Spectroscopy and Chemometrics News Weekly #44, 2018

CalibrationModel.com

AI-driven Machine Learning for NIR-Spectroscopy as a Service. LINK

Automated : data cleaning, data transformation, method selection, outlier removing, parameter tuning, model selection, report generation => Making NIRS Spectroscopy easier (2018.09.25) LINK

We are a start-up that has trained an AI to optimize prediction models for NIR Spectroscopy. LINK

NIR-Predictor does : NIR scan spectral data -> pre-treatment -> multivariate-analysis -> quantitative prediction results LINK

NIR Machine Learning as a Service, a Game Changer for Productivity and Accuracy/Precision! ( NIRS Spectroscopy AI MLaaS ) (2018.10.15) LINK

Near Infrared (NIR) Analysis Software, work smart with all NIR Spectrometers for quantitative sensing & detection. | AnalyticalChemistry LabManger Chemical Analysis Equipment ChemicalAnalysis Analytical Instruments Laboratory LabEquipment (2018.10.12) LINK

New : FREE NIR Predictor Software, drag&drop spectral data to plug&play calibrations, for any NIRS Analysis sensor type. | Chemometrics Prediction (2018.10.20) LINK



* * * * * 5 YEARS Anniversary of Spectroscopy and Chemometrics News Weekly * * * * *



Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 43, 2018 | NIRS Spektroskopie Chemometrie analyse Spektral Spektrometer Sensor LINK

Spettroscopia e Chemiometria Weekly News 43, 2018 | NIRS Spettroscopia Chemiometria analisi Spettrale Spettrometro Sensore LINK

Spectroscopy and Chemometrics News Weekly 43, 2018 | NIRS Spectroscopy Chemometrics Analysis Spectral Spectrometer Sensors LINK




Chemometrics

“Quantification of sterols and fatty acids of extra virgin olive oils by FT-NIR spectroscopy and multivariate statistical analyses” FTNIR LINK

“Rapid identification of geographical origins and determination of polysaccharides contents in Ganoderma lucidum based on near infrared spectroscopy and chemometrics” LINK

“Heterospectral two-dimensional correlation analysis with near-infrared hyperspectral imaging for monitoring oxidative damage of pork myofibrils during frozen storage.” LINK

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

“Infrared spectroscopy and chemometrics to evaluate paper variability in document dating” LINK

“Chemometric approach for discriminating tobacco trademarks by near infrared spectroscopy” LINK

“Phenomic Selection Is a Low-Cost and High-Throughput Method Based on Indirect Predictions: Proof of Concept on Wheat and Poplar.” LINK

New article: Infrared spectroscopy and chemometrics to evaluate paper variability in document dating | forensics infrared chemometrics LINK

“Determination of sulfur dioxide content in osmotically dehydrated papaya and its classification by near infrared spectroscopy” LINK

“Chemometric models for quantification of carbamazepine anhydrous and dihydrate forms in the formulation” calorimetry thermogravimetric |(18)30623-3/fulltext LINK




Near Infrared

“Visible+ Near Infrared Spectroscopy as taxonomic tool for identifying birch species” NIRS LINK

“Comparison of Quality Characteristics of Sesame Oil and Blend Oil by Using Component Analysis and NIR Spectroscopy” NIRS LINK

“Comparison of volumetric and FT-NIR method on iodine value of RBDPO and stearin” FTNIR LINK

“Banknote analysis by portable near infrared spectroscopy” NIRS Documentoscopy LINK

“Near Infrared Spectroscopic Evaluation of Ligament and Tendon Biomechanical Properties.” NIRS LINK

“Application of feedforward control strategy based on spectra of raw materials to optimize alcohol extraction process of Panax notoginseng” NIRS LINK

“Blending and Characterization of Pharmaceutical Powders” NIRS Raman LINK

“NIR inspection of each tablet that exits a tablet press – European Pharmaceutical Review” LINK

New article: Activated sludge settling analysis using a near infrared optoelectronic device: overview and application to wastewater treatment | NIR nearinfrared wastewater LINK

“Study of the Influence of Different Yeast Strains on Red Wine Fermentation with NIR Spectroscopy and Principal Component Analysis” LINK

“Machine-learning-based quantitative estimation of soil organic carbon content by VIS/NIR spectroscopy.” LINK




Infrared

“Integrable Near-Infrared Photodetectors Based on Hybrid Erbium/Silicon Junctions” LINK

“Non-destructive evaluation of wood stiffness and fiber coarseness, derived from SilviScan data, via near infrared hyperspectral imaging” LINK

“Application of fractional-order derivative in the quantitative estimation of soil organic matter content through visible and near-infrared spectroscopy” LINK

“A Rapid and Simple Quantitative Method for the Active Ingredients of Aescin in the Extraction Process Using Near Infrared Spectroscopy” LINK




Raman

“Development of Quantitative Analysis Techniques for Saccharification Reactions Using Raman Spectroscopy” LINK




Spectroscopy

“AI and NMR spectroscopy determine configuration of atoms in minutes” LINK

“Using fieldable spectrometers and chemometric methods to determine RON of gasoline from petrol stations: A comparison of low-field 1H NMR MHz, handheld RAMAN and benchtop NIR” LINK




Optics

“Avalanche-Photodioden für den NIR-Wellenlängenbereich” LIDAR LINK




Equipment

“A Multivariate Control Chart Approach for Calibration Transfer between NIR Spectrometers for Simultaneous Determination of Rifampicin and Isoniazid in Pharmaceutical Formulation.” LINK

“Rapid detection of the component contents in caryophylli flos by a handheld near infrared spectrometer based on digital light processing technology” LINK




Process Control

“Monitoring batch processes with dynamic time warping and k-nearest neighbours” LINK




Agriculture

“Detection of adulteration in milk: A review” LINK

“Near-infrared spectroscopy for determination of soil organic C, microbial biomass C and C and N fractions in a heterogeneous sample of German arable surface soils” LINK




Food & Feed

“Application of visible/near infrared spectroscopy to quality control of fresh fruits and vegetables in largeâ scale mass distribution channels: a preliminary test on carrots and tomatoes” LINK

“Inspektion und Dichtheitsprüfung von Pharmaprodukten – Pharma + Food online” LINK




Pharma

“Process analytical technologies and injectable drug products: is there a future?” PAT LINK




Medicinal

“Characterization of the optical properties of color pastes for the design of optical phantoms mimicking biological tissue” oximeters LINK




Laboratory

“New post-harvest aproach for high quality fresh ‘Medjhool’ date” LINK




Other

“Morphological and biochemical characterization of elite genotypes of linseed” LINK





Spectroscopy and Chemometrics News Weekly #41, 2018

CalibrationModel.com

NIR Machine Learning as a Service, a Game Changer for Productivity and Accuracy/Precision! ( NIRS Spectroscopy AI MLaaS ) LINK

Near Infrared (NIR) Analysis Software, work smart with all NIR Spectrometers for quantitative sensing & detection. | AnalyticalChemistry LabManger Chemical Analysis Equipment ChemicalAnalysis Analytical Instruments Laboratory LabEquipment LINK

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 40, 2018 | NIRS Spectroscopy Chemometrics analysis Spectral Spectrometer Sensors LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 40, 2018 | NIRS Spektroskopie Chemometrie analyse Spektral Spektrometer Sensor LINK

Spettroscopia e Chemiometria Weekly News 40, 2018 | NIRS Spettroscopia Chemiometria analisi Spettrale Spettrometro Sensore LINK




Chemometrics

“Classification of different animal fibers by near infrared spectroscopy and chemometric models” LINK

“Discrimination of organic and conventional rice by chemometric analysis of NIR spectra: a pilot study” LINK

“Discrimination between conventional and omega-3 fatty acids enriched eggs by FT-Raman spectroscopy and chemometric tools” omega3 LINK

“Quantification of mineral composition of Brazilian bee pollen by near infrared spectroscopy and PLS regression.” LINK

“Sex determination of silkworm pupae using VIS-NIR hyperspectral imaging combined with chemometrics.” LINK




Near Infrared

“Spinning-disc confocal microscopy in the second near-infrared window (NIR-II)” Fluorescence LINK

“Measuring the brain’s fast optical signal could speed up Brain–computer interfaces (BCI) response” NIRS LINK

“Bioprofiling for the quality control of Egyptian propolis using an integrated NIR-HPTLC-image analysis strategy.” LINK

“lab for the pocket” hertzstueck NIRS LINK

“NIR gas phase spectroscopy – Pressure broadening effects” LINK

“Near-infrared Band Used for Permanent, Wireless Self-charging System – R & D Magazine” LINK

“Non-Destructive NIR Spectral Imaging Assessment of Bone Water: Comparison to MRI Measurements” – NIRS vs. Magnetic Resonance Imaging LINK




Infrared

“Near-infrared spectroscopy could improve flu vaccine manufacturing” LINK

“Detection of Alone Stress and Combined Stress by CU and NI in Wheat Using Visible to Near-Infrared Spectroscopy” LINK

“A Novel Method for Classifying Driver Cognitive Distraction under Naturalistic Conditions with Information from Near-Infrared Spectroscopy” LINK




Agriculture

“Quality evaluation of fried soybean oil base on near infrared spectroscopy” LINK

Curious about new developments in various fields of spectroscopy and their application in plant sciences? Register now for the International Plant Spectroscopy Conference (IPSC) organised by our colleagues in Berlin, March 24-28th, 2019: LINK

“Application of Vibrational Spectroscopy and Imaging to Point-of-Care Medicine: A Review” LINK




Food & Feed

“From NIR spectra to singular wavelengths for the estimation of the oil and water contents in olive fruits” LINK




Medicinal

“New, noninvasive blood glucose test as effective as finger prick test – Clinical Innovation + Technology” Raman spectroscopy LINK




Laboratory

so, spent some time down the hardware rabbit hole. the core sensors, | is available in a number of combos, on a number of boards; e.g. | LINK










Spectroscopy and Chemometrics News Weekly #35, 2018

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


CalibrationModel.com

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

Reduce Workload and Response Time of NIRS Analytical Laboratory Chemistry method development. chemoinformatics digitalization DigitalTransformation spectroscopy LINK

Spectroscopy and Chemometrics News Weekly 34, 2018 | NIRS Spectroscopy Chemometrics analysis Spectral Spectrometer Sensors LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 34, 2018 | NIRS Spektroskopie Chemometrie analyse Spektral Spektrometer Sensor LINK

Spettroscopia e Chemiometria Weekly News 34, 2018 | NIRS Spettroscopia Chemiometria analisi Spettrale Spettrometro Sensore LINK




Chemometrics

“Comparing the analytical performances of Micro-NIR and FT-NIR spectrometers in the evaluation of acerola fruit quality, using PLS and SVM regression algorithms.” LINK

“Using portable near-infrared spectroscopy to predict pig subcutaneous fat composition and iodine value” LINK

“Comparative chemometric analysis of fluorescence and near infrared spectroscopies for authenticity confirmation and geographical origin of Argentinean extra virgin olive oils” LINK

“Development of a NIR Method for the In-Line Quantification of the Total Polyphenolic Content: A Study Applied on Ajuga genevensis L. Dry Extract Obtained in a Fluid Bed Process.” LINK

“Rapid detection of adulteration in Anoectochilus roxburghii by near-infrared spectroscopy coupled with chemometric methods” LINK

“Non-destructive prediction of banana fruit quality using VIS/NIR spectroscopy” LINK

“Prediction of total soluble solids and pH in banana using near infrared spectroscopy” LINK

“Rapid determination of soil classes in soil profiles using vis–NIR spectroscopy and multiple objectives mixed support vector classification” LINK

“Screening wavelengths with consistent and stable signals to realize calibration model transfer of near infrared spectra.” LINK

“Which regression method to use? Making informed decisions in “data-rich/knowledge poor” scenarios – The Predictive Analytics Comparison framework (PAC)” LINK




Near Infrared

“A transformative approach to ageing fish otoliths using Fourier transform-near infrared spectroscopy (NIRS): a case study of eastern Bering Sea walleye pollock (Gadus chalcogrammus)” LINK

“Panoramic optical and near-infrared SETI instrument: prototype design and testing” ExtraTerrestrial LINK

“Non-destructive measurement of salt using NIR spectroscopy in the herring marinating process” LINK

“Application of miniaturized near-infrared spectroscopy for quality control of extemporaneous orodispersible films” LINK

“Optimal sample selection for measurement of soil organic carbon using on-line vis-NIR spectroscopy” LINK

“Analysis of protein glycation in human fingernail clippings with near-infrared (NIR) spectroscopy as an alternative technique for the diagnosis of diabetes mellitus” LINK

“The Optimal Local Model Selection for Robust and Fast Evaluation of Soluble Solid Content in Melon with Thick Peel and Large Size by Vis-NIR Spectroscopy” NIRS LINK

“Hand-held near-infrared spectrometers: State-of-the-art instrumentation and practical applications” handheld NIRS LINK

“Effect of waxy material and measurement position of a sugarcane stalk on the rapid determination of Pol value using a portable near infrared instrument” NIRS LINK




Infrared

“Identification of textiles by handheld near infrared spectroscopy: Protecting customers against product counterfeiting” LINK

“Determination of Total Flavonoids Contents and Antioxidant Activity of Ginkgo biloba Leaf by Near-Infrared Reflectance Method.” LINK

“Non-destructive evaluation of maturity and quality parameters of pomegranate fruit by visible/near infrared spectroscopy” LINK

“Apple Variety Identification Using Near-Infrared Spectroscopy” fruits LINK




Hyperspectral

“Classification of maize kernels using NIR hyperspectral imaging” HSI LINK




Environment

“Prediction and mapping of soil clay and sand contents using visible and near-infrared spectroscopy” LINK




Agriculture

“Infrared spectroscopy prediction of organic carbon and total nitrogen in soil and particulate organic matter from diverse Canadian agricultural regions” LINK

“Biomass, feed quality, mineral concentration and grain yield responses to potassium fertiliser of dual-purpose crops” LINK

“Quantitative Analysis of Soil Nutrition Based on FT-NIR Spectroscopy Integrated with BP Neural DeepLearning” FTNIR LINK

“Crop management effects on supplementary feed quality and crop options for dairy feeding to reduce nitrate leaching” LINK




Food & Feed

“Fruit Quality Evaluation Using Spectroscopy Technology: A Review” LINK




Medicinal

How we can use light to see deep inside our bodies and brains. ⁦Mary Lou Jepsen ⁩’s terrific TedTalk & demo. (Excited for Mary Lou joining us as Exponential Medicine ⁩faculty | Neuroscience imaging xMed LINK




Other

Kuwaiti police has shut down a fish store that was sticking googly eyes on fish to make them appear more fresh than they are. 🙂 via Al Bayan newspaper, . LINK





Spectroscopy and Chemometrics News Weekly #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




Spectroscopy and Chemometrics News Weekly #17-18, 2017

Chemometrics

Conferentia Chemometrica 2017, 3–6 September 2017, Gyöngyös, Farkasmály, Hungary. | chemometrics LINK

“Chaos theory in chemistry and chemometrics: a review” LINK

Predicting herbivore faecal nitrogen using a multispecies near-infrared reflectance spectroscopy calibration. LINK

Calibration transfer of flour NIR spectra between benchtop & portable instruments | directstandardization spectro LINK



Near Infrared

Fast Detection of Paprika Adulteration Using FT-NIR Spectroscopy LINK

How to analyze food and future requirements for NIR spectroscopy LINK



Infrared

“On-Site Analysis of Cannabis Potency Using Infrared Spectroscopy” LINK



Raman

5th International Taiwan Symposium on Raman Spectroscopy (TISRS 2017) 27–30 June 2017, Chiayi, Taiwan. LINK



Hyperspectral

Corning and PrecisionHawk and partnership enables hyperspectral imaging on drones LINK

Employing NIR-SWIR hyperspectral imaging to predict the smokiness of scotch whisky LINK

Defect detection of green coffee by NIR-hyperspectral imaging and multivariate pattern recognition LINK



Equipment

Getting ready for the LinkSquare SDK kickstarter with some beauty shots of our handheld spectrometer! LINK!

“Learning about Spectroscopy with Ocean Optics” LINK

Agriculture

Analysis of multiple soybean phytonutrients by near-infrared reflectance spectroscopy LINK



Other

A great demonstration of why we need to plot the data and never trust statistics tables! LINK





Spectroscopy and Chemometrics News Weekly #5+6, 2017


Chemometrics

Non-Destructive Sensor-Based Prediction of Maturity and Optimum Harvest Date of Sweet Cherry Fruit | sensors LINK


IDC unveils its Top 10 Predictions for global Robotics Industry Industry40 Robotics LINK


Spectroscopy

Global Molecular Spectroscopy Market is expected to reach USD 6.712 billion till 2024. htt… LINK!


Near Infrared

Assessing pre-harvest sprouting in cereals using near-infrared spectroscopy-based metabolomics LINK


Rapid screening of commercial extra virgin olive oil products for authenticity: Performance of a handheld NIR device LINK


Hyperspectral

Imec () launches TDI, multispectral and hyperspectral sensors | imaging HSI LINK


Near-infrared hyperspectral imaging of lamination and finishing processes in textile technology LINK


Spectral Imaging

Viavi Solutions and ESPROS Photonics Corporation Debut New Miniaturized Spectral Sensor and Multispectral Sensor LINK


Equipment

Meta-lenses bring benchtop performance to small, hand-held spectrometer – Science Daily LINK



Scan anywhere with Neospectra Spectrometer Case powered by XPNDBLS PhotonicsWest … LINK!


Agriculture

World feed production exceeds 1 billion MT LINK


Chemometric soil analysis on the determination of specific bands for the detection of magnesium & potassium by … LINK


Other

This app uses spectral analysis to analyze objects and their makeup HawkSpex LINK


Research details developments in the multivariate analysis software industry | MVA LINK

“The worlds first ever spectroscopy enabled iPhone!” Check out our video to see it in action: LINK


Investments in AI will triple in 2017. ($47 billion by 2020 per ) CIO CMO | LINK


Some aspects of fetal development have long puzzled scientists, but new molecular technologies are shining a light: https:/… LINK!


CalibrationModel.com

Spectroscopy and Chemometrics News Weekly 3+4, 2017 | Spectroscopy NIRS MVDA… LINK


Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 3+4, 2017 | NIRS Spektroskopie Chemometrie Multivariate LINK


Spettroscopia e Chemiometria Weekly News 3+4, 2017 | NIRS Spettroscopia Chemiometria news LINK


WHITE PAPER: A novel knowledge-based Chemometric Software Framework for quantitative NIRS Calibration Modeling LINK



Improve Accuracy of fast non-destructive NIR Measurements by Optimal Calibration | spectroscopy sensor modeling LINK


NIRS as a secondary method requires extensive calibration on an ongoing basis | foodindustry Digitalization IoT LINK


Services for Optimization of Chemometric Application Methods of Near-Infrared Spectroscopy | Quality Control NIRS LINK


► Timesaving NIRS Calibration ► near-infrared spectroscopy | protein fat moisture sensor measurement scanning LINK





Procedures for NIR calibration – Creation of NIRS spectroscopy calibration curves

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.

Interested, then do not hesitate to contact us.

NIR Spectroscopy Calibration Report for quantitative predictive models

When you send your quantitative NIR spectra data to our NIR Calibration Model Service, you get a detailed calibration report (calibration protocol) of the found optimal calibration settings, so you are able to see all insights and easily re-build the model in your NIR/Chemometric software.

Here is a part of our calibration report, that exactly describes the data used in the calibration set (CSet), the validation set (VSet) and the test set (TSet). The numbers are the number ids of the spectra in your delivered NIR data file.


The calibration method settings and parameters are
Waveselection : the variable selection or wavenumber selection or wavelength selection
Pretreatments : the spectral data pre-processing
PCs : the number of  Principal Components (PC) or Latent Variables (LV)
Method : the modeling method algorithm used, e.g. PLS

Then the statistical analysis of the PLS model by the different sets (CSet, VSet, Tset).

Calibration Report

Statistical analysis of calibration, validation and test results : 1 Name, 2 Unit, 3 N : number of spectra, 4 N : number of samples, 5 Average spectra count per sample, 6 Reference values, 7 Min, 8 Mean, 9 Median, 10 Max, 11 Standard deviation, 12 Skewness : left (-) or right (+) lack of symmetry, 13 Kurtosis : flat (-) or peaked (+) shape, 14 Model statistics, 15 RPD, 16 R², 17 RMSEC, RMSEP, RMSET : root mean square of prediction errors, 18 SEC, SEP, SET : standard error (bias corrected), 19 Bias, 20 Skewness of prediction errors, 21 Kurtosis of prediction errors, 22 Intercept, 23 Slope, 24 Intercept (reverse), 25 Slope (reverse), 26 Sample Prediction Repeatability Error, 27 Sample Prediction Repeatability Error (of Missing data MSet)

This shows how we deliver the optimal settings. With the statistical values, the NIR model predicted values of all spectra and additional plots you are able to compare with your re-built model to verify that the models perform nearly equally.