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

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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

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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 #1, 2022

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 52, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK




Near-Infrared Spectroscopy (NIRS)

“Wide band UV/Vis/NIR blazed-binary reflective gratings: two lithographic techniques investigation” LINK

“Scanning interferometric near-infrared spectroscopy for imaging of adult human forehead blood flow dynamics” LINK

“Near-infrared spectroscopy in process control and quality management of fruits and wine” LINK

“Potential of Vis-NIR to measure heavy metals in different varieties of organic-fertilizers using Boruta and deep belief network” LINK

“Quantification of Carbon Dioxide (CO2), Methane (CH4), and Nitrous Oxide (N2O) Using Near Infrared Spectroscopy and Multivariate Calibration in High …” LINK

“Development of NIRS re-calibration model for ethiopian barley (Hordeum vulgare) lines traits to determine their brewing potential” LINK

“Near-infrared spectroscopy of blood plasma with chemometrics towards HIV discrimination during pregnancy” LINK




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

“Nondestructive evaluation of the biogenic amine in crayfish (Prokaryophyllus clarkii) by nearinfrared spectroscopy” LINK




Raman Spectroscopy

“Quantitative age grading of mosquitoes using surfaceenhanced Raman spectroscopy” LINK




Hyperspectral Imaging (HSI)

“Moisture transport dynamics in wood during drying studied by long-wave near-infrared hyperspectral imaging” | LINK

“In-line monitoring of the residual moisture in impregnated black textile fabrics by hyperspectral imaging” LINK

“Hyperspectral imaging approach for the identification of construction and demolition waste from earthquake sites” LINK




Chemometrics and Machine Learning

“Class-modelling of overlapping classes. A two-step authentication approach” LINK

“Machine learning and chemometrics for electrochemical sensors: moving forward to the future of analytical chemistry” LINK




Research on Spectroscopy

“Synthesis of the Zn1. 9Cu0. 1SiO4 pigment via the sol-gel and coprecipitation methods” | LINK

“Portable vibrational spectroscopic methods can discriminate between grass-fed and grain-fed beef” LINK

“Polydimethylsiloxane tissue-mimicking phantoms with tunable optical properties” LINK

“Biocompatible POSS-gold nanocomposites synthesized by laser ablation in ethanol” LINK




Equipment for Spectroscopy

“Ultra-broadband, high-resolution microdroplet spectrometers for the Near Infrared” LINK

“Non-destructive determination of color, titratable acidity, and dry matter in intact tomatoes using a portable Vis-NIR spectrometer” LINK

“Diffuse Reflectance Illumination Module Improvements in Near-Infrared Spectrometer for Heterogeneous Sample Analysis” LINK




Agriculture NIR-Spectroscopy Usage

“Detection of soybean oil adulteration in cow ghee (clarified milk fat): An ultrafast study using flash gas chromatography electronic nose coupled with multivariate chemometrics” LINK

“Non-lipid-rich low attenuation plaque with intraplaque haemorrhage assessed by multimodality imaging: a case report” LINK




Horticulture NIR-Spectroscopy Applications

“Hyperspectral imaging and machine learning for the prediction of SSC in kiwi fruits” LINK




Food & Feed Industry NIR Usage

“Animals : Measurements of Chemical Compositions in Corn Stover and Wheat Straw by Near-Infrared Reflectance Spectroscopy” LINK

“Contrasting effects of biochar-and organic fertilizer-amendment on community compositions of nitrifiers and denitrifiers in a wheat-maize rotation system” LINK




Other

“Single Crystals Heterogeneity Impacts the Intrinsic and Extrinsic Properties of MetalOrganic Frameworks” LINK

“基于高光谱数据研究应用近红外相机加装滤光片实现玉米叶片水分测量的关键参数” LINK

“Geochemical analysis and spectral characteristics of oil shale deposits in Wadi Abu Ziad, Western Irbid, Jordan” | LINK

” 可见光-近红外, 中红外光谱的土壤有机质组分反演” LINK

“Measurement of Somatic Cell Count in the 700-1,100 nm Short Wavelength Region: Comparison of At-Line and On-Line Measurement Modes” |(91)78179-4 LINK



Spectroscopy and Chemometrics/Machine-Learning News Weekly #46, 2021ews Weekly #46, 2021

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Spectroscopy and Chemometrics News Weekly 45, 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 45, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 45, 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)

“Intramuscular Near-Infrared Spectroscopy for Muscle Flap Monitoring in a Porcine Model” LINK

“Avocado dehydration negatively affects the performance of visible and near-infrared spectroscopy models for dry matter prediction” LINK

“Ultraviolet/Visible and Near-Infrared Dual Spectroscopic Method for Detection and Quantification of Low-Level Malaria Parasitemia in Whole Blood” LINK

“NIR hyperspectral imaging for predicting the composition of granular food commodities” LINK

“Multi-output 1-dimensional convolutional neural networks for simultaneous prediction of different traits of fruit based on near-infrared spectroscopy” LINK

“Non-Destructive Quality Measurement for Three Varieties of Tomato Using VIS/NIR Spectroscopy. Sustainability” | LINK

“Handheld NIRS for forage evaluation” LINK

“Rapid and Nondestructive Determination of Polyacrylonitrile Molecular Weight by Fourier Transform near-Infrared (NIR) Spectroscopy” LINK

“Determination of alcohols-diesel oil by near infrared spectroscopy based on gramian angular field image coding and deep learning” LINK

“Exploring Effects of Sample Storage, Preparation, and Tissue Type on Fourier Transform-Near Infrared Spectroscopy (Ft-Nirs) Ageing Across Fish Taxa” LINK

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

“Explainable artificial intelligence based analysis for interpreting infant fNIRS data in developmental cognitive neuroscience” LINK

“In Situ Determination of Cannabidiol in Hemp Oil by Near-Infrared Spectroscopy” LINK

“Rapid detection of adulteration in desiccated coconut powder: vis-NIR spectroscopy and chemometric approach” LINK

“Blood volume vs. deoxygenated NIRS signal: computational analysis of the effects muscle O2 delivery and blood volume on the NIRS signals” LINK

“Near-Infrared Spectroscopy Study of Serpentine Minerals and Assignment of the OH Group” LINK

“Identification of Microbes in Wounds via Near-Infrared Spectroscopy” LINK

“What causes differences in fracture rates of silcrete during heat treatment? A near-infrared study of water-related transformations in South African silcretes” LINK

“Correction of the moisture variation in wood NIR spectra for species identification using EPO and soft PLS2-DA” LINK

“Classification of rice based on storage time by using near infrared spectroscopy and chemometric methods” LINK




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

“Coherent Raman scattering imaging with a near-infrared achromatic metalens” LINK

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

“Distinguishing between different types of multi‐layered PET‐based backsheets of PV modules with nearinfrared spectroscopy” LINK

“Rapid Prediction Method of α-Glycosidase Inhibitory Activity of Coreopsis tinctoria Extract from Different Habitats by Near Infrared Spectroscopy” LINK

“Internal Quality Classification of Apples Based on Near Infrared Spectroscopy and Evidence Theory” LINK

“NearInfrared Photoactive Semiconductor Quantum Dots for Solar Cells” LINK




Hyperspectral Imaging (HSI)

“Application of hyperspectral imaging in the detection of aflatoxin B1 on corn seed” | LINK

“Hyperspectral Imaging Simulator and Applications for Unmanned Aerial Vehicles” LINK

“SWiVIA – Sliding window variographic image analysis for real-time assessment of heterogeneity indices in blending processes monitored with hyperspectral imaging” LINK

“Prediction of coffee aroma from single roasted coffee beans by hyperspectral imaging” LINK

“Evaluation of hyperspectral imaging to quantify perfusion changes during the modified Allen test” LINK

“Near infrared hyperspectral imaging for non-destructive determination of pH value in red delicious apple fruit during shelf life” LINK




Chemometrics and Machine Learning

“Determining tangential contact force model parameters for viscoelastic materials (apples) using a rheometer” LINK

“Physicochemical properties, content, composition and partial least squares models of A. trifoliata seeds oil” LINK

“Chemometrics and Experimental Design for the Quantification of Nitrate Salts in Nitric Acid: Near-Infrared Spectroscopy Absorption Analysis” LINK

“Evaluation and Monitoring of the API Content of a Portable Near Infrared Instrument Combined with Chemometrics Based on Fluidized Bed Mixing Process” | LINK

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

“Relationship Between Image Spectroscopy Spatial Resolution and Crown Level Tree Species Classification Accuracy” LINK

“An uncertainty sampling strategy based model updating method for soluble solid content and firmness prediction of apples from different years” LINK




Optics for Spectroscopy

“Leaky Mode Resonance-Induced Sensitive Ultraviolet Photodetector Composed of Graphene/Small Diameter Silicon Nanowire Array Heterojunctions” LINK




Facts

“Superiority Verification of Deep Learning in the Identification of Medicinal Plants: Taking Paris polyphylla var. yunnanensis as an Example” | LINK




Environment NIR-Spectroscopy Application

“Remote Sensing : Study on the Pretreatment of Soil Hyperspectral and Na+ Ion Data under Different Degrees of Human Activity Stress by Fractional-Order Derivatives” LINK

“Long-term effects of water stress on hyperspectral remote sensing indicators in young radiata pine” LINK

“Rapid assessment of soil water repellency indices using Vis-NIR spectroscopy and pedo-transfer functions” LINK




Agriculture NIR-Spectroscopy Usage

“Non-destructive determination of protein and amino acids concentration and their relationship with grain yield affected by different irrigation, sowing date …” LINK

“Physiological and molecular characterization of the late ripening stages in Mangifera indica cv Keitt” LINK

” Using UAV-based hyperspectral imaging and functional regression to assist in predicting grain yield and related traits in wheat under heat-related stress …” | LINK

“Crop Production Under Urbanisation: An Experimental Approach to Understand and Model Agricultural Intensification” | LINK

“Advancements in Forage Management: Grazing Horses on Cover Crops and Exploring Hand-Held Nirs Technology” LINK




Food & Feed Industry NIR Usage

“Applied Sciences : Winter Wheat Take-All Disease Index Estimation Model Based on Hyperspectral Data” LINK

“Foods : Total Fat Gravimetric Method Workflow in Portuguese Olives Using Closed-Vessel Microwave-Assisted Extraction (MAE)” LINK

“Foods : Multiple Breeds and Countries Predictions of Mineral Contents in Milk from Milk Mid-Infrared Spectrometry” LINK




Other

“Fiber and digestibility of Piatã grass in systems in integration.” LINK

“Detection of syrup adulterants in manuka and jarrah honey using HPTLC-multivariate data analysis” | 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

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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/Machine-Learning News Weekly #42, 2021

NIR Calibration-Model Services

Spectroscopy and Chemometrics/Machine-Learning News Weekly 41, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensor QA QC Testing Quality LINK

Spektroskopie und Chemometrie/Machine-Learning Neuigkeiten Wöchentlich 41, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT sensors Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria/Machine-Learning Weekly News 41, 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)

“Development and Submission of Near Infrared Analytical Procedures Guidance for Industry” FDA LINK

“Identification prediction moisture content of Thai coconut sugar (Cocos nucifera L.) using FT-NIR spectroscopy” LINK

“Blood identification of NIR spectroscopy based on BP neural network combined with particle swarm optimization” LINK

“A preliminary study on the utilisation of near infrared spectroscopy to predict age and in vivo human metabolism” LINK

“Near-infrared guidance finalized for small molecule testing, with biologics to come” RAPS LINK

“Wavelength Selection Method for Near Infrared Spectroscopy Based on Iteratively Retains Informative Variables and Successive Projections Algorithm” LINK

“Near-infrared spectroscopy calibration strategies to predict multiple nutritional parameters of pasture species from different” LINK

” Comparison of metabolites and variety authentication of Amomum tsao-ko and Amomum paratsao-ko using GC-MS and NIR spectroscopy” LINK

“Hyperfine-Resolved Near-Infrared Spectra of H(2)(17)O” LINK

“Geographical Differentiation of Hom Mali Rice Cultivated in Different Regions of Thailand Using FTIR-ATR and NIR Spectroscopy” LINK

“Seasonal Snowpack Classification Based on Physical Properties Using Near-Infrared Proximal Hyperspectral Data” | LINK

“NIR-based sensing system for non-Invasive detection of Hemoglobin for point-of-care applications” LINK

“A promising inorganic YFeO3 pigments with high near-infrared reflectance and infrared emission” LINK

“Classification of Softwoods using Wood Extract Information and Near Infrared Spectroscopy.” LINK

“Rapid determination and origin identification of total polysaccharides contents in Schisandra chinensis by near-infrared spectroscopy” LINK

“Near-Infrared Spectroscopy Technology in Food” | LINK

“Postharvest ripeness assessment of ‘Hass’ avocado based on development of a new ripening index and Vis-NIR spectroscopy” LINK




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

“Utility of near‐infrared spectroscopy to detect the extent of lipid core plaque leading to periprocedural myocardial infarction” LINK

“Scaling up Sagebrush Chemistry with Near-Infrared Spectroscopy and Uas-Acquired Hyperspectral Imagery” LINK




Hyperspectral Imaging (HSI)

“Spatially Resolved Spectroscopic Characterization of Nanostructured Films by Hyperspectral Dark-Field Microscopy” LINK

“Visual attention-driven framework to incorporate spatial-spectral features for hyperspectral anomaly detection” LINK




Spectral Imaging

“Spectral Super-Resolution of Multispectral Images Using Spatial-Spectral Residual Attention Network” LINK




Chemometrics and Machine Learning

“Feasibility of a chromameter and chemometric techniques to discriminate pure and mixed organic and conventional red pepper powders: A pilot study” LINK

“Cyanobacteria cell prediction using interpretable deep learning model with observed, numerical, and sensing data assemblage” LINK

“Applied Sciences : A Novel Principal Component Analysis Integrating Long Short-Term Memory Network and Its Application in Productivity Prediction of Cutter Suction Dredgers” LINK

“Plants : Morpho-Physiological Classification of Italian Tomato Cultivars (Solanum lycopersicum L.) According to Drought Tolerance during Vegetative and Reproductive Growth” LINK

“Automatic food and beverage authentication and adulteration detection by classification hybrid fusion” LINK

“Remote Sensing : Improving Accuracy of Herbage Yield Predictions in Perennial Ryegrass with UAV-Based Structural and Spectral Data Fusion and Machine Learning” LINK

“Estimating Fat Components of Potato Chips Using Visible and Near-Infrared Spectroscopy and a Compositional Calibration Model” LINK

“Wavelengths selection based on genetic algorithm (GA) and successive projections algorithms (SPA) combine with PLS regression for determination the soluble …” LINK

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

“Verified the rapid evaluation of the edible safety of wild porcini mushrooms, using deep learning and PLS‐DA” LINK




Optics for Spectroscopy

“Scientists Teach AI Cameras to See Depth in Photos Better” AI Camera Depth LINK




Facts

“Deep learning accelerates super-resolution microscopy by up to ten times” | DeepLearning microscopy LINK

“Statistical Learning to Operationalize a Domain Agnostic Data Quality Scoring. (arXiv:2108.08905v1 [cs.LG])” LINK




Research on Spectroscopy

“Foods : Instrumentation for Routine Analysis of Acrylamide in French Fries: Assessing Limitations for Adoption” LINK




Process Control and NIR Sensors

“NIR spectroscopy for monitoring of the critical manufacturing steps and quality attributes of paliperidone prolonged release tablets” LINK




Environment NIR-Spectroscopy Application

“Spatial Differentiation Analysis of Water Quality in Dianchi Lake Based on GF-5 NDVI Characteristic Optimization” LINK

“Remote Sensing : Using Sentinel-2 for Simplifying Soil Sampling and Mapping: Two Case Studies in Umbria, Italy” LINK

“Sensors : Ground-Based Hyperspectral Remote Sensing for Estimating Water Stress in Tomato Growth in Sandy Loam and Silty Loam Soils” LINK

“Potential of high-spectral resolution for field phenotyping in plant breeding: Application to maize under water stress” LINK




Agriculture NIR-Spectroscopy Usage

“Study the Genetic Diversity in Protein, Zinc and Iron in Germplasm Pools of Desi Type Chickpeas as Implicated in Quality Breeding” LINK

“Additives and soy detection in powder rice beverage by vibrational spectroscopy as an alternative method for quality and safety control” LINK

“Remote Sensing : Generating Up-to-Date Crop Maps Optimized for Sentinel-2 Imagery in Israel” LINK

“Fodder biomass, nutritive value, and grain yield of dual‐purpose Pearl Millet, Sorghum and Maize cultivars across different agro‐ecologies in Burkina Faso” LINK

“Agronomy : Effects of the Foliar Application of Potassium Fertilizer on the Grain Protein and Dough Quality of Wheat” LINK

“Remote Sensing : Sentinel-2 and Landsat-8 Multi-Temporal Series to Estimate Topsoil Properties on Croplands” LINK

“Sensors : Assessment of Grain Harvest Moisture Content Using Machine Learning on Smartphone Images for Optimal Harvest Timing” LINK

“Using UAV image data to monitor the effects of different nitrogen application rates on tea quality” LINK

“A single calibration of near-infrared spectroscopy to determine the quality of forage for multiple species” LINK




Forestry and Wood Industry NIR Usage

“Teakwood Chemistry and Natural Durability” LINK




Food & Feed Industry NIR Usage

“Foods : Temporal Sensory Perceptions of Sugar-Reduced 3D Printed Chocolates” LINK

“Foods : Rapid Nondestructive Simultaneous Detection for Physicochemical Properties of Different Types of Sheep Meat Cut Using Portable Vis/NIR Reflectance Spectroscopy System” LINK

“Foods : Real-Time Gauging of the Gelling Maturity of Duck Eggs Pickled in Strong Alkaline Solutions” LINK




Chemical Industry NIR Usage

“Polymers : Drug Amorphous Solid Dispersions Based on Poly(vinyl Alcohol): Evaluating the Effect of Poly(propylene Succinate) as Plasticizer” LINK




Pharma Industry NIR Usage

” Effects of acetazolamide and furosemide on ventilation and cerebral blood volume in normocapnic and hypercapnic COPD patients” LINK




Other

“Advances, challenges and perspectives of quantum chemical approaches in molecular spectroscopy of the condensed phase” LINK

“Calidad composicional y sensorial de la carne bovina y su determinación mediante infrarrojo cercano” LINK

“Bioinspired StimuliResponsive Hydrogel with Reversible Switching and Fluorescence Behavior Served as LightControlled Soft Actuators” LINK

“Neural Efficiency in Athletes: A Systematic Review” LINK

“Tailored Chiral Copper Selenide Nanochannels for Ultrasensitive Enantioselective Recognition and Detection” LINK

“In vivo diffuse reflectance spectroscopic analysis of fatty liver with inflammation in mice” LINK

“Métodos de análise da composição química e valor nutricional de alimentos para ruminantes” LINK

“Dissociation between exercise intensity thresholds: mechanistic insights from supine exercise” LINK





Spectroscopy and Chemometrics News Weekly #48, 2020

NIR Calibration-Model Services

Do you use Molecular Spectroscopy with Multivariate Regression Models? That will save you development time LINK

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

Spectroscopy and Chemometrics News Weekly 47, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

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



Near-Infrared Spectroscopy (NIRS)

“Influence of forage particle size and residual moisture on near infrared reflectance spectroscopy (NIRS) calibration accuracy for macro-mineral determination” LINK

“Rapid detection of adulteration in Dendrobium Huoshanense using NIR spectroscopy coupled with chemometric methods” LINK

“Using near-infrared spectroscopy to discriminate closely related species: A case study of neotropical ferns” LINK

“Temperature-dependent, VIS-NIR reflectance spectroscopy of sodium sulfates” LINK




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

“Using near-infrared spectroscopy to determine intramuscular fat and fatty acids of beef applying different prediction approach.” LINK

“Estimating the sensory qualities of tomatoes using visible and near-infrared spectroscopy and interpretation based on gas chromatography–mass …” LINK

“Near Infrared Spectroscopy-Based Evaluation of Patellar Tendon and Knee Ligaments” LINK

“Predictive capacity of some wood properties by near-infrared spectroscopy” LINK

“Predicting Marian Plum Fruit Quality without Environmental Condition Impact by Handheld Visible–Near-Infrared Spectroscopy” LINK

“Near infrared reflectance spectroscopy to quantify Perkinsus marinus infecting Crassostrea virginica” LINK

“Application of genetic algorithm and multivariate methods in detection and measurement of milk‐surfactant adulteration by attenuated total reflection and near‐infrared spectroscopy” LINK

“Classifying Thermal Degradation of Polylactic Acid by Using Machine Learning Algorithms Trained on Fourier Transform Infrared Spectroscopy Data” LINK

“Classification Option for Korean Traditional Paper Based on Type of Raw Materials, Using Near-infrared Spectroscopy and Multivariate Statistical Methods” LINK

“Rapid and simultaneous quality analysis of the three active components in Lonicerae Japonicae Flos by near-infrared spectroscopy” LINK

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

“Application of near-infrared hyperspectral imaging for variety identification of coated maize kernels with deep learning” LINK




Hyperspectral Imaging (HSI)

Konica Minolta to acquire Specim, the leading global supplier of hyperspectral imaging.   “Konica Minolta shares our vision and values and will greatly support our business through improved sell-through,” said Tapio Kallonen, CEO of Specim LINK

“Research on Citrus grandis Granulation Determination Based on Hyperspectral Imaging through Deep Learning” LINK

Wavelength Selection Method Based on Partial Least Square from Hyperspectral Unmanned Aerial Vehicle Orthomosaic of Irrigated Olive Orchards” LINK




Chemometrics and Machine Learning

“Fractional order modeling and recognition of nitrogen content level of rubber tree foliage” LINK

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

“Detection and Quantification of Tomato Paste Adulteration Using Conventional and Rapid Analytical Methods” LINK

“Prediction of Acidity Level of Avomango (Gadung Klonal 21) Using Local Polynomial Estimator” LINK

“A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra” LINK

“Spectrometric Classification of Bamboo Shoot Species by Comparison of Different Machine Learning Methods” LINK

Supporting the Sensory Panel to Grade Virgin Olive Oils: An In-House-Validated Screening Tool by Volatile Fingerprinting and Chemometrics” LINK




Process Control and NIR Sensors

“A Process Analytical Concept for In-Line FTIR Monitoring of Polysiloxane Formation” Polymers LINK




Agriculture NIR-Spectroscopy Usage

“Vis–NIR spectroscopy: from leaf dry mass production estimate to the prediction of macro-and micronutrients in soybean crops” LINK

“Comparison of benchtop and handheld near‐infrared spectroscopy devices to determine forage nutritive value” LINK




Food & Feed Industry NIR Usage

“Rapid Authentication of 100% Italian Durum Wheat Pasta by FT-NIR Spectroscopy Combined with Chemometric Tools” LINK

“Identification of Corn Seeds with Different Freezing Damage Degree Based on Hyperspectral Reflectance Imaging and Deep Learning Method” LINK

“Near-and mid-infrared determination of some quality parameters of cheese manufactured from the mixture of different milk species” LINK

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




Laboratory and NIR-Spectroscopy

“A Novel Spectral Matching Approach for Pigment: Spectral Subsection Identification Considering Ion Absorption Characteristics” LINK




Other

“Development of a novel quantitative function between spectral value and metmyoglobin content in Tan mutton” LINK

” 基于稀疏网络的可见光/近红外反射光谱土壤有机质含量估算” LINK

“基于可见-近红外光谱的茄子叶绿素荧光参数 Fv/Fm 预测方法” LINK

“A Miniaturized and Fast System for Thin Film Thickness Measurement” LINK

“Molecular spectroscopy with optical frequency combs” 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