for Detecting Undesirable Substances in Forages of Dairy Farms” LINK

“Hyperspectral imaging using multivariate analysis for simulation and prediction of agricultural crops in Ningxia, China” LINK

“Robustness of visible/near and midinfrared spectroscopic models to changes in the quantity and quality of crop residues in soil” LINK




Horticulture NIR-Spectroscopy Applications

” The Effect of Spent Mushroom Substrate and Cow Slurry on Sugar Content and Digestibility of Alfalfa Grass Mixtures” LINK




Food & Feed Industry NIR Usage

“Quantification of Ash and Moisture in Wheat Flour by Raman Spectroscopy” LINK




Laboratory and NIR-Spectroscopy

“The influence analysis of reflectance anisotropy of canopy on the prediction accuracy of Cu stress based on laboratory multi-directional measurement” LINK





.

Spectroscopy and Chemometrics News Weekly #14, 2020

CalibrationModel.com

NIR User? Get better results faster | Food Science QC Lab Laboratory Manager chemist LabWork Chemie analytik LINK

NIR-Predictor Software supports spectral file formats out of the box from: and others – Mobile spectroscopy NIRS portable Analyzers H2020 LINK

Timesaving Calibration Modeling Method: for near-infrared NIR Spectroscopy (NIRS) Multivariate Quantitative Prediction. food quality laboratory LINK

Spectroscopy and Chemometrics News Weekly 13, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 13, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse analytic LINK

Spettroscopia e Chemiometria Weekly News 13, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

We have updated the free NIR-Predictor-Software Spectral Data format support list for many mobile and benchtop NIR Spectroscopy Sensors. | Used in QualityControl for Food Fruits Milk Meat LINK



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

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


Near Infrared Spectroscopy (NIRS)

“Aplicação da espectroscopia de reflectância no infravermelho próximo (NIRS) na determinação do potencial bioquímico de metano–Revisão” LINK

“Prediction of soil organic matter and clay contents by near-infrared spectroscopy-NIRS” LINK

“Fast detection and quantification of pork meat in other meats by reflectance FT-NIR spectroscopy and multivariate analysis” LINK

“Improved GA-SVM Algorithm and Its Application of NIR Spectroscopy in Orange Growing Location Identification” LINK

“Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors.” Tobacco LINK

“Data analysis on near infrared spectroscopy as a part of technology adoption for cocoa farmer in Aceh Province, Indonesia” LINK

“Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors” LINK

“Identification of a Glass Substrate to Study Cells Using Fourier Transform Infrared Spectroscopy: Are We Closer to Spectral Pathology?” LINK

“Raman-Infrared spectral fusion combined with partial least squares (PLS) for quantitative analysis of polycyclic aromatic hydrocarbons in soil” LINK

“Identification metliod of ginger-processed Pinelliaternata based on infrared spectroscopy data fusion.” LINK

“Terahertz Time of Flight Spectroscopy as a Coating Thickness Reference Method for Partial Least Squares Near Infrared Spectroscopy Models” LINK

“Quantitative detection of apple watercore and soluble solids content by near infrared transmittance spectroscopy” LINK




Hyperspectral

“Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging.” LINK

“Hyperspectral monitoring of maize leaves under copper stress at different growth stages” LINK

“Classification of small-scale hyperspectral images with multi-source deep transfer learning” LINK




Chemometrics

“Detection of fat content in peanut kernels based on chemometrics and hyperspectral imaging technology” LINK

“Hyperspectral Imaging Feature Selection Using Regression Tree Algorithm: Prediction of Carotenoid Content Velvet Apple Leaf” LINK

“Modelos de calibración para la cuantificación nutricional de praderas frescas mediante espectroscopía de infrarojo cercano” LINK

“Performance Evaluation of Chemometric Prediction Models—Key Components of Wheat Grain” LINK




Equipment

“Rapid Nondestructive Analysis of Intact Canola Seeds Using a Handheld NearInfrared Spectrometer” LINK

“Confirmatory non-invasive and non-destructive differentiation between hemp and cannabis using a handheld Raman spectrometer” LINK




Process Control

“Monitoring of CO2 Absorption Solvent in Natural Gas Process Using Fourier Transform Near-Infrared Spectrometry” LINK




Environment

“Comparing laboratory and airborne hyperspectral data for the estimation and mapping of topsoil organic carbon: Feature selection coupled with random forest” LINK




Agriculture

“Predicting Forage Quality of Warm-Season Legumes by Near Infrared Spectroscopy Coupled with Machine Learning Techniques.” LINK

“Les défis de la technologie de l’aliment en nutrition volaille: pertinence et enjeux pour répondre aux attentes industrielles et sociétales” LINK

“CHANGES IN THE CONTENT OF STRUCTURAL CARBOHYDRATES AND LIGNIN IN THE BIOMASS OF Lolium multiflorum (Lam.) AFTER APPLYING SLURRY …” LINK

“Rapid Analysis of Alcohol Content During the Green Jujube Wine Fermentation by FT-NIR” LINK

“Spectral Analysis and Deconvolution of the Amide I Band of Proteins Presenting with High-Frequency Noise and Baseline Shifts” LINK




Petro

“Spectroscopic evidence of special intermolecular interaction in iodomethane-ethanol mixtures: the cooperative effect of halogen bonding, hydrogen bonding, and …” LINK




Pharma

“Defocused Spatially Offset Raman Spectroscopy in Media of Different Optical Properties for Biomedical Applications Using a Commercial Spatially Offset Raman Spectroscopy Device” LINK




Medicinal

“A single oral dose of beetroot-based gel does not improve muscle oxygenation parameters, but speeds up handgrip isometric strength recovery in recreational combat …” LINK




Other

“Spectral differentiation of oak wilt from foliar fungal disease and drought is correlated with physiological changes” LINK

“Wearing a headset containing both electroencephalography (EEG) and near-infrared spectroscopy (NIRS) sensors, the user simply imagines moving either his right hand, left hand, tongue or feet – and ASIMO makes a corresponding movement. ” BrainInterface LINK

KnowItAll Software / Spectral Libraries & ChemWindow are now part of Wiley Science Solutions. See press release: LINK

“The uses of near infra-red spectroscopy in postharvest decision support: A review” LINK





Spectroscopy and Chemometrics News Weekly #11, 2020

CalibrationModel.com

How to Develop Near-Infrared Spectroscopy Calibrations in the 21st Century? | Chemometrics Analytische Chemie LINK

Simplify the process of training machine learning models for NIR spectra data with applied near-infrared spectroscopy (NIRS) knowledge. quantitative multivariate prediction equations LINK

Spectroscopy and Chemometrics News Weekly 10, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 10, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 10, 2020 | NIRS NIR Spettroscopia analisi chimica Spettrale Spettrometro IoT Sensore Attrezzatura analitica nearinfrared foodscience foodprocessing foodsafety foodproduction farming agriculture LINK




Near Infrared

“Klasifikasi Kopi Green Beans Arabika Sumatera Utara Menggunakan FT-Nirs dan Analisis Diskriminan” LINK

“APLIKASI NEAR INFRARED SPECTROSCOPY (NIRS) UNTUK MENGETAHUI KANDUNGAN HARA NITROGEN FOSFOR DAN KALIUM PADA INSTALASI …” LINK

” Identification of common wood species in northeast China using Vis/NIR spectroscopy” LINK

“Efficient Super Broadband NIR Ca2LuZr2Al3O12:Cr3+,Yb3+ Garnet Phosphor for pc‐LED Light Source toward NIR Spectroscopy Applications” LINK

“Performance comparison of sampling designs for quality and safety control of raw materials in bulk: a simulation study based on NIR spectral data and geostatistical …” LINK

“Prediction of drug dissolution from Toremifene 80 mg tablets using NIR spectroscopy” LINK

“Nearinfrared spectroscopy (NIRS) for taxonomic entomology: A brief review” LINK

“Determination of tomato quality attributes using portable NIR-sensors” LINK

“Exploring the potential of NIR hyperspectral imaging for automated quantification of rind amount in grated Parmigiano Reggiano cheese” LINK

“Rapid detection of adulteration of minced beef using Vis/NIR reflectance spectroscopy with multivariate methods.” LINK

“Evaluation of quinclorac toxicity and alleviation by salicylic acid in rice seedlings using ground-based visible/near-infrared hyperspectral imaging.” LINK

“An optimized non-invasive glucose sensing based on scattering and absorption separating using near-infrared spectroscopy” LINK

“Identification of waxy cassava genotypes using fourier‐transform near‐infrared spectroscopy” LINK

“Kernel functions embedded in support vector machine learning models for rapid water pollution assessment via near-infrared spectroscopy” LINK

“Near-infrared-based Identification of Walnut Oil Authenticity” LINK

“Detection of flaxseed oil multiple adulteration by near-infrared spectroscopy and nonlinear one class partial least squares discriminant analysis” LINK

“Application research of sensor output digitization for compact near infrared IOT node” LINK

“Refining Transfer Set in Calibration Transfer of Near Infrared Spectra by Backward Refinement of Samples” LINK

“Highly identification of keemun black tea rank based on cognitive spectroscopy: Near infrared spectroscopy combined with feature variable selection” LINK

“Optimizing Rice Near-Infrared Models Using Fractional Order SavitzkyGolay Derivation (FOSGD) Combined with Competitive Adaptive Reweighted Sampling (CARS)” LINK

“Fourier transform near infrared spectroscopy as a tool to discriminate olive wastes: The case of monocultivar pomaces.” LINK

“Evaluation of quinclorac toxicity and alleviation by salicylic acid in rice seedlings using ground-based visible/near-infrared hyperspectral imaging” LINK

“Near Infrared Spectrometric Investigations on the behaviour of Lactate.” LINK

“Nondestructive rapid and quantitative analysis for the curing process of silicone resin by nearinfrared spectra” LINK

“An introduction to handheld infrared and Raman instrumentation” LINK




Hyperspectral

“Hyperspectral anomaly detection by local joint subspace process and support vector machine” LINK

“Assessment of matcha sensory quality using hyperspectral microscope imaging technology” LINK




Chemometrics

“Application of Infrared Spectroscopy and Chemometrics to the Cocoa Industry for Fast Composition Analysis and Fraud Detection” LINK

“Calibration models for the nutritional quality of fresh pastures by nearinfrared reflectance spectroscopy” LINK

“Achieving robustness to temperature change of a NIRS-PLSR model for intact mango fruit dry matter content” LINK

“Application of hyperspectral imaging combined with chemometrics for the non-destructive evaluation of the quality of fruit in postharvest” LINK




Equipment

“Characterization of Deep Green Infection in Tobacco Leaves Using a Hand-Held Digital Light Projection Based Near-Infrared Spectrometer and an Extreme Learning Machine Algorithm” LINK

“MEMS technology for fabricating plasmonic near-infrared spectrometers” LINK

“Sensors, Vol. 20, Pages 545: Development of Low-Cost Portable Spectrometers for Detection of Wood Defects” LINK




Environment

“Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging” LINK

“Determining mandatory nutritional parameters for Iberian meat products using a new method based on near infra-red reflectance spectroscopy and data mining” LINK




Agriculture

“Instrumental Procedures for the Evaluation of Juiciness in Peach and Nectarine Cultivars for Fresh Consumption” LINK

“The creation of the FT-NIR calibration for the determination of the amount of corn grain in concentrated feed” LINK

In this 9th clip from his presentation at the 2019 IFS Agronomic Conference, Wouter Saeys explains which type of NIR is best for measuring the nutrient content of manure, and why. Info on this paper is here; it’s free for Society Members to download: LINK

“Remote Sensing, Vol. 12, Pages 928: Machine Learning Algorithms to Predict Forage Nutritive Value of In Situ Perennial Ryegrass Plants Using Hyperspectral Canopy Reflectance Data” LINK

“Development of a Method To Prioritize Protein-Ligand Pairs on Beads Using Protein Conjugated to a Near-IR Dye.” LINK

“Agronomy, Vol. 10, Pages 148: Performance Evaluation of Two Commercially Available Portable Spectrometers to Non-Invasively Determine Table Grape and Peach Quality Attributes” LINK

“Investigation of a Medical Plant for Hepatic Diseases with Secoiridoids Using HPLC and FT-IR Spectroscopy for a Case of Gentiana rigescens” LINK




Food & Feed

“Comparison of sensory evaluation techniques for Hungarian wines” LINK




Laboratory

“Roadmap of cocoa quality and authenticity control in the industry: A review of conventional and alternative methods” LINK





Spectroscopy and Chemometrics News Weekly #9, 2020

CalibrationModel.com

Efficient development of new quantitative prediction equations for multivariate NIR spectra data NIRS NIR NIT LINK

NIR Calibration Service explained | NIRS NIR Near Infrared Spectroscopy Prediction Analysis Results Calibration Model multivariate Chemometrics equations SaaS LINK

Service für professionelle Entwicklung von Nah-Infrarot Spektroskopie Kalibrations Methoden | NIRS Qualität Prüfen LINK

Spectroscopy and Chemometrics News Weekly 8, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory automation Software IoT Sensors QA QC QAQC qualitycontrol LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 8, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot WetChemistry Lab Lab40 FoodTech FoodAnalysis Analysentechnik Analysemethode Nahinfrarotspektroskopie LINK

Spettroscopia e Chemiometria Weekly News 8, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK




Near Infrared

“Prediction of starch reserves in intact and ground grapevine cane wood tissues using near infrared reflectance spectroscopy (NIRS).” LINK

“An application to analyzing and correcting for the effects of irregular topographies on NIR hyperspectral images to improve identification of moldy peanuts” LINK

“Data Fusion Approach Improves the Prediction of Single Phenolic Compounds in Honey: A Study of NIR and Raman Spectroscopies” LINK

Monitoring roasting of coffee beans by NIR spectroscopy using a method called REP-ASCA coffee ☕️ chemical information related to flavors and aromas. LINK

“The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods when using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods.” LINK

“Immediate measurement of fuel characteristics of bio-char using NIRS” LINK

“Nonlinear Manifold Dimensionality Reduction Methods for Quick Discrimination of Tea at Different Altitude by Near Infrared Spectroscopy” LINK

Phenome2020 Yufeng Ge Univ Nebraska VIS-NIR-SWIR spectroscopy to analyse leaf chemistry and physiology LINK

“Rapid assessment of pork freshness using miniaturized NIR spectroscopy” LINK

“On-line prediction of hazardous fungal contamination in stored maize by integrating Vis/NIR spectroscopy and computer vision” LINK

“Chemical Characterization of Wine Vinegars Belonging to the Vinagre de Montilla-Moriles Protected Designation of Origin, Using Near-Infrared Spectroscopy” LINK

“Application of Near-Infrared Spectroscopy for the identification of rock mineralogy from Kos Island, Aegean Sea, Greece” LINK

“Assessment of Spinal Cord Ischemia With Near-Infrared Spectroscopy: Myth or Reality?” LINK

“Portable Near-Infrared spectroscopy for rapid authentication of adulterated paprika powder” LINK

“Study on the Quality Assessment of Canola Oil after Prolonged Frying Using Near-Infrared Spectroscopy” LINK

” CRUDE PROTEIN PREDICTION OF HETEROGENEOUS MOUNTAIN GRASSLAND WITH VISIBLE-NEAR-INFRARED SPECTROSCOPY” LINK

“Statistical Analysis of Amylose and Protein Content in Breeding Line Rice Germplasm Collected from East Asian Countries Based on Near-infrared reflectance …” LINK

“Assessment of pork freshness based on changes in constituting chromophores using visible to near-infrared spectroscopy” LINK

“Sensors, Vol. 20, Pages 273: Near-Infrared Transmittance Spectral Imaging for Nondestructive Measurement of Internal Disorder in Korean Ginseng” LINK

“APPLICATION of NEAR INFRARED SPECTROSCOPY TO DETERMINE SUGARCANE QUALITY in CORE SAMPLER SYSTEM” LINK




Raman

On NationalScienceDay, we celebrate the discovery of the RamanEffect by great scientist CVRaman. Do you know? The Raman effect forms the basis for Raman spectroscopy which is used by chemists & physicists to gain information about materials. LINK

“Handheld device weeds out cannabis from hemp. Raman device that measures levels of tetrahydrocannabinol could be a useful tool for police and customs agents” LINK




Hyperspectral

“Essential processing methods of hyperspectral images of agricultural and food products” LINK

“Determine Reducing Sugar Content in Potatoes Using Hyperspectral Combined with VISSA Algorithm” LINK

“Spatial variation of wood density for Eucalyptus grandis by near infra red hyperspectral imaging combined with X-ray analysis” LINK




Chemometrics

“The detection of cannabinoids in veterinary feeds by microNIR/chemometrics: a new analytical platform.” LINK

“Qualitative discrimination of Chinese dianhong black tea grades based on a handheld spectroscopy system coupled with chemometrics” LINK

” Validation of NutriOpt dietary formulation strategies on broiler growth and economic performance” LINK

” A preliminary near infrared spectroscopy calibration for the prediction of un-dried fresh grass quality” LINK

“Water content prediction of ‘crystal’guava using visible-near infrared spectroscopy and chemometrics approach” LINK

“Spectroscopy based novel spectral indices, PCA- and PLSR-coupled machine learning models for salinity stress phenotyping of rice.” LINK




Facts

“When Will AutoML Replace Data Scientists (if ever)?” MachineLearning Poll LINK




Equipment

“Probeless non-invasive near-infrared spectroscopic bioprocess monitoring using microspectrometer technology” LINK

“Confirmatory non-invasive and non-destructive differentiation between hemp and cannabis using a handheld Raman spectrometer” LINK

“Simple Defocused Fiber Optic Volume Probe for Subsurface Raman Spectroscopy in Turbid Media” LINK




Environment

“Applications of Vis-NIR spectroscopy proximal sensing to estimate and mapping the calcium carbonates (CaCO3) in the semi-arid soils of the Triffa Plain …” LINK




Agriculture

“IJMS, Vol. 21, Pages 408: Genome-Wide Identification of QTLs for Grain Protein Content Based on Genotyping-by-Resequencing and Verification of qGPC1-1 in Rice” LINK

” INFLUENCE OF PLANT SPACING AND GENETIC MATERIAL ON WOOD DENSITY AND STIFFNESS IN Eucalyptus STANDS” LINK

“Comparative data on effects of alkaline pretreatments and enzymatic hydrolysis on bioemulsifier production from sugarcane straw by Cutaneotrichosporon mucoides” LINK

” MODERN STATISTICAL ANALYSIS OF FORAGE QUALITY ASSESSMENT WITH NIR SPECTROSCOPY” LINK




Chemical

” Detection and Separation of Recyclable Plastics from Municipal Solid Waste” | Report.pdf LINK




Other

“Falsified tadalafil tablets distributed in Japan via the internet” LINK





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

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

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

    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

Configuration:

  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)

Usage:

  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.


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.

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

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.

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.

    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

Further References