Spectroscopy and Chemometrics News Weekly #13, 2021

NIR Calibration-Model Services

NIRS Analytical Laboratory Method Development : Reduce Workload and Response Time | MethodDevelopment modeling LINK

Spectroscopy and Chemometrics News Weekly 12, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Application Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Service Software Sensors QA QC Testing Quality LINK

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

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




Near-Infrared Spectroscopy (NIRS)

“Differentiation between Fresh and Thawed Cephalopods Using NIR Spectroscopy and Multivariate Data Analysis. Foods 2021, 10, 528” LINK

“Ethanol-soluble carbohydrates of cool-season grasses: prediction of concentration by near-infrared reflectance spectroscopy (NIRS) and evaluation of effects of …” LINK

“An Evaluation of Different NIR-Spectral Pre-Treatments to Derive the Soil Parameters C and N of a Humus-Clay-Rich Soil” LINK

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

“Comparison between single and mixed-species NIRS databases’ accuracy of dairy fiber feed value detection” LINK

“Using autoencoders to compress soil VNIR–SWIR spectra for more robust prediction of soil properties” LINK

“Prediction of some quality properties of rice and its flour by near-infrared spectroscopy (NIRS) analysis.” ricequality Amylose viscosity LINK




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

“Nitrogen Management Based on Visible/Near Infrared Spectroscopy in Pear Orchards” Remote Sensing LINK

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

“Near Infrared Spectroscopy as a PAT Tool for Monitoring and Control of Protein and Excipient Concentration in Ultrafiltration of Highly Concentrated Antibody Formulations” LINK

“Determination of soluble solid content in market tomatoes using Near-infrared Spectroscopy” LINK

“Discriminating Coalho cheese by origin through near and middle infrared spectroscopy and analytical measures” LINK

“Current and future research directions in computer-aided near-infrared spectroscopy: A perspective” LINK

“Estimation of the Relative Abundance of Quartz to Clay Minerals Using the Visible–Near-Infrared–Shortwave- Infrared Spectral Region” LINK

“Estimating the Lactate Threshold Using Wireless Near-Infrared Spectroscopy and Threshold Detection Analyses” LINK

“Smart SelfAssembly Amphiphilic CyclopeptideDye for NearInfrared WindowII Imaging” LINK

“Application of Long-Wave Near Infrared Hyperspectral Imaging for Determination of Moisture Content of Single Maize Seed” LINK

“Near Infrared Spectroscopy as a PAT Tool for Monitoring and Control of Protein and Excipient Concentration in Ultrafiltration of Highly Concentrated Antibody …” LINK

” Achieving the potential multifunctional near-infrared materials Ca 3 In 2− x Ga x Ge 3 O 12: Cr 3+ using a solid state method” LINK

“ATR-FTIR Microspectroscopy Brings a Novel Insight Into the Study of Cell Wall Chemistry at the Cellular Level” LINK

“Development and performance tests of an on-the-go detector of soil total nitrogen concentration based on near-infrared spectroscopy” LINK

“Mid-Infrared Scattering in -Al2O3 Catalytic Powders” LINK

“Rapid tannin profiling of tree fodders using untargeted mid-infrared spectroscopy and partial least squares regression” LINK

“Intelligent evaluation of taste constituents and polyphenols-to-amino acids ratio in matcha tea powder using near infrared spectroscopy” LINK




Raman Spectroscopy

“In vivo diagnosis of skin cancer with a portable Raman spectroscopic device” LINK




Hyperspectral Imaging (HSI)

” A chemometric view of hyperspectral images” LINK




Chemometrics and Machine Learning

” A synergistic use of chemometrics and deep learning improved the predictive performance of near-infrared spectroscopy models for dry matter prediction in …” LINK

“Soil exchangeable cations estimation using Vis-NIR spectroscopy in different depths: Effects of multiple calibration models and spiking” LINK

“Prediction of Tea Theanine Content using Near-Infrared Spectroscopy and Flower Pollination Algorithm” LINK

“Predicting Oil Content In Ripe Macaw Fruits (Acrocomia Aculeata) From Unripe Ones By Near Infrared Spectroscopy And Pls Regression” LINK

“A Model Based on Clusters of Similar Color and NIR to Estimate Oil Content of Single Olives” LINK

“Quick Determination and Discrimination of Commercial Hand Sanitisers Using Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy and Chemometrics” LINK

“A synergistic use of chemometrics and deep learning improved the predictive performance of near-infrared spectroscopy models for dry matter prediction in mango fruit” LINK

“Comparative study between Partial Least Squares and Rational function Ridge Regression models for the prediction of moisture content of woodchip samples using a handheld spectrophotometer” LINK

“Classification of Lingwu long jujube internal bruise over time based on visible near-infrared hyperspectral imaging combined with partial least squares-discriminant …” LINK

“Nondestructive qualitative and quantitative analysis of Yaobitong capsule using near-infrared spectroscopy in tandem with chemometrics” LINK

“Near infrared reflectance spectroscopy: classification and rapid prediction of patchouli oil content” LINK

“Chemometric classification of geothermal and non-geothermal ethanol leaf extract of seurapoh (Chromolaena odorata Linn) using infrared spectroscopy” LINK




Process Control and NIR Sensors

“In-Line Technologies for the Analysis of Important Milk Parameters during the Milking Process: A Review” LINK




Environment NIR-Spectroscopy Application

“Remote Sensing, Vol. 13, Pages 1105: Water Conservation Estimation Based on Time Series NDVI in the Yellow River Basin” LINK

“A novel framework to estimate soil mineralogy using soil spectroscopy” LINK




Agriculture NIR-Spectroscopy Usage

“Pentosan polysulfate maculopathy: Prevalence, spectrum of disease, and choroidal imaging analysis based on prospective screening: Pentosan maculopathy: disease spectrum & choroidal analysis” LINK

“An Alternative Approach to Evaluate the Quality of Protein-Based Raw Materials for Dry Pet Food. Animals 2021, 11, 458” LINK

“The use of NIR sensor technology for soil test-based decision making in agriculture” LINK

“Estimation of Starch Hydrolysis in Sweet Potato (Beni haruka) Based on Storage Period Using Nondestructive Near-Infrared Spectrometry. Agriculture 2021, 11, 135” LINK

“Handheld vs. Benchtop NearInfrared Spectrometers – How Do They Compare for Analyzing Forage Nutritive Value?” LINK

“Foods, Vol. 10, Pages 612: Preliminary Insights in Sensory Profile of Sweet Cherries” LINK

“Comparing CalReg performance with other multivariate methods for estimating selected soil properties from Moroccan agricultural regions using NIR spectroscopy” LINK

“Potential of Multivariate Statistical Technique Based on the Effective Spectra Bands to Estimate the Plant Water Content of Wheat Under Different Irrigation Regimes” LINK

“Agriculture, Vol. 11, Pages 239: In-Line Technologies for the Analysis of Important Milk Parameters during the Milking Process: A Review” LINK

“Foods, Vol. 10, Pages 496: Fatty Acid Composition from Olive Oils of Portuguese Centenarian Trees Is Highly Dependent on Olive Cultivar and Crop Year” LINK

“Automated in-field leaf-level hyperspectral imaging of corn plants using a Cartesian robotic platform” LINK

“A novel compact intrinsic safety full range Methane microprobe sensor using “trans-world” processing method based on near- infrared spectroscopy” LINK

“Organic carbon in agricultural and agroforestry soils: Effect of different management practices” LINK

“Machine Learning-Based Approach to Predict Insect-Herbivory-Damage and Insect-Type Attack in Maize Plants Using Hyperspectral Data” LINK

” Spectral reflectance of marine macroplastics in the VNIR and SWIR measured in a controlled environment” LINK




Forestry and Wood Industry NIR Usage

“Chemometric development using portable molecular vibrational spectrometers for rapid evaluation of AVC (Valsa mali Miyabe et Yamada) infection of apple trees” LINK




Food & Feed Industry NIR Usage

“Quantitative Analysis of Colony Number in Mouldy Wheat based on Near Infrared Spectroscopy combined with Colorimetric Sensor” LINK




Pharma Industry NIR Usage

” Integration of transcriptomes analysis with spectral signature of total RNA for generation of affordable remote sensing of Hepatocellular carcinoma in serum …” LINK




Laboratory and NIR-Spectroscopy

” Prediction of meat quality traits in the abattoir using portable near-infrared spectrometers: heritability of predicted traits and genetic correlations with laboratory …” LINK




Other

“Ultrasonic-assisted catalytic transfer hydrogenation for upgrading pyrolysis-oil” LINK

“Quantitation of volatile aldehydes using chemoselective response dyes combined with multivariable data analysis” LINK

“Evaluation and optimization on the reflection and durability of reflective coatings for cool pavement” LINK

“Polyvinyl chloride: chemical modification and investigation of structural and thermal properties” LINK





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Spectroscopy and Chemometrics News Weekly #35, 2020

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 34, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

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

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




Near-Infrared Spectroscopy (NIRS)

“Modelos NIRS para as características químicas da madeira de Eucalyptus benthamii Maiden & Cambage” LINK

“Application of in situ near infra-red spectroscopy (NIRS) for monitoring biopharmaceuticals production by cell cultures” LINK

“Using the NIRS for analyzes of soil clay content” LINK

“Determination of compost maturity using near infrared spectroscopy (NIRS)” LINK

“Screening Risk Assessment of Agricultural Areas under a High Level of Anthropopressure Based on Chemical Indexes and VIS-NIR Spectroscopy” LINK

“… an algorithm for processing Red Green Blue (RGB) images for the estimation of crude protein in grasses vs Near Infrared Reflectance Spectroscopy Technology (NIRS …” LINK

“Monitoring of cheese maturation using near infrared-hyperspectral imaging (NIR-HIS)” LINK

“Selection of sugarcane clones via multivariate models using near-infrared (NIR) spectroscopy data” LINK




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

“Rapid and simultaneous analysis of multiple wine quality indicators through near-infrared spectroscopy with twice optimization for wavelength model” LINK

“Manuka honey adulteration detection based on near-infrared spectroscopy combined with aquaphotomics” LINK

” Identification of Marine Fish Taxa by Linear Discriminant Analysis of Reflection Spectra in the Near-Infrared Region” LINK

“Assessment of Intact Macadamia Nut Internal Defects Using Near-Infrared Spectroscopy” LINK

“Rational design of near-infrared platinum(ii)-acetylide conjugated polymers for photoacoustic imaging-guided synergistic phototherapy under 808 nm irradiation.” LINK

“Classification of fish species from different ecosystems using the near infrared diffuse reflectance spectra of otoliths” LINK

“Three new Amazonian species of Myrcia sect. Myrcia (Myrtaceae) based on morphology and near-infrared spectroscopy” LINK

“Rapid Online Determination of Feed Concentration in Nitroguanidine Spray Drying Process by Near Infrared Spectroscopy” LINK




Raman Spectroscopy

“Monitoring the Caustic Dissolution of Aluminum Alloy in a Radiochemical Hot Cell Using Raman Spectroscopy” LINK




Hyperspectral Imaging (HSI)

“Hyperspectral Imaging and Deep Learning for Food Safety Assessment” LINK




Chemometrics and Machine Learning

“Rapid and Nondestructive Freshness Determination of Tilapia Fillets by a Portable Near-Infrared Spectrometer Combined with Chemometrics Methods” LINK

“Non-Targeted Detection of Adulterants in Almond Powder Using Spectroscopic Techniques Combined with Chemometrics.” LINK




Environment NIR-Spectroscopy Application

“Mobile Proximal Sensing with Visible and Near Infrared Spectroscopy for Digital Soil Mapping” LINK




Agriculture NIR-Spectroscopy Usage

“Imaging Techniques for Chicken Products Detection” LINK

“Usage of visual and near-infrared spectroscopy to predict soil properties in forest stands” LINK

“NUTRIENT CONTENT OF SOYBEAN MEAL FROM DIFFERENT ORIGINS BASED ON NEAR INFRARED REFLECTANCE SPECTROSCOPY” LINK

“Robustness of visible near-infrared and mid-infrared spectroscopic models to changes in the quantity and quality of crop residues in soil” LINK

“Use of leaf hyperspectral data and different regression models to estimate photosynthetic parameters (Vcmax and Jmax) in three different row crops” LINK

“Rapid and direct detection of small microplastics in aquatic samples by a new near infrared hyperspectral imaging (NIR-HSI) method” LINK




Horticulture NIR-Spectroscopy Applications

“Prediction of Soluble Solids Content During Storage of Apples with Different Maturity Based on VIS/NIR Spectroscopy” LINK

“A new spectral pretreatment method for detecting soluble solids content of pears using Vis/NIR spectroscopy” LINK

“Research on the Performance of Juicy Peach Sugar Content Detection Model Based on Near Infrared Spectroscopy” LINK




Forestry and Wood Industry NIR Usage

“The Effect of Construction and Demolition Waste Plastic Fractions on Wood-Polymer Composite Properties” LINK




Food & Feed Industry NIR Usage

“Non-destructive Assessment of Flesh Firmness and Dietary Antioxidants of Greenhouse-grown Tomato (Solanum lycopersicum L.) at Different Fruit Maturity Stages” LINK

“Comparative analysis of rice seed viability detection based on different spectral bands” LINK

“Detection of chocolate powder adulteration with peanut using near-infrared hyperspectral imaging and Multivariate Curve Resolution” LINK





Spectroscopy and Chemometrics News Weekly #17, 2020

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 16, 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 16, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 16, 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)

“Potential of Vis-NIR spectroscopy for detection of chilling injury in kiwifruit” LINK

“The application of NIR spectroscopy in moisture determining of vegetable seeds” LINK

“Detection and quantification of active pharmaceutical ingredients as adulterants in Garcinia cambogia slimming preparations using NIR spectroscopy combined with …” LINK

Analysis by NIRS shows the effectiveness of Lysozyme supplementation in dogs, responsible for the improvement of the coat, improved absorption in the hindgut and in microflora composition. pets petfood dogs LINK

Premier Nutrition NIR technology checks each batch of ingredient to confirm its identity ensuring highest level quality control. LINK

” La spectroscopie dans le proche infrarouge (NIRS) détermine avec précision la valeur nutritive des matières premières et des aliments pour porcs” LINK

“Handheld near-infrared spectrometers: Where are we heading?” | portable NIR NIRS NearInfrared instrumentation evaluation miniaturization LINK

“Qualitative and quantitative assessment of cork anomalies using near infrared spectroscopy (NIRS)” LINK

“Comparative Assessment on Smart Pre-processing Methods for Extracting Information in FT-NIR Measured Data” LINK




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

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

“Near-infrared wavelength-selection method based on joint mutual information and weighted bootstrap sampling” LINK

“Rapid and simultaneous analysis of direct and indirect bilirubin indicators in serum through reagent-free visible-near-infrared spectroscopy combined with …” LINK

“Sensors, Vol. 20, Pages 1472: Fusion of Mid-Wave Infrared and Long-Wave Infrared Reflectance Spectra for Quantitative Analysis of Minerals” LINK

“Near-infrared spectroscopy as a quantitative spasticity assessment tool: A systematic review.” LINK

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

“Classification of plastics using infrared spectroscopy” LINK

“Determination of routine chemicals, physical indices and macromolecular substances in reconstituted tobacco using near infrared spectroscopy combined with sample …” LINK

” Adulteration detection of mustard oil using near infrared spectroscopy” LINK

“The use of two-dimensional spectroscopy to interpret the effect of temperature on the near infrared spectra of whisky” LINK




Raman Spectroscopy

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




Hyperspectral Imaging (HSI)

“Dual-camera design for hyperspectral and panchromatic imaging, using a wedge shaped liquid crystal as a spectral multiplexer” | |)/S/URI LINK

“Hyperspectral image classification based on pre-post combination process” LINK

“A novel nonlinear hyperspectral unmixing approach for images of oil spills at sea” LINK

“Hyperspectral technique combined with deep learning algorithm for detection of compound heavy metals in lettuce” LINK




Chemometrics and Machine Learning

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

“ATR-MIR spectroscopy to predict commercial milk major components: A comparison between a handheld and a benchtop instrument” LINK

“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

“In vivo cancer detection in animal model using hyperspectral image classification with wavelet feature extraction” LINK




Environment NIR-Spectroscopy Application

“Improved mapping of soil heavy metals using a Vis-NIR spectroscopy index in an agricultural area of eastern China” LINK

“Combining visible near‐infrared spectroscopy and water vapor sorption for soil specific surface area estimation” LINK




Agriculture NIR-Spectroscopy Usage

“The Use of Multi-temporal Spectral Information to Improve the Classification of Agricultural Crops in Landscapes” LINK




Horticulture NIR-Spectroscopy Applications

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

“Spatial mapping of Brix and moisture content using hyperspectral imaging system in sugarcane stalk” LINK




Food & Feed Industry NIR Usage

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




Medicinal Spectroscopy

“Near-infrared spectroscopy-derived total haemoglobin as an indicator of changes in muscle blood flow during exercise-induced hyperaemia” LINK




Laboratory and NIR-Spectroscopy

“Laboratory-Scale Preparation and Characterization of Dried Extract of Muirapuama (Ptychopetalumolacoides Benth) by Green Analytical Techniques” LINK





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Spectroscopy and Chemometrics News Weekly #10, 2020

CalibrationModel.com

Develop customized NIR applications and freeing up hours of spectroscopy analysts time LINK

Spectroscopy and Chemometrics News Weekly 9, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Analytical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors materialsensing QA QC Quality foodtechnologies LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 9, 2020 | NIRS NIR Spektroskopie MachineLearning AI FoodScience Spektrometer IoT Sensor Nahinfrarot Analytik foodtech Analysentechnik Analysemethode Nahinfrarotspektroskopie FutureLab LINK

Spettroscopia e Chemiometria Weekly News 9, 2020 | NIRS NIR Spettroscopia MachineLearning analisi Spettrale Spettrometro Chem IoT Sensore analitica Lab Laboratorio Labtechnician analisi prova qualità QualityControl meatindustry LINK




Near Infrared

“Differentiation of Muscle Abnormalities in Turkey Breast Meat in Palestinian Market by Using VIS-NIR Spectroscopy” LINK

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

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

“Nitrate (NO3-) prediction in soil analysis using near-infrared (NIR) spectroscopy” LINK

“Can Near Infrared Spectroscopy (NIRS) Quantify The Quality of Fishmeal Circulating in Jember, Indonesia?” LINK

“Nondestructive VIS/NIR spectroscopy estimation of intravitelline vitamin E and cholesterol concentration in hen shell eggs” LINK

“NIR spectroscopy-multivariate analysis for rapid authentication, detection and quantification of common plant adulterants in saffron (Crocus sativus L.) stigmas” LINK

“Rapid discrimination of coal geographical origin via near-infrared spectroscopy combined with machine learning algorithms” LINK

“Applied Sciences, Vol. 10, Pages 616: The Brewing Industry and the Opportunities for Real-Time Quality Analysis Using Infrared Spectroscopy” LINK

“A Global Model for the Determination of Prohibited Addition in Pesticide Formulations by Near Infrared Spectroscopy” LINK

” Visible and near-infrared spectroscopy in Poland: from the beginning to the Polish Soil Spectral Library” LINK

“Visible and Near-Infrared Spectroscopic Discriminant Analysis Applied to Brand Identification of Wine” LINK

“Interaction between tau and water during the induced aggregation revealed by near-infrared spectroscopy” LINK




Raman

Using Raman Spectroscopy to Evaluate Packaging for Frozen Hamburgers – – LINK




Hyperspectral

“Hyperspectral imaging for Ink Identification in Handwritten Documents” LINK

“Effective hyperspectral band selection and multispectral sensing based data reduction and applications in food analysis” LINK

“Foods, Vol. 9, Pages 94: Rapid Screen of the Color and Water Content of Fresh-Cut Potato Tuber Slices Using Hyperspectral Imaging Coupled with Multivariate Analysis” LINK

“Detection of Microplastics Using Machine Learning” hyperspectral LINK

“Non-destructive determination of volatile oil and moisture content and discrimination of geographical origins of Zanthoxylum bungeanum Maxim. by hyperspectral …” LINK

“The hype in spectral imaging” Hyperspectral HSI LINK

“Multi-sensor integration approach based on hyperspectral imaging and electronic nose for quantitation of fat and peroxide value of pork meat” LINK




Chemometrics

“Investigating aquaphotomics for temperature-independent prediction of soluble solids content of pure apple juice” LINK

“Accuracy of Estimating Soil Properties with Mid‐Infrared Spectroscopy: Implications of Different Chemometric Approaches and Software Packages Related to Calibration Sample Size” LINK

“A correlation-analysis-based wavelength selection method for calibration transfer” LINK

“Vibrational spectroscopy and chemometrics tools for authenticity and improvement the safety control in goat milk” LINK

“Prediction and Analysis of Bamboo heating value Near Infrared Spectroscopy Based on Competitive Adaptive Weighted Sampling Algorithm” LINK




Environment

“Influence of two‐phase behavior of ethylene ionomers on diffusion of water” LINK




Agriculture

“Detection of Diseases on Wheat Crops by Hyperspectral Data” LINK

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

“Prediction of soil macronutrient (nitrate and phosphorus) using near-infrared (NIR) spectroscopy and machine learning” LINK

“最小二乘支持向量机的核桃露饮品中脂肪成分的定量分析” “Determination of Fat in Walnut Beverage Based on Least Squares Support Vector Machine” LINK

“Scaling up of NIRS facility in Mali for analysis of biomass quality for GLDC crops” LINK

“Low cost hyper-spectral imaging system using linear variable bandpass filter for agriculture applications” LINK




Other

“Avaliação estatística das variáveis relacionadas a qualidade de farelo de soja para frangos de corte” LINK

“Diets selected and growth of steers grazing buffel grass (Cenchus ciliarus cv Gayndah)-Centro (Centrosema brasilianum cv Oolloo) pastures in a seasonally dry …” LINK

“Identification of Flax Oil by Linear Multivariate Spectral Analys” LINK

“Spectroscopic Investigations of Solutions of Lithium bis(fluorosulfonyl)imide (LiFSI) in Valeronitrile” LINK

“Quantitative analysis of the interaction of ammonia with 1-(2-hydroxyethyl)-3-methylimidazolium tetrafluoroborate ionic liquid. Understanding the volumetric and …” LINK





NIR Calibration Service explained

Our Service is different

  • We build the optimal quantitative prediction models for your NIR analytical needs (No need for mathematical/statistical model building software usage at your site).
  • The NIR-Predictor software and the calibration models are at your site. No internet connection needed to our service. You can do unlimited predictions, that allows fast measurement cycles with no extra cost (Not payed per prediction).
  • You own your NIR + Lab data and the calibrations. You can have access to the detailed Calibration Report with all the settings and statistics (No Black-Box Models).

Get NIR Calibrations

Get NIR Calibrations - Workflow
Your 4 steps to the applicable NIR calibration:
  1. Download free NIR-Predictor here
  2. Combine your NIR-Spectra with Lab-Reference values, see Video for 2. and 3. (manual)
  3. Create a Calibration Request and sent it to info@CalibrationModel.com (CM)
  4. After processing you will get a link to the Web Shop to download the calibrations.



Use NIR Calibrations

Use NIR Calibrations Workflow
see more Videos


In other words

Calibration Model simplifies the process of training machine learning models for NIRS data while providing an opportunity to trying different algorithms and applied near-infrared spectroscopy (NIRS) knowledge. It’s more than an AutoML platform, it’s a full service where you can download the optimal model and its describing Calibration Report that provide insights into the data preparation, feature engineering, model training, and hyperparameter tuning.

Start Calibrate

Start Calibrate – NIR Quick Guide


More Details

Quick Start NIR Workflow: step by step

1. Check if your NIR-Device Data Format is directly supported (anyway you can convert to JCAMP) : NIR-Predictor supported Spectral Data File Formats

2. Download the free NIR-Predictor Software that contains demo data so you can play with it to see if it is the way you want analyse your NIR spectra (no registration needed) : NIR-Predictor Download

3. With your “NIR device” measurement software:

  • measure samples with NIR, that gets you spectra files,
  • label them with a proper sample name, so you know which is which,
  • and determine quantitative reference values by Laboratory reference method.
  • at least 60 samples with different contents is needed for a minimal calibration.
  • NIR-Predictor helps to create a template file (.csv) to enter the Lab values.

4. Creating your own Calibrations with NIR-Predictor to combine your NIR and Lab data for a calibration request : watch Video read Manual



Videos


Spectroscopy and Chemometrics News Weekly #37, 2019

CalibrationModel.com

The new Version V2.4 of the free NIR-Predictor supports multiple native file formats of miniature, mobile and desktop spectrometers get your spectra analyzed as easy as Drag’n’Drop. LINK

Spectroscopy and Chemometrics News Weekly 36, 2019 | NIRS NIR Spectroscopy Chemometrics Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software Sensors QA QC Testing Quality Checking LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 36, 2019 | NIRS NIR Spektroskopie Spektrometer Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse Qualitätslabor LINK

Spettroscopia e Chemiometria Weekly News 36, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem predictionmodel LINK




Chemometrics

“Identification of Passion Fruit Oil Adulteration by Chemometric Analysis of FTIR Spectra” LINK

“Investigating the use of visible and near infrared spectroscopy to predict sensory and texture attributes of beef M.longissimus thoracis et lumborum.” LINK

“Optimized prediction of sugar content in ‘Snow’ pear using near-infrared diffuse reflectance spectroscopy combined with chemometrics” LINK

“FT-NIR spectroscopy and multivariate classification strategies for the postharvest quality of green-fleshed kiwifruit varieties” FTNIR LINK

“An Approach to Rapid Determination of Tween-80 for the Quality Control of Traditional Chinese Medicine Injection by Partial Least Squares Regression in Near-Infrared Spectral Modeling” LINK

“Assessing macro-element content in vine leaves and grape berries of vitis vinifera by using near-infrared spectroscopy and chemometrics” LINK

“Investigating the use of visible and near infrared spectroscopy to predict sensory and texture attributes of beef M. longissimus thoracis et lumborum” LINK

“Rapid classification of commercial Cheddar cheeses from different brands using PLSDA, LDA and SPA-LDA models built by hyperspectral data” LINK




Near Infrared

“A Comparison of Sparse Partial Least Squares and Elastic Net in Wavelength Selection on NIR Spectroscopy Data.” LINK

“Reliability of NIRS portable device for measuring intercostal muscles oxygenation during exercise” LINK

“Ability of near-infrared spectroscopy for non-destructive detection of internal insect infestation in fruits: Meta-analysis of spectral ranges and optical measurement modes.” LINK

“Analysis of the Acid Detergent Fibre Content in Turnip Greens and Turnip Tops (Brassica rapa L. Subsp. rapa) by Means of Near-Infrared Reflectance.” LINK

“Lipid-Core Plaque Assessed by Near-Infrared Spectroscopy and Procedure Related Microvascular Injury.” LINK

“Analysis of the Acid Detergent Fibre Content in Turnip Greens and Turnip Tops (Brassica rapa L. Subsp. rapa) by Means of Near-Infrared Reflectance” Foods LINK

“Online monitoring of multiple component parameters during ethanol fermentation by near-infrared spectroscopy.” LINK




Hyperspectral

“Identifying Freshness of Spinach Leaves Stored at Different Temperatures Using Hyperspectral Imaging.” LINK

“Identifying Freshness of Spinach Leaves Stored at Different Temperatures Using Hyperspectral Imaging” Foods LINK




Equipment

“Investigations into the use of handheld near-infrared spectrometer and novel semi-automated data analysis for the determination of protein content in different cultivars of Panicum miliaceumL.” LINK




Agriculture

“Use of near-infrared spectroscopy for the rapid evaluation of soybean [Glycine max (L.) Merri.] water soluble protein content.” LINK




Food & Feed

“Rapid visible-near infrared (Vis-NIR) spectroscopic detection and quantification of unripe banana flour adulteration with wheat flour” LINK





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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

Spectroscopy and Chemometrics News Weekly #28, 2019

CalibrationModel.com

Why NIR vendors should use ready-to-use dedicated NIR modeling software solution that works already. LINK

Spectroscopy and Chemometrics News Weekly 27, 2019 | NIRS NIR Spectroscopy Chemometrics Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software Sensors QA QC Testing Quality Checking LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 27, 2019 | NIRS NIR Spektroskopie Chemometrie Spektrometer Messtechnik Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Labor Analyse LINK

Spettroscopia e Chemiometria Weekly News 27, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem prediction models LINK

This week’s NIR news Weekly is sponsored by YourCompanyNameHere – BestNIRinstruments. Check out their product page … link

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




Chemometrics

“Prediction of chemical content in patchouli oil (Pogostemon cablin Benth) by portable NIR spectroscopy” LINK

“Carbohydrate Analysis by NIRS-Chemometrics” LINK

“Chemosensors, Vol. 7, Pages 29: The Electronic Nose Coupled with Chemometric Tools for Discriminating the Quality of Black Tea Samples InSitu” LINK

fruit sensor calibration for monitoring fruit development continuously in the orchard see poster at ecpa2019 LINK

New Journal of Spectral Imaging–JSI Paper: Adaptive hierarchical clustering for hyperspectral image classification: Umbrella Clustering | Remotesensing LINK

“Prediction of Dissolution of Sustained Release Coated Ciprofloxacin Beads Using Near-infrared Spectroscopy and Process Parameters: a Data Fusion Approach” LINK




Near Infrared

“The effect of particle aspect ratio on spatially and angularly resolved vis-NIR spectroscopy of suspensions” LINK

nIRCat, a near-infrared (1000-1300 nm) probe to image dopamine in the brain LINK

“交通渋滞場面での怒りの接近動機づけ (攻撃性) における加齢の影響: 近赤外線分光法 (NIRS)・心拍出量・唾液中コルチゾールを用いた検討 (ヒューマンコミュニケーション基礎)” LINK

“Desarrollar un modelo de predicción mediante Espectroscopia en Infrarrojo Cercano (NIRS) para la determinación de proteína cruda en subproductos de arroz (Oriza sativa)” LINK

Detection of intracranial hematomas in the emergency department using near infrared spectroscopy LINK

Coating excipients How to Measure Coating Thickness of Tablets: Method Comparison of Optical Coherence Tomography, Near-infrared Spectroscopy and Weight-, Height- and Diameter Gain LINK

“NIRS identification of black textiles: Improvements for waste textiles sorting” LINK

“Near-infrared spectroscopy for determining the oxidation stability of diesel, biodiesel and their mixtures” LINK

“Influence of surface roughness and surface moisture of plastics on sensor-based sorting in the near infrared range.” LINK

“Present and future of portable/handheld near-infrared spectroscopy in chicken meat industry” LINK




Hyperspectral

“Remote Sensing, Vol. 11, Pages 1622: A Lightweight Hyperspectral Image Anomaly Detector for Real-Time Mission” LINK




Spectral Imaging

“Multispectral Imaging for Detection of Adulterants in Turmeric Powder” LINK




Facts

It’s hype nowadays to play with all the fancy shiny new open source releases, but how long will a company let their technicians play with it? LINK




Equipment

“Investigations into the use of handheld near-infrared spectrometer and novel semi-automated data analysis for the determination of protein content in different cultivars of Panicum miliaceum L.” LINK

“Extension of the Measurable Wavelength Range for a Near-Infrared Spectrometer Using a Plasmonic Au Grating on a Si Substrate” LINK




Process Control

“Integrated Robotic Mini Bioreactor Platform for Automated, Parallel Microbial Cultivation With Online Data Handling and Process Control” LINK




Agriculture

“Nutrients, Vol. 11, Pages 1476: Nutritional Properties and Consumers Acceptance of Provitamin A-Biofortified Amahewu Combined with Bambara (Vigna Subterranea) Flour” LINK

“Rapid detection of carbon-nitrogen ratio for anaerobic fermentation feedstocks using near-infrared spectroscopy combined with BiPLS and GSA” LINK




Forestry

“Soil C stocks in the sylvopastoral zone of Senegal as influences by trees” LINK




Medicinal

“Current methods for the assessment of skin microcirculation: Part 1” LINK




Other

“Kayseri Ekolojik Kosullarinda Farkli Ekim Zamanlarinin Nohut (Cicer arietinum L.) Bitkisinde verim, verim unsurlari ve kalite üzerine etkileri” LINK

“Seed composition of different Camelina sativa and Crambe abyssinica cultivars” LINK





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