Spectroscopy and Chemometrics News Weekly #13, 2021

NIR Calibration-Model Services

NIRS Analytical Laboratory Method Development : Reduce Workload and Response Time | MethodDevelopment modeling LINK

Spectroscopy and Chemometrics News Weekly 12, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Application Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Service Software 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 Spectroscopy and Chemometrics News Weekly in real time on Twitter @ CalibModel and follow us.




Near-Infrared Spectroscopy (NIRS)

“Differentiation between Fresh and Thawed Cephalopods Using NIR Spectroscopy and Multivariate Data Analysis. Foods 2021, 10, 528” LINK

“Ethanol-soluble carbohydrates of cool-season grasses: prediction of concentration by near-infrared reflectance spectroscopy (NIRS) and evaluation of effects of …” LINK

“An Evaluation of Different NIR-Spectral Pre-Treatments to Derive the Soil Parameters C and N of a Humus-Clay-Rich Soil” LINK

“Prediction of Physicochemical Properties in Honeys with Portable Near-Infrared (microNIR) Spectroscopy Combined with Multivariate Data Processing” LINK

“Comparison between single and mixed-species NIRS databases’ accuracy of dairy fiber feed value detection” LINK

“Using autoencoders to compress soil VNIR–SWIR spectra for more robust prediction of soil properties” LINK

“Prediction of some quality properties of rice and its flour by near-infrared spectroscopy (NIRS) analysis.” ricequality Amylose viscosity LINK




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

“Nitrogen Management Based on Visible/Near Infrared Spectroscopy in Pear Orchards” Remote Sensing LINK

“Applications of near infrared spectroscopy for fish and fish products quality: a review” LINK

“Near Infrared Spectroscopy as a PAT Tool for Monitoring and Control of Protein and Excipient Concentration in Ultrafiltration of Highly Concentrated Antibody Formulations” LINK

“Determination of soluble solid content in market tomatoes using Near-infrared Spectroscopy” LINK

“Discriminating Coalho cheese by origin through near and middle infrared spectroscopy and analytical measures” LINK

“Current and future research directions in computer-aided near-infrared spectroscopy: A perspective” LINK

“Estimation of the Relative Abundance of Quartz to Clay Minerals Using the Visible–Near-Infrared–Shortwave- Infrared Spectral Region” LINK

“Estimating the Lactate Threshold Using Wireless Near-Infrared Spectroscopy and Threshold Detection Analyses” LINK

“Smart SelfAssembly Amphiphilic CyclopeptideDye for NearInfrared WindowII Imaging” LINK

“Application of Long-Wave Near Infrared Hyperspectral Imaging for Determination of Moisture Content of Single Maize Seed” LINK

“Near Infrared Spectroscopy as a PAT Tool for Monitoring and Control of Protein and Excipient Concentration in Ultrafiltration of Highly Concentrated Antibody …” LINK

” Achieving the potential multifunctional near-infrared materials Ca 3 In 2− x Ga x Ge 3 O 12: Cr 3+ using a solid state method” LINK

“ATR-FTIR Microspectroscopy Brings a Novel Insight Into the Study of Cell Wall Chemistry at the Cellular Level” LINK

“Development and performance tests of an on-the-go detector of soil total nitrogen concentration based on near-infrared spectroscopy” LINK

“Mid-Infrared Scattering in -Al2O3 Catalytic Powders” LINK

“Rapid tannin profiling of tree fodders using untargeted mid-infrared spectroscopy and partial least squares regression” LINK

“Intelligent evaluation of taste constituents and polyphenols-to-amino acids ratio in matcha tea powder using near infrared spectroscopy” LINK




Raman Spectroscopy

“In vivo diagnosis of skin cancer with a portable Raman spectroscopic device” LINK




Hyperspectral Imaging (HSI)

” A chemometric view of hyperspectral images” LINK




Chemometrics and Machine Learning

” A synergistic use of chemometrics and deep learning improved the predictive performance of near-infrared spectroscopy models for dry matter prediction in …” LINK

“Soil exchangeable cations estimation using Vis-NIR spectroscopy in different depths: Effects of multiple calibration models and spiking” LINK

“Prediction of Tea Theanine Content using Near-Infrared Spectroscopy and Flower Pollination Algorithm” LINK

“Predicting Oil Content In Ripe Macaw Fruits (Acrocomia Aculeata) From Unripe Ones By Near Infrared Spectroscopy And Pls Regression” LINK

“A Model Based on Clusters of Similar Color and NIR to Estimate Oil Content of Single Olives” LINK

“Quick Determination and Discrimination of Commercial Hand Sanitisers Using Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy and Chemometrics” LINK

“A synergistic use of chemometrics and deep learning improved the predictive performance of near-infrared spectroscopy models for dry matter prediction in mango fruit” LINK

“Comparative study between Partial Least Squares and Rational function Ridge Regression models for the prediction of moisture content of woodchip samples using a handheld spectrophotometer” LINK

“Classification of Lingwu long jujube internal bruise over time based on visible near-infrared hyperspectral imaging combined with partial least squares-discriminant …” LINK

“Nondestructive qualitative and quantitative analysis of Yaobitong capsule using near-infrared spectroscopy in tandem with chemometrics” LINK

“Near infrared reflectance spectroscopy: classification and rapid prediction of patchouli oil content” LINK

“Chemometric classification of geothermal and non-geothermal ethanol leaf extract of seurapoh (Chromolaena odorata Linn) using infrared spectroscopy” LINK




Process Control and NIR Sensors

“In-Line Technologies for the Analysis of Important Milk Parameters during the Milking Process: A Review” LINK




Environment NIR-Spectroscopy Application

“Remote Sensing, Vol. 13, Pages 1105: Water Conservation Estimation Based on Time Series NDVI in the Yellow River Basin” LINK

“A novel framework to estimate soil mineralogy using soil spectroscopy” LINK




Agriculture NIR-Spectroscopy Usage

“Pentosan polysulfate maculopathy: Prevalence, spectrum of disease, and choroidal imaging analysis based on prospective screening: Pentosan maculopathy: disease spectrum & choroidal analysis” LINK

“An Alternative Approach to Evaluate the Quality of Protein-Based Raw Materials for Dry Pet Food. Animals 2021, 11, 458” LINK

“The use of NIR sensor technology for soil test-based decision making in agriculture” LINK

“Estimation of Starch Hydrolysis in Sweet Potato (Beni haruka) Based on Storage Period Using Nondestructive Near-Infrared Spectrometry. Agriculture 2021, 11, 135” LINK

“Handheld vs. Benchtop NearInfrared Spectrometers – How Do They Compare for Analyzing Forage Nutritive Value?” LINK

“Foods, Vol. 10, Pages 612: Preliminary Insights in Sensory Profile of Sweet Cherries” LINK

“Comparing CalReg performance with other multivariate methods for estimating selected soil properties from Moroccan agricultural regions using NIR spectroscopy” LINK

“Potential of Multivariate Statistical Technique Based on the Effective Spectra Bands to Estimate the Plant Water Content of Wheat Under Different Irrigation Regimes” LINK

“Agriculture, Vol. 11, Pages 239: In-Line Technologies for the Analysis of Important Milk Parameters during the Milking Process: A Review” LINK

“Foods, Vol. 10, Pages 496: Fatty Acid Composition from Olive Oils of Portuguese Centenarian Trees Is Highly Dependent on Olive Cultivar and Crop Year” LINK

“Automated in-field leaf-level hyperspectral imaging of corn plants using a Cartesian robotic platform” LINK

“A novel compact intrinsic safety full range Methane microprobe sensor using “trans-world” processing method based on near- infrared spectroscopy” LINK

“Organic carbon in agricultural and agroforestry soils: Effect of different management practices” LINK

“Machine Learning-Based Approach to Predict Insect-Herbivory-Damage and Insect-Type Attack in Maize Plants Using Hyperspectral Data” LINK

” Spectral reflectance of marine macroplastics in the VNIR and SWIR measured in a controlled environment” LINK




Forestry and Wood Industry NIR Usage

“Chemometric development using portable molecular vibrational spectrometers for rapid evaluation of AVC (Valsa mali Miyabe et Yamada) infection of apple trees” LINK




Food & Feed Industry NIR Usage

“Quantitative Analysis of Colony Number in Mouldy Wheat based on Near Infrared Spectroscopy combined with Colorimetric Sensor” LINK




Pharma Industry NIR Usage

” Integration of transcriptomes analysis with spectral signature of total RNA for generation of affordable remote sensing of Hepatocellular carcinoma in serum …” LINK




Laboratory and NIR-Spectroscopy

” Prediction of meat quality traits in the abattoir using portable near-infrared spectrometers: heritability of predicted traits and genetic correlations with laboratory …” LINK




Other

“Ultrasonic-assisted catalytic transfer hydrogenation for upgrading pyrolysis-oil” LINK

“Quantitation of volatile aldehydes using chemoselective response dyes combined with multivariable data analysis” LINK

“Evaluation and optimization on the reflection and durability of reflective coatings for cool pavement” LINK

“Polyvinyl chloride: chemical modification and investigation of structural and thermal properties” LINK





.

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



Spectroscopy and Chemometrics News Weekly #49, 2020

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 48, 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 48, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 48, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK




Near-Infrared Spectroscopy (NIRS)

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

“A fast determination of insecticide deltamethrin by spectral data fusion of UV-vis and NIR based on extreme learning machine” LINK

“Assessing Laser Cleaning of a Limestone Monument by Fiber Optics Reflectance Spectroscopy (FORS) and Visible and Near-Infrared (VNIR) Hyperspectral Imaging …” LINK

“Near infrared spectroscopy combined with chemometrics to detect and quantify adulteration of maca powder ” LINK

“Potential of smartphone-coupled micro NIR spectroscopy for quality control of green tea” LINK

“APPLICATION OF VIS–NIR HYPERSPECTRAL IMAGING FOR PREDICTION OF FLAVONOIDS, ANTHOCYANINS AND SOLUBLE SOLIDS CONTENT IN …” LINK




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

“Determination of Adenosine and Cordycepin Concentrations in Cordyceps militaris Fruiting Bodies Using Near-Infrared Spectroscopy.” LINK

“… : Prediction of α-Lactalbumin and β-Lactoglobulin Composition of Aqueous Whey Solutions Using Fourier Transform Mid-Infrared and Near-Infrared Spectroscopy” LINK

“The Effect of Freeze-Drying Pretreatment on the Accuracy of Near Infrared Spectroscopic Food Analysis to Predict the Nutritive Values of Japanese Cooked Foods” LINK

“Estimating hardness and density of wood and charcoal by near-infrared spectroscopy” LINK

“Assessing the interaction between drying and addition of maltodextrin to Kakadu plum powder samples by two dimensional and near infrared spectroscopy” LINK

“Near-infrared Spectroscopy and Hyperspectral Imaging for Sugar Content Evaluation in Potatoes over Multiple Growing Seasons” LINK

“Determination of radial profiles of wood properties using a near infrared scanning system” LINK

“FTIR combined with chemometric tools (Fingerprinting spectroscopy) in comparison to HPLC; Which strategy offers more opportunities as a green analytical chemistry technique for the pharmaceutical analysis” LINK

“Prediction of high-biomass sorghum quality using near infrared spectroscopy to monitoring calorific value, moisture, and ash content.” LINK




Raman Spectroscopy

“Preliminary Assessment of Parmigiano Reggiano Authenticity by Handheld Raman Spectroscopy” LINK




Hyperspectral Imaging (HSI)

“Hyperspectral Imaging for Minced Meat Classification Using Nonlinear Deep Features” LINK

“Monitoring microstructural changes and moisture distribution of dry-cured pork-A combined confocal laser scanning microscopy and hyperspectral imaging study.” LINK

“Monitoring Urban Black-Odorous Water by Using Hyperspectral Data and Machine Learning” LINK


Chemometrics and Machine Learning

“Development of multi-product calibration models of various root and tuber powders by fourier transform near infra-red (FT-NIR) spectroscopy for the quantification of polysaccharide contents.” LINK


Research on Spectroscopy

“Extraction of rheological-optical characteristics of rice single kernel, in order to develop an instrumental method for determining grain quality” LINK




Equipment for Spectroscopy

“Near-infrared spectroscopy in quality control of Piper nigrum: A Comparison of performance of benchtop and handheld spectrometers” Pepper LINK




Process Control and NIR Sensors

“De-risking excipient particle size distribution variability with automated robust mixing: Integrating quality by design and process analytical technology.” LINK

“Evaluation of IoT-Enabled Monitoring and Electronic Nose Spoilage Detection for Salmon Freshness During Cold Storage” Foods LINK




Agriculture NIR-Spectroscopy Usage

“Impact of Goji Berries (Lycium barbarum) Supplementation on the Energy Homeostasis of Rabbit Does: Uni- and Multivariate Approach” Animals LINK

“Chemometrics in NIR Hyperspectral Imaging: Theory and Applications in the Agricultural Crops and Products Sector” LINK




Horticulture NIR-Spectroscopy Applications

“Watermelon ripeness detector using near infrared spectroscopy” LINK




Food & Feed Industry NIR Usage

“Portable NIR spectrometer for quick identification of fat bloom in chocolates.” LINK

“Non-destructive identification of slightly sprouted wheat kernels using hyperspectral data on both sides of wheat kernels” LINK




Pharma Industry NIR Usage

“Application of Process Analytical Technology in Active Pharmaceutical Ingredient Production (PAT)” LINK




Medicinal Spectroscopy

“Microwave Ablation Efficacy Evaluation of Bone Tissue Based on Near Infrared Spectrum” LINK




Laboratory and NIR-Spectroscopy

“Simultaneous prediction of several soil properties related to engineering uses based on laboratory Vis-NIR reflectance spectroscopy” LINK




Other

“ニューラルネットワークを用いた近赤外ハイパースペクトル画像におけるプラーク検出” Dental Plaque Detection LINK

“Preparation and characterization of triamterene complex with ascorbic acid derivatives” LINK

Spectroscopy and Chemometrics News Weekly #41, 2020

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 40, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

NIR-Spectroscopy and Chemometrics News Weekly 40, 2020 Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 40, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 40, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK




Near-Infrared Spectroscopy (NIRS)

“Comparison of four mobile, non‐invasive diagnostic techniques for differentiating glass types in historical leaded windows: MA‐XRF, UV–Vis–NIR, Raman …” LINK

“NIR associated to PLS and SVM for fast and non-destructive determination of C, N, P, and K contents in poultry litter” LINK

“Feasibility of FT-NIR spectroscopy and Vis/NIR hyperspectral imaging for sorting unsound chestnuts” LINK

“Estimating purple-soil moisture content using Vis-NIR spectroscopy” | LINK




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

“Classification approaches for sorting maize (Zea mays subsp. mays) haploids using single‐kernel near‐infrared spectroscopy” LINK

“Near Infrared and Aquaphotomic analysis of water absorption in lactate containing media” LINK

“Classification of Imbalanced Near-infrared Spectroscopy Data” LINK

“Meat freshness revealed by visible to near-infrared spectroscopy and principal component analysis” LINK

“Measurement of the diffusion coefficient and hydrogen bonds of water in a dry-protective solution by microscopic near-infrared spectroscopy” LINK

“Authentication of Antibiotics Using Portable Near-Infrared Spectroscopy and Multivariate Data Analysis” LINK

“Infrared spectroscopy approaches support soil organic carbon estimations to evaluate land degradation” LINK

“Assessment of some wood properties by near infrared spectroscopy” LINK

“Impact of spectroscopic information on total column water vapor retrieval in the near-infrared spectral region” LINK

“Prediction of intramuscular fat in lamb by visible and near-infrared spectroscopy in an abattoir environment” LINK




Raman Spectroscopy

“Principal Component Regression for Forensic Age Determination Using the Raman Spectra of Teeth” LINK




Hyperspectral Imaging (HSI)

“Rapid and nondestructive evaluation of hygroscopic behavior changes of thermally modified softwood and hardwood samples using near-infrared hyperspectral imaging (NIR-HSI)” LINK

“Differentiation of Environmental Bacteria Using Hyperspectral Imaging Technology And Multivariate Analysis” LINK




Chemometrics and Machine Learning

“Comprehensive chemometric classification of snack products based on their near infrared spectra” LINK

“Adulteration detection of corn oil, rapeseed oil and sunflower oil in camellia oil by in situ diffuse reflectance near-infrared spectroscopy and chemometrics” LINK

“Near-Infrared Spectroscopy Coupled Chemometric Algorithms for Rapid Origin Identification and Lipid Content Detection of Pinus Koraiensis Seeds” Sensors LINK

“Application of Generative Adversarial Network for the Prediction of Gasoline Properties” LINK

“Near Infrared Reflectance Spectroscopy Coupled to Chemometrics as a Cost-Effective, Rapid and Non-Destructive Tool for Fish Fraud Control: Monitoring Source …” LINK

“Using chemometrics to characterise and unravel the near infra-red spectral changes induced in aubergine fruit by chilling injury as influenced by storage time and …” LINK




Equipment for Spectroscopy

“Water as a probe to understand the traditional Chinese medicine extraction process with near infrared spectroscopy: a case of Danshen (Salvia miltiorrhiza Bge) …” LINK




Process Control and NIR Sensors

“Sample Mass Estimate for the Use of Near-Infrared and Raman Spectroscopy to Monitor Content Uniformity in a Tablet Press Feed Frame of a Drug Product Continuous Manufacturing Process” LINK




Environment NIR-Spectroscopy Application

“ESTIMATION OF SOIL HEAVY METAL COMBINING FRACTIONAL ORDER DERIVATIVE” LINK

“Metabolomics approaches for analysing effects of geographic and environmental factors on the variation of root essential oils of Ferula assa-foetida L.” LINK




Agriculture NIR-Spectroscopy Usage

“Principles and Applications of Vibrational Spectroscopic Imaging in Plant Science: A Review.” LINK




Food & Feed Industry NIR Usage

“Multi-block classification of chocolate and cocoa samples into sensory poles” LINK




Pharma Industry NIR Usage

“Rapid quantification of active pharmaceutical ingredient for sugar-free Yangwei granules in commercial production using FT-NIR spectroscopy based on machine …” LINK




Laboratory and NIR-Spectroscopy

“Resin and volatile content of melamine-impregnated paper assessed by near infrared spectroscopy, a simulation of the industrial process using a laboratory-scale …” | LINK




Other

“Development of an automatic sorting robot for construction and demolition waste” LINK

“SEASONAL SPECTRAL SEPARABILITY OF SELECTED GRASSES: CASE STUDY FROM THE KRKONOŠE MTS. TUNDRA ECOSYSTEM” LINK

“Polymer types ingested by northern fulmars (Fulmarus glacialis) and southern hemisphere relatives.” LINK

“High-sensitive spectroscopy for remote sensing of concrete structures” LINK





Spectroscopy and Chemometrics News Weekly #40, 2020

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 39, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK




Near-Infrared Spectroscopy (NIRS)

“Development of a Near Infrared Reflectance Spectroscopy (NIRS) Platform for Rapid Wheat Quality Analysis” LINK

Near infrared spectroscopy (NIRS), however, is relatively cheap (once you have the machine), and non-destructive. In this article, we demonstrate that adequate calibrations can be obtained for total terpene content and some specific terpenoids for pines, spruces and thuja LINK

“Special Issue on Brain Machine/Computer Interface and its Application” fNIRS LINK

“Performance of near-infrared (NIR) spectroscopy in pork shoulder as a predictor for pork belly softness” LINK

“Omega-3 and Omega-6 Determination in Nile Tilapia’s Fillet Based on MicroNIR Spectroscopy and Multivariate Calibration” LINK

“Single-Kernel FT-NIR Spectroscopy for Detecting Supersweet Corn (Zea mays L. Saccharata Sturt) Seed Viability with Multivariate Data Analysis” LINK

“Untargeted classification for paprika powder authentication using visible–Near infrared spectroscopy (VIS-NIRS)” LINK

“Monitoring the composition, authenticity and quality dynamics of commercially available Nigerian fat-filled milk powders under inclement conditions using NIRS, chemometrics, packaging and …” LINK

“Predicting total petroleum hydrocarbons in field soils with Vis–NIR models developed on laboratory‐constructed samples” LINK

“Modeling mass loss of biomass by NIRspectrometry during the torrefaction process” LINK




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

“Rapid determination of the chemical compositions of peanut seed (Arachis hypogaea.) using portable Near-Infrared Spectroscopy” LINK

“Simultaneous detection of trace adulterants in food based on multi-molecular infrared (MM-IR) spectroscopy” LINK

“Monitoring the quality of ethanol-based hand sanitizers by low-cost near-infrared spectroscopy” LINK

“Prediction of neutral detergent fiber content in corn stover using near-infrared spectroscopy technique” LINK

“Applied Sciences, Vol. 10, Pages 5801: Potential Use of Near-Infrared Spectroscopy to Predict Fatty Acid Profile of Meat from Different European Autochthonous Pig Breeds” LINK

“Rapid and non-destructive seed viability prediction using near-infrared hyperspectral imaging coupled with a deep learning approach” LINK

“Multivariate classification for the direct determination of cup profile in coffee blends via handheld near-infrared spectroscopy” LINK

“QUANTITATIVE CHARACTERIZATION OF SUSTAINED RELEASE TABLETS WITH DICLOFENAC SODIUM BY MEANS OF NEAR-INFRARED SPECTROSCOPY AND …” LINK




Raman Spectroscopy

“Raman spectroscopy and machine-learning for edible oils evaluation” LINK




Hyperspectral Imaging (HSI)

“Application of hyperspectral imaging for detecting and visualizing leaf lard adulteration in minced pork” LINK

“Detection of Shape Characteristics of Kiwifruit Based on Hyperspectral Imaging Technology” LINK




Chemometrics and Machine Learning

“A rapid food chain approach for authenticity screening: the development, validation and transferability of a chemometric model using two handheld near infrared spectroscopy …” LINK




Equipment for Spectroscopy

“Principles and applications of miniaturized nearinfrared (NIR) spectrometers” LINK

“A comparison of different optical instruments and machine learning techniques to identify sprouting activity in potatoes during storage” | LINK




Environment NIR-Spectroscopy Application

“Water-based measured-value fuzzification improves the estimation accuracy of soil organic matter by visible and near-infrared spectroscopy.” LINK




Agriculture NIR-Spectroscopy Usage

“In situ effective snow grain size mapping using a compact hyperspectral imager” LINK

“Estimation of soil nitrogen in agricultural regions by VNIR reflectance spectroscopy” LINK




Food & Feed Industry NIR Usage

“Rapid screening of DON contamination in whole wheat meals by Vis/NIR spectroscopy and computer vision coupling technology” LINK

“Quantitative Analysis of Perennial Buckwheat Leaves Protein and GABA Using Near Infrared Spectroscopy” LINK




Laboratory and NIR-Spectroscopy

“Near-infrared laboratory measurements of feldspathic rocks as a reference for hyperspectral Martian remote sensing data interpretation.” LINK




Other

“Determinación de la calidad de carne bovina y la aceptación por parte del consumidor mediante el uso de pruebas con base en infrarrojo cercano” LINK

“Validación de un algoritmo de procesamiento de imágenes Red Green Blue (RGB), para la estimación de proteína cruda en gramíneas vs la tecnología de …” LINK

“IonQ claims it has built the most powerful quantum computer yet” QuantumComputing LINK

“D-Wave’s 5,000-qubit quantum computing platform handles 1 million variables” LINK

“The Sample, the Spectra and the Maths-The Critical Pillars in the Development of Robust and Sound Applications of Vibrational Spectroscopy.” LINK





.

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.

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