Spectroscopy and Chemometrics/Machine Learning News Weekly #35, 2021

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Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 34, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

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Near-Infrared Spectroscopy (NIRS)

“Minerals : The Role of Solar Energy (UV-VIS-NIR) as an Assistant for Sulfide Minerals Leaching and Its Potential Application for Metal Extraction” LINK

“Development of Attenuated Total Reflectance Mid-Infrared (ATR-MIR) and Near-Infrared (NIR) Spectroscopy for the Determination of Resistant Starch Content …” | LINK

“Particle Swarm Optimization and Multiple Stacked Generalizations to Detect Nitrogen and Organic-Matter in Organic-Fertilizer Using Vis-NIR” | LINK

“Inversion Method for Cellulose Content of Rice Stem in Northeast Cold Region Based on Near Infrared Spectroscopy” LINK

“Sensors : Particle Swarm Optimization and Multiple Stacked Generalizations to Detect Nitrogen and Organic-Matter in Organic-Fertilizer Using Vis-NIR” LINK




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

“Smart Detection of Faults in Beers Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Artificial Intelligence” LINK

“Fault Isolation for Desalting Processes Using Near-Infrared Measurements” | LINK

“Temporal Changes in Near-Infrared Spectroscopy Signals in Recurrent In-Stent Restenosis Attributable to Calcified Nodule” LINK




Raman Spectroscopy

“Multivariate Analysis Aided Surface-Enhanced Raman Spectroscopy (MVA-SERS) Multiplex Quantitative Detection of Trace Fentanyl in Illicit Drug Mixtures Using a Handheld Raman Spectrometer” LINK




Hyperspectral Imaging (HSI)

“SWiVIA-Sliding Window Variographic Image Analysis for real-time assessment of heterogeneity indices in blending processes monitored with hyperspectral …” LINK

“Assessing produce freshness using hyperspectral imaging and machine learning” LINK

“Nondestructive prediction and visualization of total flavonoids content in Cerasus Humilis fruit during storage periods based on hyperspectral imaging technique” LINK

“Altered mineral mapping based on ground-airborne hyperspectral data and wavelet spectral angle mapper tri-training model: Case studies from Dehua-Youxi …” LINK




Chemometrics and Machine Learning

“Artificial bionic taste sensors coupled with chemometrics for rapid detection of beef adulteration” LINK

“Identification and Classification of Technical Lignins by means of Principle Component Analysis and kNearest Neighbor Algorithm” LINK

“A 50-year personal journey through time with principal component analysis. Ian Jolliffe. Journal of Multivariate Analysis.” LINK

“A feasibility quantitative analysis of free fatty acids in polished rice by fourier transform near‐infrared spectroscopy and chemometrics” LINK

“Forensics Applications of Raman Spectroscopy, ATR FT-IR, and Chemometrics” LINK

“Fast and non-destructive near infrared spectroscopic analysis associated with chemometrics: an efficient tool in assisting breeding programs” LINK

“Chemosensors : Environmental Odour Quantification by IOMS: Parametric vs. Non-Parametric Prediction Techniques” LINK

“ACD/Labs Partners with Science Data Experts to Aid Life Sciences Companies in Accelerating Their Implementation of Machine Learning and Artificial Intelligence Technologies” | MachineLearning ArtificialIntelligence Partnership LINK

“Remote Sensing : UCalib: Cameras Autocalibration on Coastal Video Monitoring Systems” LINK

“Medical urine analysis method based on Vis-NIR optical spectroscopy using machine learning algorithms.” LINK

“A feasibility quantitative analysis of free fatty acids in polished rice by fourier transform nearinfrared spectroscopy and chemometrics” LINK

“Diffuse reflectance spectroscopy based rapid coal rank estimation: A machine learning enabled framework” LINK

“Remote Sensing : Mapping Plastic Greenhouses with Two-Temporal Sentinel-2 Images and 1D-CNN Deep Learning” LINK




Research on Spectroscopy

“Polymers : Role of the Anilinium Ion on the Selective Polymerization of Anilinium 2-Acrylamide-2-methyl-1-propanesulfonate” LINK




Environment NIR-Spectroscopy Application

“A knowledge-based, validated classifier for the identification of aliphatic and aromatic plastics by WorldView-3 satellite data” LINK

“Sustainability : Assessment of Soil Pollution Levels in North Nile Delta, by Integrating Contamination Indices, GIS, and Multivariate Modeling” LINK

“Sensing and data fusion opportunities for raw material characterisation in mining: Technology and data-driven approach” LINK




Agriculture NIR-Spectroscopy Usage

“Evaluation of non-invasive bioforensic techniques for determining the age of hot-iron brand burn scars in cattle” LINK

“Nutrients : Diet and Leukocyte Telomere Length in a Population with Extended Longevity: The Costa Rican Longevity and Healthy Aging Study (CRELES)” LINK

“Spectral and lifetime resolution of fundus autofluorescence in advanced age‐related macular degeneration revealing different signal sources” LINK

“Linking insect herbivory with plant traits: phylogenetically structured trait syndromes matter” LINK

“An in vitro Propagation of Aspilia africana (Pers.) C. D. Adams, and Evaluation of Its Anatomy and Physiology of Acclimatized Plants” | LINK

“Urban Science : Effects of Urbanization on Ecosystem Services in the Shandong Peninsula Urban Agglomeration, in China: The Case of Weifang City” LINK

“Binding to Amyloid Protein by Photothermal BloodBrain BarrierPenetrating Nanoparticles for Inhibition and Disaggregation of Fibrillation” LINK




Food & Feed Industry NIR Usage

“Molecules : The Effect of Fat Content and Fatty Acids Composition on Color and Textural Properties of Butter” LINK

“Nondestructive identification of barley seeds variety using nearinfrared hyperspectral imaging coupled with convolutional neural network” LINK

“Effect of Weather Conditions on the Fatty Acid Composition of Medium-Growth Chicken Reared in Organic Production System” LINK




Beverage and Drink Industry NIR Usage

“Chemosensors : Multi-Sensor Characterization of Sparkling Wines Based on Data Fusion” LINK

“コーヒー生豆の品質基準に関する研究” “Coffee flavor is considerably influenced by the quality of green coffee beans. ” LINK




Other

AI computers can’t patent their own inventions …. LINK



“Rape Variety Identification Based on Canopy Spectral Parameters” LINK

“Critical Review of LASSO and Its Derivatives for Variable Selection Under Dependence Among Covariates.” LINK

“近红外光谱法快速测定养生酒的酒精度方法研究” LINK

“全球食品领域近红外光谱应用研究文献计量分析” LINK

“Photochemical Synthesis of Nonplanar Small Molecules with Ultrafast Nonradiative Decay for Highly Efficient Phototheranostics” LINK

“Fluorescent Silicon Carbide Nanoparticles” LINK





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Spectroscopy and Chemometrics News Weekly #12, 2021

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Spectroscopy and Chemometrics News Weekly 11, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Application Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software Sensors QA QC QualityTesting LINK

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Near-Infrared Spectroscopy (NIRS)

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

“Assessment of frying oil quality by FT-NIR spectroscopy” LINK

“Differentiation between Fresh and Thawed Cephalopods Using NIR Spectroscopy and Multivariate Data Analysis” LINK

“A High Efficiency Trivalent Chromium-Doped Near-Infrared-Emitting Phosphor and Its NIR Spectroscopy Application” LINK

“Machine learning applied as an in-situ monitoring technique for the water content in oil recovered by means of NIR spectroscopy” LINK

“Prediction of a wide range of compounds concentration in raw coffee beans using NIRS, PLS and variable selection” LINK

“Fast and nondestructive determination of simultaneous physicochemical parameters of Manihot esculenta flour using FTNIR spectroscopy and multivariate analysis” LINK

“Intramuscular fat prediction of the semimembranosus muscle in hot lamb carcases using NIR.” LINK

“Fast and non‐destructive determination of simultaneous physicochemical parameters of Manihot esculenta flour using FT‐NIR spectroscopy and multivariate analysis” LINK

“Application of ANOVA-simultaneous component analysis to quantify and characterise effects of age, temperature, syrup adulteration and irradiation on near-infrared (NIR) spectral data of honey” LINK

“Near‐Infrared Spectroscopy (NIRS) and Optical Sensors for Estimating Protein and Fiber in Dryland Mediterranean Pastures” LINK

” Genetic variation of seed phosphorus concentration in winter oilseed rape and development of a NIRS calibration” | 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

“A review of the application of near-infrared spectroscopy (NIRS) in forestry” LINK




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

“Development of a passive optical heterodyne radiometer for near and mid-infrared spectroscopy” LINK

“Investigation of Active Compounds in Black Galingale (Kaempferia parviflora Wall. ex Baker) Using Color Analysis and Near Infrared Spectroscopy Techniques” LINK

” Quality control of milk powder with near-infrared spectroscopy” LINK

” An empirical investigation of deviations from the Beer-Lambert law in near-infrared spectroscopy: A case study of lactate in aqueous solutions, serum, blood and …” LINK

“Understanding the effect of urea on the phase transition of poly (N-isopropylacrylamide) in aqueous solution by temperature- dependent near-infrared spectroscopy” LINK

“Mechanically robust amino acid crystals as fiber-optic transducers and wide bandpass filters for optical communication in the near-infrared” LINK

“Optimal Combination of Band-Pass Filters for Theanine Content Prediction using Near-Infrared Spectroscopy” LINK

“Unlocking the Secrets of Terminalia Kernels Using Near-Infrared Spectroscopy” LINK

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

“Comparative study of degradation between the carbon black back coat layer and magnetic layer in magnetic audio tapes using attenuated total reflectance Fourier transform infrared spectroscopy and machine learning techniques” LINK

“Predicting grapevine canopy nitrogen status using proximal sensors and nearinfrared reflectance spectroscopy” LINK

“Sensors, Vol. 21, Pages 1413: The Use of a Micro Near Infrared Portable Instrument to Predict Bioactive Compounds in a Wild Harvested FruitKakadu Plum (Terminalia ferdinandiana)” LINK

“Prediction of specialty coffee flavors based on nearinfrared spectra using machine and deeplearning methods” LINK

“Soil characterization by near-infrared spectroscopy and principal component analysis” LINK

“DISCRIMINATION OF INFECTED SILKWORM CHRYSALISES USING NEAR INFRARED SPECTROSCOPY COMBINED WITH MULTIVARIATE ANALYSIS DURING …” LINK

“Evaluation of melon drying using hyperspectral imaging technique in the near infrared region” LINK




Raman Spectroscopy

“Raman spectroscopy in the detection of adulterated essential oils: The case of nonvolatile adulterants” LINK




Hyperspectral Imaging (HSI)

“Combining Multi-Dimensional Convolutional Neural Network (CNN) With Visualization Method for Detection of Aphis gossypii Glover Infection in Cotton Leaves Using Hyperspectral Imaging” LINK




Chemometrics and Machine Learning

“Detection and quantification of cow milk adulteration using portable near-infrared spectroscopy combined with chemometrics” LINK

” Realizing transfer learning for updating deep learning models of spectral data to be used in a new scenario” LINK

“Employing supervised classification techniques in determining playability status of polyesterurethane magnetic audio tapes” LINK

“Detection of Adulteration in Honey by Infrared Spectroscopy and Chemometrics: Effect on Human Health” LINK

“Rapid Biochemical Methane Potential Evaluation of Anaerobic Co-Digestion Feedstocks Based on Near Infrared Spectroscopy and Chemometrics” LINK

“Application of electronic nose with chemometrics methods to the detection of juices fraud” LINK

“Anisotropic effect on the predictability of intramuscular fat content in pork by hyperspectral imaging and chemometrics” LINK

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




Research on Spectroscopy

“Efficient utilization of date palm waste for the bioethanol production through Saccharomyces cerevisiae strain” LINK

” Spectral data analysis methods for soil properties assessment using remote sensing” DOI: 10.9790/0661-2301011418 LINK




Equipment for Spectroscopy

“Handheld arduino-based near infrared spectrometer for non-destructive quality evaluation of siamese oranges” LINK




Environment NIR-Spectroscopy Application

“Estimation of Soil Salt and Ion Contents Based on Hyperspectral Remote Sensing Data: A Case Study of Baidunzi Basin, China. Water 2021, 13, 559” LINK

“Spectroscopic anatomical mapping of left atrium endocardial substrate and lesion using an optically integrated mapping catheter”LINK

“Predicting the contents of soil salt and major water-soluble ions with fractional-order derivative spectral indices and variable selection” LINK

“Mid-Infrared Spectroscopy Supports Identification of the Origin of Organic Matter in Soils. Land 2021, 10, 215” LINK




Agriculture NIR-Spectroscopy Usage

“Identification and classification of Asian soybean rust using leaf-based hyperspectral reflectance” LINK

“Diagnosis of early blight disease in tomato plant based on visible/near-infrared spectroscopy and principal components analysis-artificial neural network prior to visual …” LINK

“Fourier Transform Infrared Spectroscopy as a Tool to Study Milk Composition Changes in Dairy Cows Attributed to Housing Modifications to Improve Animal Welfare” LINK

“Detection of soil organic matter using hyperspectral imaging sensor combined with multivariate regression modeling procedures” LINK

“Corrigendum to: Optimisation of dry matter and nutrients in feed rations through use of a near-infrared spectroscopy system mounted on a self-propelled feed mixer” LINK

“staling of white wheat bread crumb and effect of maltogenic α-amylases. part 3: spatial evolution of bread staling with time by near infrared hyperspectral imaging” LINK

” Prediction performance of portable near infrared reflectance instruments using preprocessed dried, ground forage samples” LINK




Food & Feed Industry NIR Usage

“Near Infrared (NIR) Spectroscopy as a Tool to Assess Blends Composition and Discriminate Antioxidant Activity of Olive Pomace Cultivars” LINK

“Enhanced moisture loss and oil absorption of deep-fried food by blending extra virgin olive oil in rapeseed oil” LINK




Pharma Industry NIR Usage

“GRAZING PATTERNS, DIET QUALITY, AND PERFORMANCE OF COW-CALF PAIRS GRAZING SHORT GRASS PRAIRIE USING CONTINUOUS OR HIGH …” LINK

“PENDUGAAN KADAR PATCHOULI ALCOHOL PADA MINYAK NILAM HASIL FRAKSINASI MENGGUNAKAN METODE PRINCIPAL COMPONENT REGRESSION” LINK




Medicinal Spectroscopy

“Noninvasive Monitoring of Glucose Using Near-Infrared Reflection Spectroscopy of Skin-Constraints and Effective Novel Strategy in Multivariate Calibration” LINK



Spectroscopy and Chemometrics News Weekly #4, 2021

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Spectroscopy and Chemometrics News Weekly 3, 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 3, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 3, 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

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Near-Infrared Spectroscopy (NIRS)

“Transcutaneous Near Infra-Red Spectroscopy (NIRS) for monitoring paediatric renal allograft perfusion” LINK

“Multivariate calibration: Identification of phenolic compounds in PROPOLIS using FTNIR” LINK

“Feasibility study of detecting palm oil adulteration with recycled cooking oil using a handheld NIR spectroscopy” LINK

“PREDICTION OF BIOACTIVE COMPOUNDS IN BARLEY BY NEAR-INFRARED REFLECTANCE SPECTROSCOPY (NIRS)” LINK

“A Systematic and Consistent Assay for High-throughput Characterization of Stalk Quality in Sugarcane by Near-infrared Spectroscopy” LINK

“Ultraviolet-visible/near infrared spectroscopy and hyperspectral imaging to study the different types of raw cotton” LINK




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

“Rapid qualitative identification and quantitative analysis of Flos Mume based on fourier transform near infrared spectroscopy” LINK

” Distinction of surgically resected gastrointestinal stromal tumor by near-infrared hyperspectral imaging” LINK

” Dataset of chemical and near-infrared spectroscopy measurements of fresh and dried poultry and cattle manure” LINK

“Utilizing near infrared hyperspectral imaging for quantitatively predicting adulteration in tapioca starch” LINK

“Near-infrared hyperspectral imaging for detection and visualization of offal adulteration in ground pork” LINK

“Comparative study of degradation between the carbon black back coat layer and magnetic layer in magnetic audio tapes using attenuated total reflectance Fourier transform infrared spectroscopy and machine learning techniques” LINK




Hyperspectral Imaging (HSI)

“Near-infrared hyperspectral imaging (NIR-HSI) and normalized difference image (NDI) data processing: an advanced method to map collagen in archaeological …” LINK

“Effect of curvature on hyperspectral reflectance images of cereal seed-sized objects” LINK

” Application of hyperspectral imaging to detect toxigenic Fusarium infection on cornmeal” LINK




Chemometrics and Machine Learning

“Determination of Conformational Changes of Polyphenol Oxidase and Peroxidase in Peach Juice during Mild Heat Treatment Using FTIR Spectroscopy Coupled with Chemometrics” LINK

“Model-Based Pre-Processing in Vibrational Spectroscopy” LINK

“Challenging handheld NIR spectrometers with moisture analysis in plant matrices: performance of PLSR vs. GPR vs. ANN modelling” LINK

“Maturity determination of single maize seed by using near-infrared hyperspectral imaging coupled with comparative analysis of multiple classification models” LINK

“Comparison of Soil Total Nitrogen Content Prediction Models Based on Vis-NIR Spectroscopy.” LINK

“Machine Learning-Based Presymptomatic Detection of Rice Sheath Blight Using Spectral Profiles.” LINK




Equipment for Spectroscopy

“Green analytical assay for the quality assessment of tea by using pocket-sized NIR spectrometer.” Caffeine Catechins Theanine LINK




Process Control and NIR Sensors

” Impact of Processing and Extraction on the Minor Components of Green Spanish-Style Gordal Table Olive Fat, as Assessed by Innovative Approaches” LINK




Environment NIR-Spectroscopy Application

“National-scale spectroscopic assessment of soil organic carbon in forests of the Czech Republic” LINK

“Image and Point Data Fusion for Enhanced Discrimination of Ore and Waste in Mining” LINK




Agriculture NIR-Spectroscopy Usage

” Evaluation of the physicochemical content and solid-state fermentation stage of Zhenjiang aromatic vinegar using near-infrared spectroscopy” LINK

“Non-invasive spectroscopic and imaging systems for prediction of beef quality in a meat processing pilot plant” LINK

“Compositional Analyses Reveal Relationships among Components of Blue Maize Grains” LINK

“Rapid Detection of the Quality of Miso (Japanese Fermented Soybean Paste) Using Visible/Near-Infrared Spectroscopy” LINK




Horticulture NIR-Spectroscopy Applications

“Optical Absorption and Scattering Properties at 900-1650 nm and Their Relationships with Soluble Solid Content and Soluble Sugars in Apple Flesh during Storage” LINK




Food & Feed Industry NIR Usage

“Feasibility of fraud detection in rice using a handheld near-infrared spectroscopy” LINK

“Feasibility of fraud detection in milk powder using a handheld near-infrared spectroscopy” LINK

“Wild barley, a resource to optimize yield stability and quality of elite barley: kumulative Dissertation” LINK

” Assessment of the vigor of rice seeds by near-infrared hyperspectral imaging combined with transfer learning” LINK




Laboratory and NIR-Spectroscopy

” Identification of peat type and humification by laboratory VNIR/SWIR hyperspectral imaging of peat profiles with focus on fen-bog transition in aapa mires” LINK





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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

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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 #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: