Spectroscopy and Chemometrics News Weekly #12, 2020Spektroskopie und Chemometrie Neuigkeiten Wöchentlich #12, 2020Spettroscopia e Chemiometria Weekly News #12, 2020

CalibrationModel.com

CalibrationModel.com has changed the pricing structure and NIRS-Calibration licensing options (including new perpetual and unlimited systems). | #NIR #Spectroscopy #Chemometric #AutoML #Calibration #Development #Service #milk #meet #food #qualitycontrol LINK

Do you work with Near Infra-red Reflectance Spectrometry (NIRS) and need better Accuracy? NIR Spectroscopy | mill agriculture LINK

Spectroscopy and Chemometrics News Weekly 11, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical foodprocessing foodsafety Analysis Lab Labs Laboratories Laboratory Software IoT Sensors Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 11, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor nearinfrared Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 11, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK




Near Infrared

"Characterization of the Fruits and Seeds of Alpinia Oxyphylla Miq by High-Performance Liquid Chromatography (HPLC) and near-Infrared Spectroscopy (NIRS) with Partial Least …" LINK

"Yenidoğan Yoğun Bakım Ünitesinde Yeni Bir Yaklaşım: Hemşirelik Bakımında Yakın Kızılötesi Spektroskopisi (Near-Infrared Spectroscopy-NIRS) Kullanımı" LINK

"Individual Wheat Kernels Vigor Assessment Based on NIR Spectroscopy Coupled with Machine Learning Methodologies" LINK

"Characteristion of Hydrogen Bond of L–Methionium Hydrogen Selenite by Temperature Dependent Two-dimensional Correlation FT-NIR Spectroscopy" LINK

"Authentication of Iberian pork official quality categories using a portable near infrared spectroscopy (NIRS) instrument." LINK

"Determination of grated hard cheeses adulteration by near infrared spectroscopy (NIR) and multivariate analysis" LINK

"Prekalibrasi Rumput Gajah Menggunakan NIRS dan Perbandingannya dengan Pengujian Kimia" LINK

"Near‐infrared spectroscopy (NIRS) for taxonomic entomology: A brief review" LINK

"NIR spectroscopy can detect acrylamide" Visible Spectrophotometer H2020 LINK

"Evaluation and classification of five cereal fungi on culture medium using Visible/Near-Infrared (Vis/NIR) hyperspectral imaging" LINK

"APLIKASI NEAR INFRARED SPECTROSCOPY (NIRS) UNTUK MENDETEKSI PENCEMARAN TANAH" LINK

"Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties." LINK

"Structural and visible-near infrared optical properties of (Fe, Mo)-co-doped TiO2 for colored cool pigments" LINK

"Rapid Evaluation of Biomass Properties Used for Energy Purposes Using Near-Infrared Spectroscopy" DOI: 10.5772/intechopen.90828 LINK

"Visible/near infrared spectroscopy and machine learning for predicting polyhydroxybutyrate production cultured on alkaline pretreated liquor from corn stover" LINK

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

"Determination of the viability of retinispora (Hinoki cypress) seeds using shortwave infrared hyperspectral imaging spectroscopy" LINK

"PREDIKSI KADAR SALINITAS, PH DAN C-ORGANIK TANAH MENGGUNAKAN NEAR INFRARED KECAMATAN BAITUSSALAM KABUPATEN ACEH BESAR" LINK

"Determination of pH and acidity in green coffee by near infrared spectroscopy and multivariate regression" LINK




Chemometrics

"Prediction of soil properties with machine learning models based on the spectral response of soil samples in the near infrared range" LINK

The statistics mantra "Correlation does NOT mean Causation" explained with an example. LINK

"Evaluation of aroma styles in flue-cured tobacco by near infrared spectroscopy combined with chemometric algorithms" LINK

"Sensors, Vol. 20, Pages 686: Fuzzy Evaluation Output of Taste Information for Liquor Using Electronic Tongue Based on Cloud Model" LINK

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

"Determination of Total Phenolic Content and NIR-Chemometrics Classification Model of Queen and Local Varieties of Soursop (Annonamuricata L.) Leaf Powder" LINK

"Recognition of different Longjing fresh tea varieties using hyperspectral imaging technology and chemometrics" LINK




Facts

"Visualizing the History of Pandemics" #infoGrafics LINK




Equipment

"Development of Low-Cost Portable Spectrometers for Detection of Wood Defects." LINK




Process Control

"Preparation of Celecoxib Tablet by Hot Melt Extrusion Technology and Application of Process Analysis Technology to Discriminate Solubilization Effect" LINK

"Internet of Things — Leap towards a hyper-connected world" IoT Spectral Sensors SpectralSensing qualitycontrol analysis Production ProcessControl foodprocessing foodsafety foodproduction AI BigData DataScience #INFOGRAPHICS LINK




Agriculture

"Estimating fatty acid content and related nutritional indexes in ewe milk using different near infrared instruments" LINK

"Indirect measures of methane emissions of Sahelian zebu cattle in West Africa, role of environment and management" LINK

"Detection of mycotoxins and toxigenic fungi in cereal grains using vibrational spectroscopic techniques: a review" LINK

"NIR spectroscopy and management of bioactive components, antioxidant activity, and macronutrients in fruits" LINK

"Scald-Cold: Joint Austrian-Italian consortium in the Euregio project for the comprehensive dissection of the superficial scald in apples" postharvest food LINK

"Both genetic and environmental conditions affect wheat grain texture: Consequences for grain fractionation and flour properties" LINK

"A phenotyping tool for water status determination in soybean by vegetation indexes and NIR-SWIR spectral bands." LINK




Pharma

"Non-destructive dose verification of two drugs within 3D printed polyprintlets" LINK




Laboratory

"Forward and Backward Interval Partial Least Squares Method for Quantitative Analysis of Frying Oil Quality" LINK




Other

"Study of simple detection of gasoline fuel contaminants contributing to increase Particulate Matter Emissions" LINK

"COVID-19 Open Research Dataset (CORD-19)" LINK







.

CalibrationModel.com

CalibrationModel.com has changed the pricing structure and NIRS-Calibration licensing options (including new perpetual and unlimited systems). | #NIR #Spectroscopy #Chemometric #AutoML #Calibration #Development #Service #milk #meet #food #qualitycontrol LINK

Do you work with Near Infra-red Reflectance Spectrometry (NIRS) and need better Accuracy? NIR Spectroscopy | mill agriculture LINK

Spectroscopy and Chemometrics News Weekly 11, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical foodprocessing foodsafety Analysis Lab Labs Laboratories Laboratory Software IoT Sensors Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 11, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor nearinfrared Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 11, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK




Near Infrared

"Characterization of the Fruits and Seeds of Alpinia Oxyphylla Miq by High-Performance Liquid Chromatography (HPLC) and near-Infrared Spectroscopy (NIRS) with Partial Least …" LINK

"Yenidoğan Yoğun Bakım Ünitesinde Yeni Bir Yaklaşım: Hemşirelik Bakımında Yakın Kızılötesi Spektroskopisi (Near-Infrared Spectroscopy-NIRS) Kullanımı" LINK

"Individual Wheat Kernels Vigor Assessment Based on NIR Spectroscopy Coupled with Machine Learning Methodologies" LINK

"Characteristion of Hydrogen Bond of L–Methionium Hydrogen Selenite by Temperature Dependent Two-dimensional Correlation FT-NIR Spectroscopy" LINK

"Authentication of Iberian pork official quality categories using a portable near infrared spectroscopy (NIRS) instrument." LINK

"Determination of grated hard cheeses adulteration by near infrared spectroscopy (NIR) and multivariate analysis" LINK

"Prekalibrasi Rumput Gajah Menggunakan NIRS dan Perbandingannya dengan Pengujian Kimia" LINK

"Near‐infrared spectroscopy (NIRS) for taxonomic entomology: A brief review" LINK

"NIR spectroscopy can detect acrylamide" Visible Spectrophotometer H2020 LINK

"Evaluation and classification of five cereal fungi on culture medium using Visible/Near-Infrared (Vis/NIR) hyperspectral imaging" LINK

"APLIKASI NEAR INFRARED SPECTROSCOPY (NIRS) UNTUK MENDETEKSI PENCEMARAN TANAH" LINK

"Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties." LINK

"Structural and visible-near infrared optical properties of (Fe, Mo)-co-doped TiO2 for colored cool pigments" LINK

"Rapid Evaluation of Biomass Properties Used for Energy Purposes Using Near-Infrared Spectroscopy" DOI: 10.5772/intechopen.90828 LINK

"Visible/near infrared spectroscopy and machine learning for predicting polyhydroxybutyrate production cultured on alkaline pretreated liquor from corn stover" LINK

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

"Determination of the viability of retinispora (Hinoki cypress) seeds using shortwave infrared hyperspectral imaging spectroscopy" LINK

"PREDIKSI KADAR SALINITAS, PH DAN C-ORGANIK TANAH MENGGUNAKAN NEAR INFRARED KECAMATAN BAITUSSALAM KABUPATEN ACEH BESAR" LINK

"Determination of pH and acidity in green coffee by near infrared spectroscopy and multivariate regression" LINK




Chemometrics

"Prediction of soil properties with machine learning models based on the spectral response of soil samples in the near infrared range" LINK

The statistics mantra "Correlation does NOT mean Causation" explained with an example. LINK

"Evaluation of aroma styles in flue-cured tobacco by near infrared spectroscopy combined with chemometric algorithms" LINK

"Sensors, Vol. 20, Pages 686: Fuzzy Evaluation Output of Taste Information for Liquor Using Electronic Tongue Based on Cloud Model" LINK

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

"Determination of Total Phenolic Content and NIR-Chemometrics Classification Model of Queen and Local Varieties of Soursop (Annonamuricata L.) Leaf Powder" LINK

"Recognition of different Longjing fresh tea varieties using hyperspectral imaging technology and chemometrics" LINK




Facts

"Visualizing the History of Pandemics" #infoGrafics LINK




Equipment

"Development of Low-Cost Portable Spectrometers for Detection of Wood Defects." LINK




Process Control

"Preparation of Celecoxib Tablet by Hot Melt Extrusion Technology and Application of Process Analysis Technology to Discriminate Solubilization Effect" LINK

"Internet of Things — Leap towards a hyper-connected world" IoT Spectral Sensors SpectralSensing qualitycontrol analysis Production ProcessControl foodprocessing foodsafety foodproduction AI BigData DataScience #INFOGRAPHICS LINK




Agriculture

"Estimating fatty acid content and related nutritional indexes in ewe milk using different near infrared instruments" LINK

"Indirect measures of methane emissions of Sahelian zebu cattle in West Africa, role of environment and management" LINK

"Detection of mycotoxins and toxigenic fungi in cereal grains using vibrational spectroscopic techniques: a review" LINK

"NIR spectroscopy and management of bioactive components, antioxidant activity, and macronutrients in fruits" LINK

"Scald-Cold: Joint Austrian-Italian consortium in the Euregio project for the comprehensive dissection of the superficial scald in apples" postharvest food LINK

"Both genetic and environmental conditions affect wheat grain texture: Consequences for grain fractionation and flour properties" LINK

"A phenotyping tool for water status determination in soybean by vegetation indexes and NIR-SWIR spectral bands." LINK




Pharma

"Non-destructive dose verification of two drugs within 3D printed polyprintlets" LINK




Laboratory

"Forward and Backward Interval Partial Least Squares Method for Quantitative Analysis of Frying Oil Quality" LINK




Other

"Study of simple detection of gasoline fuel contaminants contributing to increase Particulate Matter Emissions" LINK

"COVID-19 Open Research Dataset (CORD-19)" LINK







.

CalibrationModel.com

CalibrationModel.com has changed the pricing structure and NIRS-Calibration licensing options (including new perpetual and unlimited systems). | #NIR #Spectroscopy #Chemometric #AutoML #Calibration #Development #Service #milk #meet #food #qualitycontrol LINK

Do you work with Near Infra-red Reflectance Spectrometry (NIRS) and need better Accuracy? NIR Spectroscopy | mill agriculture LINK

Spectroscopy and Chemometrics News Weekly 11, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical foodprocessing foodsafety Analysis Lab Labs Laboratories Laboratory Software IoT Sensors Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 11, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor nearinfrared Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 11, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK




Near Infrared

"Characterization of the Fruits and Seeds of Alpinia Oxyphylla Miq by High-Performance Liquid Chromatography (HPLC) and near-Infrared Spectroscopy (NIRS) with Partial Least …" LINK

"Yenidoğan Yoğun Bakım Ünitesinde Yeni Bir Yaklaşım: Hemşirelik Bakımında Yakın Kızılötesi Spektroskopisi (Near-Infrared Spectroscopy-NIRS) Kullanımı" LINK

"Individual Wheat Kernels Vigor Assessment Based on NIR Spectroscopy Coupled with Machine Learning Methodologies" LINK

"Characteristion of Hydrogen Bond of L–Methionium Hydrogen Selenite by Temperature Dependent Two-dimensional Correlation FT-NIR Spectroscopy" LINK

"Authentication of Iberian pork official quality categories using a portable near infrared spectroscopy (NIRS) instrument." LINK

"Determination of grated hard cheeses adulteration by near infrared spectroscopy (NIR) and multivariate analysis" LINK

"Prekalibrasi Rumput Gajah Menggunakan NIRS dan Perbandingannya dengan Pengujian Kimia" LINK

"Near‐infrared spectroscopy (NIRS) for taxonomic entomology: A brief review" LINK

"NIR spectroscopy can detect acrylamide" Visible Spectrophotometer H2020 LINK

"Evaluation and classification of five cereal fungi on culture medium using Visible/Near-Infrared (Vis/NIR) hyperspectral imaging" LINK

"APLIKASI NEAR INFRARED SPECTROSCOPY (NIRS) UNTUK MENDETEKSI PENCEMARAN TANAH" LINK

"Near-Infrared Hyperspectral Imaging Combined with Deep Learning to Identify Cotton Seed Varieties." LINK

"Structural and visible-near infrared optical properties of (Fe, Mo)-co-doped TiO2 for colored cool pigments" LINK

"Rapid Evaluation of Biomass Properties Used for Energy Purposes Using Near-Infrared Spectroscopy" DOI: 10.5772/intechopen.90828 LINK

"Visible/near infrared spectroscopy and machine learning for predicting polyhydroxybutyrate production cultured on alkaline pretreated liquor from corn stover" LINK

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

"Determination of the viability of retinispora (Hinoki cypress) seeds using shortwave infrared hyperspectral imaging spectroscopy" LINK

"PREDIKSI KADAR SALINITAS, PH DAN C-ORGANIK TANAH MENGGUNAKAN NEAR INFRARED KECAMATAN BAITUSSALAM KABUPATEN ACEH BESAR" LINK

"Determination of pH and acidity in green coffee by near infrared spectroscopy and multivariate regression" LINK




Chemometrics

"Prediction of soil properties with machine learning models based on the spectral response of soil samples in the near infrared range" LINK

The statistics mantra "Correlation does NOT mean Causation" explained with an example. LINK

"Evaluation of aroma styles in flue-cured tobacco by near infrared spectroscopy combined with chemometric algorithms" LINK

"Sensors, Vol. 20, Pages 686: Fuzzy Evaluation Output of Taste Information for Liquor Using Electronic Tongue Based on Cloud Model" LINK

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

"Determination of Total Phenolic Content and NIR-Chemometrics Classification Model of Queen and Local Varieties of Soursop (Annonamuricata L.) Leaf Powder" LINK

"Recognition of different Longjing fresh tea varieties using hyperspectral imaging technology and chemometrics" LINK




Facts

"Visualizing the History of Pandemics" #infoGrafics LINK




Equipment

"Development of Low-Cost Portable Spectrometers for Detection of Wood Defects." LINK




Process Control

"Preparation of Celecoxib Tablet by Hot Melt Extrusion Technology and Application of Process Analysis Technology to Discriminate Solubilization Effect" LINK

"Internet of Things — Leap towards a hyper-connected world" IoT Spectral Sensors SpectralSensing qualitycontrol analysis Production ProcessControl foodprocessing foodsafety foodproduction AI BigData DataScience #INFOGRAPHICS LINK




Agriculture

"Estimating fatty acid content and related nutritional indexes in ewe milk using different near infrared instruments" LINK

"Indirect measures of methane emissions of Sahelian zebu cattle in West Africa, role of environment and management" LINK

"Detection of mycotoxins and toxigenic fungi in cereal grains using vibrational spectroscopic techniques: a review" LINK

"NIR spectroscopy and management of bioactive components, antioxidant activity, and macronutrients in fruits" LINK

"Scald-Cold: Joint Austrian-Italian consortium in the Euregio project for the comprehensive dissection of the superficial scald in apples" postharvest food LINK

"Both genetic and environmental conditions affect wheat grain texture: Consequences for grain fractionation and flour properties" LINK

"A phenotyping tool for water status determination in soybean by vegetation indexes and NIR-SWIR spectral bands." LINK




Pharma

"Non-destructive dose verification of two drugs within 3D printed polyprintlets" LINK




Laboratory

"Forward and Backward Interval Partial Least Squares Method for Quantitative Analysis of Frying Oil Quality" LINK




Other

"Study of simple detection of gasoline fuel contaminants contributing to increase Particulate Matter Emissions" LINK

"COVID-19 Open Research Dataset (CORD-19)" LINK







.

Spectroscopy and Chemometrics News Weekly #10, 2020Spektroskopie und Chemometrie Neuigkeiten Wöchentlich #10, 2020Spettroscopia e Chemiometria Weekly News #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





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





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





Spectroscopy and Chemometrics News Weekly #8, 2020Spektroskopie und Chemometrie Neuigkeiten Wöchentlich #8, 2020Spettroscopia e Chemiometria Weekly News #8, 2020

CalibrationModel.com

Knowledge-Based Variable Selection and Model Selection for near infrared spectroscopy NIRS LINK

Stop wasting too much time for NIRS Chemometrics Method development | foodanalyticaltechnologies analytic qualitycontrol foodindustry beverageindustry materialsensing LINK

Spectroscopy and Chemometrics News Weekly 7, 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 7, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 7, 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)

"Determination of Glucose by NIR Spectroscopy Under Magnetic Field" LINK

"Sensors, Vol. 20, Pages 230: The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods when using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods" LINK

"Quantum mechanical modeling of NIR spectra of thymol" LINK

"Using a handheld near-infrared spectroscopy (NIRS) scanner to predict meat quality" LINK

"NIR spectroscopy in simulation–a new way for augmenting near-infrared phytoanalysis" LINK

"Using visible-near-infrared spectroscopy to classify lichens at a Neotropical Dry Forest" LINK

"Near infrared spectroscopy as a rapid method for detecting paprika powder adulteration with corn flour" LINK

"Application of deep learning and near infrared spectroscopy in cereal analysis" LINK

"Using near infrared spectroscopy to determine the scots pine place of growth" LINK

"Chagas disease vectors identification using visible and near-infrared spectroscopy" LINK

"Ensemble of extreme learning machines for multivariate calibration of near-infrared spectroscopy" LINK

"Quantification of Silymarin in Silybum marianum with near-infrared spectroscopy: a comparison of benchtop vs. handheld devices" LINK

"N-way partial least squares combined with new self-construction strategy—A promising approach of using near infrared spectral data for quantitative determination of …" LINK

"Identification of rice flour types with near-infrared spectroscopy associated with PLS-DA and SVM methods" LINK

" Multivariate Classification of Prunus Dulcis Varieties using Leaves of Nursery Plants and Near Infrared Spectroscopy" LINK




Hyperspectral

"Frost damage to maize in northeast India: assessment and estimated loss of yield by hyperspectral proximal remote sensing" LINK

"Identification of authenticity, quality and origin of saffron using hyperspectral imaging and multivariate spectral analysis" LINK




Chemometrics

"Early detection of chilling injury in green bell peppers by hyperspectral imaging and chemometrics" LINK

"Evaluating photosynthetic pigment contents of maize using UVE-PLS based on continuous wavelet transform" LINK

"Near-infrared spectroscopy coupled with chemometrics algorithms for the quantitative determination of the germinability of Clostridium perfringens in four different …" LINK

"Analysis of residual moisture in a freeze-dried sample drug using a multivariate fitting regression model" LINK

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

"Standardisation of near infrared hyperspectral imaging for quantification and classification of DON contaminated wheat samples" LINK

"Vibrational spectroscopy and chemometric data analysis: the principle components of rapid quality control of herbal medicines" LINK

"A Model for Yellow Tea Polyphenols Content Estimation Based on Multi-Feature Fusion" LINK




Process Control

"Review of near-infrared spectroscopy as a process analytical technology for real-time product monitoring in dairy processing" LINK




Environment

"POTENTIAL OF SENSOR-BASED SORTING IN ENHANCED LANDFILL MINING" LINK

"Characterization of the salt marsh soils and visible-near-infrared spectroscopy along a chronosequence of Spartina alterniflora invasion in a coastal wetland of …" LINK




Agriculture

"Remote Sensing, Vol. 12, Pages 126: Predicting Forage Quality of Grasslands Using UAV-Borne Imaging Spectroscopy" LINK

"Novel implementation of laser ablation tomography as an alternative technique to assess grain quality and internal insect development in stored products" LINK

"Comparative Study of Two Different Strategies for Determination of Soluble Solids Content of Apples From Multiple Geographical Regions by Using FT-NIR Spectroscopy" LINK




Food & Feed

"Adulteration of Olive Oil" LINK




Laboratory

"Laboratory Raman and VNIR spectroscopic studies of jarosite and other secondary mineral mixtures relevant to Mars" LINK




Other

"Combining analytical tools to identify adulteration: some practical examples" LINK

"... questioned whether the growth and sustainability of AI technology will lead to the need for two copyright systems — one to address human creation and one to address machine creation." LINK





CalibrationModel.com

Knowledge-Based Variable Selection and Model Selection for near infrared spectroscopy NIRS LINK

Stop wasting too much time for NIRS Chemometrics Method development | foodanalyticaltechnologies analytic qualitycontrol foodindustry beverageindustry materialsensing LINK

Spectroscopy and Chemometrics News Weekly 7, 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 7, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 7, 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)

"Determination of Glucose by NIR Spectroscopy Under Magnetic Field" LINK

"Sensors, Vol. 20, Pages 230: The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods when using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods" LINK

"Quantum mechanical modeling of NIR spectra of thymol" LINK

"Using a handheld near-infrared spectroscopy (NIRS) scanner to predict meat quality" LINK

"NIR spectroscopy in simulation–a new way for augmenting near-infrared phytoanalysis" LINK

"Using visible-near-infrared spectroscopy to classify lichens at a Neotropical Dry Forest" LINK

"Near infrared spectroscopy as a rapid method for detecting paprika powder adulteration with corn flour" LINK

"Application of deep learning and near infrared spectroscopy in cereal analysis" LINK

"Using near infrared spectroscopy to determine the scots pine place of growth" LINK

"Chagas disease vectors identification using visible and near-infrared spectroscopy" LINK

"Ensemble of extreme learning machines for multivariate calibration of near-infrared spectroscopy" LINK

"Quantification of Silymarin in Silybum marianum with near-infrared spectroscopy: a comparison of benchtop vs. handheld devices" LINK

"N-way partial least squares combined with new self-construction strategy—A promising approach of using near infrared spectral data for quantitative determination of …" LINK

"Identification of rice flour types with near-infrared spectroscopy associated with PLS-DA and SVM methods" LINK

" Multivariate Classification of Prunus Dulcis Varieties using Leaves of Nursery Plants and Near Infrared Spectroscopy" LINK




Hyperspectral

"Frost damage to maize in northeast India: assessment and estimated loss of yield by hyperspectral proximal remote sensing" LINK

"Identification of authenticity, quality and origin of saffron using hyperspectral imaging and multivariate spectral analysis" LINK




Chemometrics

"Early detection of chilling injury in green bell peppers by hyperspectral imaging and chemometrics" LINK

"Evaluating photosynthetic pigment contents of maize using UVE-PLS based on continuous wavelet transform" LINK

"Near-infrared spectroscopy coupled with chemometrics algorithms for the quantitative determination of the germinability of Clostridium perfringens in four different …" LINK

"Analysis of residual moisture in a freeze-dried sample drug using a multivariate fitting regression model" LINK

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

"Standardisation of near infrared hyperspectral imaging for quantification and classification of DON contaminated wheat samples" LINK

"Vibrational spectroscopy and chemometric data analysis: the principle components of rapid quality control of herbal medicines" LINK

"A Model for Yellow Tea Polyphenols Content Estimation Based on Multi-Feature Fusion" LINK




Process Control

"Review of near-infrared spectroscopy as a process analytical technology for real-time product monitoring in dairy processing" LINK




Environment

"POTENTIAL OF SENSOR-BASED SORTING IN ENHANCED LANDFILL MINING" LINK

"Characterization of the salt marsh soils and visible-near-infrared spectroscopy along a chronosequence of Spartina alterniflora invasion in a coastal wetland of …" LINK




Agriculture

"Remote Sensing, Vol. 12, Pages 126: Predicting Forage Quality of Grasslands Using UAV-Borne Imaging Spectroscopy" LINK

"Novel implementation of laser ablation tomography as an alternative technique to assess grain quality and internal insect development in stored products" LINK

"Comparative Study of Two Different Strategies for Determination of Soluble Solids Content of Apples From Multiple Geographical Regions by Using FT-NIR Spectroscopy" LINK




Food & Feed

"Adulteration of Olive Oil" LINK




Laboratory

"Laboratory Raman and VNIR spectroscopic studies of jarosite and other secondary mineral mixtures relevant to Mars" LINK




Other

"Combining analytical tools to identify adulteration: some practical examples" LINK

"... questioned whether the growth and sustainability of AI technology will lead to the need for two copyright systems — one to address human creation and one to address machine creation." LINK





CalibrationModel.com

Knowledge-Based Variable Selection and Model Selection for near infrared spectroscopy NIRS LINK

Stop wasting too much time for NIRS Chemometrics Method development | foodanalyticaltechnologies analytic qualitycontrol foodindustry beverageindustry materialsensing LINK

Spectroscopy and Chemometrics News Weekly 7, 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 7, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 7, 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)

"Determination of Glucose by NIR Spectroscopy Under Magnetic Field" LINK

"Sensors, Vol. 20, Pages 230: The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods when using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods" LINK

"Quantum mechanical modeling of NIR spectra of thymol" LINK

"Using a handheld near-infrared spectroscopy (NIRS) scanner to predict meat quality" LINK

"NIR spectroscopy in simulation–a new way for augmenting near-infrared phytoanalysis" LINK

"Using visible-near-infrared spectroscopy to classify lichens at a Neotropical Dry Forest" LINK

"Near infrared spectroscopy as a rapid method for detecting paprika powder adulteration with corn flour" LINK

"Application of deep learning and near infrared spectroscopy in cereal analysis" LINK

"Using near infrared spectroscopy to determine the scots pine place of growth" LINK

"Chagas disease vectors identification using visible and near-infrared spectroscopy" LINK

"Ensemble of extreme learning machines for multivariate calibration of near-infrared spectroscopy" LINK

"Quantification of Silymarin in Silybum marianum with near-infrared spectroscopy: a comparison of benchtop vs. handheld devices" LINK

"N-way partial least squares combined with new self-construction strategy—A promising approach of using near infrared spectral data for quantitative determination of …" LINK

"Identification of rice flour types with near-infrared spectroscopy associated with PLS-DA and SVM methods" LINK

" Multivariate Classification of Prunus Dulcis Varieties using Leaves of Nursery Plants and Near Infrared Spectroscopy" LINK




Hyperspectral

"Frost damage to maize in northeast India: assessment and estimated loss of yield by hyperspectral proximal remote sensing" LINK

"Identification of authenticity, quality and origin of saffron using hyperspectral imaging and multivariate spectral analysis" LINK




Chemometrics

"Early detection of chilling injury in green bell peppers by hyperspectral imaging and chemometrics" LINK

"Evaluating photosynthetic pigment contents of maize using UVE-PLS based on continuous wavelet transform" LINK

"Near-infrared spectroscopy coupled with chemometrics algorithms for the quantitative determination of the germinability of Clostridium perfringens in four different …" LINK

"Analysis of residual moisture in a freeze-dried sample drug using a multivariate fitting regression model" LINK

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

"Standardisation of near infrared hyperspectral imaging for quantification and classification of DON contaminated wheat samples" LINK

"Vibrational spectroscopy and chemometric data analysis: the principle components of rapid quality control of herbal medicines" LINK

"A Model for Yellow Tea Polyphenols Content Estimation Based on Multi-Feature Fusion" LINK




Process Control

"Review of near-infrared spectroscopy as a process analytical technology for real-time product monitoring in dairy processing" LINK




Environment

"POTENTIAL OF SENSOR-BASED SORTING IN ENHANCED LANDFILL MINING" LINK

"Characterization of the salt marsh soils and visible-near-infrared spectroscopy along a chronosequence of Spartina alterniflora invasion in a coastal wetland of …" LINK




Agriculture

"Remote Sensing, Vol. 12, Pages 126: Predicting Forage Quality of Grasslands Using UAV-Borne Imaging Spectroscopy" LINK

"Novel implementation of laser ablation tomography as an alternative technique to assess grain quality and internal insect development in stored products" LINK

"Comparative Study of Two Different Strategies for Determination of Soluble Solids Content of Apples From Multiple Geographical Regions by Using FT-NIR Spectroscopy" LINK




Food & Feed

"Adulteration of Olive Oil" LINK




Laboratory

"Laboratory Raman and VNIR spectroscopic studies of jarosite and other secondary mineral mixtures relevant to Mars" LINK




Other

"Combining analytical tools to identify adulteration: some practical examples" LINK

"... questioned whether the growth and sustainability of AI technology will lead to the need for two copyright systems — one to address human creation and one to address machine creation." 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


Unser Service ist anders

  • Wir erstellen die optimalen quantitativen Vorhersagemodelle für Ihre NIR-Analysebedürfnisse (kein Bedarf an mathematisch/statistischer Modellbildungssoftware an Ihrem Standort).
  • Die NIR-Predictor-Software und die Kalibrierungsmodelle sind bei Ihnen vor Ort. Für unseren Service ist keine Internetverbindung erforderlich. Sie können unbegrenzt Vorhersagen machen, die schnelle Messzyklen ohne zusätzliche Kosten ermöglichen (Nicht pro Vorhersage bezahlt).
  • Sie besitzen Ihre NIR + Lab Daten und die Kalibrierungen. Sie können auf den detaillierten Kalibrierbericht mit allen Einstellungen und Statistiken zugreifen (keine Black-Box-Modelle).

NIR-Kalibrierungen erhalten

Workflow für NIR-Kalibrierungen

Ihre 4 Schritte zur anwendbaren NIR Kalibrierung:

  1. Download kostenloser NIR-Prädiktor hier
  2. Kombiniere deine NIR-Spektren mit Labor-Referenzwerten, siehe Video für 2. und 3.
    (Handbuch)

  3. Erstellen Sie eine Kalibrierungsanfrage und senden Sie sie an info@CalibrationModel.com (CM)
  4. Nach der Bearbeitung erhalten Sie einen Link zum Web Shop zum Herunterladen der Kalibrierungen.

NIR-Kalibrierungen verwenden

NIR-Kalibrierungs-Workflow verwenden



weitere Videos ansehen

Mit anderen Worten

Calibration Model vereinfacht den Prozess des Trainings von maschinellen Lernmodellen für NIRS-Daten
und bietet gleichzeitig die Möglichkeit, verschiedene Algorithmen und angewandtes Wissen aus der Nahinfrarotspektroskopie (NIRS) auszuprobieren.
Es ist mehr als nur eine AutoML-Plattform, es ist ein umfassender Service, bei dem Sie das optimale Modell und seinen beschreibenden Kalibrierungsbericht herunterladen können,
die Einblicke in die Datenaufbereitung, Feature-Engineering, Modelltraining und Hyperparameter-Tuning geben.

NIR Kalibrierung Starten


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

How to develop near-infrared spectroscopy calibrations in the 21st Century? / Wie werden Nahinfrarotspektroskopie Kalibrierungen im 21. Jahrhundert entwickelt?

The Problem

Calibration modeling is a complex and very important part of NIR spectroscopy, especially for quantitative analysis. If the model is badly designed the best instrument precision and highest data quality does not help getting good and robust measurement results. And NIR Spectroscopy requires periodically recalibration and validation.

How are NIR models built today?

In a typical usage in industry, a single person is responsible to develop the models (see survey). He or she uses a Chemometric software that has a click-and-wait working process to adjust all the possible settings for the used algorithms in dialogs and wait for calculations and graphics and then to think about the next modeling steps and the time is limited to do so. Do we expect to find the best use-able or optimal model that way? How to develop near-infrared spectroscopy calibrations in the 21st Century?

Our Solution

Why not put all the knowledge a good model builder is using into software and let the machines do the possibilities of calculations and presenting the result? Designing the software that way, that the domain knowledge is built-in, not just only the algorithms for machine learning and make it possible to scale the calculations to multi-core computers and up to cloud servers. Extend the Chemometric Software with the Domain Knowledge and make as much computer power available as needed.

As it was since the beginning

User  → Chemometric Software → one Computer → some results to choose from

==> User's time needed to click-and-wait for creating results

Our Solution

User → (Domain Knowledge → automatized Chemometric Software) → many Computers → the best models

==> User's time used to study the best models and reasoning about his product / process

Note that the “Domain Knowledge” here does perfectly support the User's product and process knowledge to get the things done right and efficient.

Scaling at three layers

  • Knowledge : use the domain knowledge to drive the Chemometric Software
  • Chemometric Software : support many machine learning algorithms and data pre-processings and make it automatic
  • Computer : support multi-core calculations and scale it to the cloud
The hard part in doing this, is of course the aggregation of the needed domain knowledge and transform it into software. The Domain Knowledge for building Chemometric NIR Spectroscopic models is well known and it's huge and spreads multiple disciplines. Knowledge-driven software for computing helps to find the gold needle in the haystacks. It's all about scaling that makes it possible. See Proof of Concept.

New possibilities

  • NIR users can get help working more efficient and getting better models.
  • New types of applications for NIR can be discovered.
  • Evaluation of NIR Applications to replace conventional analytical methods.
  • Hopeless calibrations development efforts can be re-started.
  • Higher model accuracy and robustness can be delivered.
  • Automate the experimental data part of your application study.
  • Person independent optimization will show new solutions, because it's not limited by a single mindset => combining all the aggregated knowledge and its combinations.
  • Software independent optimization will show new solutions, because none of vendor specific limitations and missing algorithms are present => combining all open available algorithms and there permutations.
  • Computing service is included.

Contact us for trial

Your NIR data is modeled by thousands of different useful calibration models and you get the best of them! That was not possible before in such a easy and fast way! Start Calibrate See How it works

Customized NIR CalibrationsAngepasste NIR KalibrationenTarature NIR personalizzate

Increase Your Profit with optimized NIR Accuracy


We help you to find the optimal settings for higher NIR accuracy and reliability.

You can build your own custom NIR calibration model with this valuable settings.

We offer a quantitative NIR Calibration development and optimization service.

New: free NIR-Predictor Software

White Paper about the details, what's behind.

Start Calibrate


Improve NIR Measurement Accuracy

  • going closer to your product specification limits and maximize profitability
  • optimizing your models yield to process optimization and optimizing productivity
  • compete against other NIR vendors in a feasibility study (NIR salesman)

Easy to use

  • compatible with any NIR vendor
  • no installation, no learning
  • quantitative NIR Calibration Development as a Service

Safety

  • help users avoid common pitfalls of method development
  • before you validate and approve your solution for use in production process:
    • check if a better calibration can be found,
    • compare your calibration with other experts solutions.

Speed

  • no cumbersome trial-and-error modeling steps
  • calculation time is spent on our high performance infrastructure
  • fast results, developed calibrations within days

Fix price

  • fix costs, depends only on data size (not hourly rate for service)
  • huge saving in method development costs
  • easy to plan
More benefits, for whom and where, learn more , contact

Steigern Sie Ihren Gewinn mit optimierter NIR Genauigkeit


Wir helfen Ihnen, die optimalen Einstellungen für eine höhere NIR Genauigkeit und Zuverlässigkeit zu finden.

Sie können Ihre eigenen NIR-Kalibrierungs Modelle mit diesen optimierten Einstellungen erzeugen.

Wir bieten einen quantitative NIR-Kalibrierung und Optimierungs Service.

Neu: free NIR-Predictor Software

White Paper (English) über die Details, was dahinter steckt.

Start Calibrate


NIR Messgenauigkeit Verbessern

  • näher an Ihre Produkt Spezifikationsgrenzwerte gehen und Rentabilität maximieren
  • Optimierung Ihrer Modelle ergeben eine Prozessoptimierung und Optimierung der Produktivität
  • Wettbewerb gegen andere NIR-Anbieter in einer Machbarkeitsstudie

Einfach anzuwenden

  • kompatibel mit jedem NIR Anbieter
  • keine Installation, kein Lernen
  • quantitative NIR Calibration Development as a Service

Sicherheit

  • hilft häufige Fehler bei der Methodenentwicklung zu vermeiden
  • bevor Sie Ihre Lösung validieren und freigeben für den Einsatz in der Produktion:
    • überprüfen Sie ob eine bessere Kalibrierung gefunden werden kann
    • vergleichen Sie Ihre Kalibrierung mit Lösungen anderer Experten

Geschwindigkeit

  • keine umständliche Versuch-und-Irrtum Modellierungs Schritte
  • Rechenzeit auf unseren Hochleistungs-Infrastruktur auslagern
  • schnelle Ergebnisse, Kalibrierungen innerhalb weniger Tage entwickelt

Festpreisangebote

  • Fixkosten, hängt nur von Datengröße ab (nicht Stundensatz für Service)
  • enorme Einsparung bei den Methodenentwicklungs Kosten
  • einfach zu planen
Mehr Vorteile, für wen und wo, erfahren Sie mehr, Kontakt

Increase Your Profit with optimized NIR Accuracy


We help you to find the optimal settings for higher NIR accuracy and reliability.

You can build your own custom NIR calibration model with this valuable settings.

We offer a quantitative NIR Calibration development and optimization service.

New: free NIR-Predictor Software

White Paper about the details, what's behind.

Start Calibrate


Improve NIR Measurement Accuracy

  • going closer to your product specification limits and maximize profitability
  • optimizing your models yield to process optimization and optimizing productivity
  • compete against other NIR vendors in a feasibility study (NIR salesman)

Easy to use

  • compatible with any NIR vendor
  • no installation, no learning
  • quantitative NIR Calibration Development as a Service

Safety

  • help users avoid common pitfalls of method development
  • before you validate and approve your solution for use in production process:
    • check if a better calibration can be found,
    • compare your calibration with other experts solutions.

Speed

  • no cumbersome trial-and-error modeling steps
  • calculation time is spent on our high performance infrastructure
  • fast results, developed calibrations within days

Fix price

  • fix costs, depends only on data size (not hourly rate for service)
  • huge saving in method development costs
  • easy to plan
More benefits, for whom and where, learn more , contact

NIR InstrumentsNIR-SpektrometerSpettrometro NIR

The NIR-Analysis (NIRA) also known as Near Infrared Reflectance (NIRS-Analysis) or NIR Transmission (NIT-Analysis) uses so called NIR-Spectrometer (see also NIR-Spectroscopy, NIR Spectrometry).


The supported NIR-instruments, NIR-analysers, NIR-sensors and NIR-spectrometer (near-infrared spectroscopy) are full range NIR (780-2500 [nm] or 12'820-4'000 [1/cm]) from any manufacturer and technology and also Short Wave InfraRed (SWIR) (900-1700 [nm]), that is typically used in Hyperspectral Imaging (HSI) or VIS-NIR (400-2500 [nm]) or UV-VIS-NIR (200-2500 [nm]). Definitions by IUPAC


The supported NIR-technology can be Fourier transform (FT-NIR), dispersive NIR (DLP, MEMS), NIR-diode-array, Acousto-optic tunable filter (AOTF, AOTFNIR), etc. (on-line, in-line or at-line)



Time-saving Calibration Support for all NIRS

New : NIR-Predictor Software for all NIR spectrometers! Analyze your samples.


Start Calibrate


Example overview list of NIR-spectrometer manufacturers / vendors / brands / supplier (we DO NOT SELL instruments) :



New : NIR-Predictor Software for all NIR spectrometers! Analyze your samples.


Start Calibrate


Example overview list of Miniature Near-Infrared (NIR) Spectrometer Engine (spectral sensor) manufacturers / vendors / brands (we DO NOT SELL instruments) :

Disclaimer: We have no affiliation with any of these sites or their companies.
All trademarks belong to their respective owners and are used for information only.
We DO NOT SELL instruments.




Die NIR Analyse (NIRA) auch bekannt als Nahinfrarot Reflectance (NIRS-Analyse) oder NIR Transmission (NIT-Analyse) verwendet sogenannte NIR-Spektrometer (siehe auch NIR-Spektroskopie).


Die unterstützten NIR-Analysegeräte, NIR-Sensoren und NIR-Spektrometer (Nahinfrarotspektroskopie) sind Full-Range NIR (780-2500 [nm] oder 12'820-4'000 [1/cm]) von beliebigem Hersteller und Technologie und auch Kurzwellen-Infrarot (SWIR) (900-1700 [nm]), das bei Hyperspektraler Bildgebung verwendet wird oder VIS-NIR (400-2500 [nm]) oder UV-VIS-NIR (200-2500 [nm]). Definitionen von IUPAC


Die unterstützte NIR-Technologie umfasst Fourier Transform (FT-NIR), Dispersiv NIR (DLP, MEMS), NIR Diodenarray, Acousto-optic tunable filter (AOTF, AOTFNIR), etc. (on-line, in-line oder at-line)



Zeitsparender Calibration Support für alle NIRS

Neu : NIR-Predictor Software für alle NIR-Spektrometer! Analysieren Sie Ihre Proben.


Start Calibrate


Beispiel Übersicht Liste von NIR-Spektrometer Marken Herstellern / Lieferanten (we DO NOT SELL instruments) :



Neu : NIR-Predictor Software für alle NIR-Spektrometer! Analysieren Sie Ihre Proben.


Start Calibrate


Beispiel Übersicht Liste von Miniatur Nah-Infrarot (NIR) Spektrometer Engine (Spektralsensor) Hersteller / Anbieter / Marken (we DO NOT SELL instruments):

Disclaimer: Wir haben keine Verbindung mit jeder dieser Sites oder deren Gesellschaften.
Alle Warenzeichen gehören ihren jeweiligen Inhabern und werden nur zu Informationszwecken benutzt.
We DO NOT SELL instruments.

The NIR-Analysis (NIRA) also known as Near Infrared Reflectance (NIRS-Analysis) or NIR Transmission (NIT-Analysis) uses so called NIR-Spectrometer (see also NIR-Spectroscopy, NIR Spectrometry).


The supported NIR-instruments, NIR-analysers, NIR-sensors and NIR-spectrometer (near-infrared spectroscopy) are full range NIR (780-2500 [nm] or 12'820-4'000 [1/cm]) from any manufacturer and technology and also Short Wave InfraRed (SWIR) (900-1700 [nm]), that is typically used in Hyperspectral Imaging (HSI) or VIS-NIR (400-2500 [nm]) or UV-VIS-NIR (200-2500 [nm]). Definitions by IUPAC


The supported NIR-technology can be Fourier transform (FT-NIR), dispersive NIR (DLP, MEMS), NIR-diode-array, Acousto-optic tunable filter (AOTF, AOTFNIR), etc. (on-line, in-line or at-line)



Time-saving Calibration Support for all NIRS

New : NIR-Predictor Software for all NIR spectrometers! Analyze your samples.


Start Calibrate


Example overview list of NIR-spectrometer manufacturers / vendors / brands / supplier (we DO NOT SELL instruments) :



New : NIR-Predictor Software for all NIR spectrometers! Analyze your samples.


Start Calibrate


Example overview list of Miniature Near-Infrared (NIR) Spectrometer Engine (spectral sensor) manufacturers / vendors / brands (we DO NOT SELL instruments) :

Disclaimer: We have no affiliation with any of these sites or their companies.
All trademarks belong to their respective owners and are used for information only.
We DO NOT SELL instruments.




NIR Calibration Service / NIR Kalibrations Service / Servizio di calibrazione NIR

New : free NIR-Predictor Software for all NIR instrument types! Analyze your samples.

Services and software for data analysis and analytical modeling for spectroscopy.

This NIR calibration service provides the custom development of optimal quantitative NIR calibration models based on your collected NIR and reference data for vendor independent full range NIR spectrometer analyzers (NIR = Near Infra Red spectroscopy) based on chemometric multivariate methods like Partial Least Square Regression (PLS, PLSR) and Principal Component Regression (PCR).

The key points

The NIR calibration model is decisive for the analysis accuracy.

NIR analysis results make the difference.

Near-Infrared Data Modeling Calibration Service

The problems

Imagine how many publications and literature of NIR spectroscopy (JNIRS) and chemometrics (Journal of Chemometrics) is present.

Did you find the time for the right to designate to read, to study, to incorporate them into practice?

Do you have all this knowledge at your calibration developments always present, that you consider anything important, the statistical results, interpret them correctly, analyze the graphs accurately and apply all the tips & tricks of optimizing correctly?

We have the solution for you!

We'll help you to create and optimize your calibrations.
We will help you for the time-consuming and knowledge-intensive part.
You get the best calibration solution and decide for yourself

Try it and see for yourself

New : free NIR-Predictor Software for all NIR instrument types! Analyze your samples.


Dienstleistungen und Software für die Datenanalyse und analytische Modellierung für die Spektroskopie.

Dieser NIR Kalibrations Service bietet die kundenspezifische Entwicklung von optimalen quantitativen NIR-Kalibrierungs Modellen für Ihre gesammelten NIR und Referenzdaten für herstellerunabhängige full-range NIR-Spektrometer Analysatoren an (NIR = Nah InfraRot-Spektroskopie) basierend auf chemometrischen multivariaten Methoden wie Partial Least Square Regression (PLS, PLSR) und Principal Component Regression (PCR).

Die Kernpunkte

Das NIR Kalibrations Modell ist entscheidend für die Analysen Genauigkeit.

NIR-Analysen Ergebnisse machen den Unterschied.

Near-Infrared Data Modeling Calibration Service

Die Probleme

Stellen Sie sich vor, wie viele Publikationen und Literatur zum Thema NIR (JNIRS) und Chemometrie (Journal of Chemometrics) vorhanden ist.

Haben Sie die Zeit, die für Sie die passende zu finden, zu bestellen, zu lesen, zu studieren, in die Praxis einfließen zu lassen?

Haben Sie dieses ganze Knowhow bei Ihren Kalibrations-Entwicklungen immer präsent, dass Sie alles wichtige Berücksichtigen, die statistischen Ergebnisse richtig deuten, die Grafiken genau analysieren und alle Tips & Tricks und Regeln der Kunst des Optimierens korrekt anwenden?

Wir haben die Lösung für Sie!

Wir helfen Ihnen, Ihre Kalibrationen zu erstellen und zu optimieren.
Wir helfen Ihnen für den zeitaufwendigen und Knowhow Intensiven Teil.

Sie erhalten die optimale Kalibrations Lösung und entscheiden selbst.
Probieren Sie es aus und sehen Sie selbst.

New : free NIR-Predictor Software for all NIR instrument types! Analyze your samples.


Servizi e software per l'analisi dei dati e la modellazione analitica per spettroscopia.

Il servizio di calibrazione NIR fornisce lo sviluppo personalizzato di modelli quantitativi di calibrazione NIR ottimizzati in base alla banca dati NIR presente nei vostri data base e con ulteriori dati di riferimento di fornitori indipendenti di spettrofotometri NIR (NIR = spettroscopia nel vicino infrarosso) sulla base di metodi chemiometrici multivariati come la funzione di regressione Least Square (PLS, PLSR) e delle Componenti Principali (PCR).

Il modello di calibrazione NIR è determinante per l'accuratezza dell'analisi. Sono i risultati delle analisi NIR a fare la differenza.

Near-Infrared Data Modeling Calibration Service

Immaginate quante pubblicazioni e letteratura sulla spettroscopia NIR (JNIRS) e persone che si occupano di chemiometria (Journal of Chemiometrics) siano presenti.

Non avete il tempo per leggere, studiare e far pratica su questo?

Avete tutta questa conoscenza a portata di mano per avere sviluppi di calibrazione sempre aggiornati da esperti; Cosa credete sia più importante: i risultati statistici, interpretarli correttamente, analizzare i grafici accuratamente e applicare tutti i consigli e i trucchi per ottimizzare correttamente i metodi di calibrazione?

Noi abbiamo la soluzione per voi!

Vi aiuteremo a creare e ottimizzare le tarature.
È possibile ottenere la migliore soluzione di calibrazione per Voi. Prova e vedere di persona