Spectroscopy and Chemometrics News Weekly #39, 2018Spektroskopie und Chemometrie Neuigkeiten Wöchentlich #39, 2018Spettroscopia e Chemiometria Weekly News #39, 2018

CalibrationModel.com

Automated : data cleaning, data transformation, method selection, outlier removing, parameter tuning, model selection, report generation => Making NIRS Spectroscopy easier LINK

Spectroscopy and Chemometrics News Weekly 38, 2018 | NIRS Spectroscopy Chemometrics analysis Spectral Spectrometer Sensors LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 38, 2018 | NIRS Spektroskopie Chemometrie analyse Spektral Spektrometer Sensor LINK

Spettroscopia e Chemiometria Weekly News 38, 2018 | NIRS Spettroscopia Chemiometria analisi Spettrale Spettrometro Sensore LINK

Chemometrics

"Estimating Avocado Sales Using Machine Learning Algorithms and Weather Data" LINK

"Models of near infrared spectroscopy of fatty acid contents in rapeseed" LINK

"Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm." LINK

"Non-targeted NIR spectroscopy and SIMCA classification for commercial milk powder authentication: A study using eleven potential adulterants" NIRS LINK

"Support Vector Machine Optimized by Genetic Algorithm for Data Analysis of Near-Infrared Spectroscopy Sensors" LINK

"TRANSFERABILITY OF PREDICTION EQUATIONS BETWEEN NIR INSTRUMENTS FOR PREDICTING INTRAMUSCULAR FAT AND MOISTURE IN HOMOGENISED BEEF M. LONGISSIMUS THORACIS" LINK

Near Infrared

"The application of FT-NIRS for the detection of bruises and the prediction of rot susceptibility of 'Hass' avocado fruit." fruits LINK

"Following the path of chemicals through the soil" NIRS LINK

"Application of reflectance spectroscopies (FTIR-ATR & FT-NIR) coupled with multivariate methods for robust invivo detection of begomovirus infection in papaya leaves." fruits LINK

"Long-Length Fiber Optic Near-Infrared (NIR) Spectroscopy Probes for On-Line Quality Control of Processed Land Animal Proteins" LINK

"Fault Detection Based on Near-Infrared Spectra for the Oil Desalting Process" NIRS LINK

"Diagnostics at Your Fingertips Thanks to Ultrathin Organic Photodetectors" NIR LINK

"Inline Spectroscopy: From Concept to Function - This article discusses inline spectroscopy from an instrumentation perspective." NIRS LINK

"A system using in situ NIRS sensors for the detection of product failing to meet quality standards and the prediction of optimal postharvest shelf-life in the case of oranges kept in cold storage" insitu fruits shelflife LINK

"Pre-harvest screening on-vine of spinach quality and safety using NIRS technology" portable LINK

"Predicting calcium in grape must and base wine by FT-NIR spectroscopy" FTNIR NIRS LINK

"NIR spectroscopy applied to the determination of 2‐Phenylethanol and L‐Phenylalanine concentrations in culture medium of Yarrowia lipolytica" LINK

"Application of FT-NIR spectroscopy for simultaneous estimation of taste quality and taste-related compounds content of black tea." FTNIR LINK

Infrared

"Prototype of the Near-Infrared Spectroscopy Expert System for Particleboard Identification" LINK

"Rapid Detecting Soluble Solid Content of Pears Based on Near-Infrared Spectroscopy" fruits LINK

"Improved production of polysaccharides in Ganoderma lingzhi mycelia by plasma mutagenesis and rapid screening of mutated strains through infrared spectroscopy" LINK

"Feasibility study on the use of a portable micro near infrared spectroscopy device for the "in vineyard" screening of extractable polyphenols in red grape skins" LINK

"Evaluation of salt content of curry soup containing coconut milk by near infrared spectroscopy" LINK

Hyperspectral

We have just published the 1st study exploring the SWIR spectra of ocean plastics in-situ. Our research highlights the potential of hyperspectral sensors to remotely quantity marine plastic pollution. LINK

Equipment

"On-site evaluation of Wagyu beef carcasses based on the monounsaturated, oleic, and saturated fatty acid composition using a handheld fiber-optic near-infrared spectrometer." LINK

Environment

"Assessment of cyanide contamination in soils with a handheld mid-infrared spectrometer." LINK

Pharma

"Accounting for spatial dependency in multivariate spectroscopic data" LINK

Medicinal

"A self-powered heart monitor taped to the skin" LINK

Other

"Differentiation of Apple Varieties and Investigation of Organic Status Using Portable Visible Range Reflectance Spectroscopy." LINK

"Al-light: An Alcohol-Sensing Smart Ice Cube" LINK

CalibrationModel.com

Chemometrics

Near Infrared

Infrared

Hyperspectral

Equipment

"On-site evaluation of Wagyu beef carcasses based on the monounsaturated, oleic, and saturated fatty acid composition using a handheld fiber-optic near-infrared spectrometer." LINK

Environment

"Assessment of cyanide contamination in soils with a handheld mid-infrared spectrometer." LINK

Pharma

"Accounting for spatial dependency in multivariate spectroscopic data" LINK

Medicinal

"A self-powered heart monitor taped to the skin" LINK

Other

"Differentiation of Apple Varieties and Investigation of Organic Status Using Portable Visible Range Reflectance Spectroscopy." LINK

"Al-light: An Alcohol-Sensing Smart Ice Cube" LINK

CalibrationModel.com

Chemometrics

Near Infrared

Infrared

Hyperspectral

Equipment

"On-site evaluation of Wagyu beef carcasses based on the monounsaturated, oleic, and saturated fatty acid composition using a handheld fiber-optic near-infrared spectrometer." LINK

Environment

"Assessment of cyanide contamination in soils with a handheld mid-infrared spectrometer." LINK

Pharma

"Accounting for spatial dependency in multivariate spectroscopic data" LINK

Medicinal

"A self-powered heart monitor taped to the skin" LINK

Other

"Differentiation of Apple Varieties and Investigation of Organic Status Using Portable Visible Range Reflectance Spectroscopy." LINK

"Al-light: An Alcohol-Sensing Smart Ice Cube" LINK

August 27, 2018

Spectroscopy and Chemometrics News Weekly #34, 2018Spektroskopie und Chemometrie Neuigkeiten Wöchentlich #34, 2018Spettroscopia e Chemiometria Weekly News #34, 2018

CalibrationModel.com

New : FREE NIR Predictor Software, drag&drop spectral data to plug&play calibrations, for any NIRS Analysis sensor type. | Chemometrics Prediction LINK

Spectroscopy and Chemometrics News Weekly 33, 2018 | NIRS Spectroscopy Chemometrics analysis Spectral Spectrometer Sensors LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 33, 2018 | NIRS Spektroskopie Chemometrie analyse Spektral Spektrometer Sensor LINK

Spettroscopia e Chemiometria Weekly News 33, 2018 | NIRS Spettroscopia Chemiometria analisi Spettrale Spettrometro Sensore LINK

We updated the Near Infrared (NIR) Spectrometer Directory of Suppliers / Manufacturers / Vendors. The list includes now also mobile miniature NIR spectrometer sensors. | NIRS NIR FTNIR NIT NearInfrared MEMS Spectral Sensor IoT LINK

Chemometrics

"Enhancing near infrared spectroscopy models to identify omega-3 fish oils used in the nutraceutical industry by means of calibration range extension" omega3 LINK

"Near infrared spectroscopy coupled with chemometric algorithms for predicting chemical components in black goji berries (Lycium ruthenicum Murr.)" LINK

"Towards innovation in paper dating: a MicroNIR analytical platform and chemometrics" nondestructive diagnostic forensic LINK

"Least-squares support vector machine and successive projection algorithm for quantitative analysis of cotton-polyester textile by near infrared spectroscopy" LINK

"Direct calibration transfer to principal components via canonical correlation analysis" NIRS corn tobacco LINK

"Collaborative representation based classifier with partial least squares regression for the classification of spectral data" LINK

Near Infrared

"Rapid and non-destructive discrimination of special-grade flat green tea using Near-infrared spectroscopy." LINK

"HOW DID SCIENTISTS DISCOVER WATER ON THE SURFACE OF THE MOON? .... used near-infrared reflectance spectroscopy (NIRS) to find surface water at the moon's polar regions. .... electromagnetic spectrum, from about 700 to 2,500 manometers." LINK

Infrared

"DSC, FTIR and Raman Spectroscopy Coupled with Multivariate Analysis in a Study of Co-Crystals of Pharmaceutical Interest" LINK

Equipment

"Calibration transfer of near infrared spectrometers for the assessment of plasma ethanol precipitation process" LINK

Laboratory

"ILS: An R package for statistical analysis in Interlaboratory Studies" | outliers ANOVA LINK

Other

"Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination" LINK

CalibrationModel.com

Chemometrics

Near Infrared

Infrared

"DSC, FTIR and Raman Spectroscopy Coupled with Multivariate Analysis in a Study of Co-Crystals of Pharmaceutical Interest" LINK

Equipment

"Calibration transfer of near infrared spectrometers for the assessment of plasma ethanol precipitation process" LINK

Laboratory

"ILS: An R package for statistical analysis in Interlaboratory Studies" | outliers ANOVA LINK

Other

"Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination" LINK

CalibrationModel.com

Chemometrics

Near Infrared

Infrared

"DSC, FTIR and Raman Spectroscopy Coupled with Multivariate Analysis in a Study of Co-Crystals of Pharmaceutical Interest" LINK

Equipment

"Calibration transfer of near infrared spectrometers for the assessment of plasma ethanol precipitation process" LINK

Laboratory

"ILS: An R package for statistical analysis in Interlaboratory Studies" | outliers ANOVA LINK

Other

"Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination" LINK

August 5, 2018August 7, 2018

Spectroscopy and Chemometrics News Weekly #31, 2018Spektroskopie und Chemometrie Neuigkeiten Wöchentlich #31, 2018Spettroscopia e Chemiometria Weekly News #31, 2018

Chemometrics

How to Configure the Number of Layers and Nodes in NeuralNetworks: BigData DataScience AI MachineLearning DeepLearning Algorithms by Source for graphic: | abdsc (2018.08.02) LINK

"Visible-Near-Infrared Spectroscopy can predict Mass Transport of Dissolved Chemicals through Intact Soil." (2018.08.02) LINK

"Classification and compositional characterization of different varieties of cocoa beans by near infrared spectroscopy and multivariate statistical analyses." (2018.08.02) LINK

"Classification of Chicken Parts Using a Portable Near-Infrared (NIR) Spectrophotometer and Machine Learning." (2018.08.02) LINK

"Rapid Prediction of Low (<1%) trans Fat Content in Edible Oils and Fast Food Lipid Extracts by Infrared Spectroscopy and Partial Least Squares Regression" (2018.07.31) LINK

"Evaluating the performance of a consumer scale SCiO™ molecular sensor to predict quality of horticultural products" (2018.07.30) LINK

"Estimation of Total Phenols, Flavanols and Extractability of Phenolic Compounds in Grape Seeds Using Vibrational Spectroscopy and Chemometric Tools" (2018.07.26) LINK

Near Infrared

"FT-NIR, MicroNIR and LED-MicroNIR for Detection of Adulteration in Palm Oil via PLS and LDA" FTNIR NIRS (2018.08.03) LINK

"Long-Length Fiber Optic Near-Infrared (NIR) Spectroscopy Probes for On-Line Quality Control of Processed Land Animal Proteins" (2018.08.03) LINK

"Near-infrared spectroscopy for rapid and simultaneous determination of five main active components in rhubarb of different geographical origins and processing." (2018.08.02) LINK

"Marktech Optoelectronics Introduces Silicon Avalanche Photodiodes for Low-Level Light and Short Pulse Detection" UV NIR NIRS SWIR (2018.08.02) LINK

"Innovative Technology Promises Fast, Cost-Efficient Age Data for Fisheries Management" FTNIR (2018.07.31) LINK

"Rapid qualitative and quantitative analysis of methamphetamine, ketamine, heroin, and cocaine by near-infrared spectroscopy." (2018.07.31) LINK

We (led by ) have been independently assessing thew value of consumer scale NIR devices for horticultural quality assessment. Here is our published work assessing (2018.07.30) LINK

Infrared

"Fault Detection Based on Near-Infrared Spectra for the Oil Desalting Process" (2018.08.05) LINK

"Common Infrared Optical Materials and Coatings: A Guide to Properties, Performance and Applications" (2018.08.04) LINK

Raman

SpectraBase - FREE, fast text access to hundreds of thousands of NMR, IR, Raman, UV-Vis, and mass spectra! spectroscopy (2018.08.02) LINK

Agriculture

"Three-Dimensional Reconstruction of Soybean Canopies Using Multisource Imaging for Phenotyping Analysis" (2018.08.03) LINK

"Smartphone Spectroscopy Promises a Data-Rich Future - An upsurge of portable, consumer-facing devices at the intersection of smartphone computing and spectroscopy is now leveraging integration. " (2018.08.02) LINK

Innovative Technology Promises Fast, Cost-Efficient Age Data for Fisheries Management (2018.07.31) LINK

"Smartphone-Based Food Diagnostic Technologies: A Review" (2018.07.30) LINK

Petro

"Detection, Purity Analysis, and Quality Assurance of Adulterated Peanut (Arachis Hypogaea) Oils" (2018.08.02) LINK

Pharma

"Potential of near infrared spectroscopy and pattern recognition for rapid discrimination and quantification of Gleditsia sinensis thorn powder with adulterants." (2018.08.02) LINK

Medicinal

A micro-spectrometer fit for a smartphone: Could the power to measure things like CO2, food freshness, and blood sugar levels soon be in the palm of our hands? |rld/magazine/article/323/micro-spectrometer-opens-door-to-a-wealth-of-new-smartphone-functions?utm_source=twitter.com/CalibModel health safety medicine spectroscopy (2018.02.25) LINK

"Near-infrared spectroscopy detects age-related differences in skeletal muscle oxidative function: promising implications for geroscience." (2018.02.08) LINK

Other

69% of decision makers say industrial analytics will be crucial for business in 2020. | IoT IIoT MT LINK

CalibrationModel.com

Free Chemometric NIR Predictor Software! Simple plug&play calibrations, drag&drop spectral data, for any NIRS sensor device. Easy to use software for off-line and real-time prediction without limits. offline realtime (2018.08.04) LINK

Automated NIRS spectroscopy chemometrics method development with MachineLearning for spectrometer Spectral IoT sensor SmartSensor SmartSensors (2018.07.25) LINK

Automatic NIR Spectroscopy Calibration-Development as a Service. Applicable with free NIR-Predictor software or via OEM API. | NIRSpectroscopy NearInfrared NIRanalysis spectrometers DataAnalytics Regression Spectral Sensors QualityControl Lab (2018.07.26) LINK

Increase Your Profit with optimized NIRS Accuracy with Calibration as a Service (CaaS) and the new free NIR-Predictor software. | foodsafety Feed Lab QC QA testing (2018.08.03) LINK

New : FREE NIR Predictor Software, drag&drop spectral data to plug&play calibrations, for any NIRS Analysis sensor type. | Chemometrics Prediction (2018.07.24) LINK

Spectroscopy and Chemometrics News (KW 11-30 2018) | NIRS Spectroscopy Chemometrics analysis Spectral Spectrometer Sensors (2018.07.25) LINK

Spektroskopie und Chemometrie Neuigkeiten (KW 11-30 2018) | NIRS Spektroskopie Chemometrie analyse Spektral Spektrometer Sensor (2018.07.25) LINK

Spettroscopia e Chemiometria Weekly (KW 11-30 2018) | NIRS Spettroscopia Chemiometria analisi Spettrale Spettrometro Sensore (2018.07.25) LINK

光谱学和化学计量学新闻（KW＃11-＃30 2018） | 近红外光谱化学计量学分析光谱仪传感器 (2018.07.26) LINK

分光法とケモメトリックスニュース（KW＃11-＃30 2018） | 赤外分光法・ケモメトリックスの分光センサーの近く (2018.07.26) LINK

Chemometrics

Near Infrared

Infrared

Raman

SpectraBase - FREE, fast text access to hundreds of thousands of NMR, IR, Raman, UV-Vis, and mass spectra! spectroscopy (2018.08.02) LINK

Agriculture

Petro

"Detection, Purity Analysis, and Quality Assurance of Adulterated Peanut (Arachis Hypogaea) Oils" (2018.08.02) LINK

Pharma

"Potential of near infrared spectroscopy and pattern recognition for rapid discrimination and quantification of Gleditsia sinensis thorn powder with adulterants." (2018.08.02) LINK

Medicinal

Other

69% of decision makers say industrial analytics will be crucial for business in 2020. | IoT IIoT MT LINK

CalibrationModel.com

Chemometrics

Near Infrared

Infrared

Raman

SpectraBase - FREE, fast text access to hundreds of thousands of NMR, IR, Raman, UV-Vis, and mass spectra! spectroscopy (2018.08.02) LINK

Agriculture

Petro

"Detection, Purity Analysis, and Quality Assurance of Adulterated Peanut (Arachis Hypogaea) Oils" (2018.08.02) LINK

Pharma

"Potential of near infrared spectroscopy and pattern recognition for rapid discrimination and quantification of Gleditsia sinensis thorn powder with adulterants." (2018.08.02) LINK

Medicinal

Other

69% of decision makers say industrial analytics will be crucial for business in 2020. | IoT IIoT MT LINK

CalibrationModel.com

July 25, 2018August 5, 2018

Spectroscopy and Chemometrics News (KW #11-#30 2018)Spectroscopy and Chemometrics News (KW #11-#30 2018)Spectroscopy and Chemometrics News (KW #11-#30 2018)

Chemometrics

"Identifying and filtering out outliers in spatial datasets" (2018.07.25) LINK

"Phenomic selection: a low-cost and high-throughput method based on indirect predictions. Proof of concept on wheat and poplar." (2018.07.25) LINK

"An improved variable selection method for support vector regression in NIR spectral modeling" (2018.07.25) LINK

"Near-Infrared Spectroscopy and Chemometrics for the Routine Detection of Bilberry Extract Adulteration and Quantitative Determination of the Anthocyanins" (2018.07.25) LINK

"Modern practical convolutional neural networks for multivariate regression: Applications to NIR calibration" (2018.07.25) LINK

"Automated NIR-Spectroscopy chemometrics-method development with machine-learning for spectrometers, spectral IoT-sensor/smart-sensors" - read without Hashtags (2018.07.25) LINK

An interesting explanation of "Automated Machine Learning vs Automated Data Science" | Automated MachineLearning DataScience (2018.07.03) LINK

"Evaluation of Quality Parameters of Apple Juices Using Near-Infrared Spectroscopy and Chemometrics" (2018.07.02) LINK

"Chemometric approaches for document dating: Handling paper variability" (2018.06.27) LINK

"Development and comparison of regression models for the determination of quality parameters in margarine spread samples using NIR spectroscopy" (2018.06.27) LINK

"The non-destructive technique such as Near Infrared Spectroscopy (NIRS) along with Chemometrics has been successful in predicting the quality parameters but not well established for on-vine/in-orchard fruit quality measurement." (2018.06.27) LINK

"Quantification of leghaemoglobin content in pea nodules based on near infrared hyperspectral imaging spectroscopy and chemometrics" (2018.06.15) LINK

"Exploring the Applicability of Quantitative Models Based on NIR Reflectance Spectroscopy of Plant Samples" | tobacco (2018.06.15) LINK

"Interval lasso regression based Extreme learning machine for nonlinear multivariate calibration of near infrared spectroscopic datasets" (2018.06.05) LINK

"Soft and Robust Identification of Body Fluid Using Fourier Transform Infrared Spectroscopy and Chemometric Strategies for Forensic Analysis" (2018.06.05) LINK

"Temporal decomposition sampling and chemical characterization of eucalyptus harvest residues using NIR spectroscopy and chemometric methods" (2018.06.05) LINK

: The emission spectrum of each element is a unique identifier — like the DNA of the element — and the spectral analysis of a light source is essentially a Principal Component Analysis of its components — like explanatory DataScience. Get your p… (2018.06.05) LINK

"Rice Classification Using Hyperspectral Imaging and Deep Convolutional Neural Network" DCNN (2018.05.31) LINK

"Development and comparison of regression models for determination of quality parameters in margarine spread samples using NIR spectroscopy" (2018.05.31) LINK

"Application of FTIR Spectroscopy and Chemometrics for Halal Authentication of Beef Meatball Adulterated with Dog Meat" (2018.05.31) LINK

Prediction of amino acids, caffeine, theaflavins and water extract in black tea by FT-NIR spectroscopy coupled chemometrics algorithms (2018.05.31) LINK

Chemometrics in Analytical Chemistry (CAC) Conference, Halifax, 17th CAC Meeting, June 25-29, 2018 (2018.05.31) LINK

Spatially Resolved Spectral Powder Analysis: Experiments and Modeling (2018.04.05) LINK

Calibration Transfer based on the Weight Matrix (CTWM) of PLS for near infrared (NIR) spectral analysis (2018.04.05) LINK

"A novel multivariate calibration method based on variable adaptive boosting partial least squares algorithm" (2018.03.28) LINK

"How Many ML Models You Have NOT Built?" (2018.03.28) LINK

"Calibration model maintenance in melamine resin production: Integrating drift detection, smart sample selection and model adaptation" (2018.03.27) LINK

"Application of NIR spectroscopy and chemometrics for revealing of the ‘high quality fakes’ among the medicines" (2018.03.27) LINK

"Dual-Domain Calibration Transfer Using Orthogonal Projection" (2018.03.14) LINK

"A Review of Calibration Transfer Practices and Instrument Differences in Spectroscopy" (2018.03.14) LINK

Near Infrared

"Near-infrared chemical imaging used for in-line analysis of functional finishes on textiles." (2018.07.25) LINK

"A comparison between NIR and ATR-FTIR spectroscopy for varietal differentiation of Spanish intact almonds" (2018.07.25) LINK

Worked examples of MSC and SNV correction for NIR spectroscopy in Python. nirs (2018.07.25) LINK

"Evolution of Frying Oil Quality Using Fourier Transform Near-Infrared (FT-NIR) Spectroscopy" - FryingOil FTNIR (2018.07.25) LINK

"Evaluation of drying of edible coating on bread using NIR spectroscopy" (2018.07.25) LINK

very interesting article. NIRS is cheaper than molecular markers (2018.07.13) LINK

FlowChemMondays application of a portable near-infrared spectrophotometer (MicroNIR) for in-line monitoring of the synthesis of 5-hydroxymethylfurfural (5-HMF) from D-fructose is described in OPRD | flowchemistry (2018.07.03) LINK

"Accurate and rapid detection of soil and fertilizer properties based on visible/near-infrared spectroscopy" (2018.06.25) LINK

"Detection of Veterinary Antimicrobial Residues in Milk through Near-Infrared Absorption Spectroscopy" (2018.06.05) LINK

"Calibration is key - the calibration is the most important part of the NIRS method" near-infrared reflectance spectroscopy NIRS - from the lab to the field... forage quality agchat handheldNIRS Lab2Field (2018.06.05) LINK

"Assessment of tomato soluble solids content and pH by spatially-resolved and conventional Vis/NIR spectroscopy" (2018.05.31) LINK

"A novel method for geographical origin identification of Tetrastigma hemsleyanum (Sanyeqing) by near-infrared spectroscopy" (2018.05.31) LINK

"Effects of moisture on automatic textile fiber identification by NIR spectroscopy" (2018.05.31) LINK

"Rapid, noninvasive detection of Zika virus in Aedes aegypti mosquitoes by near-infrared spectroscopy" (2018.05.31) LINK

Method for Identifying Maize Haploid Seeds by Applying Diffuse Transmission Near-Infrared Spectroscopy (2018.04.05) LINK

This article is about NIR Spectroscopy. (2018.03.27) LINK

"Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS-NIR Spectroscopy" (2018.03.27) LINK

"Concentration monitoring with near infrared chemical imaging in a tableting press" (2018.03.14) LINK

"NIR Chemical Imaging Can Help Maintain the Safety of Pharmaceutical Tablets" | NIRCI (2018.03.14) LINK

Infrared

"High Throughput Screening of Elite Loblolly Pine Families for Chemical and Bioenergy Traits with Near Infrared Spectroscopy" (2018.07.25) LINK

"Non-Destructive Methodology to Determine Modulus of Elasticity (MOE) in Static Bending of Quercus mongolica Using Near-Infrared Spectroscopy" wood (2018.06.27) LINK

"Near infrared spectroscopy as an alternative method for rapid evaluation of toluene swell of natural rubber latex and its products" (2018.06.25) LINK

"The Application of Discrete Wavelet Transform with Improved Partial Least-Squares Method for the Estimation of Soil Properties with Visible and Near-Infrared Spectral Data" (2018.06.05) LINK

"Mutual factor analysis for quantitative analysis by temperature dependent near infrared spectra." (2018.03.27) LINK

"Evaluating the Applications of the Near-Infrared Region in Mapping Foliar N in the Miombo Woodlands" (2018.03.27) LINK

Raman

"Differentiating Donor Age Groups Based on Raman Spectroscopy of Bloodstains for Forensic Purposes" (2018.06.27) LINK

Hyperspectral

"Evaluation of Near-Infrared Hyperspectral Imaging for Detection of Peanut and Walnut Powders in Whole Wheat Flour" (2018.07.03) LINK

"Potential of Visible and Near-Infrared Hyperspectral Imaging for Detection of Diaphania pyloalis Larvae and Damage on Mulberry Leaves" (2018.07.03) LINK

"Quantification of leghaemoglobin content in pea nodules based on near infrared hyperspectral imaging spectroscopy and chemometrics" (2018.06.15) LINK

Optics

"Spectral Fiber Sensors for Cancer Diagnostics" by artphotonics | Optical Fibers (2018.07.03) LINK

Facts

"This is your brain detecting patterns. It is different from other kinds of learning, study shows" (2018.06.01) LINK

Research

Android Tricorder: Google übernimmt Startup, das Körperwerte mit dem Smartphone messen kann (2017.08.16) LINK

Equipment

"Comparing the qualitative performances of handheld NIR and Raman spectrometers for the analysis of falsified pharmaceutical products." (2018.07.25) LINK

"Tiny $25 Spectrometer Aims to Identify Materials with Ease" (2018.05.31) LINK

"Comparison of Portable and Bench-Top Spectrometers for Mid-Infrared Diffuse Reflectance Measurements of Soils" (2018.03.28) LINK

Environment

Nonlinear Regression with High-Dimensional Space Mapping for Blood Component Spectral Quantitative Analysis (2018.02.02) LINK

Agriculture

"Bluret Protein Measurement Machine, a technological disrupter of its day" (2018.07.03) LINK

"Identification and quantification of microplastics in table sea salts using micro-NIR imaging methods" (2018.05.31) LINK

Food & Feed

"Non-Invasive Methodology to Estimate Polyphenol Content in Extra Virgin Olive Oil Based on Stepwise Multilinear Regression" (2018.03.27) LINK

Rapid Determination of Active Compounds and Antioxidant Activity of Okra Seeds Using Fourier Transform Near Infrared (FT-NIR) Spectroscopy FTNIR Polyphenols Flavonoids AntioxidantActivity | (2018.03.03) LINK

Pharma

"Quantification of pharmaceuticals contaminants in wastewaters by NIR spectroscopy" (2018.07.25) LINK

Laboratory

Spectroscopy used to be confined to the laboratory. Today, portable NeoSpectra SpectralSensors bring the power of NIR out of the lab and into the field. (2018.06.20) LINK

Other

"Non-Destructive Methodology to Determine Modulus of Elasticity (MOE) in Static Bending of Quercus mongolica Usin… (2018.06.27) LINK

"The Application of Discrete Wavelet Transform with Improved Partial Least-Squares Method for the Estimation of… (2018.06.05) LINK

: The emission spectrum of each element is a unique identifier — like the DNA of the element — and the spectral analysis of a light source is essentially a Principal Component Analysis of its components — like explanatory DataScience. Get your p… (2018.06.05) LINK

An Innovative Approach to Exploit the Reflection Spectroscopy of Liquid Characteristics (2018.04.05) LINK

"Combining Fractional Order Derivative and Spectral Variable Selection for Organic Matter Estimation of Homogeneous Soil Samples by VIS-NIR Spectroscopy" (2018.03.27) LINK

Chemometrics

Near Infrared

Infrared

Raman

"Differentiating Donor Age Groups Based on Raman Spectroscopy of Bloodstains for Forensic Purposes" (2018.06.27) LINK

Hyperspectral

Optics

"Spectral Fiber Sensors for Cancer Diagnostics" by artphotonics | Optical Fibers (2018.07.03) LINK

Facts

"This is your brain detecting patterns. It is different from other kinds of learning, study shows" (2018.06.01) LINK

Research

Android Tricorder: Google übernimmt Startup, das Körperwerte mit dem Smartphone messen kann (2017.08.16) LINK

Equipment

Environment

Nonlinear Regression with High-Dimensional Space Mapping for Blood Component Spectral Quantitative Analysis (2018.02.02) LINK

Agriculture

Food & Feed

Pharma

"Quantification of pharmaceuticals contaminants in wastewaters by NIR spectroscopy" (2018.07.25) LINK

Laboratory

Spectroscopy used to be confined to the laboratory. Today, portable NeoSpectra SpectralSensors bring the power of NIR out of the lab and into the field. (2018.06.20) LINK

Other

Chemometrics

Near Infrared

Infrared

Raman

"Differentiating Donor Age Groups Based on Raman Spectroscopy of Bloodstains for Forensic Purposes" (2018.06.27) LINK

Hyperspectral

Optics

"Spectral Fiber Sensors for Cancer Diagnostics" by artphotonics | Optical Fibers (2018.07.03) LINK

Facts

"This is your brain detecting patterns. It is different from other kinds of learning, study shows" (2018.06.01) LINK

Research

Android Tricorder: Google übernimmt Startup, das Körperwerte mit dem Smartphone messen kann (2017.08.16) LINK

Equipment

Environment

Nonlinear Regression with High-Dimensional Space Mapping for Blood Component Spectral Quantitative Analysis (2018.02.02) LINK

Agriculture

Food & Feed

Pharma

"Quantification of pharmaceuticals contaminants in wastewaters by NIR spectroscopy" (2018.07.25) LINK

Laboratory

Spectroscopy used to be confined to the laboratory. Today, portable NeoSpectra SpectralSensors bring the power of NIR out of the lab and into the field. (2018.06.20) LINK

Other

Procedures for NIR calibration – Creation of NIRS spectroscopy calibration curvesArbeitsweisen zur NIR Kalibrierung – Erstellung von NIRS-Spektroskopie Kalibrierungskurven Le procedure di calibrazione NIR – Realizzazione di curve di calibrazione NIRS spettroscopia

Do you know the effect that you prefer to try out their favorite data pretreatments in combination and often try the same wavelength selections based spectra of the visualized?

You try as six to ten combinations until one of them selects his favorite calibration model, to then continue to optimize. Since then suddenly fall to outliers, because it goes in depth, so is familiar with the data, we know now the spectra of numbers of outliers and is familiar with the extreme values.

Now, the focus is on the major components (principal components, Latent Variables, factors) and makes sure not to over-fit and under-fit not to. The whole takes a few hours and finally one is content with the model found.

So what would happen if you all in the beginning tried variants found outliers removed and re-evaluated and compared? The results would be better than that of the previous model choice? One does not try out? Because it is cumbersome and takes hours again?

We have developed a software which simplifies this so that also the number of model variations can be increased as desired. The variants generation is automated with an intelligent control system, as well as the optimization and comparing the models and finally the final selection of the best calibration model.

Our software includes all the usual known data pretreatment methods (data pre-processing) and can combine them useful. Since many Preteatments are directly dependent on the wavelength selection, such as the normalization the determined within a wavelength range of the scaling factors to normalize the spectra so that pretreatments with the wavelength ranges may be combined. So a variety of settings sensible model comes together that are all calculated and optimized. For the automatic selection of the relevant wavelength ranges, different methods are used, which are based on the spectral intensities. Thus, for example, regions with total absorption is not used, and often interfering water bands removed or retained.

Over all the calculated model variations as a summary outlier analysis can be made. Are there any new outliers (hidden outlier) discovered, all previous models can be automatically recalculated, optimized and compared without these outliers.

From this great number of calculated models with the statistical quality reviews (prediction performance) the optimum calibration can now be selected. For this purpose, not simply sorting by the prediction error (prediction error, SEP RMSEP) or the coefficient of determination (coefficient of determination r²), but by several statistical and test values are used jointly toward the final assessment of optimal calibration.

Thus we have created a platform that allows the highly automated work what a man can never do with a commercial software.

We therefore offer the largest number of matched to your application problem modeling calculations and choose the best calibration for you!

This means that our results are faster, more accurate, robust and objective basis (person independent) and quite easy for you to apply.

You have the full control of the models supplied by us, because we provide a clearly structured and detailed blueprint of the complete calibration, with all settings and parameters, with all necessary statistical characteristics and graphics.

Using this blueprint, you can adjust the quantitative calibration model itself in the software you use, understand and compare. You have everything under control form model creation, model validation and model refinement.

Your privacy is very important to us. The NIR data that you briefly provide us for the custom calibration development will remain of course your property. Your NIR data will be deleted after the job with us.

Start Calibrate

Interested, then do not hesitate to contact us.

Kennen Sie den Effekt, dass Sie bevorzugt ihre Lieblings-Datenvorbehandlungen in Kombination durchprobieren und oft die gleichen Wellenlängen-Selektionen anhand der visualisierten Spektren ausprobieren?

Man probiert z.B. sechs bis zehn Kombinationen aus, bis man davon sein favorisiertes Kalibrationsmodell auswählt, um es dann weiter zu optimieren. Da fallen dann plötzlich Ausreisser (Outlier) auf, weil man in die Tiefe geht, also mit den Daten vertraut ist, man kennt mittlerweile die Spektren-Nummern der Ausreisser und ist mit den Extremwerten vertraut.

Jetzt fokussiert man sich auf die Hauptkomponenten (Principal Components, Latent Variables, Faktoren) und achtet darauf nicht zu über-fitten und nicht zu unter-fitten. Das ganze dauert ein paar Stunden und schliesslich begnügt man sich mit dem gefundenen Modell.

Was wäre nun, wenn man in all den zu Beginn ausprobierten Varianten, die gefundenen Ausreisser entfernt und nochmals berechnet und vergleicht? Wären die Ergebnisse besser als die von der bisherigen Modell Wahl? Man probiert es nicht aus? Weil es mühsam ist und wieder Stunden dauert?

Wir haben eine Software entwickelt die dies so vereinfacht, dass auch die Anzahl der Modell Variationen beliebig erhöht werden kann. Die Varianten Erzeugung läuft automatisiert mit einem intelligenten Regelsystem, so auch die Optimierung und das Vergleichen der Modelle und schliesslich die finale Auswahl des Besten Kalibrations Modell.

Unsere Software beinhaltet alle üblichen bekannten Datenvorbehandlungs Methoden (Preteatments) und kann diese sinnvoll kombinieren. Da viele Preteatments direkt abhängig sind von der Wellenlängen Selektion, so z.B. die Normalisierungen die innerhalb eines Wellenlängen-Bereiches die Skalierungsfaktoren ermittelt, um die Spektren damit zu normieren, werden die Pretreatments mit dem Wellenlängen-Bereichen kombiniert. So kommt eine Vielzahl von sinnvollen Modell Einstellungen zusammen die alle berechnet und optimiert werden.

Für die automatische Auswahl der relevanten Wellenlängen Bereiche kommen verschiedene Methoden zum Einsatz, die sich an den Spektren Intensitäten orientieren. So werden z.B. Bereiche mit Totalabsorption nicht verwendet, oftmals störende Wasserbanden entfernt oder beibehalten.

Über all die berechneten Modell Variationen können so zusammenfassende Outlier Analysen gemacht werden. Werden durch die gefahrenen Versuche neue Outlier (Hidden Outlier) entdeckt, können alle bisherigen Modelle automatisch ohne diese Ausreisser nachberechnet, optimiert und verglichen werden.

Aus dieser Vielzahl berechneter Modelle mit deren Statistischen Güte Bewertungen (Prediction Performance) kann nun die optimale Kalibration ausgewählt werden. Dazu wird nicht einfach nach dem Vorhersage Fehler (Prediction Error, SEP, RMSEP) oder nach dem Bestimmtheitsmaß (Coefficient of Determination r²) sortiert, sondern mehrere Statistik- und Testwerte gemeinsam zur umfänglichen Beurteilung der optimalen Kalibration herangezogen.

Somit haben wir eine Plattform geschaffen, die es ermöglicht hochgradig automatisiert das zu tun, was ein Mensch niemals mit einer handelsüblichen Software tun kann.

Wir bieten damit die grösste Anzahl auf Ihr Applikations-Problem angepasste Modellierungs-Berechnungen und wählen die beste Kalibration für Sie aus!

Das heisst, unsere Ergebnisse sind schneller, genauer, robuster und objektiv ausgewählt (Personen unabhängig) und für Sie ganz einfach anzuwenden.

Die Kontrolle über die von uns gelieferten Modelle haben Sie vollumfänglich, denn wir liefern einen klar strukturierten und detaillierten Bauplan der kompletten Kalibration, mit allen Einstellungen und Parametern, mit allen notwendigen Statistischen Kenngrössen und Grafiken.

Anhand dieses Bauplans können Sie das quantitative Kalibrations Modell selbst in der von Ihnen verwendeten Software nachstellen, nachvollziehen und vergleichen. Sie haben so alles im Griff, für die Modell-Validierung und die Modellpflege.

Der Datenschutz ist uns sehr wichtig. Die NIR Daten, die Sie uns für die Kalibrations-Erstellung kurzzeitig zu Verfügung stellen bleiben selbstverständlich Ihr Eigentum. Ihre NIR Daten werden nach Abschluss des Auftrags bei uns gelöscht.

Start Calibrate

Interessiert, dann zögern Sie nicht uns zu kontaktieren.

Do you know the effect that you prefer to try out their favorite data pretreatments in combination and often try the same wavelength selections based spectra of the visualized?

You try as six to ten combinations until one of them selects his favorite calibration model, to then continue to optimize. Since then suddenly fall to outliers, because it goes in depth, so is familiar with the data, we know now the spectra of numbers of outliers and is familiar with the extreme values.

Now, the focus is on the major components (principal components, Latent Variables, factors) and makes sure not to over-fit and under-fit not to. The whole takes a few hours and finally one is content with the model found.

So what would happen if you all in the beginning tried variants found outliers removed and re-evaluated and compared? The results would be better than that of the previous model choice? One does not try out? Because it is cumbersome and takes hours again?

We have developed a software which simplifies this so that also the number of model variations can be increased as desired. The variants generation is automated with an intelligent control system, as well as the optimization and comparing the models and finally the final selection of the best calibration model.

Our software includes all the usual known data pretreatment methods (data pre-processing) and can combine them useful. Since many Preteatments are directly dependent on the wavelength selection, such as the normalization the determined within a wavelength range of the scaling factors to normalize the spectra so that pretreatments with the wavelength ranges may be combined. So a variety of settings sensible model comes together that are all calculated and optimized. For the automatic selection of the relevant wavelength ranges, different methods are used, which are based on the spectral intensities. Thus, for example, regions with total absorption is not used, and often interfering water bands removed or retained.

Over all the calculated model variations as a summary outlier analysis can be made. Are there any new outliers (hidden outlier) discovered, all previous models can be automatically recalculated, optimized and compared without these outliers.

From this great number of calculated models with the statistical quality reviews (prediction performance) the optimum calibration can now be selected. For this purpose, not simply sorting by the prediction error (prediction error, SEP RMSEP) or the coefficient of determination (coefficient of determination r²), but by several statistical and test values are used jointly toward the final assessment of optimal calibration.

Thus we have created a platform that allows the highly automated work what a man can never do with a commercial software.

We therefore offer the largest number of matched to your application problem modeling calculations and choose the best calibration for you!

This means that our results are faster, more accurate, robust and objective basis (person independent) and quite easy for you to apply.

You have the full control of the models supplied by us, because we provide a clearly structured and detailed blueprint of the complete calibration, with all settings and parameters, with all necessary statistical characteristics and graphics.

Using this blueprint, you can adjust the quantitative calibration model itself in the software you use, understand and compare. You have everything under control form model creation, model validation and model refinement.

Your privacy is very important to us. The NIR data that you briefly provide us for the custom calibration development will remain of course your property. Your NIR data will be deleted after the job with us.

Start Calibrate

Interested, then do not hesitate to contact us.

NIRS Calibration Model Equation – Optimal Predictive Model SelectionNIRS Calibration Model Equation – Optimal Predictive Model SelectionNIRS Calibration Model Equation – Optimal Predictive Model Selection

To give you an insight what we do to find the optimal model, imagine a NIR data set, where a NIR specialist works hard for 4 hours in his chemometric software to try what he can with his chemometric-, NIR spectroscopic- and his product-knowledge to get a good model. During the 4 hours he finds 3 final candidate models for his application. With the RMSEP of 0.49 , 0.51 and 0.6. Now he has to choose one or to test all his three models on new measured NIR spectra.

That is common practice. But is this good practice?

And nobody asks, how long, how hard have you tried, how many trial have you done, if this really the best model that is possible from the data?
And imagine the cost of the data collection including the lab analytics!
And behind this costs, have you really tried hard enough to get the best out of your data? Was the calibration done quick and dirty on a Friday afternoon? Yes, time is limited and manually clicking around and wait in such kind of software is not really fun, so what are the consequences?

Now I come to the most important core point ever, if you own expensive NIR spectrometer system, or even many of them, and your company has collected a lot of NIR spectra and expensive Lab-reference data over years, do you spend just a few hours to develop and build that model, that will define the whole system's measurement performance for the future? And ask yourself again (and your boss will ask you later), have you really tried hard enough, to get the best out of your data? really?

What else is possible? What does your competition do?

There is no measure (yet) what can be reached with a specific NIR data set.
And this is very interesting, because there are different beliefs if a secondary method like NIR or Raman can be more precise and accurate, as the primary method.

What we do different is, that our highly specialized software is capable of creating large amounts of useful calibrations to investigate this limits – what is possible. It's done by permutation and combination of spectra-selection, wave-selection, pre-processing sequences and PC selections. If you are common with this, then you know that the possibilities are huge.

For a pre-screening, we create e.g. 42'000 useful calibrations for the mentioned data set. With useful we mean that the model is usable, e.g. R² is higher than 0.8, which shows a good correlation between the spectra and the constituent and it is well fitted (neither over-fitted nor under-fitted) because the PC selection for the calibration-set is estimated by the validation-set and the predictive performance of the test-set is used for model comparisons.

Here the sorted RMSEP values of the Test Set is shown for 42'000 calibrations.
You can immediately see that the manually found performance of 0.49 is just in the starting phase of our optimization. Interesting is the steep fall from 1.0 to 0.5 where manually optimization found it's solutions. A range where ca. 2500 different useful calibrations exist. The following less steep fall from 0.5 to 0.2 contains a lot more useful models and between 0.2 to 0.08 the obvious high accurate models are around 2500 different ones. So the golden needle is not in the first 2500 models, it must be somewhere in the last 2500 models in the haystack.

Sorted RMSEP plot of 42'000 NIR Calibration Model Candidates

That allows us to pick the best calibration out of 42'000 models, depending on multiple statistical evaluation criteria, that is not just the R² or RPD, SEC, SEP or RMSEP, (or Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Multivariate AIC (MAIC) etc.) we do the model selection based on multiple statistical parameters.

Dengrogram plot of similar NIR Calibration Models

To compare the calibration models by similarity it is best viewed with dendrogram plots like this (zoomed in), where the settings are shown versus the models overall performance similarity. In the settings you can see a lot of different permutations of pre-processings combined with different wave-selections.

To give you an insight what we do to find the optimal model, imagine a NIR data set, where a NIR specialist works hard for 4 hours in his chemometric software to try what he can with his chemometric-, NIR spectroscopic- and his product-knowledge to get a good model. During the 4 hours he finds 3 final candidate models for his application. With the RMSEP of 0.49 , 0.51 and 0.6. Now he has to choose one or to test all his three models on new measured NIR spectra.

That is common practice. But is this good practice?

And nobody asks, how long, how hard have you tried, how many trial have you done, if this really the best model that is possible from the data?
And imagine the cost of the data collection including the lab analytics!
And behind this costs, have you really tried hard enough to get the best out of your data? Was the calibration done quick and dirty on a Friday afternoon? Yes, time is limited and manually clicking around and wait in such kind of software is not really fun, so what are the consequences?

Now I come to the most important core point ever, if you own expensive NIR spectrometer system, or even many of them, and your company has collected a lot of NIR spectra and expensive Lab-reference data over years, do you spend just a few hours to develop and build that model, that will define the whole system's measurement performance for the future? And ask yourself again (and your boss will ask you later), have you really tried hard enough, to get the best out of your data? really?

What else is possible? What does your competition do?

There is no measure (yet) what can be reached with a specific NIR data set.
And this is very interesting, because there are different beliefs if a secondary method like NIR or Raman can be more precise and accurate, as the primary method.

What we do different is, that our highly specialized software is capable of creating large amounts of useful calibrations to investigate this limits – what is possible. It's done by permutation and combination of spectra-selection, wave-selection, pre-processing sequences and PC selections. If you are common with this, then you know that the possibilities are huge.

For a pre-screening, we create e.g. 42'000 useful calibrations for the mentioned data set. With useful we mean that the model is usable, e.g. R² is higher than 0.8, which shows a good correlation between the spectra and the constituent and it is well fitted (neither over-fitted nor under-fitted) because the PC selection for the calibration-set is estimated by the validation-set and the predictive performance of the test-set is used for model comparisons.

Here the sorted RMSEP values of the Test Set is shown for 42'000 calibrations.
You can immediately see that the manually found performance of 0.49 is just in the starting phase of our optimization. Interesting is the steep fall from 1.0 to 0.5 where manually optimization found it's solutions. A range where ca. 2500 different useful calibrations exist. The following less steep fall from 0.5 to 0.2 contains a lot more useful models and between 0.2 to 0.08 the obvious high accurate models are around 2500 different ones. So the golden needle is not in the first 2500 models, it must be somewhere in the last 2500 models in the haystack.

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

NIR Calibration Modeling (Part 2)NIR Kalibrationsentwicklung (Teil 2)NIR calibrazione Modeling (parte 2)

( to part 1 )

All the below categories are implemented by using multiple different algorithms and formulas which leads to many different calibrations.

Steps in modeling

Data Cleaning - (bad data, missing values, duplicate elimination, spectral quality / intensity / noise, input value typing errors, …)
Initial Calibration set up - selection of calibration, validation and test samples
Wavelengths selection
Data preprocessing, pretreatments
Method calculation
Choosing the number of Principal Components / Latent Variables
Validation of calibration model / Statistics of performance - (accuracy, precision, linearity, repeatability, range, distribution, robustness / stability, sensitivity, simplicity, etc.)
Outlier examination and removal

The problem of choosing the optimal number of factors to find the optimum between underfitting and overfitting is solved by having multiple methods and protocols implemented leading to multiple calibrations.

The evaluation and the selection of the best calibration is based on many individual statistical values including the most popular RMSEP, SEP, Bias, SEC, R² and PCs etc.

Results and Reporting

A detailed calibration report is provided detailing the best available calibration containing all calibration parameter settings and statistics of prediction performance of the calibration set, the validation set and the test set. A visual expression of the calibration is provided with the most importance plots.

Our service works with any quantitative NIR spectra data set in the standard JCAMP-DX format and uses mainly PLS and PCR to be compatible with other chemometric calibration software.

( zu Teil 1 )

Alle folgenden Kategorien werden durch die Verwendung mehrerer verschiedener Algorithmen und Formeln umgesetzt, was zu vielen unterschiedlichen Kalibrierungen führt.

Arbeitsschritte bei der Modell Erstellung

Daten Bereinigung - (schlechte Daten, fehlende Werte, Duplikateliminierung, spektrale Qualität / Intensität / Rauschen, Eingabewert Tippfehler, ...)
Initial Kalibrierung einrichten - Auswahl der Kalibrierungs-, Validierungs- und Test-Sets
Wellenlängen Auswahl
Datenvorverarbeitung, Datenvorbehandlungen
Method Berechnung
Die Wahl der Anzahl der Hauptkomponenten / Latente Variablen / Faktoren
Validierung des Kalibrierungs Modell / Performance Statistiken - (Genauigkeit, Präzision, Linearität, Wiederholbarkeit, Reichweite, Verteilung, Robustheit / Stabilität, Empfindlichkeit, Einfachheit, etc.)
Ausreißer Untersuchung und Beseitigung

Das Problem der Wahl der optimalen Anzahl von Faktoren, um das Optimum zwischen Unterfittung und Überfittung zu finden, wird gelöst durch mehrere implementierte Methoden und Protokollen, was zu mehreren Kalibrierungen führt.

Die Auswertung und die Auswahl der besten Kalibrierung basiert auf vielen einzelnen statistischen Werten, einschließlich der beliebtesten RMSEP, SEP, Bias, SEC, R² und PCs usw.

Ergebnisse und Berichte

Eine detailliertes Kalibrierprotokoll wird bereitgestellt, das die beste verfügbare Kalibrierung detailliert mit allen Kalibrierparameter Einstellungen und Statistiken der Vorhersage Leistung des Kalibrier-Sets, des Validierungs-Sets und des Test-Sets beinhaltet. Eine visuelle Betrachtung der Kalibrierung wird mit den wichtigsten Grafiken zur Verfügung gestellt.

Unser Service funktioniert mit jedem quantitative NIR-Spektren Daten Satz im Standardformat JCAMP-DX-Format und verwendet hauptsächlich PLS und PCR um kompatibel zu sein mit anderen chemometrischen Kalibrierungssoftwaren.

( to part 1 )

All the below categories are implemented by using multiple different algorithms and formulas which leads to many different calibrations.

Steps in modeling

Data Cleaning - (bad data, missing values, duplicate elimination, spectral quality / intensity / noise, input value typing errors, …)
Initial Calibration set up - selection of calibration, validation and test samples
Wavelengths selection
Data preprocessing, pretreatments
Method calculation
Choosing the number of Principal Components / Latent Variables
Validation of calibration model / Statistics of performance - (accuracy, precision, linearity, repeatability, range, distribution, robustness / stability, sensitivity, simplicity, etc.)
Outlier examination and removal

BenefitNutzenBenefici

The NIR Calibration service offers the following benefit: Saving money

Improving the accuracy and reliability of already used NIR calibration models have high potential in various manufacturing processes as well as in quality assurance.
increased accuracy of analysis => better control of the production process => optimum process flow => better quality => less waste => more throughput.
quick and inexpensive to create professional NIR calibration models.
relief of their own staff

Time savings

for data cleaning (increasing data quality) - missing data, outlier search, wrong data (conflicting information), outlier removal
for the search for the optimal NIR model parameter settings (calibration set, wavelength selection, data pretreatments, factor selection)
for the calculation of different variations of the model
for the validation, evaluation and selection of the optimal model (error, SEP, RMSEP, RMSEC, RPD, fit, R², bias, slope, ...)
time-consuming calculation of huge calibration models
no long trial and error and waiting in the used NIR software until the calibrations seems to work

NIR analytical accuracy

higher reliability due to accuracy and robustness of NIR calibration models
the possibility of comparison with your own created or already existing or purchased NIR calibrations
what performance increase of analytical accuracy is possible
improvement of robustness with respect to change of the product matrix and possible instruments drift

Professional NIR calibration models

decades of experience in chemometrics for NIR spectroscopy
based on theoretical and applied good practice and know-how
application of various guidelines and rules
application of vendor-independent NIR chemometric software
outsourcing of NIR calibration method development and calibration equation maintenance
improving the robustness of NIR prediction model
avoid traps and pitfalls of the complicated chemometrics

Detailed results

The service provides optimal calibration settings for your NIR data.
You get full insight into the NIR calibration, as it is produced and detailed statistical values as a performance index assisted with graphics.

A further aspect of the chemometric modeling services.

Der NIR Kalibrations Service bietet Ihnen folgende Vorteile: Geldersparnis

Die Verbesserung der Genauigkeit und Zuverlässigkeit bereits eingesetzter/angewandter NIR Kalibrationsmodelle hat hohes Einsparpotenzial bei verschiedenen Produktions Prozessen wie auch in der Qualitätssicherung.
Erhöhte Analysen Genauigkeit => den Produktionsprozess besser im Griff => optimaler Prozessablauf => bessere Qualität => weniger Ausschuss => mehr Durchsatz
Schnell und günstig professionelle NIR Kalibrations Modelle erstellen.
Entlasten von eigenem Personal

Zeitersparnis

beim Datenbereinigung (Steigerung der Datenqualität) - Fehlende Daten, Ausreisser Erkennung, fehlerhafte Daten (widersprüchliche Informationen), Ausreisser Elimination
beim Suchen nach den optimalen Parameter Kombinationen für das Modell (calibration set, wavelength selection, data pretreatments, factor selection)
beim zeitintensiven Berechnungen von diversen Variationen des Modells
bei der Validation, Bewertung und Auswahl des optimalen Modells (error, SEP, RMSEP, RMSEC, RPD, fit, R², bias, slope, ...)
Nicht lange rumprobieren in der verwendeten NIR Software bis die Kalibration einigermassen funktioniert.

NIR Analysen Genauigkeit

höhere Zuverlässigkeit durch Genauigkeit und Robustheit der NIR Kalibrations Modelle
Vergleichsmöglichkeit mit ihren eigenen erstellten oder schon vorhandenen oder gekauften NIR Kalibrationen
was ist an Performance Steigerung der Analysen Genauigkeit möglich
was ist an Robustheit bzgl. ändernder Produkt Matrix, Instrumenten Drift möglich

Professionelle NIR Kalibrations Modelle

Jahrzehnte lange Erfahrung in Chemometrics for NIR-Spectroscopy
basierend auf theoretischer und angewandter Good Practice und Know How
Anwendung verschiedenster Richtlinien und Regeln
Anwendung von Hersteller unabhängiger NIR Chemometrie Software
Auslagerung (Outsourcing) der NIR-Kalibrations Methoden Entwicklung und NIR Kalibrations Pflege
Verbesserung der Robustheit von NIR-Kalibrationen
Vermeidung von Fallstricken und Fallgruben der komplexen Chemometrie

Detaillierte Ergebnisse

Der Service liefert die optimalen Kalibrations Einstellungen für ihre NIR Daten.
Sie erhalten vollen Einblick in die NIR Kalibration, wie sie erzeugt wird und detaillierte statistische Werte als Performance Übersicht unterstützt mit Grafiken.

Ein weiterer Aspekt des chemometrischen Modellierung Services.Il servizio di calibrazione NIR offre i seguenti vantaggi: Risparmiare soldi

Migliorando la precisione e l’affidabilità di modelli di calibrazione NIR pre-esistenti si possono aumentare le potenzialità in vari processi di produzione, nonché la garanzia di qualità.
Maggiore accuratezza dell’analisi assicura un miglior controllo del processo produttivo, flusso di processo ottimale e meno scarti di produzione.
Velocità e poco costo per creare modelli di calibrazione professionali.
Sollievo del personale.

Risparmiare tempo

Ripulendo i dati e aumentandone la qualità – dati mancanti, ricerca di valori anomali, dati errati (informazioni contraddittorie), rimozione di valori anomali.
Ricercando impostazioni ottimali dei parametri del modello NIR (set di calibrazione, selezione lunghezza d'onda, pre-trattamentidi dati, selezione fattore).
Calcolando diverse varianti del modello.
Facendo valutazioni di convalida e selezione del modello ottimale (errore, SEP, RMSEP, RMSEC, RPD, fit, R², bias, slope, ...).
Per il calcolo dei modelli di calibrazioni enormi.

Precisione analitica NIR

Maggiore affidabilità grazie alla precisione e robustezza dei modelli di calibrazione NIR.
Possibilità di confronto del metodo creato e quello eventualmente da acquistare.
Aumento della performance di accuratezza analitica quanto possibile.
Miglioramento della robustezza cambiando matrice e possibile derivata.

Modelli di calibrazione NIR professionali

Decenni di esperienza nella chemiometria per la spettroscopia NIR.
Su base teorica e applicata di buone tecniche e know-how.
Applicazione di linee guida.
Presenza di venditori indipendenti di software chemiometrici NIR.
Manutenzione equazione di taratura.
Migliorare la solidità del modello di predizione NIR.
Evitare trappole e trabocchetti della complicata chemiometria.

Risultati nel dettaglio

Il servizio fornisce le impostazioni di calibrazione ottimali per i vostri dati NIR.
È possibile avere una piena conoscenza della calibrazione NIR, in quanto vengono forniti in modo dettagliato valori statistici come indice di performance assistita e relativi grafici.

Un ulteriore aspetto dei servizi di modellazione chemiometrica.

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.

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.

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