Spectroscopy and Chemometrics News Weekly #3, 2020

Near Infrared (NIR) Spectroscopy

“Fourier-transform near infrared spectroscopy (FT-NIRS) rapidly and non-destructively predicts daily age and growth in otoliths of juvenile red snapper Lutjanus …” LINK

“Desarrollo de Modelos NIRS de Predicción para el Análisis de la Finura de Fibras Textiles de Vicuña y Llama” LINK

“fNIRS-GANs: Data augmentation using generative adversarial networks for classifying motor tasks from functional near-infrared spectroscopy.” LINK

” Simulated NIR spectra as sensitive markers of the structure and interactions in nucleobases” LINK

“Penentuan Parameter Mutu Buah Jeruk Siam Garut Secara Nondestruktif Menggunakan Spektroskopi NIR” LINK

“Rapid non-destructive moisture content monitoring using a handheld portable Vis–NIR spectrophotometer during solar drying of mangoes (Mangifera indica L.)” LINK

“VIS-NIR wave spectrometric features of acorns (Quercus robur L.) for machine grading” LINK

“Portable NIR Spectroscopy: Affordable Technology for Developing Countries” LINK

“NIR spectroscopy used for non-destructive evaluation of the chemical composition of the investigated papers in addition to typically used standard methods.” LINK

“Feasibility study on prediction of gasoline octane number using NIR spectroscopy combined with manifold learning and neural network” LINK

“Authentication of Tokaj Wine (Hungaricum) with the Electronic Tongue and Near Infrared Spectroscopy” LINK

“Environmental advantages of visible and near infrared spectroscopy for the prediction of intact olive ripeness” LINK

“Rapid screening and quantitative analysis of adulterant Lonicerae Flos in Lonicerae Japonicae Flos by Fourier-transform near infrared spectroscopy” LINK

“Rapid analysis of soluble solid content in navel orange based on visible-near infrared spectroscopy combined with a swarm intelligence optimization method” LINK

“Establishment and relevant analysis of plant’s spectral reflectivity database in visible and near-infrared bands” LINK




Hyperspectral Imaging

“Mineral Mapping of Drill Core Hyperspectral Data with Extreme Learning Machines” LINK

“Deep learning classifiers for hyperspectral imaging: A review” LINK

“Texture and Shape Features for Grass Weed Classification Using Hyperspectral Remote Sensing Images” LINK

“Avoiding Overfitting When Applying Spectral-Spatial Deep Learning Methods on Hyperspectral Images with Limited Labels” LINK

“Estimation Model of Winter Wheat Yield Based on Uav Hyperspectral Data” LINK




Chemometrics and Machine Learning

“Validation of near infrared spectroscopy as an age-prediction method for plastics” LINK

Whoever leads in ArtificialIntelligence in 2030 will rule the world until 2100 | fintech AI MachineLearning DeepLearning robotics LINK

“Chemical Composition of Hexene-Based Linear Low-Density Polyethylene by Infrared Spectroscopy and Chemometrics” LINK

“Establishment of Plot-Yield Prediction Models in Soybean Breeding Programs Using UAV-Based Hyperspectral Remote Sensing” LINK

“DEVELOPING NEAR INFRARED SPECTROSCOPIC MODELS FOR PREDICTING DENSITY OF Eucalyptus WOOD BASED ON INDIRECT MEASUREMENT” LINK

“NIR reflectance spectroscopy and SIMCA for classification of crops flour” LINK




NIR Equipment

“Prediction of meat quality traits in the abattoir using portable and hand-held near-infrared spectrometers” LINK

“A Plug-and-play Hyperspectral Imaging Sensor Using Low-cost Equipment” LINK




NIR in Environment

“The use of hyperspectral remote sensing to detect PCB contaminated soils in the 0.35 to 12 micron spectral range” LINK




NIR in Agriculture

“Fast measurement of phosphates and ammonium in fermentation-like media: a feasibility study” LINK

“Detection of early decay on citrus using hyperspectral transmittance imaging technology coupled with principal component analysis and improved watershed …” LINK

“Determination of Nutritive Value of Some Feedstuffs Used in Poultry Nutrition by Near Infrared Reflectance Spectroscopy (NIRS) and Chemical Methods” LINK

“근적외선분광법을 이용한 사료용 벼의 사료가치 평가” “Evaluation of Feed Values for Whole Crop Rice Using Near Infrared Reflectance Spectroscopy” LINK

“Classification of crop flours based on protein contents using near infra-red spectroscopy and principle component analysis” LINK

“In-field detection of Alternaria solani in potato crops using hyperspectral imaging” LINK




NIR in Laboratory

“Use of Remote sensing technology to assess grapevine quality” LINK


CalibrationModel.com

Spectroscopy and Chemometrics News Weekly 2, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical AI Application Chemical Analysis Lab Labs Laboratories Laboratory Software Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 2, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 2, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem predictionmodel LINK


Start Calibrate

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

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




Spectroscopy and Chemometrics News Weekly #1, 2020

CalibrationModel.com

Five Mistakes to avoid on Digitalization in NIR-Spectroscopy – that Lab Managers, Executives and CEOs must know! NIRSpectroscopy NIRS Sensors NearInfrared Analyzers DigitalTransformation QualityControl foodtech machinelearning AI datascience LINK

SAFE COST IN MAINTAINING NIR-SPECTROSCOPY METHODS | NIRSpectroscopy NIRS Spectroscopy DigitalTransformation Analysis Lab Laboratory Application Quantitative Analysis Methods Measurements Analytical Parameters Spectrometer Quality Accuracy LINK

Do you develop NIR / NIRS calibrations by yourself? Can you sell it? No? Buy it! Digitalization LabManager LabAutomation CEO Digitalisation Spectroscopy AutoML LowCost lowerCost SaveMoney SaveTime Efficiency Effectivity LINK

5 Fehler, die es bei der Digitalisierung in der NIR-Spektroskopie zu vermeiden gilt – das müssen Labormanager, Führungskräfte und CEOs wissen! NIRSpectroscopy NIRS Sensor NearInfrared Analyzers DigitalTransformation foodtech machinelearning LINK

Spectroscopy & Chemometrics News Weekly 52, 2019 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Food Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality Check LINK

Spectroscopy & Chemometrics News Weekly 51, 2019 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory AI Software IoT Sensors QA QC Testing Quality LINK

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

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



Near Infrared

“Estimation and classification of popping expansion capacity in popcorn breeding programs using NIR spectroscopy” LINK

“Simultaneous detection of quality and safety in spinach plants using a new generation of NIRS sensors” LINK

“Identification of Genuine and Adulterated Pinellia ternata by Mid-Infrared (MIR) and Near-Infrared (NIR) Spectroscopy with Partial Least Squares – Discriminant …” LINK

“Monitoring coffee roasting cracks and predicting with in situ near-infrared spectroscopy” LINK

“Assessment of a soil fertility index using visible and near-infrared spectroscopy in the rice paddy region of southern China” LINK

“Conversion of the Felixton Mill laboratory from conventional to NIRS analysis.” LINK

“Automatic cancer discrimination based on near-infrared spectrum and class-modeling technique” LINK

“Food powders classification using handheld Near-Infrared Spectroscopy and Support Vector Machine” LINK

“Development and validation of a method for separation of pregabalin and gabapentin capsules using Near Infrared hyperspectral imaging” LINK

“Field-resolved infrared spectroscopy of biological systems” Nature LINK

“Infrared spectroscopy finally sees the light” nature LINK

“Journal Highlight: Estimation of protein and fatty acid composition in shellintact cottonseed by near Infrared reflectance spectroscopy” LINK

” Visible/near-infrared Spectroscopy as a Novel Technology for Nondestructive Detection of Escherichia coli ATCC 8739 in Lettuce Samples” LINK

“The model updating based on near infrared spectroscopy for the sex identification of silkworm pupae from different varieties by a semi-supervised learning with pre …” LINK

“… Study on the Determination of ppm-Level Concentration of Histamine in Tuna Fish Using a Dry Extract System for Infrared Coupled with Near-Infrared Spectroscopy” LINK




Raman

“Transmission Raman Spectroscopic Quantification of Active Pharmaceutical Ingredient in Coated Tablets of Hot-Melt Extruded Amorphous Solid Dispersion” LINK




Hyperspectral

“Plastic waste monitoring and recycling by hyperspectral imaging technology” LINK

“Comparison of Ink Classification Capabilities of Classic Hyperspectral Similarity Features” LINK




Chemometrics

“DEVELOPING NEAR INFRARED SPECTROSCOPIC MODELS FOR PREDICTING DENSITY OF Eucalyptus WOOD BASED ON INDIRECT MEASUREMENT” LINK

“Development of Partial Least Square (PLS) Prediction Model to Measure the Ripeness of Oil Palm Fresh Fruit Bunch (FFB) by Using NIR Spectroscopy” LINK




Facts

“Bombs and cocaine: detecting nefarious nitrogen sources using remote sensing and machine learning” LINK




Agriculture

“Laboratory-based hyperspectral image analysis for the classification of soil texture” LINK

“Research on simultaneous detection of SSC and FI of blueberry based on hyperspectral imaging combined MS-SPA” LINK

“Polymers, Vol. 12, Pages 78: Insight into the Intermolecular Interaction and Free Radical Polymerizability of Methacrylates in Supercritical Carbon Dioxide” LINK

“Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging.” LINK




Other

“绿泥石矿物近红外光谱吸收谱带的位移机理与控制机制研究” LINK

“Spektroskopie – Unverwechselbarer molekularer Fingerabdruck” LINK





Five Mistakes to avoid on Digitalization in NIR-Spectroscopy – that Lab Managers, Executives and CEOs must know!

The fast and non-destructive NIR-Spectroscopy analysis method is based on predictive models that are built upon spectral and Lab reference data.

Developing such models with Chemometric techniques or Machine Learning Algorithms can now be fully automatized with many advantages.

If your company uses NIR-Spectroscopy, do you know if your company yet tried this calibration service? And if not, why not?

Here are some possible reasons why such services are blocked in your company.

With the knowledge where and why it may be blocked, you are able to overcome the barriers and go for innovation.

Misbeliefs of People that may block Advanced Services (AI and Automatic Machine Learning)


5 Mistakes to avoid on Digitalization in NIR-Spectroscopy
Misbeliefs People Remove barriers
1 You need to program or scripting with data modeling tools (R, Phyton, TensorFlow, Matlab, sas, SPSS, H2O, scikit, …..) The opinion that only PhD (statisticians, data scientists) are smart enough to create models. Give the service a try, and compare! There are free trials for calibration development.
2 We have done it always manually by hand. We must see the plots and interpret the stats in every step.
Job protectionism : “My job will dramatically change if that will work, so I will never prepare real-life data to give someone a possibility to try such a service.”
AI / ML haters (NIR-Specialists, NIR-Experts) don’t belive in automated calibration. Give the service a try, and compare! You will see plots and stats! There are free trials for calibration development.
3 Against any new hardware, software or service because of supposed new problems. Preserving ones – the permission gate keepers (IT) Give it a try, without an account, download the free NIR-Predictor software (full version, no limitations) with included demo data, it installs and runs without Administrator priviledge!
4 Ensures that new methodologies follow the rules. Any new thing will give extra work for them. Compliance and Regulation officers. Get access to the detailed model blueprint (ISO 12099) – there is no vendor lock-in or black-box model. You can even anonymize your data for Knowledge Protection – see JCAMP-Anonymizer.
5 Our employees are open for new things. Our employees do not block. Managers, executives, board level. Remove the Barriers to Innovation!


Start Calibrate

Cost comparision / Price comparison of Chemometrics / Machine Learning / Data Science for NIR-Spectroscopy

CalibrationModel.com (CM) versus Others

Costs are not everything, there are other important factors listed in the table.

CM fix € pricing Others € Price Range (approx.)
Software
included
Chemometric Package not‑needed
3500 – 6500 per user
Chemometric Predictor
free‑software
1500 – 2500 per NIR device
Knowledge
included
Chemometric Training not‑needed
1500 – 2500 per user
Chemometrician* Salary not‑needed
1 years Salary / year
(+ risk of Employee Turnover)
Computation
included
Powerful Computer (many Processors, lot of RAM for big data) not‑needed
1500 – 4500 per computer
Development and Usage
fix price
Development of a Calibration
299
80 – 150 / hour
of Chemometrician* using a Chemometric Software (click and wait) and applying it’s knowledge
Usage of a Calibration
299 / year
Total 598 in first year
299 in second year
initial (min 8000 , max 15500)
+ 2 * (2 – 4)(hour to cost same! as CM service) * (80 – 150) Chemometrician* work
no initial cost
very high initial costs
no personnel cost
high personnel* costs
constant CM services
risk of Employee Turnover
global knowledge
risk of only use personal knowledge
easy to calculate fix cost on demand
difficult to calculate variable cost on demand plus Chemometrician* Recruitment needed
Results :
calibration prediction performance
always reproducible highly optimized
only as good as your Chemometrician* daily condition
better prediction performance, due to best-of 10’000x calibrations
small size of experiments, non-optimal calibrations

Start Calibrate

*) Personnel / Chemometrician / Data Scientist / Data Analyst / Machine Learning Engineer : We are not against it, we are one of them a long time ago, but the way the work is done is changing (see below).

2019 Digitalization and the Future of Work: Macroeconomic Consequences
2019 The Digitalization of the American Workforce
2017 Digitalization and the American workforce , full-report

Start Calibrate – NIR Quick Guide

Quick Start NIR Workflow: step by step

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

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

3. With your “NIR device” measurement software:

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

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



Videos


Spectroscopy and Chemometrics News Weekly #40, 2019

CalibrationModel.com

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

Spectroscopy and Chemometrics News Weekly 39, 2019 | NIRS NIR Spectroscopy Chemometrics Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software Sensors QA QC Testing Quality Checking LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 39, 2019 | NIRS NIR Spektroskopie Chemometrie Spektrometer Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 39, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem predictionmodel HSI LINK




Chemometrics

“Near-infrared spectroscopy to determine residual moisture in freeze-dried products: model generation by statistical design of experiments LINK

“制浆材木质素含量近红外分析模型传递研究” “Study on Near-infrared Calibration Model Transfer for Lignin Content in Pulpwood” LINK

“Prediction of Mineral Composition in Commercial Extruded Dry Dog Food by Near-Infrared Reflectance Spectroscopy” LINK




Near Infrared

“Influence of Particles Size on NIR Spectroscopic Estimations of Charcoal Properties” LINK

“Measurement of refractive index of hemoglobin in the visible/NIR spectral range.” LINK

“Feasibility on using NIR spectroscopy for the measurement of the textural parameters in mango” LINK

“NIR Spectroscopy as a Suitable Tool for the Investigation of the Horticultural Field” LINK

“Handheld Near-Infrared Spectroscopy for Distinction of Extra Virgin Olive Oil from Other Olive Oil Grades Substantiated by Compositional Data” LINK




Infrared

“Short-wave near infrared spectroscopy for the quality control of milk” LINK

“Rapid detection of infrared inactive sodium chloride content in frozen tuna fish for determining commercial value using short wavelengths” LINK




Equipment

Visible/near Infrared Reflection Spectrometer and Electronic Nose Data Fusion as an Accuracy Improvement Method for Portable Total Soluble Solid Content Detection of Orange” LINK




Environment

“Prediction of soil available water-holding capacity from visible near-infrared reflectance spectra.” LINK




Agriculture

“Local anomaly detection and quantitative analysis of contaminants in soybean meal using near infrared imaging: The example of non-protein nitrogen.” LINK

“Challenges and opportunities in clinical translation of biomedical optical spectroscopy and imaging.” LINK

“The potential of CIELAB colour scores to gauge the quality of sorghum as a feed grain for chicken-meat production” LINK




Food & Feed

“Control and Monitoring of Milk Renneting Using FT-NIR Spectroscopy as a Process Analytical Technology Tool” Foods LINK

Food quality digitized at the “speed of light” This shows how using spectroscopy for measuring food quality can become the future for many businesses – With the speed of light LINK

“A Study on the use of Spectroscopic Techniques to Identify Food Adulteration” LINK

“Recent advances in micro-level experimental investigation in food drying technology” LINK




Laboratory

“In situ reaction monitoring using spectroscopy can be very useful in an industrial laboratory, but there are many factors to take into account.” Click Here for Seven Essential Steps for In Situ Reaction Monitoring: LINK





Spectroscopy and Chemometrics News Weekly #32, 2019

CalibrationModel.com

Spectroscopy and Chemometrics News Weekly 31, 2019 | NIRS NIR Spectroscopy Chemometrics Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software Sensors QA QC Testing Quality Checking LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 31, 2019 | NIRS NIR Spektroskopie Chemometrie Spektrometer Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysemethode Nahinfrarotspektroskopie Laboranalyse Qualitätslabor LINK

Spettroscopia e Chemiometria Weekly News 31, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore analitica Laboratorio analisi prova qualità Analysesystem prediction models Calibrazione LINK




Chemometrics

“Near infrared spectroscopic investigation of lipid oxidation in model solid food systems” LINK

Rapid quantification of the adulteration of fresh coconut water by dilution and sugars using Raman spectroscopy and chemometrics, published in Food Chemistry, is now OpenAccess via LINK

“Recent Progress in Rapid Analyses of Vitamins, Phenolic, and Volatile Compounds in Foods Using Vibrational Spectroscopy Combined with Chemometrics: a Review” LINK

” Beef Tenderness Prediction by a Combination of Statistical Methods: Chemometrics and Supervised Learning to Manage Integrative Farm-To-Meat Continuum Data” Foods LINK

“P-Wave VisibleShortwaveNear-Infrared (Vis-SW-NIR) Detection System for the Prediction of Soluble Solids Content and Firmness on Wax Apples” LINK

“Model development for soluble solids and lycopene contents of cherry tomato at different temperatures using near-infrared spectroscopy” LINK




Near Infrared

“Preliminary Assessment of Visible, Near-Infrared, and Short-Wavelength-Infrared Spectroscopy with a Portable Instrument for the Detection of Staphylococcus aureus Biofilms on Surfaces.” LINK

” Inline monitoring of powder blend homogeneity in continuous drug manufacture using near infrared spectroscopy” LINK

“Nondestructive real-time assessment of sausage quality based on visible-near infrared spectrographic technique” LINK

“Lipid oxidation degree of pork meat during frozen storage investigated by near-infrared hyperspectral imaging: Effect of ice crystal growth and distribution” LINK

“High prevalence of cholesterol-rich atherosclerotic lesions in ancient mummies: A near-infrared spectroscopy study” LINK

“Application of Infrared Spectroscopy for Functional Compounds Evaluation in Olive Oil: A Current Snapshot” LINK

“Towards online Near-Infrared spectroscopy to optimise food product mixing” LINK




Hyperspectral

“Estimation of chlorophyll content in intertidal mangrove leaves with different thicknesses using hyperspectral data” LINK

“Snapshot Multispectral and Hyperspectral Data Processing for Estimating Food Quality Parameters” LINK




Environment

“Field Spectroscopy: A Non-Destructive Technique for Estimating Water Status in Vineyards” LINK

“Optical detection of contamination event in water distribution system using online Bayesian method with UVVis spectrometry” LINK




Agriculture

“GrassQ-A holistic precision grass measurement and analysis system to optimize pasture based livestock production” LINK

“Monitoring of Nitrogen and Grain Protein Content in Winter Wheat Based on Sentinel-2A Data” Remote Sensing LINK

“Identification and characterization of a fast-neutron-induced mutant with elevated seed protein content in soybean.” LINK

“Sensors, Vol. 19, Pages 3147: NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Study the Residues of Different Concentrations of Omethoate on Wheat Grain Surface” LINK




Food & Feed

“Multi-target Prediction of wheat flour quality parameters with near infrared spectroscopy” LINK




Other

” Spectroscopic determination of leaf chlorophyll content and color for genetic selection on Sassafras tzumu” LINK

Record-breaking new analytical method for fingerprinting petroleum and other complex mixtures LINK

A record-breaking 244,779 molecular compositions within a sample of petroleum have been assigned using a powerful method of analysing and ‘fingerprinting’ chemical mixtures developed by at . Read more: LINK

“Optical properties of living corals determined with diffuse reflectance spectroscopy” LINK





NIR-Predictor

New: NIR-Predictor V2.4 with new features

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

  • NIR-Predictor is an easy to use NIR software for all NIR devices
    to produce quantitative results out of NIR data.

  • CalibrationModel Service provides development of
    customized calibrations out of NIR and Lab data.

  • It allows to use NIR with your own customized
    models without the need of Chemometric Software!

  • We do the Machine Leraning for your NIR-Spectrometer
    and with the free NIR-Predictor you are
    able to analyze new measured samples.

  • For NIR-Vendors we also offer the
    Software Development Kit (SDK) for OEM Predictor use
    via the Application Programming Interface (API).
    Think of a sencod predictor engine,
    as a second heart in your system.



Download

Key Features of NIR-Predictor

  • Super flexible prediction with automatic file format detection
  • Support for many mobile and desktop NIR Spectrometers file format
  • Application concept allows to group multiple Calibrations together for an Application
  • Prediction Report shows Histogram Charts of the tabulated prediction results
  • Sample based Properties File Creator for combining NIR and Lab reference data
  • Checked creation of a single file Calibration Request

Super flexible prediction

Loads multiple files at once in

  • different file-formats and …
  • different wave-ranges and wave-resolutions and …
  • predicts each spectrum with all compatible calibrations and …
  • merges the results in a report and …
  • saves the report as HTML.

It allows you to

  • comparing measurements
  • compare different calibrations
  • compare different spectrometers,
    carry out your own round-robin amongst the vendors’ instruments.
  • compare different spectra file formats

With no configuration and no special menu command,
just drag & drop your data files.

Videos


Properties File Creator

A tool for the NIR-User to create the property file easily. It helps to create a CSV file from the measured spectra files with sample names and properties to edit in Spreadsheet/EXCEL software. Lets you enter Lab-Reference-Values in a sample-based manner, corresponding to your sample spectra for calibration. It contains clever automatic analysis mechanisms of inconsistencies in your raw-data to increase the data quality for calibration. Provides detailed analyzer information for manual data cleanup when needed.

It’s time saving and less error prone because you DON’T need to open each spectrum file separately in an editor and copy the spectral values into a table grid beside the Lab-values.

Properties File Creator saves you from:

  • manually error prone and boring tasks
  • importing multiple data files and combining it’s content manually into a single data file to append the lab reference values (aka properties)
  • programming and writing scripts to transform the data into the shape needed
  • no trouble with data handling of
    • Wavelength / Wavenumber information (x-axis)
    • Absorbance / Reflectance labeling (y-axis)
    • checking compatibility of the raw data before merging
    • Averaging Spectral Intensities of a Sample
    • coping, flipping and transposing rows and colums to get the X-Block and Y-Block data sets ready for calibration modeling
    • limited and error prone table grid functionality

Because it’s all automatic and you can check the results and get the analysis information!

Properties File Creator provides you – a individual template based on your raw-data for combining NIR and Lab-values – analysis and checks for better data quality for calibration

Top 8 Reasons why you should use
Automated NIR Calibration Service

  • No subjective model selection
  • No variation in results and interpretation
  • No overfitting model
  • Better accuracy
  • Better precision
  • Time saving!
  • No software cost (no need for Chemometric software and training)
  • One free prediction software for all your NIR systems

Reduce Total Cost of Ownership (TCO) of your NIR

To be ahead of competitors – by not owning a chemometric software – by not struggling days with these complicated software – by not deep dive into chemometrics theory

It takes significant know-how and continous investment to develop calibrations – You need to have the relevant skill sets in your organization. – That means salaries (the biggest expense in most organizations)

To get most out of it, start now! – use the free NIR-Predictor together with your NIR-Instrument software – as an NIR-Vendor, integrate the free NIR-Predictor OEM into your NIR-Instrument software – don’t delay time-to-market

Download


About the included Demo-Spectra and Demo-Calibrations

The demo calibrations for the spectrometers from

  • Si-Ware Systems
  • Spectral Engines
  • Texas Instruments
  • VIAVI

are built with the raw data, thankfully provided from Prof. Heinz W Siesler, from this publication

“Hand-held near-infrared spectrometers:
State-of-the-art instrumentation and practical applications”
Hui Yan, Heinz W Siesler
First Published August 20, 2018 Research Article
https://doi.org/10.1177/0960336018796391

The demo calibrations for the FOSS are built with the

ANSIG Kaji Competition 2014 shootout data
http://www.anisg.com.au/the-kaji-competition


References

Quickstart: NIR-Predictor – Manual

Features and Version History: NIR-Predictor – Release Notes History

Supported NIR Spectra Formats: NIR-Predictor supported Spectral Data File Formats

Frequently Asked Questions: NIR-Predictor – FAQ

WebShop : CalibrationModel WebShop