Spectroscopy and Chemometrics News Weekly #11, 2020

CalibrationModel.com

How to Develop Near-Infrared Spectroscopy Calibrations in the 21st Century? | Chemometrics Analytische Chemie LINK

Simplify the process of training machine learning models for NIR spectra data with applied near-infrared spectroscopy (NIRS) knowledge. quantitative multivariate prediction equations LINK

Spectroscopy and Chemometrics News Weekly 10, 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 10, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 10, 2020 | NIRS NIR Spettroscopia analisi chimica Spettrale Spettrometro IoT Sensore Attrezzatura analitica nearinfrared foodscience foodprocessing foodsafety foodproduction farming agriculture LINK




Near Infrared

“Klasifikasi Kopi Green Beans Arabika Sumatera Utara Menggunakan FT-Nirs dan Analisis Diskriminan” LINK

“APLIKASI NEAR INFRARED SPECTROSCOPY (NIRS) UNTUK MENGETAHUI KANDUNGAN HARA NITROGEN FOSFOR DAN KALIUM PADA INSTALASI …” LINK

” Identification of common wood species in northeast China using Vis/NIR spectroscopy” LINK

“Efficient Super Broadband NIR Ca2LuZr2Al3O12:Cr3+,Yb3+ Garnet Phosphor for pc‐LED Light Source toward NIR Spectroscopy Applications” LINK

“Performance comparison of sampling designs for quality and safety control of raw materials in bulk: a simulation study based on NIR spectral data and geostatistical …” LINK

“Prediction of drug dissolution from Toremifene 80 mg tablets using NIR spectroscopy” LINK

“Nearinfrared spectroscopy (NIRS) for taxonomic entomology: A brief review” LINK

“Determination of tomato quality attributes using portable NIR-sensors” LINK

“Exploring the potential of NIR hyperspectral imaging for automated quantification of rind amount in grated Parmigiano Reggiano cheese” LINK

“Rapid detection of adulteration of minced beef using Vis/NIR reflectance spectroscopy with multivariate methods.” LINK

“Evaluation of quinclorac toxicity and alleviation by salicylic acid in rice seedlings using ground-based visible/near-infrared hyperspectral imaging.” LINK

“An optimized non-invasive glucose sensing based on scattering and absorption separating using near-infrared spectroscopy” LINK

“Identification of waxy cassava genotypes using fourier‐transform near‐infrared spectroscopy” LINK

“Kernel functions embedded in support vector machine learning models for rapid water pollution assessment via near-infrared spectroscopy” LINK

“Near-infrared-based Identification of Walnut Oil Authenticity” LINK

“Detection of flaxseed oil multiple adulteration by near-infrared spectroscopy and nonlinear one class partial least squares discriminant analysis” LINK

“Application research of sensor output digitization for compact near infrared IOT node” LINK

“Refining Transfer Set in Calibration Transfer of Near Infrared Spectra by Backward Refinement of Samples” LINK

“Highly identification of keemun black tea rank based on cognitive spectroscopy: Near infrared spectroscopy combined with feature variable selection” LINK

“Optimizing Rice Near-Infrared Models Using Fractional Order SavitzkyGolay Derivation (FOSGD) Combined with Competitive Adaptive Reweighted Sampling (CARS)” LINK

“Fourier transform near infrared spectroscopy as a tool to discriminate olive wastes: The case of monocultivar pomaces.” LINK

“Evaluation of quinclorac toxicity and alleviation by salicylic acid in rice seedlings using ground-based visible/near-infrared hyperspectral imaging” LINK

“Near Infrared Spectrometric Investigations on the behaviour of Lactate.” LINK

“Nondestructive rapid and quantitative analysis for the curing process of silicone resin by nearinfrared spectra” LINK

“An introduction to handheld infrared and Raman instrumentation” LINK




Hyperspectral

“Hyperspectral anomaly detection by local joint subspace process and support vector machine” LINK

“Assessment of matcha sensory quality using hyperspectral microscope imaging technology” LINK




Chemometrics

“Application of Infrared Spectroscopy and Chemometrics to the Cocoa Industry for Fast Composition Analysis and Fraud Detection” LINK

“Calibration models for the nutritional quality of fresh pastures by nearinfrared reflectance spectroscopy” LINK

“Achieving robustness to temperature change of a NIRS-PLSR model for intact mango fruit dry matter content” LINK

“Application of hyperspectral imaging combined with chemometrics for the non-destructive evaluation of the quality of fruit in postharvest” LINK




Equipment

“Characterization of Deep Green Infection in Tobacco Leaves Using a Hand-Held Digital Light Projection Based Near-Infrared Spectrometer and an Extreme Learning Machine Algorithm” LINK

“MEMS technology for fabricating plasmonic near-infrared spectrometers” LINK

“Sensors, Vol. 20, Pages 545: Development of Low-Cost Portable Spectrometers for Detection of Wood Defects” LINK




Environment

“Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging” LINK

“Determining mandatory nutritional parameters for Iberian meat products using a new method based on near infra-red reflectance spectroscopy and data mining” LINK




Agriculture

“Instrumental Procedures for the Evaluation of Juiciness in Peach and Nectarine Cultivars for Fresh Consumption” LINK

“The creation of the FT-NIR calibration for the determination of the amount of corn grain in concentrated feed” LINK

In this 9th clip from his presentation at the 2019 IFS Agronomic Conference, Wouter Saeys explains which type of NIR is best for measuring the nutrient content of manure, and why. Info on this paper is here; it’s free for Society Members to download: LINK

“Remote Sensing, Vol. 12, Pages 928: Machine Learning Algorithms to Predict Forage Nutritive Value of In Situ Perennial Ryegrass Plants Using Hyperspectral Canopy Reflectance Data” LINK

“Development of a Method To Prioritize Protein-Ligand Pairs on Beads Using Protein Conjugated to a Near-IR Dye.” LINK

“Agronomy, Vol. 10, Pages 148: Performance Evaluation of Two Commercially Available Portable Spectrometers to Non-Invasively Determine Table Grape and Peach Quality Attributes” LINK

“Investigation of a Medical Plant for Hepatic Diseases with Secoiridoids Using HPLC and FT-IR Spectroscopy for a Case of Gentiana rigescens” LINK




Food & Feed

“Comparison of sensory evaluation techniques for Hungarian wines” LINK




Laboratory

“Roadmap of cocoa quality and authenticity control in the industry: A review of conventional and alternative methods” LINK





Spectroscopy and Chemometrics News Weekly #50, 2019

CalibrationModel.com

Total cost of ownership (TCO) of NIR-Spectroscopy Systems in the Age of Digitalization – Compare Operating Costs of NIRS LINK

Spectroscopy and Chemometrics News Weekly 49, 2019 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 49, 2019 | NIRS NIR Spektroskopie MachineLearning Spektrometer Sensor Nahinfrarot LaborAnalytik Analysengeräte Analysentechnik Analysemethode Labor Nahinfrarotspektroskopie Qualitätslabor LINK

Spettroscopia e Chemiometria Weekly News 49, 2019 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem prediction models 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

” Raman Spectroscopy and NIR Spectroscopy as Possible AID in Localisation of Solitary Pulmonary Nodules” LINK

NIR spectroscopy has potential for rapid on farm analysis of slurry nutrient content. Wouter Saeys, IFSConf LINK

“Modeling for SSC and Firmness Detection of Persimmon Based on NIR Hyperspectral Imaging by Sample Partitioning and Variables Selection” LINK

” Application of the NIR Spectroscopy in the Researches of Orthopedics Diseases” LINK

“FT-NIR による油脂の迅速な品質管理” LINK

“Accuracy improvement of quantitative analysis in VIS-NIR spectroscopy using the GKF-WTEF algorithm.” LINK

“Rapid determination of the content of digestible energy and metabolizable energy in sorghum fed to growing pigs by near-infrared reflectance spectroscopy.” LINK

“Characterization of the Processing Conditions upon Textural Profile Analysis (TPA) Parameters of Processed Cheese Using Near-Infrared Hyperspectral Imaging” LINK

“Total aromatics of diesel fuels analysis by deep learning and near-infrared spectroscopy” LINK

“Rapid Assessment of Soil Quality Indices Using Infrared Reflectance Spectroscopy” LINK

“Quantitative Determination of the Fiber Components in Textiles by Near-Infrared Spectroscopy and Extreme Learning Machine” LINK

“Non-Destructive Method for Predicting Sapodilla Fruit Quality Using Near Infrared Spectroscopy” LINK

“Qualitative analysis for sweetness classification of longan by near infrared hyperspectral imaging” LINK

” MENGUKUR BERAT VOLUME TANAH DI LAPANGAN MENGGUNAKAN NEAR INFRARED SPECTROSCOPY MEASUREMENT OF SOIL BULK DENSITY IN …” LINK

“Hyperspectral Characteristics of Coastal Saline Soil with Visible/near Infrared Spectroscopy” LINK

“Monitoring Soil Surface Mineralogy at Different Moisture Conditions Using Visible Near-Infrared Spectroscopy Data” LINK

“Near infrared spectroscopy for assessing mechanical properties of Castanea sativa wood samples” Modulus of elasticity LINK

” Development of near-infrared spectroscopic sensing system for online real-time monitoring of milk quality during milking” LINK

” Advances in Near-Infrared Spectroscopy and Related Computational Methods” LINK

“Morphological, Physicochemical and FTIR Spectroscopic Properties of Bee Pollen Loads from Different Botanical Origin” LINK

“Fourier transform infrared imaging and quantitative analysis of pre-treated wood fibers: A comparison between partial least squares and multivariate curve resolution with alternating least squares methods in a case study” LINK

“Antioxidant Activity of Blueberry (Vaccinium spp.) Cultivar Leaves: Differences Across the Vegetative Stage and the Application of Near Infrared Spectroscopy.” LINK




Hyperspectral

“Development of Simplified Models for Nondestructive Testing of Rice with Husk Starch Content Using Hyperspectral Imaging Technology” LINK




Spectral Imaging

“Model for estimation of total nitrogen content in sandalwood leaves based on nonlinear mixed effects and dummy variables using multispectral images” LINK

“Sources of Variation in Assessing Canopy Reflectance of Processing Tomato by Means of Multispectral Radiometry” LINK




Chemometrics

“Sampling for spectroscopic analysis: consequences for multivariate calibration” LINK

“Combination of hyperspectral sensing images and chemometrics for measuring tensile strength indices of organic plastic sheeting in the field” LINK

” Comparison of prediction power of three multivariate calibrations for estimation of leaf anthocyanin content with visible spectroscopy in Prunus cerasifera” LINK

” Prediction of quality parameters of food residues using NIR spectroscopy and PLS models based on proximate analysis” LINK

“Pre-processing spectroscopic data: for good or ill?” LINK




Process Control

“Optimization of pickled herring production-Approaches for process and quality control-DTU Orbit (26/09/2019)” LINK




Environment

“Response surface methodology for optimizing LIBS testing parameters: A case to conduct the elemental contents analysis in soil” LINK




Pharma

“Rapid detection of cAMP content in red jujube using near-infrared spectroscopy” LINK

“Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning.” LINK




Other

“Determination of Carvacrol Content in Alaska Yellow Cedar (Callitropsis nootkatensis) Extractives” LINK





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

CalibrationModel.com (CM) versus Others

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

CM fix € pricing Others € Price Range (approx.)
Software
included
Chemometric Package not‑needed
€3500 – €6500 per user
Chemometric Predictor
free‑software
€1500 – €2500 per NIR device
Knowledge
included
Chemometric Training not‑needed
€1500 – €2500 per user
Chemometrician* Salary not‑needed
1 years Salary / year
(+ risk of Employee Turnover)
Computation
included
Powerful Computer (many Processors, lot of RAM for big data) not‑needed
€1500 – €4500 per computer
Development and Usage
Development of a Calibration
€128
€80 – €150 / hour
of Chemometrician* using a Chemometric Software (click and wait) and applying it’s knowledge
Usage of a Calibration
€60 / year
Total €178 in first year
€60 in second year
initial (min €8000 , max €15500)
+ 2 * (2 – 4)(hour to cost same! as CM service) * (€80 – €150) Chemometrician* work
no initial cost
very high initial costs
no personnel cost
high personnel* costs
constant CM services
risk of Employee Turnover
global knowledge
risk of only use personal knowledge
easy to calculate fix cost on demand
difficult to calculate variable cost on demand plus Chemometrician* Recruitment needed
Results :
calibration prediction performance
always reproducible highly optimized
only as good as your Chemometrician* daily condition
better prediction performance, due to best-of 10’000x calibrations
small size of experiments, non-optimal calibrations

See also: pricing

Start Calibrate

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

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

Spectroscopy and Chemometrics News Weekly #34, 2019

CalibrationModel.com

Develop customized NIR applications and freeing up hours of spectroscopy analysts time. chemometric software LINK

Spectroscopy and Chemometrics News Weekly 33, 2019 | NIRS NIR Spectrometer Analytical Chemistry Chemical Analysis Lab Labs Laboratories QAQC Testing Quality LabManager LabManagers laboratory digitalization labdata laboratorydata LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 33, 2019 | NIRS NIR FTNIR Spektroskopie Chemometrie Spektrometer Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysemethode Laborleiter Laboranalyse Qualitätskontrolle LINK

Spettroscopia e Chemiometria Weekly News 33, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità prediction controllo qualità LINK




Chemometrics

“A Spectral Fitting Algorithm to Retrieve the Fluorescence Spectrum from Canopy Radiance” Remote Sensing RemoteSensing LINK

“A hyperspectral GA-PLSR model for prediction of pine wilt disease” LINK

“Hyperspectral Anomaly Detection via Convolutional Neural Network and Low Rank With Density-Based Clustering” LINK

“Use of near-infrared hyperspectral (NIR-HS) imaging to visualize and model the maturity of long-ripening hard cheeses” LINK

“Identification of lactic acid bacteria Enterococcus and Lactococcus by near-infrared spectroscopy and multivariate classification.” LINK

“A practical convolutional neural network model for discriminating Raman spectra of human and animal blood” LINK

“Non-destructive prediction of texture of frozen/thaw raw beef by Raman spectroscopy” LINK

“Incorporating brand variability into classification of edible oils by Raman spectroscopy” LINK

“Three-way data splits (training, test and validation) for model selection and performance estimation” LINK

“Importance of spatial predictor variable selection in machine learning applications — Moving from data reproduction to spatial prediction.” LINK

“Tracing the dune activation of Badain Jaran Desert and Tengger Desert by using near infrared spectroscopy and chemometrics” LINK




Near Infrared

“On-The-Go VIS + SW – NIR Spectroscopy as a Reliable Monitoring Tool for Grape Composition within the Vineyard.” LINK

“Improved Functional Near Infrared Spectroscopy Enables Enhanced Brain Imaging” fNIR FDNIR LINK

“Estabilishing A Calibration For Neutral Detergent Fiber (NDF) Value by Using Near Infrared Spectroscopy (NIR) in Corn Grain” LINK

“Using Near Infrared Spectroscopy and Machine Learning to diagnose Systemic Sclerosis.” LINK

“Strategies for the efficient estimation of soil organic carbon at the field scale with vis-NIR spectroscopy: Spectral libraries and spiking vs. local calibrations” LINK

“Sensomics-from conventional to functional NIR spectroscopy-shining light over the aroma and taste of foods” LINK




Infrared

“Assessment of Spinal Cord Ischemia With Near-Infrared Spectroscopy: Myth or Reality?” LINK

“Identification of antibiotic mycelia residues in cottonseed meal using Fourier transform near-infrared microspectroscopic imaging.” LINK

“Application of near-infrared spectroscopy for frozen-thawed characterization of cuttlefish (Sepia officinalis)” Aquaphotomics LINK

” Identification of Tilletia foetida, Ustilago tritici, and Urocystis tritici Based on Near-Infrared Spectroscopy” LINK

“Assessment of meat freshness and spoilage detection utilizing visible to near-infrared spectroscopy” LINK




Hyperspectral

“Estimating the severity of apple mosaic disease with hyperspectral images” LINK

“Spectral filter design based on in-field hyperspectral imaging and machine learning for mango ripeness estimation” LINK




Equipment

“Feasibility Study of the Use of Handheld NIR Spectrometer for Simultaneous Authentication and Quantification of Quality Parameters in Intact Pineapple Fruits” LINK




Agriculture

“Evidence on the discrimination of quinoa grains with a combination of FT-MIR and FT-NIR spectroscopy” FTNIR FTMIR LINK




Forestry

“Near-infrared spectroscopy analysis-a useful tool to detect apple proliferation diseased trees?”LINK

“Evaluation of near infrared spectroscopy to non-destructively measure growth strain in trees” LINK




Other

“Spectral Screening Based on Comprehensive Similarity and Support Vector Machine” LINK

“Aquaphotomics-From Innovative Knowledge to Integrative Platform in Science and Technology.” LINK





Spectroscopy and Chemometrics News Weekly #27, 2019

CalibrationModel.com

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 26, 2019 | NIRS NIR Spektroskopie Chemometrie Spektrometer Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spectroscopy and Chemometrics News Weekly 26, 2019 | NIRS NIR Spectroscopy Chemometrics Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software Sensors QA QC Testing Quality Checking LINK

Spettroscopia e Chemiometria Weekly News 26, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem predictionmodels LINK

This week’s NIR news Weekly is sponsored by YourCompanyNameHere – BestNIRinstruments. Check out their product page … link

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




Chemometrics

“Non-destructive Classification for Maturity of Pomelo CV. Tubtim Siam” Brix LINK

“Authentication of “Avola Almonds” by Near Infrared (NIR) Spectroscopy and chemometrics” LINK

“Feature selection based convolutional neural network pruning and its application in calibration modeling for NIR spectroscopy” LINK

” Geographical origin traceability of Cabernet Sauvignon wines based on Infrared fingerprint technology combined with chemometrics” LINK




Near Infrared

“Compositional Analysis of Cement Raw Meal by Near-Infrared (NIR) Spectroscopy” LINK

A joint project by researchers from and IMB_CNM will explore the possibility of developing silicon sensors for near-infrared light detection with single photon resolution funded by the LINK

“Molecular (Raman, NIR, and FTIR) spectroscopy and multivariate analysis in consumable products analysis” LINK

“Influences of Detection Position and Double Detection Regions on Determining Soluble Solids Content (SSC) for Apples Using On-line Visible/Near-Infrared (Vis/NIR) …” LINK

“Non-destructive determination of strawberry fruit and juice quality parameters using ultraviolet, visible, and near infrared spectroscopy” LINK

“Determination of API Gravity and Total and Basic Nitrogen Content by Mid-and Near-Infrared Spectroscopy in Crude Oil with Multivariate Regression and Variable Selection” LINK

“Selecting Near-infrared Hyperspectral Wavelengths Based on One-way ANOVA to Identify the Origin of Lycium Barbarum” LINK

“Rapid and Nondestructive Measurement of Rice Seed Vitality of Different Years Using Near-Infrared Hyperspectral Imaging.” LINK

“Rapid estimation of soil heavy metal nickel content based on optimized screening of near-infrared spectral bands” LINK

“Distinguishing watermelon maturity based on acoustic characteristics and near infrared spectroscopy fusion technology” LINK

“Development of a methodology to analyze leaves from Prunus dulcis varieties using near infrared spectroscopy” LINK

“Rapid fingerprinting technology of heavy oil spill by Mid-infrared spectroscopy” LINK

“Evaluation of high alcohol concentration using a 1.7-µm band near-infrared spectroscopy system using multi-mode optical fibers” LINK




Raman

“Journal Highlight: Raman Open Database: first interconnected RamanXray diffraction openaccess resource for material identification” LINK




Equipment

“Water as a probe for serum-based diagnosis by temperature-dependent near-infrared spectroscopy” LINK




Process Control

“Detecting special-cause variation ‘events’ from process data signatures” LINK




Environment

“Soil macrofauna and leaf functional traits drive the decomposition of secondary metabolites in leaf litter” LINK




Agriculture

“Plants, Vol. 8, Pages 205: Comparison of Sugar Profile between Leaves and Fruits of Blueberry and Strawberry Cultivars Grown in Organic and Integrated Production System” LINK

“Sensors, Vol. 19, Pages 2934: Combining Fourier Transform Mid-Infrared Spectroscopy with Chemometric Methods to Detect Adulterations in Milk Powder” LINK

“Utilising near-infrared hyperspectral imaging to detect low-level peanut powder contamination of whole wheat flour” LINK




Chemical

“All Donor Electrochromic Polymers Tunable across the Visible Spectrum via Random Copolymerization” LINK




Other

“Effet du moment d’acquisition des spectres proche infrarouge sur la qualité de prédiction du taux de lipides et du taux de fonte du foie gras chez le canard” LINK

“Investigation of the prevalence and characterisation of infection by Kudoa thyrsites and K. paniformis in South African marine fish species” LINK





Spectroscopy and Chemometrics News Weekly #26, 2019

CalibrationModel.com

NIR Method Development Service for Labs and NIR-Vendors (OEM) LINK

Spectroscopy and Chemometrics News Weekly 25, 2019 | NIRS NIR Spectroscopy Chemometrics Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software Sensors QA QC Testing Quality Checking LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 25, 2019 | NIRS NIR Spektroskopie Chemometrie Spektrometer Sensoren Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Qualitätslabor LINK

Spettroscopia e Chemiometria Weekly News 25, 2019 | NIRS NIR Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Chem Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem prediction models LINK




Chemometrics

“X-ray fluorescence and visible near infrared sensor fusion for predicting soil chromium content” LINK

“Staling of white wheat bread crumb and effect of maltogenic a-amylases. Part 2: Monitoring the staling process by using near infrared spectroscopy and chemometrics” LINK

“Rapid and Nondestructive Quantification of Trimethylamine by FT-NIR Coupled with Chemometric Techniques” Fish quality LINK

“Prediction of yerba mate caffeine content using near infrared spectroscopy” LINK

“Journal Highlight: A new flow cell and chemometric protocol for implementing inline Raman spectroscopy in chromatography” LINK

Teaching Machine Learning at the moment and a student asks whether “PCA” stands for “Pretty Cool Algorithm” after I apparently used that phrase… That should really have been deliberate (it wasn’t). I will never use “Principal Component Analysis” again. PrettyCoolAlgorithm LINK

“A screening method based on Visible-NIR spectroscopy for the identification and quantification of different adulterants in high-quality honey.” LINK

“Chemometric studies of the effects of milk fat replacement with different proportions of vegetable oils in the formulation of fat-filled milk powders: Implications for quality assurance.” LINK

“Comparison of Bayesian and partial least squares regression methods for mid-infrared prediction of cheese-making properties in Montbéliarde cows” LINK

“NIR model transfer of alkali-soluble polysaccharides in Poria cocos with piecewise direct standardization” LINK

“Comparison of three different classification methods performance for the determination of biofuel quality by means of NIR spectroscopy” LINK

“Application of hierarchical classification models and reliability estimation by bootstrapping, for authentication and discrimination of wine vinegars by UV-vis spectroscopy” LINK

“Geographical origin traceability of Cabernet Sauvignon wines based on Infrared fingerprint technology combined with chemometrics.” LINK

“Determination of Adulteration Content in Extra Virgin Olive Oil Using FT-NIR Spectroscopy Combined with the BOSSPLS Algorithm” LINK




Near Infrared

“NIR-based Sudan I to IV and Para-Red food adulterants screening.” Paprika adulteration LINK

“Nondestructive detection of rape leaf chlorophyll level based on Vis-NIR spectroscopy.” LINK

“High-throughput analysis of leaf physiological and chemical traits with VIS–NIR–SWIR spectroscopy: a case study with a maize diversity panel” | New phenomics paper from Ge, Schnable, Sigmon and Yang labs of & LINK

High-throughput analysis of leaf physiological and chemical traits with VIS–NIR–SWIR spectroscopy: a case study with a maize diversity panel LINK

“Estimating dry matter and fat content in blocks of Swiss cheese during production using on-line near infrared spectroscopy” LINK

“Temperature-dependent near-infrared spectroscopy for studying the interactions in protein aqueous solutions” LINK

” 滑皮金桔糖度的近红外光谱无损检测技术.” “Non-destructive testing technology of sugar content in Huapikumquat by near infrared spectroscopy” LINK

“Grading and Sorting of Grape Berries Using Visible-Near Infrared Spectroscopy on the Basis of Multiple Inner Quality Parameters” LINK

“Modified silver nanoparticles enhanced single drop micro extraction of tartrazine in food samples coupled with diffuse reflectance Fourier transform infrared spectroscopic analysis” LINK

“Multicolor lanthanide-doped CaS and SrS near-infrared stimulated luminescent nanoparticles with bright emission: application in broad-spectrum lighting, information coding, and bio-imaging.” LINK

“The use of mid-infrared spectra to map genes affecting milk composition” |(19)30485-0/fulltext?rss=yes LINK




Raman

“Semi-Automated Heavy-Mineral Analysis by Raman Spectroscopy” Minerals LINK




Hyperspectral

“Discrimination of astringent and deastringed hard Rojo Brillante persimmon fruit using a sensory threshold by means of hyperspectral imaging” LINK

“Remote Sensing, Vol. 11, Pages 1485: Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction” LINK




Agriculture

“Applied Sciences, Vol. 9, Pages 2472: Comparison of Raman and Mid-Infrared Spectroscopy for Real-Time Monitoring of Yeast Fermentations: A Proof-of-Concept for Multi-Channel Photometric Sensors” LINK

“Agronomy, Vol. 9, Pages 293: Field Spectroscopy to Determine Nutritive Value Parameters of Individual Ryegrass Plants” LINK




Pharma

“Quantification of Inkjet-Printed Pharmaceuticals on Porous Substrates Using Raman Spectroscopy and Near-Infrared Spectroscopy” LINK




Laboratory

“Adapted-Consumer-Technology Approach to Making Near-Infrared-Reflectography Visualization of Paintings and Murals Accessible to a Wider Audience” – Journal of Chemical Education LINK





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get your spectra analyced as easy as Drag’n’Drop.

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

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

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

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

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



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Key Features of NIR-Predictor

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

Super flexible prediction

Loads multiple files at once in

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

It allows you to

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

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

Videos


Properties File Creator

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

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

Properties File Creator saves you from:

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

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

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

Top 8 Reasons why you should use
Automated NIR Calibration Service

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

Reduce Total Cost of Ownership (TCO) of your NIR

To be ahead of competitors
  • by not owning a chemometric software
  • by not struggling days with these complicated software
  • by not deep dive into chemometrics theory
It takes significant know-how and continous investment to develop calibrations
  • You need to have the relevant skill sets in your organization.
  • That means salaries (the biggest expense in most organizations)
To get most out of it, start now!
  • use the free NIR-Predictor together with your NIR-Instrument software
  • as an NIR-Vendor, integrate the free NIR-Predictor OEM into your NIR-Instrument software
  • don’t delay time-to-market
Read more about NIR Total cost of ownership (TCO)

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About the included Demo-Spectra and Demo-Calibrations

The demo calibrations for the spectrometers from

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

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

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

The demo calibrations for the FOSS are built with the

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


References

Quickstart: NIR-Predictor – Manual

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

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

Frequently Asked Questions: NIR-Predictor – FAQ

WebShop : CalibrationModel WebShop