Spectroscopy and Chemometrics/Machine-Learning News Weekly #2, 2022

NIR Calibration-Model Services

NIR Spectrometry Custom Applications for chemical analysis | laboratory analyzer analyser QA QC Testing QAQC LINK

Protip: For NIR Spectroscopy Data Analysis use a Data Analytics Service that is NIR Domain related LINK

Spectroscopy and Chemometrics News Weekly 1, 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 1, 2022 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 1, 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

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




Near-Infrared Spectroscopy (NIRS)

“Minerals : VIS-NIR/SWIR Spectral Properties of H2O Ice Depending on Particle Size and Surface Temperature” LINK

“AKURASI KESAMAAN KELOMPOK DATA BERDASARKAN FCM DAN PCA-FCM PADA DATA GULA DARAH HASIL PEMINDAIAN NIRS TERHADAP DATA GULA …” LINK

“Prediksi Kehilangan Hara Pada Tanah Tererosi Menggunakan Near Infrared Reflectance Spectroscopy (NIRS)” LINK

“Aplikasi Teknologi Near Infrared Reflectance Spectroscopy Dengan Metode Partial Least Square Untuk Prediksi Kadar Patchouli Alkohol Minyak Nilam” LINK

“Near infrared spectroscopy and aquaphotomics evaluation of the efficiency of solar dehydration processes in pineapple slices” LINK

“Comparisons of commercially available NIRS-based analyte predictions of haylage quality for equid nutrition” LINK

“Rapid discrimination of wood species from native forest and plantations using near infrared spectroscopy” LINK

“Identification of cocoa bean quality by near infrared spectroscopy and multivariate modeling” LINK

“The Influence of Ingredients, Corn Particle Size, and Sample Preparation on the Predictability of the Near Infrared Reflectance Spectroscopy” LINK

“Machine Learning Approach Using a Handheld Near-Infrared (NIR) Device to Predict the Effect of Storage Conditions on Tomato Biomarkers” LINK

“Solid Physical State Transformation in Hot Melt Extrusion Revealed by Inline Near-Infrared (NIR) Spectroscopy and Offline Terahertz (THz) Raman Imaging” LINK

“Thermal Insulation Performance of Novel Coated Fabrics Based on Fe-Doped BaSnO3 Near-Infrared Reflectance Pigments” LINK

“Non-destructive method for discrimination of weedy rice using near infrared spectroscopy and modified self-organizing maps (SOMs)” LINK

“Potential of VIS/NIR spectroscopy to detect and predict bitter pit in ‘Golden Smoothee’apples” LINK

“Litterbag-NIRS to Forecast Yield: a Horticultural Case with Biofertilizer Effectors” | LINK

“Determination of SSC and TA content of pear by Vis-NIR spectroscopy combined CARS and RF algorithm” LINK

“Evaluation of the robustness of a novel NIR-based technique to measure the residual moisture in freeze-dried products” LINK

“Comparison of Partial Least Square, Artificial Neural Network and Support Vector Regressions for real time monitoring of CHO cell culture processes using in situ Near Infrared spectroscopy” LINK

“Evaluating the impact of NIR pre-processing methods via multiblock partial least-squares” LINK

“Differentiation of Organic Cocoa Beans and Conventional Ones by Using Handheld NIR Spectroscopy and Multivariate Classification Techniques” | LINK




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

“NearInfrared II Plasmonic Phototheranostics with Glutathione Depletion for Multimodal ImagingGuided HypoxiaTolerant ChemodynamicPhotocatalyticPhotothermal Cancer Therapy Triggered by a Single Laser” LINK

“Nearinfrared spectroscopy aids ecological restoration by classifying variation of taxonomy and phenology of a native shrub” LINK

“Optimization of sweet basil harvest time and cultivar characterization using nearinfrared spectroscopy, liquid and gas chromatography, and chemometric statistical methods” LINK




Hyperspectral Imaging (HSI)

“Prediction and Distribution Visualization of Salmon Quality Based on Hyperspectral Imaging Technology” LINK

“A data fusion method of electronic nose and hyperspectral to identify the origin of rice” LINK

“Plants : Hyperspectral Reflectance Response of Wild Rocket (Diplotaxis tenuifolia) Baby-Leaf to Bio-Based Disease Resistance Inducers Using a Linear Mixed Effect Model” LINK

“Prediction of moisture content in steamed and dried purple sweet potato using hyperspectral imaging analysis” | LINK

“Integration of textural and spectral features of Raman hyperspectral imaging for quantitative determination of a single maize kernel mildew coupled with chemometrics” LINK




Chemometrics and Machine Learning

“Near infrared spectroscopy combined with chemometrics for quantitative analysis of corn oil in edible blend oil” LINK

“Applications of NIR spectroscopy and chemometrics to illicit drug analysis: an example from inhalant drug screening tests” LINK

“Remote Sensing : Extraction of Kenyan Grassland Information Using PROBA-V Based on RFE-RF Algorithm” LINK

“Monitoring Molecular Weight Changes during Technical Lignin Depolymerization by Operando Attenuated Total Reflectance Infrared Spectroscopy and Chemometrics” LINK

“Modification of the effect of maturity variation on nondestructive detection of apple quality based on the compensation model” LINK

“Foods : Rapid Detection of Thermal Treatment of Honey by Chemometrics-Assisted FTIR Spectroscopy” LINK

“Applied Sciences : Dual Image-Based CNN Ensemble Model for Waste Classification in Reverse Vending Machine” LINK




Optics for Spectroscopy

Five highly cited papers in the fields of biosensors materials sensors (a thread) LINK




Facts

“Remote Sensing : Towards a Deep-Learning-Based Framework of Sentinel-2 Imagery for Automated Active Fire Detection” LINK




Research on Spectroscopy

“Residence Time Distribution as a Traceability Method for Lot Changes in A Pharmaceutical Continuous Manufacturing System” LINK




Environment NIR-Spectroscopy Application

“Remote Sensing : Inversion Evaluation of Rare Earth Elements in Soil by Visible-Shortwave Infrared Spectroscopy” LINK

“Heavy rainfall in peak growing season had larger effects on soil nitrogen flux and pool than in the late season in a semiarid grassland” LINK

“Rachis browning and water loss description during postharvest storage of ‘Krissy’and ‘Thompson Seedless’ table grapes” LINK

“Retrieving zinc concentrations in topsoil with reflectance spectroscopy at Opencast Coal Mine sites” | LINK

“Remote Sensing : Soil Organic Carbon Content Prediction Using Soil-Reflected Spectra: A Comparison of Two Regression Methods” LINK




Agriculture NIR-Spectroscopy Usage

“The Promise of Hyperspectral Imaging for the Early Detection of Crown Rot in Wheat” LINK

“Ecological effects on the nutritional value of bromeliads, and its influence on Andean bears’ diet selection” LINK

“Agronomy : Complex Spectroscopic Study for Fusarium Genus Fungi Infection Diagnostics of “Zalp” Cultivar Oat” LINK

“Predicting Protein Content in Grain Using Hyperspectral Deep Learning” LINK

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




Horticulture NIR-Spectroscopy Applications

“Determination of Sugar Adulteration in Honey Using Conductivity Meter and pH Meter” LINK

“A Comparative Analysis of Hybrid SVM and LS-SVM Classification Algorithms to Identify Dried Wolfberry Fruits Quality Based on Hyperspectral Imaging Technology” LINK




Chemical Industry NIR Usage

“Polymers : Hybrid Proton-Exchange Membrane Based on Perfluorosulfonated Polymers and Resorcinol-Formaldehyde Hydrogel” LINK




Laboratory and NIR-Spectroscopy

“Improving Quality Inspection of Textiles by an Augmented RGB-IR-HS-AI Approach” LINK




Other

“External beam irradiation angle measurement using Cerenkov emission I: Signal dependencies consideration” LINK

“Applications of Sensing for Disease Detection” LINK

“Functionalized Tris (anilido) triazacyclononanes as Hexadentate Ligands for the Encapsulation of U (III), U (IV) And La (III) Cations” LINK

“การ ประยุกต์ ใช้ เทคนิค สเปก โทร ส โค ปี อินฟราเรด ย่าน ใกล้ สำหรับ ทำนาย ปริมาณ แค โร ที น อย ด์ ใน เชื้อ พันธุกรรม ข้าวโพด หวาน” LINK

“抑郁症的近红外光谱研究进展” LINK

“高光谱成像技术在医药领域中的应用进展研究” LINK



Spectroscopy and Chemometrics/Machine-Learning News Weekly #51, 2021

NIR Calibration-Model Services

How to improve calibration models for NIR Instrument Devices | Wheat Food Security multispectral NIRS LINK

Spectroscopy and Chemometrics News Weekly 50, 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 50, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 50, 2021 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK




Near-Infrared Spectroscopy (NIRS)

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

“Application of near-infrared spectroscopy and multivariate methods for the estimation of isopropyl alcohol content in hand sanitizer formulation” LINK

“Akurasi Deteksi Kualitas Nutrisi dan Kecernaan Hijauan Pakan menggunakan Near Infrared Reflectance Spectroscopy (NIRS)” LINK

“Rapid estimation of a soil− water retention curve using visible− near infrared spectroscopy” LINK

“Non-destructive detection of multi-component heavy metals in corn oil using nano-modified colorimetric sensor combined with near-infrared spectroscopy” LINK

“Classification of Browning on Intact Table Grape Bunches Using Near-Infrared Spectroscopy Coupled With Partial Least Squares-Discriminant Analysis and Artificial Neural Networks” LINK

“Qualitative barometry of high P/T rocks with field based NIR spectroscopy of white mica” LINK

“Applications and Mechanisms of Near Infrared Spectroscopy For Age Estimation in Otoliths of Red Snapper Lutjanus Campechanus” LINK

“An Improved Residual Network for Pork Freshness Detection Using Near-Infrared Spectroscopy” LINK

“Report on the construction of portable NIRS equation to estimate forage quality in Brachiaria decumbens-ruzisiensis-brizantha species complex” LINK

“Sensors : Enhanced Sensitivity and Detection of Near-Infrared Refractive Index Sensor with Plasmonic Multilayers” LINK

“Feasibility of near-infrared spectroscopy for species identification and parasitological diagnosis of freshwater snails of the genus Biomphalaria (Planorbidae)” LINK

“Evaluation of the robustness of a novel NIR-based technique to measure the residual moisture in freeze-dried products” LINK

“Application of Near Infrared Reflectance (NIR) spectroscopy to predict the moisture, protein, and fat content of beef for gourmet hamburger preparation” LINK

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

“Genotypic classification of wheat using near-infrared spectroscopy and PLS-DA” LINK

“Study of Muscular Fatigue Effect on Human-Machine Interface Using Electromyography and Near-Infrared Spectroscopy” LINK

“Functional near-infrared spectroscopy brain imaging predicts symptom severity in youth exposed to traumatic stress” LINK

“Dry mechanochemical synthesis of ethenzamide and saccharin 1: 1 cocrystal and their evaluation using powder X-ray diffraction and FT-IR and NIR spectroscopy” LINK

“Influence of long-term natural degradation processes on near-infrared spectra and sorting of post-consumer plastics” LINK




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

“Near-infrared studies of nova V1674 Herculis: A shocking record-breaker” LINK

“A New Vegetation Index in Short-Wave Infrared Region of Electromagnetic Spectrum” LINK

“Identification of Pu’er raw tea with different storage years by infrared spectroscopy” LINK

“Analisa Gula Kristal Putih Secara Cepat Menggunakan Near Infrared Spectroscopy” LINK

“Rapid and nondestructive freshness evaluation of squid by FTIR coupled with chemometric techniques” LINK




Hyperspectral Imaging (HSI)

“Rapid ripening stage classification and dry matter prediction of durian pulp using a pushbroom near infrared hyperspectral imaging system” LINK

“An Effective Feature Extraction Approach Based on Spectral-Gabor Space Discriminant Analysis for Hyperspectral Image” LINK

“Fast detection of water loss and hardness for cucumber using hyperspectral imaging technology” LINK

“Assessment of diabetic small‐fiber neuropathy by using short‐wave infrared (SWIR) hyperspectral imaging” LINK




Chemometrics and Machine Learning

“Evaluation of Optimized Preprocessing and Modeling Algorithms for Prediction of Soil Properties Using VIS-NIR Spectroscopy” LINK

“Who is wining? A comparison of humans versus computers for calibration model building” LINK

“In-situ spectral calibration module for an earth observation satellite” LINK

“Who is wining? A comparison of humans versus computers for calibration model building” LINK

“Evaluation of Optimized Preprocessing and Modeling Algorithms for Prediction of Soil Properties Using VIS-NIR Spectroscopy” LINK

“Organic matter estimation of surface soil using successive projection algorithm” LINK

“Determination of the total viable count of Chinese meat dishes by near‐infrared spectroscopy: A predictive model” LINK

“Improving soil organic carbon mapping with a fieldspecific calibration approach through diffuse reflectance spectroscopy and machine learning algorithms” LINK

“Soil profile analysis using interactive visualizations, machine learning, and deep learning” LINK




Research on Spectroscopy

“Study on a fast non-contact detection method for key parameters of refractory organic wastewater treatment” LINK




Equipment for Spectroscopy

“Monitoring the ripening attributes of Turkish white cheese using miniaturized vibrational spectrometers” LINK

“Applied Sciences : Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm” LINK




Process Control and NIR Sensors

“A survey of neuromonitoring practices in North American pediatric intensive care units” LINK




Environment NIR-Spectroscopy Application

“Denitrification of Permeable Sand Sediment in a Headwater River Is Mainly Influenced by Water Chemistry, Rather Than Sediment Particle Size and Heterogeneity” LINK

“Remote Sensing : An Implementation of Open Source-Based Software as a Service (SaaS) to Produce TOA and TOC Reflectance of High-Resolution KOMPSAT-3/3A Satellite Image” LINK




Agriculture NIR-Spectroscopy Usage

“Hyperspectral imagery applications for precision agriculture-a systemic survey” | LINK

“Fodder biomass, nutritive value, and grain yield of dual-purpose improved cereal crops in Burkina Faso” LINK

“Remote Sensing : Estimating Crop Biophysical Parameters Using Machine Learning Algorithms and Sentinel-2 Imagery” LINK

“… gryllotalpa (Orthoptera: Gryllotalpidae) on plant and crop characteristics of sugar beet, Beta vulgaris L. and detection of associated damage using hyperspectral …” LINK

“Measurement of absorption and scattering properties of milk using a hyperspectral spatial frequency domain imaging system” LINK

“The relationship of the underlying lipidic plaque at the implanted newer-generation drug-eluting stents with future stent-related events: insights from the REASSURE …” LINK




Chemical Industry NIR Usage

“Polymers : Multifunctional Optical Device with a Continuous Tunability over 500 nm Spectral Range Using Polymerized Cholesteric Liquid Crystals” LINK

“In Situ Spectroelectrochemical-Conductance Measurements as an Efficient Tool for the Evaluation of Charge Trapping in Conducting Polymers” LINK




Medicinal Spectroscopy

“Cancers : A Tissue Section-Based Near-Infrared Spectroscopical Analysis of Salivary Gland Tumors” LINK




Laboratory and NIR-Spectroscopy

“Establishment of instrument operation qualification and routine performance qualification procedures for handheld near-infrared spectrometers used at different locations within a laboratory network” LINK




Other

“JMMP : Filament Development for Laser Assisted FFF 3D Printing” LINK

“Sensing Technology Survey for Obstacle Detection in Vegetation” LINK

” 联合特征子空间分布对齐的标定迁移方法” LINK

“Research on Adulteration Detection of Rattan Pepper Oil Based on BAS_WOA_SVR” LINK

“EUNADICS-AV early warning system dedicated to supporting aviation in the case of a crisis from natural airborne hazards and radionuclide clouds” LINK

“近红外光谱技术在危重新生儿的应用及研究进展” LINK

“近红外光谱在川贝母及非川贝母品种鉴别中的应用” LINK

“便携式近红外仪在反刍动物饲料质量分析中的应用研究” LINK

“Multifunctional Optical Device with a Continuous Tunability over 500 nm Spectral Range Using Polymerized Cholesteric Liquid Crystals” LINK

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


Spectroscopy and Chemometrics/Machine-Learning News Weekly #45, 2021


NIR Calibration-Model Services

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



Spectroscopy and Chemometrics News Weekly 44, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

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


Near-Infrared Spectroscopy (NIRS)

“Study on Characteristic Wavelength Extraction Method for Near Infrared Spectroscopy Identification Based on Genetic Algorithm” LINK

“Uji Karakteristik Biochar dengan Pendekatan Near Infrared Spectroscopy (NIRS)” LINK

“Establishment of online quantitative model for moisture content determination of hydroxychloroquine sulfate particles by near infrared spectroscopy” LINK

“Nondestructive detection model of soluble solids content of an apple using visible/near-infrared spectroscopy combined with CARS and MPGA” LINK

“Detecting cadmium contamination in loessal soils using near-infrared spectroscopy in the Xiaoqinling gold area” LINK

“Egg Freshness Evaluation Using Transmission and Reflection of NIR Spectroscopy Coupled Multivariate Analysis” LINK

“Determination of Alcohol Content in Beers of Different Styles Based on Portable Near-Infrared Spectroscopy and Multivariate Calibration” | LINK

“Analysing the Water Spectral Pattern by Near-Infrared Spectroscopy and Chemometrics as a Dynamic Multidimensional Biomarker in Preservation: Rice Germ …” LINK

“Remote Sensing : The Application of NIRS to Determine Animal Physiological Traits for Wildlife Management and Conservation” LINK

“Wavelength selection method for near-infrared spectroscopy based on standard-sample calibration transfer of mango and apple” LINK

“A portable NIR-system for mixture powdery food analysis using deep learning” LINK




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

“Compositional and sensory quality of beef and its determination by near infrared” LINK

“Butyrylcholinesterase responsive supramolecular prodrug with targeted nearinfrared cellular imaging property” LINK




Raman Spectroscopy

“Quantitative analysis of binary and ternary organo-mineral solid dispersions by Raman spectroscopy for robotic planetary exploration missions on Mars” | OpenAccess LINK




Hyperspectral Imaging (HSI)

“A shallow network for hyperspectral image classification using an autoencoder with convolutional neural network” | LINK

“Hyperspectral camera development on an unmanned aerial vehicle” LINK

“Direct reflectance transformation methodology for drone-based hyperspectral imaging” LINK




Spectral Imaging

“Remote Sensing : Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves” LINK




Chemometrics and Machine Learning

“Sensors : Adaboost-Based Machine Learning Improved the Modeling Robust and Estimation Accuracy of Pear Leaf Nitrogen Concentration by In-Field VIS-NIR Spectroscopy” LINK

“Discrimination of Manufacturers Origin of Oxytetracycline Using Terahertz Time-Domain Spectroscopy with Chemometric Methods” LINK




Spectroscopy

“Sensors : Non-Invasive Monitoring of Ethanol and Methanol Levels in Grape-Derived Pisco Distillate by Vibrational Spectroscopy” LINK




Equipment for Spectroscopy

“Improving the thermoelectric performances of polymer via synchronously realizing of chemical doping and side-chain cleavage” LINK




Environment NIR-Spectroscopy Application

“Determining physical and mechanical volcanic rock properties via reflectance spectroscopy” LINK

“Unauthorized landfills of solid household and industrial wastes detection in the Arctic and Subarctic territories using remote sensing technologies” LINK

“Evaluating the effects of distinct water saturation states on the light penetration depths of sand-textured soils” LINK




Agriculture NIR-Spectroscopy Usage

“Sorghum Grains Grading for Food, Feed, and Fuel Using NIR Spectroscopy” LINK

“Estimation of leaf area index at the late growth stage of crops using unmanned aerial vehicle hyperspectral images” LINK

“Analysis of Spectral Reflectance Characteristics Using Hyperspectral Sensor at Diverse Phenological Stages of Soybeans” LINK




Horticulture NIR-Spectroscopy Applications

“Data fusion for fruit quality authentication: combining non-destructive sensing techniques to predict quality parameters of citrus cultivars” | LINK




Food & Feed Industry NIR Usage

“Buckwheat Identification by Combined UV-VIS-NIR Spectroscopy and Multivariate Analysis” LINK




Other

“Effect of the annealing temperature on the growth of the silver nanoparticles synthesized by physical route” LINK



Spectroscopy and Chemometrics/Machine-Learning News Weekly #43, 2021

NIR Calibration-Model Services

Espectroscopia e Quimiometria/Máquina-Aprendizagem Semanal 42, 2021 | NIRS NIR Spectroscopia MaquinaLearning Espectrometria Analítica Química Análise Lab Labs Laboratórios Laboratório Software IoT Sensores QA QC Teste Qualidad LINK

Noticias semanales sobre espectroscopia y quimiometría 42, 2021 | NIRS NIR Espectroscopia AprendizajeMáquina Espectrómetro Espectrométrico Analítica Química Análisis Laboratorio Laboratorios Software IoT Sensores QA QC Testing Calidad LINK

Spectroscopy and Chemometrics News Weekly 42, 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 42, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 42, 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.

Еженедельник новостей спектроскопии и хемометрии/машинного обучения LINK

光谱学和化学计量学/机器学习新闻周刊 | 近红外光谱 机器学习光谱仪 光谱分析化学 化学分析实验室 实验室 实验室软件 物联网传感器 QA QC 测试质量 LINK

分光法とケモメトリックス/機械学習ニュースウィークリー | NIR-分光法機械学習分光計分光分析化学化学分析ラボラボラボラボラボソフトウェアIoTセンサーQAQCテスト品質 LINK




Near-Infrared Spectroscopy (NIRS)

“Rapid prediction of soil available sulphur using visible near-infrared reflectance spectros copy” LINK

“Multispectral and Hyperspectral Reflectance Imaging Spectrometry (VIS, VNIR, SWIR) in Painting Analyses: Undergraduate Teaching and Interfacial Undergraduate …” LINK

“The effectiveness of drug-coated balloons for two dissimilar calcific lesions assessed by near-infrared spectroscopy intravascular ultrasound and optical coherence …” LINK

“The visible and near-infrared optical absorption coefficient spectrum of Parylene C measured by transmitting light through thin films in liquid filled cuvettes” thinfilms LINK

“Predicting heavy metals in dark sun-cured tobacco by near-infrared spectroscopy modeling based on the optimized variable selections” LINK

“Penentuan Indeks Panen Buah Jambu Kristal secara Non Destruktif dengan Spektroskopi NIR” LINK

“In vivo non-invasive near-infrared spectroscopy distinguishes normal, post-stroke, and botulinum toxin treated human muscles” LINK

“Soil Classification Based on Deep Learning Algorithm and Visible Near-Infrared Spectroscopy” | LINK

“… SERAT KASAR DEDAK PADI MENGGUNAKAN JARINGAN SYARAF TIRUAN (JST) BERDASARKAN DATA ABSORBAN NIRS (NEAR INFRARED REFLECTANCE …” LINK

“Correlation of Near-Infrared Spectroscopy Oximetry and Corresponding Venous Oxygen Saturations in Children with Congenital Heart Disease” | LINK

“High Near-Infrared Reflectance Orange Pigments of Fe-Doped La2W2O9: Preparation, Characterization, and Energy Consumption Simulation” LINK




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

“On the Limit of Detection in Infrared Spectroscopic Imaging” LINK

“Minerals : Coupled Substitutions in Natural MnO(OH) Polymorphs: Infrared Spectroscopic Investigation” LINK

“Sensors : Application of High-Speed Quantum Cascade Detectors for Mid-Infrared, Broadband, High-Resolution Spectroscopy” LINK

“Infrared Spectroscopy and Chemometric Applications for the Qualitative and Quantitative Investigation of Grapevine Organs” | LINK

“Aging Pacific cod (Gadus macrocephalus) from otoliths using Fourier‐transformed near‐infrared spectroscopy” LINK




Hyperspectral Imaging (HSI)

“CHEMOMETRIC STRATEGIES FOR NEAR INFRARED HYPERSPECTRAL IMAGING ANALYSIS: CLASSIFICATION OF SEED GENOTYPES” LINK

“Near-infrared hyperspectral imaging technology combined with deep convolutional generative adversarial network to predict oil content of single maize kernel” LINK

“Detecting total acid content quickly and accurately by combining hyperspectral imaging and an optimized algorithm method” LINK

“Hyperspectral-enhanced dark field analysis of individual and collective photo-responsive gold-copper sulfide nanoparticles” LINK

“Ripeness evaluation of kiwifruit by hyperspectral imaging” LINK

“Hyperspectral reflectance imaging for water content and firmness prediction of potatoes by optimum wavelengths” | LINK

“Soluble solid content and firmness index assessment and maturity discrimination of Malus micromalus Makino based on near-infrared hyperspectral imaging” LINK




Chemometrics and Machine Learning

“Comparison of variable selection methods in predictive models applied to near-infrared and genomic data” LINK

“Building kinetic models to determine moisture content in apples and predicting shelf life based on spectroscopy” LINK

“Applied Sciences : Sample Reduction for Physiological Data Analysis Using Principal Component Analysis in Artificial Neural Network” LINK

“MultiTempLSTM: prediction and compression of multitemporal hyperspectral images using LSTM networks” LINK




Facts

“Stop Sending Samples” Off to the Lab” for Analysis: A Machine Learning Solution” LINK




Research on Spectroscopy

“Research on the online rapid sensing method of moisture content in famous green tea spreading” LINK




Process Control and NIR Sensors

“Microcirculatory Monitoring to Assess Cardiopulmonary Status” | LINK

“Advanced Process Analytical Tools for Identification of Adulterants in Edible Oils-A Review” LINK




Environment NIR-Spectroscopy Application

“Remote Sensing : Fine-Scale Sea Ice Segmentation for High-Resolution Satellite Imagery with Weakly-Supervised CNNs” LINK

“Plants : Water Spectral Patterns Reveals Similarities and Differences in Rice Germination and Induced Degenerated Callus Development” LINK

“Remote Sensing : Combining Remote Sensing and Meteorological Data for Improved Rice Plant Potassium Content Estimation” LINK




Agriculture NIR-Spectroscopy Usage

“Nutrients : Plant-Derived and Dietary Hydroxybenzoic AcidsA Comprehensive Study of Structural, Anti-/Pro-Oxidant, Lipophilic, Antimicrobial, and Cytotoxic Activity in MDA-MB-231 and MCF-7 Cell Lines” LINK




Horticulture NIR-Spectroscopy Applications

“Biology : Phylogenetic Analysis and Genetic Diversity of Colletotrichum falcatum Isolates Causing Sugarcane Red Rot Disease in Bangladesh” LINK




Food & Feed Industry NIR Usage

“Feasibility study on quantification and authentication of the cassava starch content in wheat flour for bread-making using NIR spectroscopy and digital images” LINK

“Technological innovations or advancement in detecting frozen and thawed meat quality: A review” LINK




Laboratory and NIR-Spectroscopy

“Separations : Quality Assessment of Camellia oleifera Oil Cultivated in Southwest China” LINK




Other

“A Strategy to Detect and Monitor Coca Production in Colombia, Peru, and Bolivia” LINK

“Measuring Nd(III) Solution Concentration in the Presence of Interfering Er(III) and Cu(II) Ions: A Partial Least Squares Analysis of UltravioletVisible Spectra” UVvis Ultraviolet LINK

“Spectral Properties of Anhydrous Carbonates and Nitrates” LINK

“การจำแนกสายพันธุ์อ้อยด้วยการตรวจวัดลำอ้อยโดยใช้เครื่องอินฟาเรดย่านใกล้สเปกโทรมิเตอร์แบบพกพา” LINK





Spectroscopy and Chemometrics News Weekly #25, 2021

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 24, 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 24, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 24, 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)

” CARACTERIZACIÓN QUIMICA DE SUELOS VOLCANICOS UTILIZANDO ESPECTROSCOPIA DE INFRARROJO CERCANO (NIRS)” LINK

“Development of an FT-NIR Method to Predict Process Cheese Functionality” LINK

“An effective classification framework for brain-computer interface system design based on combining of fNIRS and EEG signals” LINK

“Coronary artery disease and its impact on the pulsatile brain: A functional NIRS study” LINK

“Predicting anemia using NIR spectrum of spent dialysis fluid in hemodialysis patients” | LINK

“Automated Detection of Tetranychus urticae Koch in Citrus Leaves Based on Colour and VIS/NIR Hyperspectral Imaging” LINK




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

” A novel spectral index for estimating fractional cover of non-photosynthetic vegetation using near-infrared bands of Sentinel satellite” LINK

“Spatial distribution of total polyphenols in multi-type of tea using near-infrared hyperspectral imaging” LINK

“Investigation on the Mechanisms of Mg(OH)2 Dehydration and MgO Hydration by Near-Infrared Spectroscopy” LINK

“Near Infrared Reflectance Spectroscopy Analysis to Predict Diet Composition of a Mountain Ungulate Species” LINK

“Nondestructive determination of SSC in Korla Fragrant Pear using a portable near-infrared spectroscopy system” LINK

“Applied Sciences, Vol. 11, Pages 4717: 808-Nm Near-Infrared Laser Photobiomodulation versus Switched-Off Laser Placebo in Major Aphthae Management: A Randomized Double-Blind Controlled Trial” LINK

“Titration of Inspired Oxygen in Preterm Infants with Hypoxemic Respiratory Failure Using Near Infrared Spectroscopy and Pulse Oximetry: A New Approach” LINK

“Shedding light on neuroscience: Two decades of functional nearinfrared spectroscopy applications and advances from a bibliometric perspective” LINK




Hyperspectral Imaging (HSI)

“Hyperspectral Detection and Monitoring of Salt Stress in Pomegranate Cultivars” Agronomy LINK

“Applied Sciences, Vol. 11, Pages 4588: Beef Quality Grade Classification Based on Intramuscular Fat Content Using Hyperspectral Imaging Technology” LINK




Spectral Imaging

“Artificial Intelligence Empowered Multispectral Vision Based System for Non-Contact Monitoring of Large Yellow Croaker (Larimichthys crocea) Fillets” Foods LINK




Chemometrics and Machine Learning

” Assessment of chicken breast shelf life based on bench-top and portable near-infrared spectroscopy tools coupled with chemometrics” LINK

” Prediction of the particle size and flow characteristics of powder blends for tableting by near-infrared spectroscopy and chemometrics” LINK

“Antibacterial Activity of Moroccan Zantaz Honey and the Influence of Its Physicochemical Parameters Using Chemometric Tools” AppliedSciences LINK

“Predicting pectin performance strength using nearinfrared spectroscopic data: A comparative evaluation of 1D convolutional neural network, partial least squares, and ridge regression modeling” LINK

“Sequential and orthogonalized PLS (SOPLS) regression for path analysis: Order of blocks and relations between effects” LINK

“The Impacts of Spatial Resolution, Viewing Angle, and Spectral Vegetation Indices on the Quantification of Woody Mediterranean Species Seasonality Using Remote Sensing” LINK

“Partial least squares and silver nanoparticles in spectrophotometric prediction of total hardness of water” LINK

“Genetic robust kernel sample selection for chemometric data analysis” LINK




Equipment for Spectroscopy

“Nearinfrared triggered drug delivery of Imatinib Mesylate by molybdenum disulfide nanosheets grafted copolymers as thermosensitive nanocarriers” LINK




Process Control and NIR Sensors

“IQR CUSUM charts: An efficient approach for monitoring variations in aquatic toxicity” LINK




Environment NIR-Spectroscopy Application

“Study on hyperspectral estimation model of soil organic carbon content in the wheat field under different water treatments” LINK

“Ecometabolic mixture design-fingerprints from exploratory multi-block data analysis in Coffea arabica beans from climate changes: Elevated carbon dioxide and reduced soil water availability” LINK




Agriculture NIR-Spectroscopy Usage

“Integrating Straw Management and Seeding to Improve Seed Yield and Reduce Environmental Impacts in Soybean Production” Agronomy LINK

” Soil N 2 O flux and nitrification and denitrification gene responses to feed-induced differences in the composition of dairy cow faeces” | LINK




Food & Feed Industry NIR Usage

“Pulsed Electric Field (PEF) Processing of Chilled and Frozen-Thawed Lamb Meat Cuts: Relationships between Sensory Characteristics and Chemical Composition of Meat” Foods LINK




Other

“Racial Differences in Hemodynamic Responses to Lower Body Negative Pressure: The Effects of Capsaicin” LINK

“苹果可溶性固形物的可见/近红外无损检测” LINK

“Quantitative vibrational spectroscopy on liquid mixtures: concentration units matter” LINK

“Enhanced light harvesting in dyesensitized solar cells enabled by TiO2:Er3+, Yb3+ upconversion phosphor particles as solar spectral converter and light scattering medium” LINK





Spectroscopy and Chemometrics News Weekly #19, 2021

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 18, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

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

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





Near-Infrared Spectroscopy (NIRS)

“Integrated (1)H NMR fingerprint with NIR spectroscopy, sensory properties, and quality parameters in a multi-block data analysis using ComDim to evaluate coffee blends” LINK

“Efficient Nearinfrared Pyroxene Phosphor LiInGe2O6:Cr3+ for NIR Spectroscopy Application” LINK

“Transfer learning and wavelength selection method in NIR spectroscopy to predict glucose and lactate concentrations in culture media using VIPBoruta” LINK

“Theranostic Near-Infrared-Active Conjugated Polymer Nanoparticles” LINK

“Integrated NIRS and QTL assays reveal minor mannose and galactose as contrast lignocellulose factors for biomass enzymatic saccharification in rice” LINK

“Age estimation of barramundi (Lates calcarifer) over multiple seasons from the southern Gulf of Carpentaria using FT-NIR spectroscopy” | LINK

“Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy” LINK

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

“Foods, Vol. 10, Pages 885: Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy” LINK




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

“RenalClearable NickelDoped Carbon Dots with Boosted Photothermal Conversion Efficiency for Multimodal ImagingGuided Cancer Therapy in the Second NearInfrared Biowindow” LINK

“Simultaneous Broadening and Enhancement of Cr3+ Photoluminescence in LiIn2SbO6 by Chemical Unit Cosubstitution: NightVision and NearInfrared Spectroscopy Detection Applications” LINK

“Applied Sciences, Vol. 11, Pages 3701: Measurement of Temperature and H2O Concentration in Premixed CH4/Air Flame Using Two Partially Overlapped H2O Absorption Signals in the Near Infrared Region” LINK

“Fourier-Transform Infrared Spectroscopy as a Discriminatory Tool for Myotonic Dystrophy Type 1 Metabolism: A Pilot Study” IJERPH LINK

“Application of machine learning to estimate fireball characteristics and their uncertainty from infrared spectral data” LINK

“Cross Target Attributes and Sample Types Quantitative Analysis Modeling of Near-infrared Spectroscopy Based on Instance Transfer Learning” LINK

“Fast at-line characterization of solid organic waste: Comparing analytical performance of different compact near infrared spectroscopic systems with different …” LINK

“On-line identification of silkworm pupae gender by short-wavelength near infrared spectroscopy and pattern recognition technology” LINK

“The Use of Multispectral Imaging and Single Seed and Bulk Near-Infrared Spectroscopy to Characterize Seed Covering Structures: Methods and Applications in Seed …” LINK

” The use of infrared reflectance spectroscopy to predict the dry matter intake of lactating grazing dairy cows” LINK

“Development of a Novel Green Tea Quality Roadmap and the Complex Sensory-associated Characteristics exploration using Rapid Near-Infrared Spectroscopy …” LINK

“… of the Neutral and Acid Detergent Fiber Fractions of Chickpea (Cicer arietinum L.) by Combining Modified PLS and Visible with Near-Infrared Spectroscopy” LINK

“Non-destructive estimation of fibre morphological parameters and chemical constituents of Tectona grandis Lf wood by near infrared spectroscopy” LINK

“NEAR INFRARED SPECTROSCOPY MEASUREMENT AND KINETIC MODELING FOR PHYSIOCHEMICAL PROPERTIES OF TABTIM FISH (HYBRID TILAPIA …” LINK

“A simple multiple linear regression model in near infrared spectroscopy for soluble solids content of pomegranate arils based on stability competitive adaptive re …” LINK

“Intelligent assessment of the histamine level in mackerel (Scomber australasicus) using near-infrared spectroscopy coupled with a hybrid variable selection strategy” LINK

“Portable Near Infrared Spectroscopy as a Tool for Fresh Tomato Quality Control Analysis in the Field” LINK

” Focused echocardiography, end-tidal carbon dioxide, arterial blood pressure or near-infrared spectroscopy monitoring during paediatric cardiopulmonary …” LINK

“EXPRESS: Fourier Transform Infrared (FT-IR) Imaging Analysis of Interactions Between Polypropylene Grafted with Maleic Anhydride (MAPP) and Silica Spheres (SS) …” LINK




Hyperspectral Imaging (HSI)

“Hazelnuts classification by hyperspectral imaging coupled with variable selection methods” LINK

“Towards the development of a sterile model cheese for assessing the potential of hyperspectral imaging as a non-destructive fungal detection method” LINK

“Geographical origin discriminant analysis of Chia seeds (Salvia hispanica L.) using hyperspectral imaging” LINK




Chemometrics and Machine Learning

“Bayesian subset selection and variable importance for interpretable prediction and classification. (arXiv:2104.10150v1 [stat.ML])” LINK

“MachineLearning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer” LINK

“Remote Sensing, Vol. 13, Pages 1598: Development of Novel Classification Algorithms for Detection of Floating Plastic Debris in Coastal Waterbodies Using Multispectral Sentinel-2 Remote Sensing Imagery” LINK

“Sensors, Vol. 21, Pages 2871: A Novel Runtime Algorithm for the Real-Time Analysis and Detection of Unexpected Changes in a Real-Size SHM Network with a Quasi-Distributed FBG Sensors” LINK

“NIR spectroscopy coupled with chemometric algorithms for the prediction of cadmium content in rice samples” LINK

” Determination of petroleum hydrocarbon contamination in soil using VNIR DRS and PLSR modeling” LINK

” Establishment and applicant of near-infrared reflectance spectroscopy models for predicting protein, linolenic acid and lignan contents of flaxseed” LINK

“Detection of chlorpyrifos and carbendazim residues in the cabbage using visible/near-infrared spectroscopy combined with chemometrics” LINK

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

“The Organochlorine Pesticide Residues of Mesona Chinensis Benth by Near Infrared (NIR) Spectroscopy and Chemometrics” LINK

“Strategies for the Development of Spectral Models for Soil Organic Matter Estimation” Remote Sensing LINK

“THE QUANTIFICATION OF NEUROCOGNITIVE IMPAIRMENT ACROSS THE SPECTRUM OF KIDNEY DISEASE” LINK




Facts

“IJMS, Vol. 22, Pages 4347: Mitochondrial Bioenergetic, Photobiomodulation and Trigeminal Branches Nerve Damage, Whats the Connection? A Review” LINK




Research on Spectroscopy

“ndothelial and microvascular function in CKD: Evaluation methods and associations with outcomes” LINK




Equipment for Spectroscopy

“On-line monitoring of egg freshness using a portable NIR spectrometer in tandem with machine learning” LINK




Environment NIR-Spectroscopy Application

” Current sensor technologies for in situ and on-line measurement of soil nitrogen for variable rate fertilization-A review.” LINK

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




Agriculture NIR-Spectroscopy Usage

“Crystals, Vol. 11, Pages 458: Boron Influence on Defect Structure and Properties of Lithium Niobate Crystals” | LINK

“Remote Sensing, Vol. 13, Pages 1620: Estimating Plant Nitrogen Concentration of Rice through Fusing Vegetation Indices and Color Moments Derived from UAV-RGB Images” LINK

“Consensus rule for wheat cultivar classification on VL, VNIR and SWIR imaging” LINK

” Changes in the Milk Market in the United States on the Background of the European Union and the World” LINK

“High-Resolution Airborne Hyperspectral Imagery for Assessing Yield, Biomass, Grain N Concentration, and N Output in Spring Wheat” RemoteSensing LINK

” Near infrared hyperspectral imaging of the hemodynamic and metabolic states of the exposed cortex: in vivo investigation on small animal models” LINK

“Engineered Protein PhotoThermal Hydrogels for Outstanding In Situ Tongue Cancer Therapy” LINK




Food & Feed Industry NIR Usage

“The quality and shelf life of biscuits with cryoground proso millet and buckwheat byproducts” LINK

“Performance of different portable and hand-held near-infrared spectrometers for predicting beef composition and quality characteristics in the abattoir without meat sampling” LINK

“New Approaches to Detect Compositional Shifts in Fish Oils” LINK




Medicinal Spectroscopy

“Aplicação de espectroscopia no infravermelho próximo e análise multivariada para identificação e quantificação de hidrocarbonetos totais do petróleo em solo” | LINK




Other

“Vibrational Analysis of Benziodoxoles and Benziodazolotetrazoles” LINK

“Recent advances in Unmanned Aerial Vehicles forest remote sensing—A systematic review. Part II: Research applications” LINK

“Physical and thermal properties of gold nanoparticles embedded Nd3+-doped borophosphate glasses: Spectroscopic parameters” LINK

“Spectral Assessment of Organic Matter with Different Composition Using Reflectance Spectroscopy” Remote Sensing LINK




NIR-Predictor – Manual


NIR-Predictor – Manual

Predicting Spectra

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

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

Use the included data to checkout how it feels:

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

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

  3. The spectra will be

    • loaded
    • pre-processed
    • predicted and
    • reported

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

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


Creating your own Calibrations

How it works – step by step

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

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

  2. Now you need to combine these data.

    NIR-spectra + Lab-references
    -> PropertiesBySamples

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

    The NIR-Predictor provides tooling for that:

    Menu > Create Properties File... (F6)

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

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

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

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

    Menu > Create Calibration Request... (F7)

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

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

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

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

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


Configure the Calibrations for prediction usage

Configuration:

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

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

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

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

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

Usage:

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

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

  3. the NIR-Predictor is now ready to predict

  4. to switch the application, goto 6.


Applications

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

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

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

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

The use-all case

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

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


Prediction Result Report

Histograms of Prediction Values per Property

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

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

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

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

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

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

Spectra Plot Thumbnail on the Prediction Report

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

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

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

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

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

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

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

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

Note

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

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

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

Spectra Plot

Outlier Detection

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

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

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

Outlier (Out) Symbol Description

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

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

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

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

The technical names in literature correspond to:

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

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

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

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

Some hints to avoid these Outliers:

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

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

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

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

Result Ordering

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

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

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

*) sorted : ascending sort

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

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

  • Rev_Date_Name
  • Rev_Name_Date
  • Rev_Date_NamesWithNumbers
  • Rev_NamesWithNumbers_Date

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

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

Archiving Reports

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


Enter lab values to NIR spectra

Entering the laboratory reference values for NIR calibrations

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

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

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

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

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

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

How it works

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

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

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

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

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

Create Properties File

Note:

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

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

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

The file header line contains :

Sample Replicates Names Prop1 Prop2 Prop3 DateFirst DateLast Hashes

Where Name and Date describes the spectrum.

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

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

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

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

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

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

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

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

Enter the Lab Reference Concentrations to the spectra/sample.

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

Hints: Data handling:

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

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

  • How to add more spectra files?

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

    Or

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

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

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


Create Calibration Request

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

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

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

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

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

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

When all is fine

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

The ZIP file contains:

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

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

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


Program Settings

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

Further References

NIR-Predictor Download

The free NIR-Predictor software
  • comes with demo data, so you can predict sample spectra with demo calibrations.
  • has no functional limitations, no nagging, no ads and needs no license-key.
  • you need no account and no registration to download and use.
  • runs on Microsoft Windows 10/8/7 (Starter, Basic, Professional) (32 bit / 64 bit).
  • no data is ever transmitted from your local machine. We don’t even collect usage data.
See more Videos



Beside the free NIR-Predictor software with Windows user interface,
the real-time Predictor Engine is also available
  • for embedded integration in application, cloud and instrument-software (ICT).
  • As a light-weigt single library file (DLL)
    with application programming interface (API),
    documentation and software development kit (SDK)
    including sample source code (C#).
  • Easy integration and deployment,
    no software license protection (no serial key, no dongle).
  • Put your spectrum as an array into the multivariate predictor,
    no specific file format needed.
  • Fast prediction speed and low latency
    because of compiled code library (direct call, no cloud API).
  • Protected prediction results with outlier detection information.
See NIR Method Development Service for Labs and NIR-Vendors (OEM, White-Label)



Software Size Date Comment
NIR-Predictor V2.6.0.2 (download)

What’s new, see Release Notes

By downloading and/or using the software
you accept the Software License Agreement (EULA)
3.7 MB 18.08.2021 public release

Minimal System Requirements
Windows 7 Starter 32Bit, 1.6 GHz, 2 GB RAM, non-Administrator account

Installation
There are no administrator rights required, unpack the zip file to a folder “NIR-Predictor” in your documents or on your desktop.
Read the ReadMe.txt and double click the NIR-Predictor.exe file.

Upgrade
If you have installed an older version of NIR-Predictor then unpack into a different folder named e.g. “NIR-PredictorVx.y”. All versions can run side-by-side. Copy the Calibrations in use to the new version into the “Calibration” folder. That’s all.

Uninstall
Make sure to backup your reports and calibrations inside your “NIR-Predictor” folder. Delete the “NIR-Predictor” folder.


Start Calibrate

See also: