Spectroscopy and Chemometrics News Weekly #11, 2021

NIR Calibration-Model Services

Custom NIR Equations for better accuracy and precision for QA QC Testing Analysis Lab PAT NIRS NIR ag food LINK

Improve Accuracy of fast Nondestructive NIR Measurements by Optimal Calibration | foodquality agtech Lab biotech LINK

Spectroscopy and Chemometrics News Weekly 10, 2021 | 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 10, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 10, 2021 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

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

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

br>

Near-Infrared Spectroscopy (NIRS)

“Fast and contactless assessment of intact mango fruit quality attributes using near infrared spectroscopy (NIRS)” LINK

“NON-INVASIVE IDENTIFICATION OF COMMERCIAL GREEN TEA BLENDS USING NIR SPECTROSCOPY AND SUPPORT VECTOR MACHINE” LINK

” Mango internal defect detection based on optimal wavelength selection method using NIR spectroscopy” LINK

“Application of Fourier Transform Near-Infrared (FT-NIR) spectroscopy for detection of adulteration in palm sugar” LINK

“Quick assessment of chicken spoilage based on hyperspectral NIR spectra combined with partial least squares regression” LINK

“PENDUGAAN KANDUNGAN GIZI AMPAS TEBU (Bagasse) MENGGUNAKAN NIRS (Near Infrared Reflectance Spectroscopy)” LINK

“… condition of leaves during black tea processing via the fusion of electronic eye (E-eye), colorimetric sensing array (CSA), and micro-near-infrared spectroscopy (NIRS)” LINK

” NIR spectroscopy of natural medicines supported by novel instrumentation and methods for data analysis and interpretation” LINK




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

“A method using near infrared hyperspectral imaging to highlight the internal quality of apple fruit slices” LINK

“Use of near-infrared spectroscopy on predicting wastewater constituents to facilitate the operation of a membrane bioreactor” LINK

“Textile Recognition and Sorting for Recycling at an Automated Line Using Near Infrared Spectroscopy” LINK

“Identification of tomatoes with early decay using visible and near infrared hyperspectral imaging and imagespectrum merging technique” LINK

“Mineral equilibrium in commercial curd and predictive ability of near-infrared spectroscopy” LINK

“Organic fertilizer from agricultural waste: determination of phosphorus content using near infrared reflectance” LINK

“Robust fraudulence detection of patchouli oil plant using near infrared spectroscopy” LINK

“Rapid quantification of epigoitrin in the extraction process of Radix Isatidis using near infrared spectroscopy” LINK




Hyperspectral Imaging (HSI)

“Nondestructive detection for egg freshness based on hyperspectral imaging technology combined with harris hawks optimization support vector regression” LINK




Chemometrics and Machine Learning

“Rapid screening of apple juice quality using ultraviolet, visible, and near infrared spectroscopy and chemometrics: A comparative study” LINK

“Portable Spectroscopy Calibration with Inexpensive and Simple Sampling Reference Alternatives for Dry Matter and Total Carotenoid Contents in Cassava Roots” LINK

“Gravity mass powder flow through conical hoppers–Part I: A mathematical model predicting the radial velocity profiles of free‐flowing granular systems as a function of …” LINK

“Simultaneous sex and species classification of silkworm pupae by” LINK

“Application of near infrared spectroscopy in chemometric modeling of tannin content and stiasny number of Pinus caribaea bark” LINK

“Monitoring UV-accelerated alteration processes of paintings by means of hyperspectral micro-FTIR imaging and chemometrics” LINK




Equipment for Spectroscopy

“Principle and optimum analysis of small near-infrared spectrometers based on digital micromirror device” LINK

We are excited to announce the release of the VIS-NIR version of our ultra compact PEBBLE spectrometer platform. The PEBBLE VIS-NIR is ideal for fluorescence and absorption spectroscopy. Read about our ultra compact PEBBLE VIS-NIR spectrometer here: LINK




Environment NIR-Spectroscopy Application

“Transfer learning strategy for plastic pollution detection in soil: Calibration transfer from high-throughput HSI system to NIR sensor” LINK

“UAS-Based Hyperspectral Environmental Monitoring of Acid Mine Drainage Affected Waters” Minerals LINK




Agriculture NIR-Spectroscopy Usage

” Comparison of UAV RGB imagery and hyperspectral remote-sensing data for monitoring winter-wheat growth” LINK

” Soil nutrient information extraction model based on transfer learning and near infrared spectroscopy” LINK

“Machine Learning-Based Prediction of Selected Parameters of Commercial Biomass Pellets Using Line Scan Near Infrared- Hyperspectral Image” LINK




Horticulture NIR-Spectroscopy Applications

” Nondestructive Assessment of Citrus Fruit Quality and Ripening by Visible–Near Infrared Reflectance Spectroscopy” LINK




Food & Feed Industry NIR Usage

“An Alternative Approach to Evaluate the Quality of Protein-Based Raw Materials for Dry Pet Food” LINK

“Predictive Ability of Four Small‐Scale Quality Tests for Dough Rheological Properties and Baking Quality in Hard Red Spring Wheat” LINK




Other

“Sequential Data Fusion Techniques for the Authentication of the P.G.I. Senise (Crusco) Bell Pepper” 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.

Spectroscopy and Chemometrics News Weekly #37, 2020

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 36, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 36, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 36, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl 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 Spectroscopy (NIRS)

“NIR Spectroscopic Techniques for Quality and Process Control in the Meat Industry” LINK

“Estimating coefficient of linear extensibility using Vis–NIR reflectance spectral data: Comparison of model validation approaches” LINK

“NIR spectroscopy and chemometric tools to identify high content of deoxynivalenol in barley” LINK

“Combining multivariate method and spectral variable selection for soil total nitrogen estimation by Vis–NIR spectroscopy” LINK

“Multi-task deep learning of near infrared spectra for improved grain quality trait predictions” LINK

“Multi-factor Fusion Models for Soluble Solid Content Detection in Pear (Pyrus bretschneideri ‘Ya’) Using Vis/NIR Online Half-transmittance Technique” LINK

“Determining regression equations for predicting the metabolic energy values of barley-producing cultivars in Iran and comparing the results with the results of NIRS method and cultivars …” LINK

“Using Near-Infrared Reflectance Spectroscopy (NIRS) to Predict Glucobrassicin Concentrations in Cabbage and Brussels Sprout Leaf Tissue” LINK

“Near-Infrared Spectroscopy for Analyzing Changes of Pulp Color of Kiwifruit in Different Depths” LINK

“Novel NIR modeling design and assignment in process quality control of Honeysuckle flower by QbD” LINK




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

“Near-infrared spectroscopy to determine cold-flow improver concentrations in diesel fuel” LINK

“Improving spatial synchronization between X-ray and near-infrared spectra information to predict wood density profiles” LINK

“Functional principal component analysis for near-infrared spectral data: a case study on Tricholoma matsutakeis” LINK

“Midinfrared spectroscopy as a tool for realtime monitoring of ethanol absorption in glycols” LINK

“Inline characterization of dispersion uniformity evolution during a twinscrew blending extrusion based on nearinfrared spectroscopy” LINK

“Development of Fourier Transform near Infrared Spectroscopy Methods for the Rapid Quantification of Starch and Cellulose in Mozzarella and Other Italian-Type CHEESES” LINK

“Prediction of Anthocyanin Content in Three Types of Blueberry Pomace by Near-Infrared Spectroscopy” LINK

“Sweetness Detection and Grading of Peaches and Nectarines by Combining Short-and Long-Wave Fourier-Transform Near-Infrared Spectroscopy” LINK




Spectral Imaging

“Use of UAS Multispectral Imagery at Different Physiological Stages for Yield Prediction and Input Resource Optimization in Corn” Remote Sensing LINK




Chemometrics and Machine Learning

“Combination of visible reflectance and acoustic response to improve nondestructive assessment of maturity and indirect prediction of internal quality of redfleshed pomelo” LINK

“Green Analytical Methods of Antimalarial Artemether-Lumefantrine Analysis for Falsification Detection Using a LowCost Handled NIR Spectrometer with DD-SIMCA and Drug Quantification by HPLC” LINK

“Data fusion of UPLC data, NIR spectra and physicochemical parameters with chemometrics as an alternative to evaluating kombucha fermentation” LINK

“Effect of physicochemical factors and use of milk powder on milk rennet-coagulation: Process understanding by near infrared spectroscopy and chemometrics” LINK

“A Digital Approach to Model Quality and Sensory Traits of Beers Fermented under Sonication Based on Chemical Fingerprinting” LINK

“Latent Variable Graphical Modeling for High Dimensional Data Analysis” LINK




Equipment for Spectroscopy

“Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer” Sensors LINK

“Developing a soil spectral library using a low-cost NIR spectrometer for precision fertilization in Indonesia” LINK

“Compact Solid Etalon Computational Spectrometer: FY19 Optical Systems Technology Line-Supported Program” LINK




Agriculture NIR-Spectroscopy Usage

“Detection of Melamine Adulteration in Milk Powder by Using Optical Spectroscopy Technologies in the Last Decade—a Review” LINK




Horticulture NIR-Spectroscopy Applications

“Accurate non-destructive prediction of peach fruit internal quality and physiological maturity with a single scan using near infrared spectroscopy” LINK

“Recent Advances in Spectral Analysis Techniques for Non-Destructive Detection of Internal Quality in Watermelon and Muskmelon: A Review” “光谱分析在西甜瓜内部品质无损检测中的研究进展” LINK




Food & Feed Industry NIR Usage

“Detection of fraud in highquality rice by nearinfrared spectroscopy” LINK

“Detecting food fraud in extra virgin olive oil using a prototype portable hyphenated photonics sensor” LINK

“Nondestructive detection of sunset yellow in cream based on near-infrared spectroscopy and interval random forest” LINK




Other

“The Detection of Substitution Adulteration of Paprika with Spent Paprika by the Application of Molecular Spectroscopy Tools.” LINK

“The Effect of Monomers on the Recognition Properties of Molecularly Imprinted Beads for Proto-hypericin and Proto-pseudohypericin” | FLOREA GAVRILA 1 20.pdf LINK





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Spectroscopy and Chemometrics News Weekly #33, 2020

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 32, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 32, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 32, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl 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 Spectroscopy (NIRS)

“Integrated soluble solid and nitrate content assessment of spinach plants using portable NIRS sensors along the supply chain” LINK

“Evaluation of Near Infrared Spectroscopy (NIRS) and Remote Sensing (RS) for Estimating Pasture Quality in Mediterranean Montado Ecosystem” LINK

“Evaluation of Homogeneity in Drug Seizures Using Near-Infrared (NIR) Hyperspectral Imaging and Principal Component Analysis (PCA)”LINK

“FT-NIRS Coupled with PLS Regression as a Complement to HPLC Routine Analysis of Caffeine in Tea Samples” Foods LINK




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

“Model based optimization of transflection near infrared spectroscopy as a process analytical tool in a continuous flash pasteurizer” LINK

“EXPRESS: Monitoring Polyurethane Foaming Reactions Using Near-Infrared Hyperspectral Imaging” LINK

“Near-infrared spectroscopy for monitoring free jejunal flap.” LINK

“Real-Time and Online Inspection of Multiple Pork Quality Parameters Using Dual-Band Visible/N ear-Infrared Spectroscopy” LINK

“An approach to quantify natural durability of Eucalyptus bosistoana by near infrared spectroscopy for genetic selection” LINK

“Rapid detection of green pea adulteration in ground pistachio nuts using near and mid-infrared spectroscopy” LINK

“Non‐invasive quality analysis of thawed tuna using near infrared spectroscopy with baseline correction” LINK




Raman Spectroscopy

“Low-Content Quantitation in Entecavir Tablets Using 1064 nm Raman Spectroscopy” LINK




Hyperspectral Imaging (HSI)

“Detecting defects on cheese using hyperspectral image analysisLINK

“Non-destructive discrimination of the variety of sweet maize seeds based on hyperspectral image coupled with wavelength selection algorithm” LINK

“Hyperspectral Imaging from a Multipurpose Floating Platform to Estimate Chlorophyll-a Concentrations in Irrigation Pond Water” Remote Sensing LINK

“Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning” LINK




Chemometrics and Machine Learning

“Maintaining the predictive abilities of near-infrared spectroscopy models for the determination of multi-parameters in White Paeony Root” LINK

“Machine Learning Modeling of Wine Sensory Profiles and Color of Vertical Vintages of Pinot Noir Based on Chemical Fingerprinting, Weather and Management Data” Sensors LINK

“Authentication of the Geographical Origin of “Vallerano” Chestnut by Near Infrared Spectroscopy Coupled with Chemometrics” LINK




Agriculture NIR-Spectroscopy Usage

“Relationship between chemical composition and standardized ileal digestible amino acid contents of corn grain in broiler chickens” LINK

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

“Determination of mechanical properties of whey protein films during accelerated aging: Application of FTIR profiles and chemometric tools” LINK

“A portable IoT NIR spectroscopic system to analyze the quality of dairy farm forage” LINK

“Exploring Relevant Wavelength Regions for Estimating Soil Total Carbon Contents of Rice Fields in Madagascar from Vis-NIR Spectra with Sequential Application of …” LINK

“Applied Sciences, Vol. 10, Pages 4345: Observation of Potential Contaminants in Processed Biomass Using Fourier Transform Infrared Spectroscopy” LINK

“Animals, Vol. 10, Pages 1095: Comparison of Fatty Acid Proportions Determined by Mid-Infrared Spectroscopy and Gas Chromatography in Bulk and Individual Milk Samples” LINK

“Manipulation of Fruit Dry Matter via Seasonal Pruning and Its Relationship to dAnjou Pear Yield and Fruit Quality” Agronomy LINK




Forestry and Wood Industry NIR Usage

“The Effect of Construction and Demolition Waste Plastic Fractions on Wood-Polymer Composite Properties” LINK




Food & Feed Industry NIR Usage

“Analysis of sorghum content in corn–sorghum flour bioethanol feedstock by near infrared spectroscopy” LINK

“Quantitative detection of fatty acid value during storage of wheat flour based on a portable near-infrared (NIR) spectroscopy system” LINK

“Integration of spectra and image features of Vis/NIR hyperspectral imaging for prediction of deoxynivalenol contamination in whole wheat flour” LINK

“Ongoing Research on Microgreens: Nutritional Properties, Shelf-Life, Sustainable Production, Innovative Growing and Processing Approaches” Foods LINK




Pharma Industry NIR Usage

“Direct Catalytic Fuel Cell Device Coupled to Chemometric Methods to Detect Organic Compounds of Pharmaceutical and Biomedical Interest” Sensors LINK




Laboratory and NIR-Spectroscopy

“Application of infrared microscopy and alternating least squares to the forensic analysis of automotive paint chips” LINK




Other

“Phenotypic plasticity and nonstructural carbohydrates in annual growth rings of the australian red cedar clones in contrasting enviroments” LINK





Spectroscopy and Chemometrics News Weekly #29, 2020

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 28, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 28, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 28, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl 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 Spectroscopy (NIRS)

“Non-invasive method to identify the type of green tea inside teabag using NIR spectroscopy, support vector machines and Bayesian optimization” LINK

“Online milk composition analysis with an on-farm near-infrared sensor” LINK

“Anonymous fecal sampling and NIRS studies of diet quality: Problem or opportunity?” LINK

“Organic and Symbiotic Fertilization of Tomato Plants Monitored by Litterbag-NIRS and Foliar-NIRS Rapid Spectroscopic Methods Running title: Litterbag-NIRS and Foliar-NIRS model in symbiotic tomato” LINK

“Determination of crude protein and metabolized energy with near infrared reflectance spectroscopy (NIRS) in ruminant mixed feeds” LINK




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

“Near Infrared Spectroscopy as an efficient tool for the Qualitative and Quantitative Determination of Sugar Adulteration in Milk” | LINK

“NEAR INFRARED SPECTROSCOPY AS A NEW FIRE SEVERITY METRIC” by Bushfire and Natural Hazards CRC LINK

“Near-infrared spectroscopy for the concurrent quality prediction and status monitoring of gasoline blending” LINK

“Application of Selective Near Infrared Spectroscopy for Qualitative and Quantitative Prediction of Water Adulteration in Milk” LINK

“Predicting Macronutrient of Baby Food using Near-infrared Spectroscopy and Deep Learning Approach” LINK

“Detection of heat treatment of honey with near infrared spectroscopy” LINK

“Use of near infrared spectroscopy in cotton seeds physiological quality evaluation” LINK

“Detection of Haemonchus contortus nematode eggs in sheep faeces using near and mid-infrared spectroscopy” LINK

“Feasibility of using near-infrared measurements to detect changes in water quality” LINK




Hyperspectral Imaging (HSI)

“Hyperspectral waveband selection algorithm based on weighted maximum relevance minimum redundancy and its stability analysis” LINK




Chemometrics and Machine Learning

“Comparative quantification of chlorophyll and polyphenol levels in grapevine leaves sampled from different geographical locations” LINK

“Screening method for determination of C18:1 trans fatty acids positional isomers in chocolate by 1H NMR and chemometrics” LINK

“Combination of efficient signal pre-processing and optimal band combination algorithm to predict soil organic matter through visible and near-infrared spectra” LINK

“A chemometric approach to the evaluation of the ageing ability of red wines” LINK

“Determination of Loline Alkaloids and Mycelial Biomass in Endophyte-Infected Schedonorus Pratensis by Near-Infrared Spectroscopy and Chemometrics” LINK

“A Feasible Approach to Detect Pesticides in Food Samples Using THz-FDS and Chemometrics” LINK

“Prediction of Soluble Solids Content in Green Plum by Using a Sparse Autoencoder” LINK




Process Control and NIR Sensors

“Real-time and field monitoring of the key parameters in industrial trough composting process using a handheld near infrared spectrometer” LINK




Environment NIR-Spectroscopy Application

“Detection and analysis of soil water content based on experimental reflectance spectrum data” LINK

” International Soil and Water Conservation Research” | LINK




Agriculture NIR-Spectroscopy Usage

“Detecting Low Concentrations of Nitrogen-Based Adulterants in Whey Protein Powder Using Benchtop and Handheld NIR Spectrometers and the Feasibility of Scanning through Plastic Bag.” LINK

“Assessment of the genetic diversity of sweetpotato germplasm collections for protein content” LINK

“Near-infrared spectroscopy and imaging in protein research” LINK

“Foods, Vol. 9, Pages 710: Application of ATR-FT-MIR for Tracing the Geographical Origin of Honey Produced in the Maltese Islands” LINK

“Agriculture, Vol. 10, Pages 193: Content of Polyphenolic Compounds and Antioxidant Potential of Some Bulgarian Red Grape Varieties and Red Wines, Determined by HPLC, UV, and NIR Spectroscopy” LINK

“Agronomy, Vol. 10, Pages 787: Assessing Soil Key Fertility Attributes Using a Portable X-Ray Fluorescence: A Simple Method to Overcome Matrix Effect” LINK




Food & Feed Industry NIR Usage

“Non‑destructive testing technology for raw eggs freshness: a review” LINK

“Quantification of multiple adulterants in beef protein powder by FT-NIR” LINK




Beverage and Drink Industry NIR Usage

“Beer Aroma and Quality Traits Assessment Using Artificial Intelligence” LINK




Other

“Tetrahedral Mn4+ as chromophore in sillenite-type compounds” LINK




NIR-Predictor – Manual


NIR-Predictor – Manual

Predicting Spectra

It’s easy to use with NIR-Predictor,
just drag & drop your data for getting the prediction results.

It supports an automatic file format detection.
So you don’t need to specify the instrument type and settings! See the list of supported formats and NIR Vendors: NIR-Predictor supported Spectral Data File Formats

Use the included data to checkout how it feels:

  1. Open the demo Spectra folder by using the Menu > Open Demo Spectra or press F8.
    There are files with spectra from different Vendors.

  2. Drag & drop a spectra file onto the NIR-Predictor window (or press Ctrl+O as for ’Open some files).

  3. The spectra will be

    • loaded
    • pre-processed
    • predicted and
    • reported

Note:
All the steps are fully automatic.
All calibrations that are compatible with the spectra, will produce prediction results in one go.
To select specific calibrations choose the Application. Where the ” ” empty means use all the calibrations.
To define a Application read more in chapter “Applications”

Hint:
To get access to Statistics of Predictions and Reports use the Menu > Show more/less (Ctrl+M) or you can simply resize the window. Here you can also re-do the Analyze step manually with changed inputs (e.g. Result Ordering).


Creating your own Calibrations

How it works – step by step

  1. You have measured your samples with you NIR-Instrument Software.
    And got the Lab-values of these samples.

    samples
    -> measured NIR-spectra
    -> Lab-references analytics

  2. Now you need to combine these data.

    NIR-spectra + Lab-references
    -> PropertiesBySamples

    Note: If you combined these data already in your NIR software used,
    and you can export it as a JCAMP-DX file then use
    Menu > Create Request File .req ... (F2)
    and read the “Help.html” and NIR-Predictor JCAMP.
    Else proceed as below.

    The NIR-Predictor provides tooling for that:

    Menu > Create Properties File... (F6)

    Select the folder with your NIR spectra measured for an application.
    NIR-Predictor creates a customized Properties file template for that data to enter the Lab values.

    Note: You don’t need to specify your instrument or vendor or an application. It’s all done automatically. And also the sample spectra are detected and grouped automatically!

  3. Use your favorite editor or spreadsheet program to enter and copy&paste
    the Lab-references Values into the columns “Prop1”, “Prop2” etc. and save the file.

  4. A final check of your entered data is done by NIR-Predictor,
    to make sure your data ist complete and all is fine.

    Menu > Create Calibration Request... (F7)

    Select the folder with the filled file.
    A CalibrationRequest.zip is created with the necessary data
    if enougth diverse Lab values are entered.

  5. Email the CalibrationRequest.zip file
    to info@CalibrationModel.com to develop the calibrations.

  6. When your calibrations are ready, you will receive an email with a link
    to the CalibrationModel WebShop where
    you can purchase and download the calibration files,
    that work with our free NIR-Predictor software without internet access.

    Note: Your sent NIR data is deleted after processing.
    We do not collect your NIR data!

Note: Further details can be found under “Create Properties File” and “Create Calibration Request”.


Configure the Calibrations for prediction usage

Configuration:

  1. in NIR-Predictor : Menu > Open Calibrations (F9)

  2. an explorer window is opened where the calibrations are located

  3. create a folder for your application, choose a name

  4. copy the calibration file(s) (*.cm) into that folder

  5. in NIR-Predictor : Menu > Search and load Applications (F4)

Usage:

  1. in NIR-Predictor : open the Application drop down list, and select your application by name

  2. if all is fine, the calibration file is valid and not expired, it shows : Calibration “1 valid calibation”

  3. the NIR-Predictor is now ready to predict

  4. to switch the application, goto 6.


Applications

The Application concept allows to group multiple Calibrations together for an Application. By selecting an Application before prediction, only the Calibrations belonging to the Application will be used for Prediction. In the Demo Data this is used to have multiple spectrometer as Application. This can be used easily as e.g. as Application “Meat Products” containing Fat and Moisture Calibration.

To create an Application, create a folder with the Application’s name inside the Calibrations folder, and move/copy all the Calibrations files to this Application folder. To remove a Calibration from the Application, remove the Calibration file from the Application folder.

After creating an new Application folder, press menu Search and load Applications (F4) to update the NIR-Predictor dialog where the Application can be selected via the dropdown list. You don’t need to close the NIR-Predictor.

After moving Calibration files around, press menu Search and load Calibrations (F5) to update the NIR-Predictor dialog.

The use-all case

In the NIR-Predictor dialog where the Application can be selected via the dropdown list, the empty "" name means that all (yes all) valid Calibrations will be used for prediction.

Note: The Prediction Report will contain only results from spectral compatible Calibrations with the given spectra. That allows to automatically handle the multi vendor NIR instrument usage.


Prediction Result Report

Histograms of Prediction Values per Property

Shows the distribution of the predicted results per calibration. The histogram range contains the range of the calibrated property and includes the predicted results.

The histogram bar (bin) color is defined as follow:

  • blue : all predictions inside calibration range.
  • red : all predictions outside calibration range.
  • orange : some overlaps with calibration range.
    So not all spectra in a orange bin are outside calibration range.
Histograms

Note: Predicted values are always shown in Histogram table and Prediction Value List table, even if the spectrum does not fit into model (spectrum different to model, aka Residual Outlier) shown as Out = X.

Note: Old browsers like Microsoft Internet Explorer 11 don’t support the grafics for Histogram charts. Use an current browser like Firefox or Chrome or Edge.

Note: If your browser opens the report too slow, try to deactivate some browser plugins, because they can filter what you look at and some add-ons are really slow.

Spectra Plot Thumbnail on the Prediction Report

Visualizes the min,median,max spectrum of the spectra dropped as files on the NIR-Predictor. This gives a minimal and good spectral overview of the predicted property results.

  • Spectra Plot color legend: min,median,max spectrum by predicted property or if no calibration is available by spectral intensity.

  • The min,median,max is determined from the predicted properties or if not available from the intensity of the spectra.

  • Beside the histogram of the predicted properties, where the distribution can be seen, the spectra shown are the ones from min,median,max predicted property.

  • This gives a minimal and good spectral overview of the predicted property results.

  • The “Spectral Range” and number of datapoints is shown in the Prediction Report Header below the listed spectra files.

  • To zoom the spectra plot a little, zoom the report in the browser (hold ctrl + mouse wheel, or pinch on touch screen).

  • The spectra plots and histograms are stored with the report and can be archived.

Note

  • Note that the spectra are shown in the raw values that are loaded, they are not shown pre-processed as the calibration model uses them to make the prediction.

  • Note that the median property spectrum is the median from the predicted property pobulation and not the “median” of the calibration property range.

  • Note that in the multi calibration prediction case, the spectra are selected for each property based on the related predicted property values and so the spectra plots shows typical different spectra.

Spectra Plot

Outlier Detection

To safeguard the prediction results, outliers are automatically checked for each individual prediction. This is based on limits that are determined when creating the calibration with the base data. Thus, a strange spectral measurement can be detected and signaled as an outlier even without base data only by means of the calibration and the NIR predictor. A prediction result with outlier warning is to be distrusted. How the various outlier tests are interpreted and how to avoid them in practice is described here.

The spectrum is an outlier to the model, if the spectrum is not similar with the spectra and lab-values the model is built with.

This legend is shown on each NIR-Predictor prediction report below the results:

Outlier (Out) Symbol Description

  • “X” : spectrum does not fit into model (spectrum different to model)
  • “O” : spectrum is wide outside model center (spectrum similar to model but far away)
  • “=” : prediction is outside upper or lower range of model (property outside model range)
  • “-” : spectrum is incompatible to calibration

Note: A prediction result with outlier warning is to be distrusted.

There are 3 outlier cases (X, O, =) and the incompatible data case “-”.

  • The bad case is “X”
  • the medium case is “O”
  • and the soft case is “=”.

The technical names in literature correspond to:

  • “X” : Spectral Residual Outlier
  • “O” : Leverage Outlier
  • “=” : Property Range Outlier

These 3 outlier cases can appear in combinations, like “XO=” or “XO” or “O=” or “X=”. The more outlier marker are shown the more likely the spectrum is an Outlier.

The default setting in NIR-Predictor Menu > “Report with Simplified Outlier Symbols”

  • is ON, that will show only the worst case instead of all combinations to have a simplified minimal information.
  • if OFF, that will show the combinations (e.g. “XO=” or “XO” or “O=” or “X=”), which is more informative for analyzing problem cases.

Some hints to avoid these Outliers:

  • “X” : spectrum does not fit into model (spectrum different to model)
    Check if the spectrum is noise only, or has no proper signal. That can happen when measured past the sample or measured into the air or at a different substance. If you have multiple NIR instruments of the same type, use spectra measured with different instruments for the calibration.

  • “O” : spectrum is wide outside model center (spectrum similar to model but far away) Sample temperature has an effect on NIR spectra shape, use spectra measured at different (typical use) temperatures (sample temperature, instrument temperature).

  • “=” : prediction is outside upper or lower range of model (property outside model range)
    Use more spectra for the calibration in the Lab value region where your special interest is. If the predicted value is only a little bit out of the calibration range, it can be Ok. Add these spectra to the calibration spectra (with the Lab values), to extend the prediction range of the calibration.

  • “-” : spectrum is incompatible to calibration
    The spectra (from the NIR instrument) has a different wavelength range or a different resolution than the spectra used for calibration. Check Instrument settings (wavelength range, resolution)

Result Ordering

To change the ordering, a drop-down-box is located below the Analyze button. If there is an analysis from the current session, and the Result Ordering is changed, the data is re-Analyzed and reported with the new Result Ordering setting. That allows to compare the different orderings. The Result Ordering is listed in the Prediction Report above the Prediction Value List and stored in the settings.

The order/sorting of the prediction results of the spectra can be defined:

  • GivenOrder (default) the given order of the spectra from file select dialog or drag&drop

*) sorted : ascending sort

  • Date_Name sorted by Date (if any) and then by Name
  • Name_Date sorted by Name and then by Date
  • Date_NamesWithNumbers sorted by Date (if any) and then by Name with number logic
  • NamesWithNumbers_Date sorted by Name with number logic (e.g. “ABC1” is before “ABC002” ) and then by Date

*) as above but sorted Rev : reverse sort = descending sort

  • Rev_Date_Name
  • Rev_Name_Date
  • Rev_Date_NamesWithNumbers
  • Rev_NamesWithNumbers_Date

E.g. with reverse sort by Rev_Date_Name, the newest spectra appear on top.

Depending on how many calibrations are used the result table is getting broader. To print the report (e.g. to Adobe PDF, FreePDF or Microsoft XPS), sometimes the landscape format is shorter in number of pages or in portrait a scale of 80% fits nicely. Or try another internet browser (Mozilla Firefox, Google Chrome, Microsoft Edge, …) to print the report and set the browser as your default browser so it will be opened by default.

Archiving Reports

Each report is contained in one file only, including the grafics. To save storage space the report file folder can be compressed to a zip file (.zip, .7z).


Enter lab values to NIR spectra

Entering the laboratory reference values for NIR calibrations

We have developed specialized tools into NIR-Predictor to combine the NIR and Lab data is a sample-based safe manner.

The main target is to improve Data Quality during the step of combining of the Lab data and the NIR data, because to model a good reliable calibration the data that build the base needs to be of high quality.

It also simplifies to enter the lab values manually to the corresponding NIR data, because of automatically grouping repeated NIR measurements of the same sample, so the lab values can be entered sample based and not by spectrum.

It helps to avoid false reference data, because of the broken relation of NIR spectra and reference values, data entry on the wrong position in the table.

And Helps to detect errors of duplicated or multiple copies of spectra files, and checks for inconsistencies in Date-Time and Sample-Naming. It also checks for missing values.

That all increases the Data Quality for the next step of Calibration Development, and makes data entry a less time consuming and less risky work.

How it works

  1. Menu > Create Properties File... (F6) select the folder with your NIR spectra measured for an application. NIR-Predictor creates a Properties file template for that data : PropertiesBySamples.csv.txt

  2. Use your favorite editor or spreadsheet program to enter and copy&paste the Lab Values into the columns and save the file.

  3. Menu > Create Calibration Request... (F7) select the folder with the filled file for a last check and a Calibration Request file is created with the needed data as a single zip file.

  4. Email the Calibration Request file to info@CalibrationModel.com to develop the calibrations.

Ok that is it, the NIR-Predictor guides you through the steps needed. And if you need to know more details, the Chapter “Create Properties File” is for you.

Create Properties File

Note:

  • If you have (exported) JCAMP-DX files containing the Lab-Values, you don’t need to do this step.
    You can send the JCAMP file with your Request (.req) file directly to the calibration service at info@CalibrationModel.com.
  • If your JCAMP-DX files does NOT contain Lab-Values, this is a way to go.

For calibrating the spectra to the lab-values you need to assign the lab-values to the spectra. The easiest way is to have a table where each spectrum (row) is linked to multiple lab-values (columns). This function Create Properties File build such a table for the selected spectra folder automatically!

This table is stored in the file PropertiesBySamples.csv.txt. This can be created for any spectra folder you like. The file extension is .csv.txt to make it easy to edit in a text editor and also in a spreadsheet (excel). The columns are standard TAB separated.

The file header line contains :

Sample Replicates Names Prop1 Prop2 Prop3 DateFirst DateLast Hashes

Where Name and Date describes the spectrum.

Prop1, Prop2, Prop3 are the place to enter the Lab Reference Concentrations properties corresponding to each spectrum. It can be extended to Prop4, Prop5, … etc. Of course you can enter real word names like “Fat (%)” instead of “Prop1”. It’s recommended to put the measurement unit beside the name.

Replicates is the number of replicated or repeated spectra of a sample that is grouped together in the Sample based property file. Sample name and the DateFirst / DateLast between the sample spectra are measured.

Date format is ISO-8601. Missing Dates are 0002-02-02T00:00:00.0000000.

If the file PropertiesBySamples.csv.txt already exist in the selected folder, the user will be notified (it will not be overwritten, because the file may contain user entered Lab-values). The Lab Reference Concentrations values are initialized to 0 (zero) and needed to be changed.

Note: 0 is not interpreted as missing value! If you have a 0 concentration value, put in 0 or 0.0 .

The entry of properties is as easy as possible, because it’s organized by Sample (and not by Spectra), so it’s like your Lab-Value Table that is sample based. The sample rows are sorted in a special way by Sample name. Sorting by Date or alphabetically by Sample can done easily in a spreadsheet program.

Note: when coping lab values to the samples make sure they correspond, so that there are no gaps and the sorting is the same.

The Spectra (rows) are initially sorted by name (and date) to have the replicates/repeats together. You can sort for your convenience in a spreadsheet program.

Enter the Lab Reference Concentrations to the spectra/sample.

Enter the Lab-Values in spreadsheet (e.g. Excel) or a text editor (e.g. Notepad++). If done, use the next menu Create Calibration Request.

Hints: Data handling:

  • The NIR-Predictor creates the PropertiesBySamples.csv.txt once, after that the user is responsible for its content. That means NIR-Predictor does not change this file anymore.

  • You can remove entire rows (spectra) in the property file. You don’t need to remove the spectra files. The NIR-Predictor is aware of the relation, the PropertiesBySamples.csv.txt defines what will be calibrated.

  • How to add more spectra files?

    The additional spectra can be handled in a separate folder, create the property file and copy the spectra to the other folder and copy/merge the property files together in your editor or spreadsheet.

    Or

    Copy the spectra into the folder, rename the PropertiesBySamples.csv.txt to e.g. “PropertiesBySamples-Part1.csv.txt” and use Create Properties File to create a new PropertiesBySamples.csv.txt with all the spectra. You can copy/merge the content of the Properties files together in your editor or spreadsheet.

  • What happens with possible duplicate rows? It does no harm to the Calibration because we do an exact checking and data cleaning in the calibration process.

  • What happens to duplicate spectra names? The spectra names are not relevant for the calibration process. The spectra names are helpful to assign the lab-values to the corresponding spectrum entry. That’s why the table is initially sorted by name. The spectra names can be edited by the user.


Create Calibration Request

The menu function Create Calibration Request packs a created Properties file (see “Create Properties File”) and it’s linked spectra files in a compressed ZIP file for sending to the CalibrationModel.com Service.

Please note that the number of measured quantitative samples need to be at least 60 . That means you need at least 60 different spectra (not counting the replicate/repeated measurements).

It shows additional property information about the data you have entered, like – the property type (Quantitative) – it’s range (min – max) and – the number of unique values and – if the Lab-values are enough diverse to get calibrated.

First select the folder with the PropertiesBySamples.csv.txt and measured spectra files of samples you have Lab-values. The data is checked and you get notified what is missing or might be wrong. If something needs to be changed, edit the PropertiesBySamples.csv.txt and do Create Calibration Request again. Your last selected folder is remembered, so you can press return in the folder selection dialog.

Hint: The keyboard shortcuts for redoing it after you edited some entries is : F7 Return – that allows you to get the property information quickly.

Hint: If you open the PropertiesBySamples.csv.txt in a spreadsheet program, you can create Histogram plots of the entered Lab-values, to see in which range are to less samples measurements.

When all is fine

When all is fine the “CalibrationRequest.zip” file is created for that data.

The ZIP file contains:

  • your PropertiesBySamples.csv.txt
  • your personal REQuest file for your computer system, that looks like
    e.g. “337dcdc06b2d6dfb0b5c4bba578642312edf2ae84d909281624d7e26283e8b07 WIN-GB0PB48GSK4.req”
  • the spectra data files

Note: If the CalibrationRequest.zip file is already created and you change the PropertiesBySamples.csv.txt make sure to delete the old CalibrationRequest.zip file first! In the dialog it states if it was successfully created or NOT because it already exist. So you are always on the safe side.

Note: CalibrationRequest.zip file name contains the property names to know what would be calibrated and at the end an identification number for referencing the file. E.g. “CalibrationRequest ‘Prop1’ – ‘Prop2’ h31T3wOH.zip”


Program Settings

  • The users program settings are stored in UserSettings.json
  • The program counters are stored in GlobalCounters.json

Further References

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: