Spectroscopy and Chemometrics Machine-Learning News Weekly #31, 2022

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 30, 2022 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 30, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 30, 2022 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK




Near-Infrared Spectroscopy (NIRS)

“Peatlands spectral data influence in global spectral modelling of soil organic carbon and total nitrogen using visible-near-infrared spectroscopy” LINK

“A NIRS Based Device for Identification of Acute Ischemic Stroke by Using a Novel Organic Dye in the Human Blood Serum” | LINK

“Interlacing the evaluation of mechanical properties of mortar cement with near-infrared spectroscopy using multivariate data analysis” LINK

“A Preliminary Study to Classify Corn Silage for High or Low Mycotoxin Contamination by Using near Infrared Spectroscopy” | LINK

“Scalable, NanometerAccurate Fabrication of AllDielectric Metasurfaces with Narrow Resonances Tunable from Near Infrared to Visible Wavelengths” LINK

“A Combined Near-Infrared and Mid-Infrared Spectroscopic Approach for the Detection and Quantification of Glycine in Human Serum” | LINK

“Sensors : Determination of Quality Parameters in Mangetout (Pisum sativum L. ssp. arvense) by Using Vis/Near-Infrared Reflectance Spectroscopy” LINK

“Study on the Effect of Apple Size Difference on Soluble Solids Content Model Based on Near-Infrared (NIR) Spectroscopy” | LINK

“Canopy VIS-NIR spectroscopy and self-learning artificial intelligence for a generalised model of predawn leaf water potential in Vitis vinifera” LINK

“Chemical composition of Andropogon gayanus cv. planaltina predicted through nirs and analyzed through wet chemistry” LINK

“A Preliminary Study to Classify Corn Silage for High or Low Mycotoxin Contamination by Using near Infrared Spectroscopy” LINK

“Model robustness in estimation of blueberry SSC using NIRS” LINK

“Non-invasive Measurement of Blood Sugar Using Near-Infrared Spectroscopy” | LINK

“Investigating the Utility of Near Infrared Reflectance (NIR) Imaging for Diabetic Retinopathy Screening” LINK

“Fourier transform near infrared spectroscopy as a tool to predict spawning status in Alaskan fishes with variable reproductive strategies” LINK

“Prediction of dry matter, carbon and ash contents and identification of Calycophyllum spruceanum (Benth) organs by Near-Infrared Spectrophotometry” LINK

“Determination of aflatoxin B1 value in corn based on Fourier transform near-infrared spectroscopy: Comparison of optimization effect of characteristic …” LINK

“Combining different pre-processing and multivariate methods for prediction of soil organic matter by near infrared spectroscopy (NIRS) in Southern Brazil” LINK

“Evaluation of coating uniformity for the digestion-aid tablets by portable near-infrared spectroscopy” LINK

“Novel strategies in near infrared spectroscopy (NIRS) and multivariate analysis (MVA) for detecting and profiling pathogens and diseases of agricultural importance.” LINK




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

“A highly efficient colloidal quantum dot imager that operates at near-infrared wavelengths” LINK

“Green tea grades identification via Fourier transform near‐infrared spectroscopy and weighted global fuzzy uncorrelated discriminant transform” LINK

“Investigation of oxygen saturation in regions of skin by near infrared spectroscopy” LINK

“Spectral variable selection for estimation of soil organic carbon content using midinfrared spectroscopy” LINK

“Plants : Uptake and Presence Evaluation of Nanoparticles in Cicer arietinum L. by Infrared Spectroscopy and Machine Learning Techniques” LINK

“Electric-field-resolved near-infrared microscopy” LINK




Raman Spectroscopy

“A comparative study based on serum SERS spectra in and on the coffee ring for high precision breast cancer detection” LINK

“Raman spectroscopy and multivariate analysis for identification and classification of pharmaceutical pain reliever tablets” LINK

“Foods : High Precisive Prediction of Aflatoxin B1 in Pressing Peanut Oil Using Raman Spectra Combined with Multivariate Data Analysis” LINK




Hyperspectral Imaging (HSI)

” Identification of pesticide residues on black tea by fluorescence hyperspectral technology combined with machine learning” LINK

“Combining hyperspectral imaging and electrochemical sensing for detection of Pseudomonas aeruginosa through pyocyanin production” LINK




Spectral Imaging

“Open-source mobile multispectral imaging system and its applications in biological sample sensing” LINK




Chemometrics and Machine Learning

“Sensors : A Model for Predicting Cervical Cancer Using Machine Learning Algorithms” LINK

“Application of miniature fiber near infrared spectroscopy combined with chemometrics in predicting antioxidant activity of Sagittaria sagittifolia L …” LINK

“Comparison of Calibration Models for Rapid Prediction of Lignin Content in Lignocellulosic Biomass Based on Infrared and Near-Infrared Spectroscopy” LINK

“Foods : Shelf-Life Prediction and Critical Value of Quality Index of Sichuan Sauerkraut Based on Kinetic Model and Principal Component Analysis” LINK




Spectroscopy

“Giorgia Stocco Rapid and non-destructive determination of Ca and P in milk using WDXRF” LINK




Facts

” A Review of Machine Learning Techniques for Identifying Weeds in Corn” LINK




Research on Spectroscopy

“Syntheses, Structures, and Properties of Coordination Polymers with 2,5-Dihydroxy-1,4-Benzoquinone and 4,4′-Bipyridyl Synthesized by In Situ Hydrolysis Method” LINK




Equipment for Spectroscopy

“Polymers : Combined Strategy of Wound Healing Using Thermo-Sensitive PNIPAAm Hydrogel and CS/PVA Membranes: Development and In-Vivo Evaluation” LINK




Future topics in Spectroscopy

“The Effect of Task Performance and Partnership on Interpersonal Brain Synchrony during Cooperation” LINK




Process Control and NIR Sensors

“Monitoring of dromedary milk clotting process by Urtica dioica extract using fluorescence, near infrared and rheology measurements” LINK




Environment NIR-Spectroscopy Application

“Remote Sensing : Combination of Hyperspectral and Machine Learning to Invert Soil Electrical Conductivity” LINK

“Sensors : Comparative Study on Recognition Models of Black-Odorous Water in Hangzhou Based on GF-2 Satellite Data” LINK




Agriculture NIR-Spectroscopy Usage

“Vis-NIR Spectroscopy and Machine Learning Methods to Diagnose Chemical Properties in Colombian Sugarcane Soils” LINK

“Nutrients : Long-Term Dietary Patterns Are Reflected in the Plasma Inflammatory Proteome of Patients with Inflammatory Bowel Disease” LINK

“Remote Sensing : Hyperspectral UAV Images at Different Altitudes for Monitoring the Leaf Nitrogen Content in Cotton Crops” LINK

“Plants : Integrated Starches and Physicochemical Characterization of Sorghum Cultivars for an Efficient and Sustainable Intercropping Model” LINK

“Remote Sensing : Estimation of Canopy Structure of Field Crops Using Sentinel-2 Bands with Vegetation Indices and Machine Learning Algorithms” LINK




Horticulture NIR-Spectroscopy Applications

“Foods : Physico-Chemical, Textural and Sensory Evaluation of Spelt Muffins Supplemented with Apple Powder Enriched with Sugar Beet Molasses” LINK




Food & Feed Industry NIR Usage

“Warming increase the N2O emissions from wheat fields but reduce the wheat yield in a rice-wheat rotation system” LINK

“Biochemical study of rapid discolouration mechanisms in bison meat” LINK

“Emerging Nondestructive Techniques for the Quality and Safety Evaluation of Pork and Beef: Recent Advances, Challenges and Future Perspectives” LINK




Pharma Industry NIR Usage

“The Conservation of Cloud Pattern-painted Boots (1800-1600 BP) Excavated in Yingpan, Xinjiang” | LINK




Other

“基于 WOS 的高光谱技术在农业方面应用的计量分析” LINK

“Modified Hybrid Strategy Integrating Online Adjustable Oil Property Characterization and Data-Driven Robust Optimization under Uncertainty: Application in Gasoline …” LINK

“Insights into the Effect of Sludge Retention Times on System Performance, Microbial Structure and Quorum Sensing in an Activated Sludge Bioreactor” | LINK

“Diclofenac Ion Hydration: Experimental and Theoretical Search for Anion Pairs” LINK

“Aort cerrahisinde derin ve ılımlı hipotermik antegrad serebral perfüzyonun nörolojik etkileri” LINK

“Vibrational spectroscopic evaluation of hydrophilic or hydrophobic properties of oxide surfaces” LINK




Spectroscopy and Chemometrics/Machine-Learning News Weekly #28, 2022

NIR Calibration-Model Services

Portable Near-Infrared Spectroscopy LINK

Efficient development of new quantitative prediction equations for multivariate data like NIR spectra | spectral LINK

Spectroscopy and Chemometrics News Weekly 27, 2022 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 27, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 27, 2022 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK




Near-Infrared Spectroscopy (NIRS)

” NNB Monitoring and prediction of sensory shelf-life in strawberry with ultraviolet-visible-near-infrared (UV-VIS-NIR) spectroscopy” LINK

“Near Infrared Spectrograph (NIRSpec)” NASAWebb LINK

“Agronomic characterization of anaerobic digestates with near-infrared spectroscopy” LINK

“Near InfraredTriggered Theranostic Nanoplatform with Controlled Release of HSP90 Inhibitor for Synergistic Mild Photothermal and Enhanced Nanocatalytic Therapy with Hypoxia Relief” LINK

“Research on Online Rapid Sorting Method of Waste Textiles Based on Near-Infrared Spectroscopy and Generative Adversity Network” | LINK

“Agriculture : A First Attempt to Combine NIRS and Plenoptic Cameras for the Assessment of Grasslands Functional Diversity and Species Composition” LINK

“Foods : Classification of Tea Quality Levels Using Near-Infrared Spectroscopy Based on CLPSO-SVM” LINK

“Near-infrared spectroscopy combined with pattern recognition algorithms to quickly classify raisins” | LINK

“Assessment by Multi‐Distance Hyperspectral NIRS of Changes in the Oxidation State of Cytochrome C Oxidase (oxCCO) to Carotid Artery Compressions” LINK

“Imaging near-infrared luminescent nanoformulations to guide drug delivery and photoinduced therapy” LINK

“Positive Association between NIRS‐derived measures of Microvascular Reactivity and Mitochondrial Capacity in the Quadriceps and Hamstrings” LINK

“Is intraoperative near infrared spectroscopy a reliable monitoring method in preventing neurocognitive dysfunction in cardiac surgery?” LINK

“Fusion models for detection of soluble solids content in mandarin by Vis/NIR transmission spectroscopy combined external factors” LINK

“Enhanced near-infrared reflectance and functional characteristics of nano metal oxide embedded alkyd coatings” LINK

“Webb spectrum showcases galaxy’s composition” NIRSpec NearInfrared Spectrograph chemical composition Webbtelescope WebbSpaceTelescope webbfirstimages LINK

“Field-scale spatial correlation between soil and Vis-NIR spectra in the Cerrado biome of Central Brazil” LINK

“Metaheuristic algorithms in visible and near infrared spectra to detect excess nitrogen content in tomato plants” LINK

“Aquaphotomics for monitoring of groundwater using short-wavelength near-infrared spectroscopy” LINK

“Classifying waste wood from Amazonian species by near-infrared spectroscopy (NIRS) to improve charcoal production” LINK

“Rapid Determination of Urea Formaldehyde Resin Content in Wood Fiber Mat Using Near-infrared Spectroscopy” LINK

“… on early identification of freshness decay of fresh‐cut kiwifruit during cold chain storage by Fourier transform‐near infrared spectroscopy combined with chemometrics” LINK

“NIR spectroscopy for rapid measurement of moisture and cannabinoid contents of industrial hemp (Cannabis sativa)” LINK

“A feasibility study on nondestructive classification of frozen Atlantic salmon (Salmo salar) fillets based on temperature history at the logistics using NIR spectroscopy” LINK

“A review on hybrid strategy-based wavelength selection methods in analysis of near-infrared spectral data” LINK

“Deep Learning-Based Multilevel Classification of Alzheimer’s Disease Using Non-invasive Functional Near-Infrared Spectroscopy” LINK

“Non-Invasive Detection of Anti-Inflammatory Bioactivity and Key Chemical Indicators of the Commercial Lanqin Oral Solution by Near Infrared Spectroscopy” | LINK




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

” The Importance of Wavelength Selection in On-Scene Identification of Drugs of Abuse with Portable Near-Infrared Spectroscopy” LINK

“Tunable Infrared Sensing Properties of MXenes Enabled by Intercalants” LINK

“Longwave Infrared Hyperspectral Emissivity Retrieval with Robustness to Spectral Uncertainty” LINK

“Rapid identification of the geographic origin of Taiping Houkui green tea using nearinfrared spectroscopy combined with a variable selection method” LINK




Raman Spectroscopy

“Highly Scalable, Wearable Surface-Enhanced Raman Spectroscopy” LINK




Hyperspectral Imaging (HSI)

“A New Method for Calculating Water Quality Parameters by Integrating Space-Ground Hyperspectral Data and Spectral-In situ Assay Data” LINK

“Research on hyperspectral band selection method for visual interpretation” LINK

“Classification of chemical species for nuclear reactor inspections using SWIR hyperspectral imaging” LINK




Chemometrics and Machine Learning

“Experimentation on Spectra Data Regression Using Dense Multilayer Neural Networks with Common Pre-processing” | LINK

“Top-down sensory prediction in the infant brain at 6 months is correlated with language development at 12 and 18 months” LINK

“Aerobic Fitness as a Predictor of Conduit, Resistance and, Microvascular Arterial Function” LINK

“In silico NIR spectroscopy-a review. Molecular fingerprint, interpretation of calibration models, understanding of matrix effects and instrumental difference” LINK

“Linear Calibration Methods” | LINK

“Performance evaluation of variable selection methods coupled with partial least squares regression to determine the target component in solid samples” LINK

“Multivariate methods with feature wavebands selection and stratified calibration for soil organic carbon content prediction by VIS‐NIR Spectroscopy” LINK




Optics for Spectroscopy

“Fibers : The Mechanical Response of Epoxy-Sisal Composites Considering Fiber Anisotropy: A Computational and Experimental Study” LINK




Facts

“Precision Farming Using Image Processing and Machine Learning” LINK

“Do Racial Differences Exist in Mechanoreflex Sensitivity in Young Healthy Males?” LINK


Research on Spectroscopy

illicitdrug analysis cocaine MDMA Ketamine methamphetamine amphetamine GHB LINK

“Coatings : Stability of Polyethylene Glycol-Coated Copper Nanoparticles and Their Optical Properties” LINK

“Selection of the Effective Characteristic Spectra Based on the Chemical Structure and Its Application in Rapid Analysis of Ethanol Content in Gasoline” LINK

“Evaluation of Blend Uniformity and Terminal Point during Continuous Mixing in Water for Modified Double-Base Propellant Components Using a Near-Infrared Method” LINK




Equipment for Spectroscopy

“A feasibility study on the use of a pocket-sized NIR spectrometer and multivariate algorithm to distinguish expired drugs from unexpired ones” LINK




Environment NIR-Spectroscopy Application

“Can in situ spectral measurements under disturbance-reduced environmental conditions help improve soil organic carbon estimation?” LINK




Agriculture NIR-Spectroscopy Usage

“Remote Sensing : Cropping Patterns of Annual Crops: A Remote Sensing Review” LINK

“Analysis of Protein Denaturation, and Chemical Visualization, of Frozen Grass Carp Surimi Containing Soluble Soybean Polysaccharides” LINK

“Can soil spectroscopy contribute to soil organic carbon monitoring on agricultural soils?” LINK




Horticulture NIR-Spectroscopy Applications

“Spectrum classification of citrus tissues infected by fungi and multispectral image identification of early rotten oranges” LINK




Forestry and Wood Industry NIR Usage

“Collecting Wood Core Samples from Macassar Ebony (Diospyros celebica Bakh.) for Multi-Purpose Analysis using Pickering Punch” LINK




Food & Feed Industry NIR Usage

“Fluoride effect indicators in Phaseolus vulgaris seeds and seedlings” | LINK

“Applications of the Remote Sensing Technology to Detect and Monitor the Rust Disease in the Wheat-a Literature Review” LINK

“Applied Sciences : Meat 4.0: Principles and Applications of Industry 4.0 Technologies in the Meat Industry” LINK




Laboratory and NIR-Spectroscopy

“Optical properties of ‘Gala'(Malus pumila) apple pulp and their relationship with internal quality” LINK




Other

“Photochromism of Coordination Compounds” LINK

“Structural and Optical Characterization of Mechanochemically Synthesized CuSbS2 Compounds” LINK

“การ ตรวจ สอบ โรค แอ น แทรก โน ส ใน มะม่วง พันธุ์ น้ำดอกไม้ สี ทอง ด้วย เนีย ร์ อินฟราเรด สเปก โทร ส โก ปี” LINK

“Growth and characterization of nonlinear optical crystal glycine sodium nitrate and its biological activity” | LINK





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Spectroscopy and Chemometrics/Machine-Learning News Weekly #23, 2022

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 22, 2022 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 22, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 22, 2022 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK




Near-Infrared Spectroscopy (NIRS)

“Sensors : Potential of Near-Infrared Spectroscopy for the Determination of Olive Oil Quality” LINK

“Physiological variable predictions using VIS-NIR spectroscopy for water stress detection on grapevine: Interest in combining climate data using multiblock method” LINK

“SpectraNet-53: A deep residual learning architecture for predicting soluble solids content with VIS-NIR spectroscopy” LINK

“Measurement of water-holding capacity in fermented milk using near-infrared spectroscopy combined with chemometric methods” LINK

“Investigating the water structures in reverse micelles by temperature-dependent near infrared spectroscopy combined with independent component analysis” LINK

“Chemosensors : Digital Detection of Olive Oil Rancidity Levels and Aroma Profiles Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Machine Learning Modelling” LINK

“A new variant position of head-up CPR may be associated with improvement in the measurements of cranial near-infrared spectroscopy suggestive of an increase in …” LINK

“Design of an Integrated Near-Infrared Spectroscopy Module for Sugar Content Estimation of Apples” | LINK

“Comparison of several strategies for the deployment of a multivariate regression model on several handheld NIR instruments. Application to the Quality Control of …” LINK

“Cross-Modal Transfer Learning From EEG to Functional Near-Infrared Spectroscopy for Classification Task in Brain-Computer Interface System” | |(08)70223-0 LINK

“Novel Application of NIR Spectroscopy for Non-Destructive Determination of ‘Maratina’ Wine Parameters” LINK

“Effects of degraded speech processing and binaural unmasking investigated using functional near-infrared spectroscopy (fNIRS)” LINK

“Rapid monitoring of flavonoid content in sweet tea (Lithocarpus litseifolius (Hance) Chun) leaves using NIR spectroscopy” | LINK

“Simultaneous determination of apparent amylose, amylose and amylopectin content and classification of waxy rice using near-infrared spectroscopy (NIRS)” LINK

“Design of an Integrated Near-Infrared Spectroscopy Module for Sugar Content Estimation of Apples” LINK

“Nondestructive Testing of Pear Based on Fourier Near-Infrared Spectroscopy” | LINK

“NIRSCAM: A Mobile Near-Infrared Sensing System for Food Calorie Estimation” LINK

“Potential of Near-Infrared Spectroscopy for the Determination of Olive Oil Quality” | LINK

“A new concept of acousto-optic tunable filter-based near-infrared hyperspectral imager for planetary surface exploration” LINK

“Multivariety and multimanufacturer drug identification based on near-infrared spectroscopy and recurrent neural network” | LINK

“Rapid identification of lamb freshness grades using visible and near-infrared spectroscopy (Vis-NIR)” LINK

“Blended Fabric with Integrated Neural Network based on Attention Mechanism Qualitative Identification Method of Near Infrared Spectroscopy” LINK

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

“Enhancing count of Aspergillus colony in wheat based on nanoparticles modified chemo-responsive dyes combined with visible/near-infrared spectroscopy” LINK




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

“Physicochemical Properties and Detection of Glucose Syrup Adulterated Kelulut (Heterotrigona Itama) Honey Using Near‐Infrared Spectroscopy” LINK

“Applied Sciences : Use of Fourier-Transform Infrared Spectroscopy for DNA Identification on Recycled PET Composite Substrate” LINK

“Identification of Five Similar Cinnamomum Wood Species Using Portable Near-Infrared Spectroscopy” LINK

“Rapid identification of the geographic origin of Taiping Houkui green tea using nearinfrared spectroscopy combined with a variable selection method” LINK

“A Fast Method to Sparse Reconstruction for Near-infrared Spectroscopy” LINK

“Thermal-Infrared Spectral Feature Analysis and Spectral Identification of Monzonite Using Feature-Oriented Principal Component Analysis” LINK

“Nondestructive quality assessment of longans using near infrared hyperspectral imaging” LINK




Raman Spectroscopy

“Biomedicines : Lung Cancer: Spectral and Numerical Differentiation among Benign and Malignant Pleural Effusions Based on the Surface-Enhanced Raman Spectroscopy” LINK

“Robust Encapsulation of Biocompatible Gold Nanosphere Assemblies for Bioimaging via Surface Enhanced Raman Scattering” LINK




Hyperspectral Imaging (HSI)

“Detecting aggressive papillary thyroid carcinoma using hyperspectral imaging and radiomic features” LINK

“Quantitative Detection of Myoglobin Content in Tan Mutton During Cold Storage by Near-infrared Hyperspectral Imaging” | LINK

“Rapid and non-destructive detection of natural mildew degree of postharvest Camellia oleifera fruit based on hyperspectral imaging” LINK

“A new quantitative index for the assessment of tomato quality using Vis-NIR hyperspectral imaging” LINK




Chemometrics and Machine Learning

“Authenticity green coffee bean species and geographical origin using near‐infrared spectroscopy combined with chemometrics” LINK

“Advanced process control for salvianolic acid A conversion reaction based on data-driven and mechanism-driven model” LINK

“A comparison of benchtop and micro NIR spectrometers for infant milk formula powder storage time discrimination and particle size prediction using chemometrics and …” LINK

“Origin identification of foxtail millet (Setaria italica) by using green spectral imaging coupled with chemometrics” LINK

“A two-dimensional sample screening method based on data quality and variable correlation” LINK




Research on Spectroscopy

“Novel regularization method for diffuse optical tomography inverse problem” LINK




Equipment for Spectroscopy

“Advances in costeffective integrated spectrometers” IoT miniature sensors applications LINK




Environment NIR-Spectroscopy Application

“Particle densities of cultivated south greenlandic soils can be explained by a three‐compartment model, pedotransfer functions, and a vis-NIR spectroscopy model” LINK




Agriculture NIR-Spectroscopy Usage

“Polymers : Preparation of a Novel Nanocomposite and Its Antibacterial Effectiveness against Enterococcus faecalis—An In Vitro Evaluation” LINK

“Synthesis of a mixedvalent europium(II, III)borate and its optical and magnetic behavior” LINK

“Performance of analytical techniques (SWIR imaging, XRD, EPMA) for the identification of minerals frequently formed during natural and technological geothermal …” LINK

“Functional N-cycle genes in soil and N2O emissions in tropical grass-maize intercropping systems” LINK

“Agriculture : Forever Young? Late Shoot Pruning Affects Phenological Development, Physiology, Yield and Wine Quality of Vitis vinifera cv. Malbec” LINK

“Aquaphotomic, E-Nose and Electrolyte Leakage to Monitor Quality Changes during the Storage of Ready-to-Eat Rocket” LINK

“Investigation of physicochemical properties, photoluminescence, laser damage threshold, antimicrobial, NLO activity of L-proline manganese chloride monohydrate …” | LINK




Horticulture NIR-Spectroscopy Applications

“Consensual Regression of Soluble Solids Content in Peach by Near Infrared Spectrocopy” | LINK




Food & Feed Industry NIR Usage

“Quantitative assessment of wheat quality using nearinfrared spectroscopy: A comprehensive review” LINK

“Calculation of the Mixing Time as a Function of the Dairy Cow Diet Chemical Homogeneity Inside the Mixing Hopper” | LINK

“Discrimination of centre composition in panned chocolate goods using nearinfrared spectroscopy” LINK

“Advanced Spectroscopic Techniques for Food Quality” FoodQuality FoodSecurity FoodSafety Spectroscopy LINK

“CONCISE REVIEWS AND HYPOTHESES IN FOOD SCIENCE” LINK

“Adulteration detection technologies used for halal/kosher food products: an overview” | LINK

“Synthesis and characterization of theJapanese rice-ball’-shaped Molybdenum Blue Na4 [Mo2O2 (OH) 4 (C6H4NO2) 2] 2 [Mo120Ce6O366H12 (OH) 2 (H2O) 76]∼ …” LINK




Pharma Industry NIR Usage

“Detection of low numbers of bacterial cells in pharmaceutical drug product using Raman Spectroscopy and PLS-DA multivariate analysis” LINK




Laboratory and NIR-Spectroscopy

“Multifunctional superhydrophobic and cool coating surfaces of the blue ceramic nanopigments based on the heulandite zeolite” LINK




Other

“Influence of anisotropy on heterogeneous nucleation of gold nanorod assemblies” LINK

“Phenomic selection: A new and efficient alternative to genomic selection” | LINK





Spectroscopy and Chemometrics/Machine-Learning News Weekly #4, 2022

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 3, 2022 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 3, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 3, 2022 | 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)

“Origin Identification of Hungarian Honey Using Melissopalynology, Physicochemical Analysis, and Near Infrared Spectroscopy” LINK

“Larch Wood Defect Definition and Microscopic Inversion Analysis Using the ELM Near-infrared Spectrum Optimization along with WOA-SVM” LINK

“Estimating texture and organic carbon of an Oxisol by near infrared spectroscopy” LINK

“Study on effects of airborne Pb pollution on quality indicators and accumulation in tea plants using Vis-NIR spectroscopy coupled with radial basis function neural network” LINK

“Sensors : Rapid Estimation of Potato Quality Parameters by a Portable Near-Infrared Spectroscopy Device” LINK

“Comparison of soil organic carbon stocks predicted using visible and near infrared reflectance (VNIR) spectra acquired in situ vs. on sieved dried samples …” LINK

“Effect of the sample measurement representativeness on soil carbon determination using near-infrared compact spectrophotometers” LINK




Hyperspectral Imaging (HSI)

“Remote Sensing : Non-Destructive Monitoring of Maize Nitrogen Concentration Using a Hyperspectral LiDAR: An Evaluation from Leaf-Level to Plant-Level” LINK




Chemometrics and Machine Learning

“A Graph-Based Feature Extraction Algorithm Towards a Robust Data Fusion Framework for Brain-Computer Interfaces” LINK

“Improving Prediction of Peroxide Value of Edible Oils Using Regularized Regression Models” LINK

“Influence of atmospheric modeling on spectral target detection through forward modeling approach in multi-platform remote sensing data” LINK

“Comparison of Machine Learning Classification Methods for Determining the Geographical Origin of Raw Milk Using Vibrational Spectroscopy” | LINK

“Quantification of the Spectral Variability of Ore-Bearing Granodiorite under Supervised and Semisupervised Conditions: An Upscaling Approach” | LINK




Optics for Spectroscopy

“On the use of polychromatic cameras for high spatial resolution spectral dose measurements” LINK




Research on Spectroscopy

“High Refractive Index Silica-Titania Films Fabricated via the Sol-Gel Method and Dip-Coating Technique-Physical and Chemical Characterization” LINK

“The synthesis and optical dispersions parameters of Cadmium doped Tin-oxide thin films by the Sol-Gel method” LINK




Environment NIR-Spectroscopy Application

“Applied Sciences : Accurate Measurements of Forest Soil Water Content Using FDR Sensors Require Empirical In Situ (Re)Calibration” LINK




Agriculture NIR-Spectroscopy Usage

“Sensors : Simultaneous Determination of Droplet Size, pH Value and Concentration to Evaluate the Aging Behavior of Metalworking Fluids” LINK

“Polymers : Development of Application Specific Intelligent Framework for the Optimized Selection of Industrial Grade Magnetic Material” LINK

“Biogenic and physicogenic aggregates: formation pathways, assessment techniques, and influence on soil properties” LINK

“Estimation of genetic parameters for carcass grading traits, image analysis traits, and monounsaturated fatty acids in Japanese Black cattle from Hyogo Prefecture” | LINK

“Optimizing Near Infrared Reflectance Spectroscopy to Predict Nutritional Quality of Chickpea Straw for Livestock Feeding” LINK

“Polymers : Properties of Biocomposite Film Based on Whey Protein Isolate Filled with Nanocrystalline Cellulose from Pineapple Crown Leaf” LINK




Pharma Industry NIR Usage

“Antibiotics : Chemical, Cytotoxic, and Anti-Inflammatory Assessment of Honey Bee Venom from Apis mellifera intermissa” LINK




Laboratory and NIR-Spectroscopy

“Development of a server for a portable near-infrared spectroscopy laboratory” LINK




Other

“Minerals : Preliminary Data on Geochemical Characteristics of Major and Trace Elements in Typical Biominerals: From the Perspective of Human Kidney Stones” LINK

“A comparison of computer workstation postures” LINK

“Breeding groundnut for drought tolerance.” LINK

“Tunable ChargeDensity PEDOT:PSS for Application in PostSynaptic Organic Neuromorphic Electrodes” | LINK

“Granulometric Discrimination of Marine Sediments Based on Trace Metal Content Measured by the Technique LIBS (Laser Induced Breakdown Spectroscopy)” LINK

“Oncometabolite Fingerprinting Using Fluorescent SingleWalled Carbon Nanotubes” | LINK





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Spectroscopy and Chemometrics/Machine-Learning News Weekly #49, 2021

NIR Calibration-Model Services

Effective development of new quantitative prediction equations for multivariate data like NIR spectra | spectrum LINK

How to improve calibration models for NIR Instrument Devices? Wheat Food Security LINK

Spectroscopy and Chemometrics News Weekly 48, 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 48, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 48, 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)

“Insight into the stability of protein in confined environment through analyzing the structure of water by temperature-dependent near-infrared spectroscopy” LINK

“Determination of starch and amylose contents in various cereals using common model of near-infrared reflectance spectroscopy.” LINK

“RESEARCH ARTICLE Near-infrared spectroscopy for the distinction of wood and charcoal from Fabaceae species: comparison of ANN, KNN AND SVM …” LINK

“Device-Independent Discrimination of Falsified Amoxicillin Capsules Using Heterogeneous Near-Infrared Spectroscopic Devices for Training and Testing of a Support Vector Machine” LINK

“High quality VO(2) thin films synthesized from V(2)O(5) powder for sensitive near-infrared detection” | LINK

“Detection of toxic chemicals in hand sanitizers using near-infrared spectroscopy” LINK

“Identification of Baha’sib mung beans based on Fourier transform near infrared spectroscopy and partial least squares” LINK

“Remote Sensing : Identification of Amaranthus Species Using Visible-Near-Infrared (Vis-NIR) Spectroscopy and Machine Learning Methods” LINK

“Nondestructive Detection of Internal Flavor in ‘Shatian’Pomelo Fruit Based on Visible/Near Infrared Spectroscopy” | LINK

“Determination of sex-enhancing drugs illegally added in health care products by TLC-NIRS combined technology” LINK

“Rapid detection of exogenous sucrose in black tea samples based on near-infrared spectroscopy” LINK

“Rapid determination of diesel fuel properties by near-infrared spectroscopy” LINK

“Biosensors : Room-Temperature Synthesis of Air-Stable Near-Infrared Emission in FAPbI3 Nanoparticles Embedded in Silica” LINK

“Potential of visible-near infrared spectroscopy for the determination of three soil aggregate stability indices” LINK

” A dataset of the chemical composition and near-infrared spectroscopy measurements of raw cattle, poultry and pig manure” LINK

“Forests : Identifying Wood Based on Near-Infrared Spectra and Four Gray-Level Co-Occurrence Matrix Texture Features” LINK

“… Selection for Referenceless Multivariate Calibration: A Case Study on Nicotine Determination in Flue-Cured Tobacco Powder by Near-Infrared (NIR) Spectroscopy” LINK

“Prediction and Utilization of Malondialdehyde in Exotic Pine Under Drought Stress Using Near-Infrared Spectroscopy” | LINK

“Quantification of irrigated lesion morphology using near-infrared spectroscopy” | LINK

“A Comparison between the Post-and Pre-dispersive Near Infrared Spectroscopy in Non-Destructive Brix Prediction Using Artificial Neural Network” LINK




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

“Efficient Broadband Near‐Infrared Emission in the GaTaO4:Cr3+ Phosphor” | LINK

“Aflatoxin contaminated cocoa beans classification using near-infrared spectroscopy” LINK

“The Effect Of Hemodynamic Parameters On Peripheral Near Infrared Spectroscopy In An Animal Model” LINK

“Iridium(III) Complexes with [2, 1, 0] Charged Ligands Realized DeepRed/NearInfrared Phosphorescent Emission” 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)

“BRCN-ERN: A Bidirectional Reconstruction Coding Network and Enhanced Residual Network for Hyperspectral Change Detection” LINK

“Rapid identification of adulterated safflower seed oil by use of hyperspectral spectroscopy” LINK

“Remote Sensing : Monitoring the Severity of Pantana phyllostachysae Chao on Bamboo Using Leaf Hyperspectral Data” LINK

“Rapid identification of the green tea geographical origin and processing month based on near-infrared hyperspectral imaging combined with chemometrics” LINK

“Chemometric strategies for near infrared hyperspectral imaging analysis: classification of cotton seed genotypes” LINK

“Applied Sciences : Determination of Sugar, pH, and Anthocyanin Contents in Port Wine Grape Berries through Hyperspectral Imaging: An Extensive Comparison of Linear and Non-Linear Predictive Methods” LINK

“… Area Index, Chlorophyll Content and Fraction of Vegetation Cover Using an Empirical-Statistical Approach from Chris-Proba Satellite Hyperspectral Images over the …” LINK

“Design and verification of a large-field hyperspectral imaging system” LINK

“Visualization of heavy metal cadmium in lettuce leaves based on wavelet support vector machine regression model and visible‐near infrared hyperspectral imaging” LINK

“Band Selection for HSI Classification using Binary Constrained Optimization” LINK

“New Approach to the Old Challenge of Free Flap Monitoring—Hyperspectral Imaging Outperforms Clinical Assessment by Earlier Detection of Perfusion Failure” LINK




Spectral Imaging

“Machine learning and hyper spectral imaging: multi spectral endoscopy in the gastro intestinal tract towards hyper spectral endoscopy” LINK




Chemometrics and Machine Learning

“… residual extreme learning machine (PLSRR-ELM) calibration algorithm applied in fast determination of gasoline octane number with near-infrared spectroscopy” LINK

“Recognition of authentic or false blood based on NIR spectroscopy and PCA-WNN-PSO algorithm” LINK

“EVALUATION OF AGARWOOD (AQUILARIA MALACCENIS) FROM BINTAN ISLAND BASED ON INDONESIAN STANDARD: PREDICTING ITS QUALITY USING …” LINK

“Comparison of PLS and SVM models for soil organic matter and particle size using vis-NIR spectral libraries” LINK

“Multivariate Calibration of Concentrations of C, Mn, Si, Cr, Ni, and Cu in Low-Alloy Steels from Raw Low-Resolution Spectra Obtained By Laser-Induced Breakdown Spectroscopy” LINK




Optics for Spectroscopy

“Foam Flows in Turbulent Liquid Exfoliation of Layered Materials and Implications for Graphene Production and Inline Characterisation” LINK

“Chemical Engineering of Cu-Sn Disordered Network Metamaterials” LINK

“High-Performance Waveguide-Integrated Bi<sub>2</sub>O<sub>2</sub>Se Photodetector for Si Photonic Integrated Circuits” LINK




Equipment for Spectroscopy

“Electrical and Mechanical Properties of Intrinsically Flexible and Stretchable PEDOT Polymers for Thermotherapy” LINK

“Application of hand-held near-infrared and Raman spectrometers in surface treatment authentication of cork stoppers” LINK




Process Control and NIR Sensors

“Dry Powder Mixing Is Feasible in Continuous Twin Screw Extruder: Towards Lean Extrusion Process for Oral Solid Dosage Manufacturing” | LINK

“Manipulating electroluminochromism behavior of viologen substituted iridium(III) complexes through ligand engineering for information display and encryption” LINK




Environment NIR-Spectroscopy Application

“Remote Sensing : Estimating Vertical Distribution of Leaf Water Content within Wheat Canopies after Head Emergence” LINK




Agriculture NIR-Spectroscopy Usage

“Towards ecological intensification of agriculture: from management to soil bacterial and nitrogen-cycling communities” LINK

“Er: YAG Laser Cleaning of Painted Surfaces: Functional Considerations to Improve Efficacy and Reduce Side Effects” LINK

“Agronomy : Evaluation of Metabolomic Profile and Growth of Moringa oleifera L. Cultivated with Vermicompost under Different Soil Types” LINK

“High-throughput phenotyping of cool-season crops using non-invasive sensing techniques” LINK




Forestry and Wood Industry NIR Usage

“Polymers : Passive Fire Protection of Taeda pine Wood by Using Starch-Based Surface Coatings” LINK




Food & Feed Industry NIR Usage

“Foods : Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis” LINK

“The potential to increase beef production in tropical Nth Australia by including Desmanthus cv JCU 2 in a Buffel grass (Cenchrus ciliaris) dominant pasture” LINK




Pharma Industry NIR Usage

“Spectroscopic characteristics of Xeloda chemodrug” | LINK




Laboratory and NIR-Spectroscopy

“Potential of laboratory hyperspectral data for in-field detection of Phytophthora infestans on potato” LINK




Other

“Beam test of carbon ion production for development of new compact ECR ion source for multi ion therapy” LINK

“Silver Peroxide Nanoparticles for Combined Antibacterial Sonodynamic and Photothermal Therapy” | LINK

“Splanchnic oxygen saturation during reoxygenation with 21% or 100% O2 in newborn piglets” | LINK





Spectroscopy and Chemometrics/Machine-Learning News Weekly #47, 2021

NIR Calibration-Model Services

Knowledge-Based Variable Selection and Model Selection for near-infrared spectroscopy (NIRS) | PLSR PCA PCR PLS SVM LINK

Spectroscopy and Chemometrics News Weekly 46, 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 46, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 46, 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)

“At-line quality assurance of deep-fried instant noodles using pilot scale visible-NIR spectroscopy combined with deep-learning algorithms” LINK

“Application of near-infrared spectroscopy technology in the complex fermentation system to achieve high-efficiency production” LINK

“Near-infrared optical spectroscopy for pancreas shrinkage estimation with multi synchrosqueezing transform and multivariate regression model” | LINK

“Translating near-infrared spectroscopy from laboratory to commercial slaughterhouse: existing challenges and solutions” LINK

“Aquaphotomics for Bio-Diagnostics in Dairy : Applications of near-Infrared Spectroscopy.” LINK

“Impact of near infrared (NIR) spectroscopy and hyperspectral (HS) imaging system to predict physicochemical composition and quality attributes of meat: A review” LINK

“Identification of lactic acid bacteria and rhizobacteria by ultraviolet-visible-near infrared spectroscopy and multivariate classification” LINK

“Applied Sciences : Aquaphotomics Reveals Subtle Differences between Natural Mineral, Processed and Aged Water Using Temperature Perturbation Near-Infrared Spectroscopy” LINK

“Comparison of different processing approaches by SVM and RF on HS-MS eNose and NIR Spectrometry data for the discrimination of gasoline samples” LINK

“An Overview of Near Infrared Spectroscopy and Its Applications in the Detection of Genetically Modified Organisms” LINK

“Use of near-infrared spectroscopy with Fourier Transform (FT-NIR) to accompany the Bovine Parasitic Sadness process.” LINK

“Near-infrared Stokes spectropolarimetry as a novel local measurement method of atomic line emission in SOL and divertor plasmas” LINK

“Handheld NIRS for forage evaluation” LINK

“Detection of Profenofos in Chinese Kale, Cabbage, and Chili Spur Pepper Using Fourier Transform Near-Infrared and Fourier Transform Mid-Infrared Spectroscopies” LINK

“Determination of the Geographical Origin of Hazelnuts (Corylus avellana L.) by Near-Infrared Spectroscopy (NIR) and a Low-Level Fusion with Nuclear Magnetic …” LINK

“Foods : On-Line Real-Time Monitoring of a Rapid Enzymatic Oil Degumming Process: A Feasibility Study Using Free-Run Near-Infrared Spectroscopy” LINK

“Determination of Moisture, Fat, Carbohydrates and Protein Contents in Flour by Near Infrared Spectroscopy” LINK




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

“Nearinfrared optical spectroscopy for pancreas shrinkage estimation with multi synchrosqueezing transform and multivariate regression model” LINK

“Prospector: A mobile application for portable, high‐throughput near‐infrared spectroscopy phenotyping” LINK

“Indandioneterminated quinoidal compounds for lowbandgap small molecules with strong nearinfrared absorption: effect of conjugation length on the properties” LINK

“Chemometric approach to characterization of the selected grape seed oils based on their fatty acids composition and FTIR spectroscopy” LINK

“Heat impact control in flash pasteurization by estimation of applied pasteurization units using near infrared spectroscopy” LINK

“Revealing the interactions of water with cryoprotectant and protein by near− infrared spectroscopy” LINK




Raman Spectroscopy

“Silk fibroin fibers decorated with urchin-like Au/Ag nanoalloys: a flexible hygroscopic SERS sensor for monitoring of folic acid in human sweat” LINK




Hyperspectral Imaging (HSI)

“HyperMixNet: Hyperspectral Image Reconstruction With Deep Mixed Network From a Snapshot Measurement” LINK

“Hyperspectral image classification based on parallel-branch expectation-maximization attention mechanism” LINK

“Applied Sciences : Variety Identification of Chinese Walnuts Using Hyperspectral Imaging Combined with Chemometrics” LINK

“Visualization of heavy metal cadmium in lettuce leaves based on wavelet support vector machine regression model and visiblenear infrared hyperspectral imaging” LINK

“Improving the estimation accuracy of SPAD values for maize leaves by removing UAV hyperspectral image backgrounds” LINK

“Leaf water content estimation using top-of-canopy airborne hyperspectral data” LINK




Terahertz Spectroscopy

“Sensors : Terahertz Imaging for Breast Cancer Detection” LINK




Chemometrics and Machine Learning

“Comparative study of preprocessing on ATRFTIR calibration model for insitu monitoring of solution concentration in cooling crystallization” LINK

“Variable selection for near-infrared spectrum modeling based on fast nondominated sorting genetic algorithm” LINK

“Ripening assessment of ‘Ortanique'(Citrus reticulata Blanco x Citrus sinensis (L) Osbeck) on tree by SW-NIR reflectance spectroscopy-based calibration models” LINK

“Molecules : Quantification of Corn Adulteration in Wet and Dry-Processed Peaberry Ground Roasted Coffees by UVVis Spectroscopy and Chemometrics” LINK

“A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression ” | LINK

“Development of a new approach for a rapid IDENTIfication and CLASSification of uranium powders using colour, image texture and spectroscopy signatures” LINK

“Foods : Electronic Tongue as a Correlative Technique for Modeling Cattle Meat Quality and Classification of Breeds” LINK

“Digital Twin of Low Dosage Continuous Powder Blending – Artificial Neural Networks and Residence Time Distribution Models” LINK




Optics for Spectroscopy

“A new penternary semiconductor Cu2CoSn (SSe) 4 nanocrystal: a study on structural, dielectric and optical properties” | LINK




Research on Spectroscopy

“Polymers : Valorization Strategy for Leather Waste as Filler for High-Density Polyethylene Composites: Analysis of the Thermal Stability, Insulation Properties and Chromium Leaching” LINK

“Multi-method exploration of the relationship between sleep and infant neurocognitive development” LINK

“Opportunities and Limitations of Mobile Neuroimaging Technologies in Educational Neuroscience” LINK

“A Triphenylphosphonium Functionalized AIE Conjugated Macrocyclic Tetramaleimide for Mitochondrialtargeting Bioimaging” LINK




Equipment for Spectroscopy

“Evaluation of MEMS NIR Spectrometers for On-Farm Analysis of Raw Milk Composition” LINK

“Light-induced off-axis cavity-enhanced thermoelastic spectroscopy in the near-infrared for trace gas sensing” LINK

“Tunable diode laser-based two-line thermometry: a noncontact thermometer for active body temperature measurement” LINK




Environment NIR-Spectroscopy Application

“Remote Sensing : Identifying the Spectral Signatures of Invasive and Native Plant Species in Two Protected Areas of Pakistan through Field Spectroscopy” LINK




Agriculture NIR-Spectroscopy Usage

“Prediction of protein and amino acid composition and digestibility in individual feedstuffs and mixed diets for pigs using Near-infrared spectroscopy” LINK

” Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods” LINK

“impact of donor nutritional balance on the growth and development of mesenchymal stem cells from caprine umbilical cord Wharton´s jelly” | LINK

“Biosensors : Distinguishing Amyloid -Protein in a Mouse Model of Alzheimers Disease by Label-Free Vibrational Imaging” LINK

“From RGB to NIR: Predicting of Near Infrared Reflectance From Visible Spectrum Aerial Images of Crops” LINK

“Remote Sensing : Ir-UNet: Irregular Segmentation U-Shape Network for Wheat Yellow Rust Detection by UAV Multispectral Imagery” LINK




Forestry and Wood Industry NIR Usage

“Adaptive Bayesian Sum of Trees Model for Covariate Dependent Spectral Analysis. (arXiv:2109.14677v1 [stat.ME])” LINK




Food & Feed Industry NIR Usage

“Study on the aqueous dispersibility of multi-walled carbon nanotubes bearing modified corn starch” | LINK

“Remote Sensing : Incorporating Multi-Scale, Spectrally Detected Nitrogen Concentrations into Assessing Nitrogen Use Efficiency for Winter Wheat Breeding Populations” LINK




Chemical Industry NIR Usage

“Polymers : Partial Polymer Blend for Fused Filament Fabrication with High Thermal Stability” LINK




Petro Industry NIR Usage

“Energy-resolved plasmonic chemistry in individual nanoreactors” | LINK




Medicinal Spectroscopy

“Measurement of Tissue Oximetry in Standing Unsedated and Sedated Horses” LINK




Other

“Structural and chemical modifications of oxides and OH generation by space weathering: Electron microscopic/spectroscopic study of hydrogen-ion-irradiated Al2O3” LINK

“Caractérisation de la composition chimique de l’espèce Aloe macra par spectrométrie proche infrarouge” LINK

“THE IMPACT OF THE ALKYL-AMMONIUM AND-PHOSPHONIUM CATIONS ON THE HYDRATION OF ORGANO-MONTMORILLONITE” LINK

“Optical and structure properties of CH3NH3PbI3 perovskite films doped with Cesium” LINK

“Cultivation and Performance Analysis of Simultaneous Partial Nitrification, ANAMMOX, and Denitratation Granular Sludge” LINK

” 近红外光谱的通用聚苯乙烯牌号在线识别方法” LINK

“Biosensors : An Optical Smartphone-Based Inspection Platform for Identification of Diseased Orchids” LINK



Spectroscopy and Chemometrics News Weekly #7, 2021

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 6, 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 6, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 6, 2021 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

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

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




Near-Infrared Spectroscopy (NIRS)

“Evaluation of swelling properties and drug release from mechanochemical pre-gelatinized glutinous rice starch matrix tablets by near infrared spectroscopy” LINK

” Multivariate method for prediction of fumonisins B1 and B2 and zearalenone in Brazilian maize using Near Infrared Spectroscopy (NIR)” | LINK

“Non-destructive determination of fatty acid composition of in-shell and shelled almonds using handheld NIRS sensors” LINK

“Breakthrough instruments and products: Near infrared spectral sensing: Advances in portable instrumentation and implementations” LINK

“Quali-quantitative monitoring of chemocatalytic cellulose conversion into lactic acid by FT-NIR spectroscopy.” LINK

“NIR Spectroscopy Detects Chlorpyrifos-Methyl Pesticide Residue in Rough, Brown, and Milled Rice” LINK

“ANALYSIS OF TEA LEAVES WITH DIFFERENT OXIDATION STATES BY FT-NIR SPECTROSCOPY” LINK




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

“Identification of peach and apricot kernels for traditional Chinese medicines using near-infrared spectroscopy” LINK

“Breakthrough instruments and products: Near infrared spectral sensing: Advances in portable instrumentation and implementations” LINK

“Two-dimensional moisture content and size measurement of pharmaceutical granules after fluid bed drying using near-infrared chemical imaging.” LINK

“Influence of steroids on hydrogen bonds in membranes assessed by near infrared spectroscopy” LINK

“Determination of the oxidative stability of biodiesel fuels by near-infrared spectroscopy” LINK

“In Vitro Spectroscopy-Based Profiling of Urothelial Carcinoma: A Fourier Transform Infrared and Raman Imaging Study” LINK

“Novel alternative use of near-infrared spectroscopy to indirectly forecast 3D printability of purple sweet potato pastes” LINK

“Shortwave infrared hyperspectral imaging system coupled with multivariable method for TVB-N measurement in pork” LINK




Chemometrics and Machine Learning

“Discrimination between wild-grown and cultivated Gastrodia elata by near-infrared spectroscopy and chemometrics” LINK

“A Single Model to Monitor Multistep Craft Beer Manufacturing using Near Infrared Spectroscopy and Chemometrics” LINK

“Noninvasive Blood Glucose sensing by Near-Infrared Spectroscopy based on PLSR Combines SAE Deep Neural Network Approach” LINK

“Comparison of a Low-cost Prototype Optical Sensor with Three Commercial Systems in Predicting Water and Nutrient Contents of Turfgrass: Prediction performance of …” LINK

“Development of NIR-HSI and chemometrics process analytical technology for drying of beef jerky” LINK

“Soil spectroscopy with the use of chemometrics, machine learning and pre-processing techniques in soil diagnosis: Recent advances-A review” LINK

“Application of NIR handheld transmission spectroscopy and chemometrics to assess the quality of locally produced antimalarial medicines in the Democratic Republic of Congo” LINK




Facts

“An Accuracy Improvement Method Based on Multi-Source Information Fusion and Deep Learning for TSSC and Water Content Nondestructive Detection in “Luogang” Orange” LINK




Research on Spectroscopy

“Nondestructive methods for determining the firmness of apple fruit flesh” LINK




Equipment for Spectroscopy

“Development of Fluorescence Imaging Technique to Detect Fresh-Cut Food Organic Residue on Processing Equipment Surface” LINK




Process Control and NIR Sensors

“Acid number, viscosity and end-point detection in a multiphase high temperature polymerisation process using an online miniaturised MEMS Fabry-Pérot interferometer.” | LINK

“Real-Time Monitoring of Yogurt Fermentation Process by Aquaphotomics Near-Infrared Spectroscopy.” LINK




Agriculture NIR-Spectroscopy Usage

“Rapid and nondestructive determination of qualities in vacuum packaged catfish (Clarias leather) fillets during slurry ice storage” LINK

“Economic and chemometric studies to supplement food-grade soybean variety development in the Mid-Atlantic region” LINK

“Online Monitoring of Fermented Grains Parameters for Chinese Liquor Brewing Based on Near Infrared Spectroscopy” LINK




Horticulture NIR-Spectroscopy Applications

“Fast, simultaneous and contactless assessment of intact mango fruit by means of near infrared spectroscopy [J]” LINK

“The development of portable detector for apples soluble solids content based on visible and near infrared spectrum.” LINK

“Non-destructive and fast method of mapping the distribution of the soluble solids content and pH in kiwifruit using object rotation near-infrared hyperspectral imaging …” LINK




Food & Feed Industry NIR Usage

“A two-tiered system of analysis to tackle rice fraud: The Indian Basmati study” LINK





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Spectroscopy and Chemometrics News Weekly #36, 2020

NIR Calibration-Model Services

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


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

Spectroscopy and Chemometrics News Weekly 35, 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 35, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 35, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK




Near-Infrared Spectroscopy (NIRS)

“Estimating Roughage Quality with Near Infrared Reflectance (NIR) Spectroscopy and Chemometric Techniques” LINK

“Detection of Insect Damage in Green Coffee Beans Using VIS-NIR Hyperspectral Imaging” LINK

“Revisiting Water Speciation in Hydrous Alumino-Silicate glasses: A Discrepancy between Solid-state 1H NMR and NIR spectroscopy in the Determination of X-OH …” LINK

“Prediction of Organic Carbon Content of Intertidal Sediments Based on Visible-Near Infrared Spectroscopy” “可见-近红外光谱的潮间带沉积物有机碳含量的几种模型预测方法” LINK




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

“Predicting soil phosphorus and studying the effect of texture on the prediction accuracy using machine learning combined with near-infrared spectroscopy” LINK

“CIC nanoGUNE reaches new depths in infrared nanospectroscopy” LINK

“Distinguishing Hemp from Marijuana by Mid-Infrared Spectroscopy” LINK

“Glucobrassicin Enhancement Using Low Red to Far-Red Light Ratio in ‘Ruby Ball’ Cabbage and High-Throughput Glucobrassicin Estimation Using Near-Infrared …” LINK

“Near-infrared spectroscopy outperforms genomic selection for predicting sugarcane feedstock quality traits” LINK

“Estimation of critical nitrogen contents in peach orchards using visible-near infrared spectral mixture analysis” LINK

“Non-destructive and rapid measurement of sugar content in growing cane stalks for breeding programmes using visible-near infrared spectroscopy” LINK

“Quantitative Analysis of Protein and Polysaccharide in Lilium Lanzhou Based on Near Infrared Spectroscopy” LINK

“Time-stretch infrared spectroscopy” LINK

“Using near infrared reflectance spectroscopy for estimating nutritional quality of Brachiaria humidicola in breeding selections” LINK

“Quantification of phenolic acids by partial least squares Fouriertransform infrared (PLSFTIR) in extracts of medicinal plants” LINK




Chemometrics and Machine Learning

“Predicting adulteration of Palm oil with Sudan IV dye using shortwave handheld spectroscopy and comparative analysis of models” LINK

“Self-adaptive models for predicting soluble solid content of blueberries with biological variability by using near-infrared spectroscopy and chemometrics” LINK

“Rapid identification and quantitative pit mud by near infrared Spectroscopy with chemometrics” LINK

“Methane emission detection and flux quantification from exploratory hydraulic fracturing in the United Kingdom, using unmanned aerial vehicle sampling” LINK




Research on Spectroscopy

“MD dating: molecular decay (MD) in pinewood as a dating method” LINK

“Improved Dimensional Stability and Mold Resistance of Bamboo via In Situ Growth of Poly(Hydroxyethyl Methacrylate-N-Isopropyl Acrylamide)” Polymers LINK




Equipment for Spectroscopy

“Applied Sciences, Vol. 10, Pages 4896: A Novel Single-Channel Arrangement in Chirp Transform Spectrometer for High-Resolution Spectrum Detection” LINK




Agriculture NIR-Spectroscopy Usage

“Angle Distribution Measurement of Scattered Light Intensity from Needle-shaped Crystals in a Magnetic Field for Gout Diagnosis” LINK

“Use of barley silage or corn silage with dry-rolled barley, corn, or a blend of barley and corn on predicted nutrient total tract digestibility and growth performance of …” LINK

“Identification of Leaf-Scale Wheat Powdery Mildew (Blumeria graminis f. sp. Tritici) Combining Hyperspectral Imaging and an SVM Classifier” Plants LINK

“Smartphone-supported portable micro-spectroscopy/imaging system to character morphology and spectra of samples at microscale” LINK

“Novel Antioxidant Packaging Films Based on Poly(-Caprolactone) and Almond Skin Extract: Development and Effect on the Oxidative Stability of Fried Almonds” LINK

“Applied Sciences, Vol. 10, Pages 4907: Experimental Comparison of Diesel and Crude Rapeseed Oil Combustion in a Swirl Burner” LINK

“Molecules, Vol. 25, Pages 3260: Comparison of Bioactive Phenolic Compounds and Antioxidant Activities of Different Parts of Taraxacum mongolicum” LINK




Horticulture NIR-Spectroscopy Applications

“Application of a Vis-NIR Spectroscopic Technique to Measure the Total Soluble Solids Content of Intact Mangoes in Motion on a Belt Conveyor” LINK




Forestry and Wood Industry NIR Usage

“Online analysis of wood extractives” LINK




Food & Feed Industry NIR Usage

Near-Infrared Spectroscopy as a Beef Quality Tool to Predict Consumer Acceptance” Foods LINK

“Rapid Vitality Estimation and Prediction of Corn Seeds Based on Spectra and Images Using Deep Learning and Hyperspectral Imaging Techniques” LINK

“Identification of Bacterial Blight Resistant Rice Seeds Using Terahertz Imaging and Hyperspectral Imaging Combined With Convolutional Neural Network.” LINK

“A simple design for the validation of a FT-NIR screening method: Application to the detection of durum wheat pasta adulteration.” LINK




Laboratory and NIR-Spectroscopy

In-line UV-Vis Spectroscopy Market Research Report 2019-2030 | Industry Report, Industry …: Success of this technology depends on the in-depth knowledge of the link between optical instrumentation design and its effect on data quality. LINK




Other

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

“Non-destructive Detection of Apple Maturity by Constructing Spectral Index based on Reflectance Spectrum” LINK





Spectroscopy and Chemometrics News Weekly #25, 2020

NIR Calibration-Model Services

Using cost saving NIR-Spectroscopy Analysis? You can Save even more Costs and Time! How? Read here | VIS NIR NIRS Spectroscopy LabManager Labs QualityControl CostSaving foodindustry foodproduct Spectrometer Sensor Analytics LINK

Machine Learning for NIR Spectroscopy as a Service, a Game Changer for Productivity and Accuracy/Precision! Use the free NIR-Predcitor software to combine NIRS + Lab data and send your Calibration Request. LabManager Analysis MachineLearning LINK

Spectroscopy and Chemometrics News Weekly 24, 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 24, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 24, 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)

“Near-Infrared (NIR) Spectroscopy to Differentiate Longissimus thoracis et lumborum (LTL) Muscles of Game Species” LINK

“Estimation of Harumanis (Mangifera indica L.) Sweetness using Near-Infrared (NIR) Spectroscopy” LINK

“Handheld Near-Infrared Spectrometers: Reality and Empty Promises” miniaturization NIRS FTNIR MEMS MOEMS LVFs LINK

BESTCentreLTU research hot off the press: | In collaboration with Assoc. Prof. Kellie Tuck from , we’ve developed new near-infrared emissive electrochemiluminophores for sensing in NIR transparent biological media. LINK

“Near-Infrared Emitter for Bioanalytical Applications” NIR ECL electrochemiluminescence LINK

“Fault detection with moving window PCA using NIRS spectra for the monitoring of anaerobic digestion process” LINK

“New applications of visnir spectroscopy for the prediction of soil properties” LINK

“Simultaneous determination of quality parameters in yerba mate (Ilex paraguariensis) samples by application of near-infrared (NIR) spectroscopy and partial least …” LINK

“Control of ascorbic acid in fortified powdered soft drinks using near-infrared spectroscopy (NIRS) and multivariate analysis.” LINK




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

“Non-Invasive Blood Glucose Monitoring using Near-Infrared Spectroscopy based on Internet of Things using Machine Learning” LINK

“Investigating the Quality of Antimalarial Generic Medicines Using Portable Near-Infrared Spectroscopy” LINK

“Rapid quantitative detection of mineral oil contamination in vegetable oil by near-infrared spectroscopy” LINK

“THE DETERMINATION OF FATTY ACIDS IN CHEESES OF VARIABLE COMPOSITION (COW, EWE’S, AND GOAT) BY MEANS OF NEAR INFRARED SPECTROSCOPY” LINK

“Detection of melamine and sucrose as adulterants in milk powder using near-infrared spectroscopy with DD-SIMCA as one-class classifier and MCR-ALS as a means to provide pure profiles of milk and of both adulterants with forensic evidence” LINK

“Protein, weight, and oil prediction by singleseed nearinfrared spectroscopy for selection of seed quality and yield traits in pea (Pisum sativum)” LINK

“Modeling bending strength of oil-heat-treated wood by near-infrared spectroscopy” LINK

“ripening stages monitoring of Lamuyo pepper using a new‐generation near‐infrared spectroscopy sensor” LINK

“Should the Past Define the Future of Interpretation of Infrared and Raman Spectra?” LINK

“Performance Improvement of Near-Infrared Spectroscopy-Based Brain-Computer Interface Using Regularized Linear Discriminant Analysis Ensemble Classifier Based on Bootstrap Aggregating.” LINK

“Continuously measurement of the dry matter content using near-infrared spectroscopy” LINK

“Rapid identification of Lilium species and polysaccharide contents based on near infrared spectroscopy and weighted partial least square method.” LINK

“A Rapid and Nondestructive Approach for the Classification of Different-Age Citri Reticulatae Pericarpium Using Portable Near Infrared Spectroscopy.” LINK




Hyperspectral Imaging (HSI)

“Rapid detection of quality index of postharvest fresh tea leaves using hyperspectral imaging” LINK

“Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms” LINK

“A deep learning based regression method on hyperspectral data for rapid prediction of cadmium residue in lettuce leaves” LINK

“Deep learning applied to hyperspectral endoscopy for online spectral classification” DOI:10.1038/s41598-020-60574-6 LINK

“Detection of fish fillet substitution and mislabeling using multimode hyperspectral imaging techniques” LINK




Chemometrics and Machine Learning

“Molecules, Vol. 25, Pages 1453: Characterization, Quantification and Quality Assessment of Avocado (Persea americana Mill.) Oils” LINK

“Comprehensive Chemometrics – Chemical and Biochemical Data Analysis Reference Work • 2nd Edition • 2020” | books Chemometrics DataAnalysis Chemical Biochemical LINK

“Identification of invisible biological traces in forensic evidences by hyperspectral NIR imaging combined with chemometrics” LINK




Research on Spectroscopy

“Automatisierte und digitale Dokumentation der Applikation organischer Düngemittel” LINK

“Plenary Lecture Methods and Tools for Sensors Information Processing” LINK




Equipment for Spectroscopy

Using NIR scanner to assess grass in sward for composition prior to baling and wrapping for EU LIFE Farm4More project. Thanks to Dinamica Generale for providing the equipment LINK

“Determination of soluble solids content in Prunus avium by Vis/NIR equipment using linear and non-linear regression methods” LINK

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




Agriculture NIR-Spectroscopy Usage

“Hyperspectral imaging using multivariate analysis for simulation and prediction of agricultural crops in Ningxia, China” LINK

“Placing Soil Information in the Hands of Farmers” LINK

“Robustness of visible/near and midinfrared spectroscopic models to changes in the quantity and quality of crop residues in soil” LINK

“Complex Food Recognition using Hyper-Spectral Imagery” LINK




Horticulture NIR-Spectroscopy Applications

” The Effect of Spent Mushroom Substrate and Cow Slurry on Sugar Content and Digestibility of Alfalfa Grass Mixtures” LINK




Laboratory and NIR-Spectroscopy

“The influence analysis of reflectance anisotropy of canopy on the prediction accuracy of Cu stress based on laboratory multi-directional measurement” LINK




Other

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