Spectroscopy and Chemometrics/Machine-Learning News Weekly #20, 2022

NIR Calibration-Model Services

NIR User? Get better results faster | Food Science QC Lab Laboratory Manager chemist LabWork Chemie analytik LINK

Spectroscopy and Chemometrics News Weekly 19, 2022 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 19, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 19, 2022 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK




Near-Infrared Spectroscopy (NIRS)

“Why People and AI Make Good Business Partners” | human AI relationships AI as a Service ( AIaaS ) LabManager NIRS MachineLearning LINK

“A novel aquaphotomics based approach for understanding salvianolic acid A conversion reaction with near infrared spectroscopy” LINK

“ex type determination in papaya seeds and leaves using near infrared spectroscopy combined with multivariate techniques and machine learnin” LINK

“DETERMINATION OF QUALITY AND RIPENING STAGES OF ‘PACOVAN’BANANAS USING VIS-NIR SPECTROSCOPY AND MACHINE LEARNING” LINK

“Rapid authentication and composition determination of cellulose films by UV-VIS-NIR spectroscopy” LINK

“Interoceptive Attentiveness Induces Significantly More PFC Activation during a Synchronized Linguistic Task Compared to a Motor Task as Revealed by Functional Near-Infrared Spectroscopy” | LINK

“Near-infrared spectroscopy and machine learning-based technique to predict quality-related parameters in instant tea” | LINK

“Sensors : LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI” LINK

“Using metaheuristic algorithms to improve the estimation of acidity in Fuji apples using NIR spectroscopy” LINK

“Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks” LINK

“Multimodal diffuse optical system integrating DSCA-NIRS and cSFDI for measuring tissue metabolism” LINK

extruded granules extruder NIR AEE audible acoustic emission granule drying process PAT LINK

“Fast Noniterative Data Analysis Method for Frequency-Domain Near-Infrared Spectroscopy with the Microscopic Beer-Lambert Law” LINK

“Vis-NIR Hyperspectral Dimensionality Reduction for Nondestructive Identification of China Northeast Rice” | LINK

“FT-NIR Spectroscopy for the Non-Invasive Study of Binders and Multi-Layered Structures in Ancient Paintings: Artworks of the Lombard Renaissance as Case Studies” LINK

“In Vivo Measurement Strategy for Near-Infrared Noninvasive Glucose Detection and Human Body Verification” LINK

“A Standard-Free Calibration Transfer Strategy for a Discrimination Model of Apple Origins Based on Near-Infrared Spectroscopy” LINK

“Comparative study on the real-time monitoring of a fluid bed drying process of extruded granules using near-infrared spectroscopy and audible acoustic emission” LINK

“Fast detection of cotton content in silk/cotton textiles by handheld near-infrared spectroscopy: a performance comparison of four different instruments” LINK

“Evaluation of optical properties of tofu samples produced with different coagulation temperatures and times using near-infrared transmission spectroscopy” LINK

“Near-Infrared Spectroscopy and Mode Cloning (NIR-MC) for In-Situ Analysis of Crude Protein in Bamboo” LINK

“Near-infrared spectroscopy to estimate the chemical element concentration in soils and sediments in a rural catchment” LINK

“Ensemble classification and regression techniques combined with portable near infrared spectroscopy for facile and rapid detection of water adulteration in bovine …” LINK

“Characterization of crude oils with a portable NIR spectrometer” CrudeOil NIRspectrometer LINK




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

“A Device for Measuring Apple Sweetness Using Near Infrared Spectroscopy” LINK

“Nearinfrared fluorophores based on heptamethine cyanine dyes: from their synthesis and photophysical properties to recent optical sensing and bioimaging applications” LINK

“Use of Attenuated Total Reflection Fourier Transform Infrared Spectroscopy and Principal Component Analysis for the Assessment of Engine Oils” | LINK

“Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview” LINK

“Analyzing the Water Confined in Hydrogel Using Near-Infrared Spectroscopy” LINK

“Near-infrared spectra of aqueous glucose solutions” LINK

“Determination of storage period of harvested plums by nearinfrared spectroscopy and quality attributes” LINK




Hyperspectral Imaging (HSI)

“Rapid Detection of Different Types of Soil Nitrogen Using Near-Infrared Hyperspectral Imaging” LINK

“Near-Infrared Hyperspectral Imaging Pipelines for Pasture Seed Quality Evaluation: An Overview” LINK

“Estimating soil moisture content under grassland with hyperspectral data using radiative transfer modelling and machine learning” LINK

“Improving rice nitrogen stress diagnosis by denoising strips in hyperspectral images via deep learning” LINK

“Identification of Soil Arsenic Contamination in Rice Paddy Field Based on Hyperspectral Reflectance Approach” LINK




Chemometrics and Machine Learning

“Remote Sensing : Early Detection of Dendroctonus valens Infestation with Machine Learning Algorithms Based on Hyperspectral Reflectance” LINK

“Applied microwave power estimation of black carrot powders using spectroscopy combined with chemometrics” LINK

DataScientist Job: Expectation vs. Reality [infographic] BigData DataScience Analytics AI MachineLearning ArtificialIntelligence Data DataAnalytics Python SQL Statistics DataViz Careers Jobs FeatureEngineering DataPrep DataCleaning LINK

“Agronomy : Detection of Adulterations in Fruit Juices Using Machine Learning Methods over FT-IR Spectroscopic Data” LINK

“Reflectance Based Models for Non-Destructive Prediction of Lycopene Content in Tomato Fruits” | LINK

“The evolution of chemometrics coupled with near infrared spectroscopy for fruit quality evaluation” LINK

“Machine Learning Strategies for the Retrieval of Leaf-Chlorophyll Dynamics: Model Choice, Sequential Versus Retraining Learning, and Hyperspectral Predictors” | LINK

“In-line near-infrared analysis of milk coupled with machine learning methods for the daily prediction of blood metabolic profile in dairy cattle” LINK

“Near-infrared spectroscopy with chemometrics for identification and quantification of adulteration in high-quality stingless bee honey” LINK

“Rapid identification and quantification of intramuscular fat adulteration in lamb meat with VIS-NIR spectroscopy and chemometrics methods” LINK




Optics for Spectroscopy

“Spectrum Reconstruction with Filter-Free Photodetectors Based on Graded-Band-Gap Perovskite Quantum Dot Heterojunctions” LINK




Facts

“Sensors : Dietary Patterns Associated with Diabetes in an Older Population from Southern Italy Using an Unsupervised Learning Approach” | LINK




Research on Spectroscopy

“A Study of C= O… HO and OH… OH (Dimer, Trimer, and Oligomer) Hydrogen Bonding in a Poly (4-vinylphenol) 30%/Poly (methyl methacrylate) 70% Blend and its …” LINK

“Deeper insights into the photoluminescence properties and (photo) chemical reactivity of cadmium red (CdS1− xSex) paints in renowned twentieth century …” | LINK




Equipment for Spectroscopy

“Green Textile Materials for Surface Enhanced Raman Spectroscopy Identification of Pesticides Using a Raman Handheld Spectrometer for In-Field Detection” LINK

“Characterization of Crude Oils with a Portable Nir Spectrometer” LINK

“Discrimination of the Red Jujube Varieties Using a Portable NIR Spectrometer and Fuzzy Improved Linear Discriminant Analysis” LINK

“Rapid authentication of the geographical origin of milk using portable near‐infrared spectrometer and fuzzy uncorrelated discriminant transformation” LINK




Environment NIR-Spectroscopy Application

“Remote Sensing : Estimating Forest Soil Properties for Humus Assessment—Is Vis-NIR the Way to Go?” LINK

“Sensors : Evaluation of Two Portable Hyperspectral-Sensor-Based Instruments to Predict Key Soil Properties in Canadian Soils” LINK

“Evaluation of Vis-Nir Pretreatments Combined with Pls Regression for Estimation SOC, Cec and Clay in Silty Soils from Eastern Croatia” LINK

“Comparing Two Different Development Methods of External Parameter Orthogonalization for Estimating Organic Carbon from Field-Moist Intact Soils by Reflectance …” LINK




Agriculture NIR-Spectroscopy Usage

“Site-specific seeding for maize production using management zone maps delineated with multi-sensors data fusion scheme” LINK

“Rapid Identification of Soybean Varieties by Terahertz Frequency-Domain Spectroscopy and Grey Wolf Optimizer-Support Vector Machine” | LINK

“A LUCASbased midinfrared soil spectral library: Its usefulness for soil survey and precision agriculture” LINK

“Identification of Microplastics in Biosolids Using Ftir and Vis-Nir Spectroscopy Enhanced by Chemometric Methods” LINK

“Agriculture : Feature Wavelength Selection Based on the Combination of Image and Spectrum for Aflatoxin B1 Concentration Classification in Single Maize Kernels” LINK




Food & Feed Industry NIR Usage

“Agronomy : Analysis of Physico-Chemical and Organoleptic Fruit Parameters Relevant for Tomato Quality” LINK




Chemical Industry NIR Usage

“Polymers : Microscopic and Structural Studies of an Antimicrobial Polymer Film Modified with a Natural Filler Based on Triterpenoids” LINK




Laboratory and NIR-Spectroscopy

“Laboratory Hyperspectral Image Acquisition System Setup and Validation” LINK




Other

“A sensor combination based automatic sorting system for waste washing machine parts” LINK





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Digitization in the field of NIR spectroscopy (smart sensors)

Digitalization is advancing, also in NIR spectroscopy, which enables trainable miniature smart sensors e.g. for analyses in the food&feed, chemical and pharmaceutical sectors.

The calibration is the core of a NIR spectroscopy sensor, it enables the numerous applications and should therefore not be the weakest link in the measurement chain.

The development of calibrations that turn NIR spectrometers into smart sensors is done manually by experts (NIR specialist, chemometrician, data scientist) with so-called chemometrics software.

This is very time-consuming (time to market) and the result is person-dependent and thus suboptimal, because each expert has his own preferred way of proceeding. In addition, the calibrations have to be maintained, as new data has been collected in the meantime, which can be used to extend and improve the calibrations.

This is where our automated service comes in, combining the knowledge and good practices of NIR spectroscopy and chemometrics collected in one software and using machine learning to generate optimal calibrations.

Based on this, we have developed a complete technology platform (Time to Market) that covers the entire process from sending NIR + Lab data, to NIR Calibration as a Service, from online purchase of calibrations, to NIR Predictor software that directly evaluates newly measured NIR data locally and generates result reports.

Besides the free desktop version with user interface, the NIR Predictor can also be integrated (OEM). This can be integrated in parallel as a complement to your current Predictor, allowing the user to choose how they want to calibrate. And give them the advantage in NIR feasibility studies and NIR spectrometer evaluations to quickly provide the customer with a solid and accurate calibration that will make their NIR system deliver better results.

Advantages for your NIR users (internal or external)
  • no initial costs (no chemometrics software license required),
  • calculable operating costs (fixed amount instead of time and hourly rate) (calibration development, calibration maintenance)
  • easy to use (no chemometrics and software training),
  • quicker to use (no calibration development work) and
  • better calibrations (precision, accuracy, robustness, …)


Our chargeable service is based on the calibration development and the annual calibration use. Calibration development and calibration use can also be carried out separately (manufacturer / user).

For you as a spectrometer manufacturer, this means that you can deliver your system pre-calibrated for certain applications without incurring software license costs. And without your application specialists having to provide additional calibration services.

The unique advantages of our calibration service together with the free NIR Predictor are:
  • no software license costs (chemometrics software, predictor software, OEM integration)
  • no chemometrics know-how necessary
  • no time needed to develop optimal NIR calibrations.


If interested in using/evaluating the service :

About CalibrationModel.com : Time and knowledge intensive creation and optimization of chemometric evaluation methods for spectrometers as a service to enable more accurate analysis and measurement results.



see also

Paradigm Change in NIR

Five Mistakes to avoid on Digitalization in NIR

NIR – Total cost of ownership (TCO)

OEM / White Label Software

White Paper



NIR Analysis in Laboratory and Laboratories – aka NIR Labs and NIR testing


Do you have a NIR spectrometer in your Lab?

How many other analytics you do in the Lab could be done faster and cheaper with NIR?

Is this possible and precise enough?

Try, we have the solution for you!
You have the NIR, scan the samples, you have the lab values and the spectra, we calibrate for you!

To see if the application is possible and how precise it can be due to knowledge based intensive model optimizations.

We do the NIR feasibility study with data for you. Fixed prices

NIR has huge application potentials and it’s a Green analytical method, that’s fast and easy to use. And has today the possibility to scale out with inexpensive mobile NIR spectrometers.

Bring the Lab to the sample. To avoid sample transport and get immediate results for decision at the place or in the process.

Just try the NIR application, use the NIR daily, collect data in parallel, we develop, optimize and maintain the calibration models for you.

How do you think?

Start Calibrate


What is possible today with NIR?
The number of different Applications exploded in the last 2-3 years!
See NIR research papers news daily on @CalibModel or the 7-day summariesNIR News Weekly” here.

How to start building a NIR Calibration (a NIR prediction model)?

With at least 60-100 different NIR measured samples with different Lab values
we can develop a quantitative NIR Calibration for you!

You don’t need to operate with expensive Chemometric Software.
Don’t worry, prices are inexpensive for that and all software you need is included.
Price comparison

The free NIR-Predictor software allows to combine your measured NIR-Spectra with the Lab values of the samples. And checks your data and creates a Calibration Request file for you. NIR-Predictor Info

With that you can order your individual customized calibration, by sending the Calibration Request file to info@CalibrationModel.com.

After processing you will get an Email with a link to access your customize calibrations in our WebShop where you can purchase and download the calibration file immediately
for use in the included free NIR-Predictor software to get results predicted out of spectra files. NIR-Predictor software

For your Calibration start-up cycle we provide low-price short living calibrations, that work for e.g. 3 or 6 months. Pricing

During that Calibration usage time you have measured more samples and collected more NIR and Lab data to build a bigger and better calibration.
E.g. you got samples that extend the range of the constituents.
Or you have collected the Lab values of additional constituents.

Yes out of an measured NIR-Spectrum you can get multiple constituents predicted at the same time (a quantitative calibration per constituent is needed).

And NIR-Predictor does handle this with ease and creates NIR Prediction Reports (printable, archive-able) of all the calibrations you have in folder named with e.g. “MyFruitApplication”.

And having multiple such applications, in NIR-Predictor you can easily switch between the applications for analyzing new NIR spectra files.

The NIR spectra files are found from the stored or exported folder from your NIR-Spectrometer software. supported File Formats



NIR Calibration Service explained


Start Calibrate

NIR-Predictor Download

The free NIR-Predictor software
  • comes with demo data, so you can predict sample spectra with demo calibrations.
  • has no functional limitations, no nagging, no ads and needs no license-key.
  • you need no account and no registration to download and use.
  • runs on Microsoft Windows 10/8/7 (Starter, Basic, Professional) (32 bit / 64 bit).
  • no data is ever transmitted from your local machine. We don’t even collect usage data.
See more Videos



Beside the free NIR-Predictor software with Windows user interface,
the real-time Predictor Engine is also available
  • for embedded integration in application, cloud and instrument-software (ICT).
  • As a light-weigt single library file (DLL)
    with application programming interface (API),
    documentation and software development kit (SDK)
    including sample source code (C#).
  • Easy integration and deployment,
    no software license protection (no serial key, no dongle).
  • Put your spectrum as an array into the multivariate predictor,
    no specific file format needed.
  • Fast prediction speed and low latency
    because of compiled code library (direct call, no cloud API).
  • Protected prediction results with outlier detection information.
See NIR Method Development Service for Labs and NIR-Vendors (OEM, White-Label)



Software Size Date Comment
NIR-Predictor V2.6.0.2 (download)

What’s new, see Release Notes

By downloading and/or using the software
you accept the Software License Agreement (EULA)
3.7 MB 18.08.2021 public release

Minimal System Requirements
Windows 7 Starter 32Bit, 1.6 GHz, 2 GB RAM, non-Administrator account

Installation
There are no administrator rights required, unpack the zip file to a folder “NIR-Predictor” in your documents or on your desktop.
Read the ReadMe.txt and double click the NIR-Predictor.exe file.

Upgrade
If you have installed an older version of NIR-Predictor then unpack into a different folder named e.g. “NIR-PredictorVx.y”. All versions can run side-by-side. Copy the Calibrations in use to the new version into the “Calibration” folder. That’s all.

Uninstall
Make sure to backup your reports and calibrations inside your “NIR-Predictor” folder. Delete the “NIR-Predictor” folder.


Start Calibrate

See also: