Spectroscopy and Chemometrics/Machine-Learning News Weekly #40, 2021

NIR Calibration-Model Services

Improve Accuracy of fast Nondestructive NIR Analytics by Optimal Calibration | Food Feed FoodSafety ag Lab LINK

Increase Your Profit with optimized NIR Accuracy Process Protein Oil plastic colors paints milk soy Soybean LINK

Spectroscopy and Chemometrics News Weekly 39, 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 39, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 39, 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)

“Convenient use of near-infrared spectroscopy to indirectly predict the antioxidant activitiy of edible rose (Rose chinensis Jacq “Crimsin Glory” HT) petals during …” LINK

“Near-infrared emission from spatially indirect excitons in type II ZnTe/CdSe/(Zn,Mg)Te core/double-shell nanowires” LINK

“Rapid Detection of Fatty Acids in Edible Oils Using Vis-NIR Reflectance Spectroscopy with Multivariate Methods” LINK

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

“Methylglyoxal Adducts Levels in Blood Measured on Dried Spot by Portable Near-Infrared Spectroscopy” LINK

“Application of Near-Infrared Spectroscopy to statistical control in freeze-drying processes” LINK

“TeaNet: Deep learning on Near-Infrared Spectroscopy (NIR) data for the assurance of tea quality” LINK

“Detecting Residual Awareness in Patients With Prolonged Disorders of Consciousness: An fNIRS Study” | LINK

“Dry Matter Estimation of Standing Corn with Near-infrared Reflectance Spectroscopy” LINK

“Conditional-GAN Based Data Augmentation for Deep Learning Task Classifier Improvement Using fNIRS Data” | LINK

“Application of Various Algorithms for Spectral Variable Selection in NIRS Modeling of Red Ginseng Extraction” LINK

“Vis-NIR hyperspectral imaging along with Gaussian process regression to monitor quality attributes of apple slices during drying” LINK

“Potential of Near Infrared Spectroscopy as a Rapid Method to Discriminate OTA and Non-OTA-Producing Mould Species in a Dry-Cured Ham Model System” LINK

“Pendugaan Tingkat Fermentasi Kakao Secara Non-Destruktif dengan NIRS” LINK

“Penentuan Tingkat Kekerasan dan Kemanisan Buah Naga Merah (Hylocereus polyrhizus) Secara Nondestruktif Menggunakan Near Infrared Spectroscopy (NIRS)” LINK




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

” A NOVEL PROTEIN STRUCTURE ELUCIDATION TECHNIQUE BY CIRCULAR DICHROISM AND NEAR INFRARED SPECTROSCOPY” LINK

“A novel strategy of “pick the best of the best” for the nondestructive identification of Poria cocos based on near-infrared spectroscopy” LINK

“The Neural Processing of Vocal Emotion After Hearing Reconstruction in Prelingual Deaf Children: A Functional Near-Infrared Spectroscopy Brain Imaging Study” LINK

“Antinutrient to mineral molar ratios of raw common beans and their rapid prediction using near-infrared spectroscopy” LINK

“Effect of spectral pretreatment on qualitative identification of adulterated bovine colostrum by near-infrared spectroscopy” LINK

“A Rotational-Linear Sample Probing Device to Improve the Performance of Compact Near-Infrared Spectrophotometers” LINK

“Machine Learning Calibration for Near TS Infrared Spectroscopy Data: A Visual kkS Programming Approach” LINK




Hyperspectral Imaging (HSI)

“Selecting informative bands for partial least squares regressions improves their goodness-of-fits to estimate leaf photosynthetic parameters from hyperspectral data” LINK

“Hyperspectral detection of salted sea cucumber adulteration using different spectral preprocessing techniques and SVM method” LINK

“An automated approach for fringe frequency estimation and removal in infrared spectroscopy and hyperspectral imaging of biological samples” LINK

“New evidence from hyperspectral imaging analysis on the effect of photobiomodulation therapy on normal skin oxygenation” LINK




Chemometrics and Machine Learning

“The Use of Chemometrics for Classification of Sidaguri (<i>Sida rhombifolia</i>) Based on FTIR Spectra and Antiradical Activities” LINK

“Massive spectral data analysis for plant breeding using parSketch-PLSDA method: Discrimination of sunflower genotypes” LINK

“Htype indices with applications in chemometrics I: hmultiple similarity index” LINK

“Near-Infrared Spectroscopy and Machine Learning-Based Classification and Calibration Methods in Detection and Measurement of Anionic Surfactant in Milk” LINK

“Remote Sensing : Validation of FY-3D MERSI-2 Precipitable Water Vapor (PWV) Datasets Using Ground-Based PWV Data from AERONET” LINK

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

“Foods : Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality” LINK

“Estimating the Forage Neutral Detergent Fiber Content of Alpine Grassland in the Tibetan Plateau Using Hyperspectral Data and Machine Learning Algorithms” LINK

“Optical spectroscopy methods for the characterization of sol-gel materials” LINK

“Sensors : Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors” LINK

“Agronomy : How Different Cooking Methods Affect the Phenolic Composition of Sweet Potato for Human Consumption (Ipomea batata (L.) Lam)” LINK




Equipment for Spectroscopy

“Miniaturized VIS-NIR Spectrometers Based on Narrowband and Tunable Transmission Cavity Organic Photodetectors with Ultrahigh Specific Detectivity above 10(14) Jones” LINK

In the market for a palm spectrometer, bandpass filter, or microscopy stage? Those and more are featured in the October Product Showcase out today. | photonics optics LINK

“PORTABLE NEAR INFRARED SPECTROMETER DENGAN SENSOR AS7263 UNTUK PENDUGAAN SIFAT KIMIA JERUK SIAM (CITRUS NOBILIS) SECARA NON …” LINK




Process Control and NIR Sensors

“Multi-modal diffuse optical spectroscopy for high-speed monitoring and wide-area mapping of tissue optical properties and hemodynamics” LINK

“Process analytical technique (PAT) miniaturization for monoclonal antibody aggregate detection in continuous downstream processing” LINK

“Development of a Robust Control Strategy for Fixed-Dose Combination Bilayer Tablets with Integrated Quality by Design, Statistical, and Process Analytical …” LINK




Environment NIR-Spectroscopy Application

“Recent Advances in Plasmonic Photocatalysis Based on TiO2 and Noble Metal Nanoparticles for Energy Conversion, Environmental Remediation, and Organic Synthesis” LINK

“Mapping liquid water content in snow: An intercomparison of mixed-phase optical property models using hyperspectral imaging and in situ measurements” LINK

“Estimating Atterberg limits of soils from reflectance spectroscopy and pedotransfer functions” LINK




Agriculture NIR-Spectroscopy Usage

“Seeing the wood for the trees: hyperspectral imaging for high throughput QTL detection in raspberry, a perennial crop species” LINK

“Agronomy : Effect of Different Edaphic Crop Conditions on the Free Amino Acid Profile of PH-16 Dry Cacao Beans” LINK




Horticulture NIR-Spectroscopy Applications

“Application of Hyperspectral Imaging for Maturity and Soluble Solids Content Determination of Strawberry With Deep Learning Approaches” LINK

“Predicting soluble solids content in “Fuji” apples of different ripening stages based on multiple information fusion” LINK




Food & Feed Industry NIR Usage

“Effects of cultivars and fertilization levels on the quality of brown and polished rice” LINK

“Foods : Physicochemical and Functional Properties of Snack Bars Enriched with Tilapia (Oreochromis niloticus) by-Product Powders” LINK

“Using data science to combat poverty” | BASE foodwaste LINK

“Foods : HPLC Fingerprints for the Characterization of Walnuts and the Detection of Fraudulent Incidents” LINK

“A novel approach to identify the spectral bands that predict moisture content in canola and wheat” LINK




Medicinal Spectroscopy

“Investigating spectroscopic measurement of sublingual veins and tissue to estimate central venous oxygen saturation” LINK

“Detection of the Communication Site by Indocyanine Green Adsorbed to Human Serum Albumin Fluorescence During Surgery for a Pleuroperitoneal …” LINK

“INFLUENCE OF SEX ON LOWER LIMB SKELETAL MUSCLE OXIDATIVE CAPACITY AND MUSCLE DIFFUSION IN ENDURANCE TRAINED INDIVIDUALS” LINK




Other

“Efecto del ambiente ruminal y la fuente de fibra sobre la dinámica de desaparición de la materia orgánica y sus componentes en bovinos en confinamiento.” LINK

“催熟对采后菠萝品质的影响与光谱识别” LINK



Spectroscopy and Chemometrics News Weekly #12, 2021

NIR Calibration-Model Services

Develop near infrared spectroscopy applications & freeing up hours of analysts time NIR NIRS QC Lab Laboratories LINK

Reduce Operating Costs & TCO of NIRS NIR-Spectroscopy in the Digitalization Age AnalyticalLabs FoodIndustry FoodScience TestingLab LaboratoryManager LabManager Laboratory Manager qualityassurance AIaaS MLaaS SaaS MachineLearning ML LINK

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

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)

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

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

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

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

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

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

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

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

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

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

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

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

“… condition of leaves during black tea processing via the fusion of electronic eye (E-eye), colorimetric sensing array (CSA), and micro-near-infrared spectroscopy (NIRS)” LINK

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




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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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




Raman Spectroscopy

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




Hyperspectral Imaging (HSI)

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




Chemometrics and Machine Learning

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

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

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

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

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

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

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

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




Research on Spectroscopy

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

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




Equipment for Spectroscopy

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




Environment NIR-Spectroscopy Application

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

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

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

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




Agriculture NIR-Spectroscopy Usage

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

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

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

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

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

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

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




Food & Feed Industry NIR Usage

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

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




Pharma Industry NIR Usage

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

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




Medicinal Spectroscopy

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



Spectroscopy and Chemometrics News Weekly #4, 2021

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 3, 2021 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 3, 2021 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 3, 2021 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

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

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


Near-Infrared Spectroscopy (NIRS)

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

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

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

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

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

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




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

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

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

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

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

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

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




Hyperspectral Imaging (HSI)

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

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

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




Chemometrics and Machine Learning

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

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

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

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

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

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




Equipment for Spectroscopy

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




Process Control and NIR Sensors

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




Environment NIR-Spectroscopy Application

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

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




Agriculture NIR-Spectroscopy Usage

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

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

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

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




Horticulture NIR-Spectroscopy Applications

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




Food & Feed Industry NIR Usage

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

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

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

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




Laboratory and NIR-Spectroscopy

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





Digitization in the field of NIR spectroscopy (smart sensors)

Digitalization is advancing, also in NIR spectroscopy, which enables trainable miniature smart sensors e.g. for analyses in the food&feed, chemical and pharmaceutical sectors.

The calibration is the core of a NIR spectroscopy sensor, it enables the numerous applications and should therefore not be the weakest link in the measurement chain.

The development of calibrations that turn NIR spectrometers into smart sensors is done manually by experts (NIR specialist, chemometrician, data scientist) with so-called chemometrics software.

This is very time-consuming (time to market) and the result is person-dependent and thus suboptimal, because each expert has his own preferred way of proceeding. In addition, the calibrations have to be maintained, as new data has been collected in the meantime, which can be used to extend and improve the calibrations.

This is where our automated service comes in, combining the knowledge and good practices of NIR spectroscopy and chemometrics collected in one software and using machine learning to generate optimal calibrations.

Based on this, we have developed a complete technology platform (Time to Market) that covers the entire process from sending NIR + Lab data, to NIR Calibration as a Service, from online purchase of calibrations, to NIR Predictor software that directly evaluates newly measured NIR data locally and generates result reports.

Besides the free desktop version with user interface, the NIR Predictor can also be integrated (OEM). This can be integrated in parallel as a complement to your current Predictor, allowing the user to choose how they want to calibrate. And give them the advantage in NIR feasibility studies and NIR spectrometer evaluations to quickly provide the customer with a solid and accurate calibration that will make their NIR system deliver better results.

Advantages for your NIR users (internal or external)
  • no initial costs (no chemometrics software license required),
  • calculable operating costs (fixed amount instead of time and hourly rate) (calibration development, calibration maintenance)
  • easy to use (no chemometrics and software training),
  • quicker to use (no calibration development work) and
  • better calibrations (precision, accuracy, robustness, …)


Our chargeable service is based on the calibration development and the annual calibration use. Calibration development and calibration use can also be carried out separately (manufacturer / user).

For you as a spectrometer manufacturer, this means that you can deliver your system pre-calibrated for certain applications without incurring software license costs. And without your application specialists having to provide additional calibration services.

The unique advantages of our calibration service together with the free NIR Predictor are:
  • no software license costs (chemometrics software, predictor software, OEM integration)
  • no chemometrics know-how necessary
  • no time needed to develop optimal NIR calibrations.


If interested in using/evaluating the service :

About CalibrationModel.com : Time and knowledge intensive creation and optimization of chemometric evaluation methods for spectrometers as a service to enable more accurate analysis and measurement results.



see also

Paradigm Change in NIR

Five Mistakes to avoid on Digitalization in NIR

NIR – Total cost of ownership (TCO)

OEM / White Label Software

White Paper



Spectroscopy and Chemometrics News Weekly #39, 2020

NIR Calibration-Model Services

Safe Cost and Time with new NIR Model Development and Maintenance in the Analytical Testing Laboratory. | NIRS NIR instruments Spectrometer Infrared Spectroscopy Food Feed Dairy Sample Analysis Analytical Testing Laboratory LINK

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

Spectroscopy and Chemometrics News Weekly 38, 2020 | 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)

“Determination of green pea and spinach adulteration in pistachio nuts using NIR spectroscopy” LINK

“Improved prediction of ‘Kent’ mango firmness during ripening by near-infrared spectroscopy supported by interval partial least square regression” LINK

“Phytochemical composition and variability in Quercus ilex acorn morphotypes as determined by NIRS and MS-based approaches” LINK

“FT-NIR spectroscopy and RP-HPLC combined with multivariate analysis reveals differences in plant cell suspension cultures of Thevetia peruviana treated with …” LINK

“Combination of Convolutional Neural Networks and Recurrent Neural Networks for predicting soil properties using Vis–NIR spectroscopy” LINK

“Non-destructive determination of the main chemical components of red dragon fruit peel flour by using Near-Infrared Reflectance Spectroscopy (NIRS)” LINK

“Comparative VIS and NIR Raman and FTIR Spectroscopy of Lunar Analog Samples” LINK

“NearInfrared spectroscopy (NIRS) applications for high throughput phenotyping (HTP) for cassava and yam: A review” LINK

“Near infrared spectroscopy for rapid determination of solids content of amino resins” LINK




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

“Dual-slope imaging in highly scattering media with frequency-domain near-infrared spectroscopy.” LINK

“Non-destructive measurement of apple internal quality by using near-infrared spectroscopy” | LINK

“Feasibility Study on Use of Near Infrared Spectroscopy for Rapid and Non-Destructive Determination of Gossypol Content in Intact Cottonseeds” LINK

“NEAR INFRARED SPECTROSCOPY FOR SEPARATION OF TAUARI WOOD IN BRAZILIAN AMAZON NATIVE FOREST” LINK

“The use of infrared spectroscopy and machine learning tools for detection of Meloidogyne infestations” LINK

“simultaneous determination of moisture and berberine content in Coptidis Rhizoma and Phellodendri Chinensis Cortex by near-infrared spectroscopy” LINK




Raman Spectroscopy

Epic video for OpenRAMAN to celebrate first year on Patreon! LINK




Hyperspectral Imaging (HSI)

“Hyperspectral Imaging Coupled with Multivariate Analysis and Image Processing for Detection and Visualisation of Colour in Cooked Sausages Stuffed in Different …” LINK




Spectral Imaging

Point of care (POC) testing is designed to diagnose patients in the field, rather than in a medical facility. Ocean Insight offers highly integrated multispectral sensors reduces the complexity, footprint and cost of PCR instrumentation. Learn more: LINK




Chemometrics and Machine Learning

“Probabilistic classification models for the in situ authentication of Iberian pig carcasses using Near Infrared Spectroscopy” LINK

“Application of Chemometrics in Biosensing: A Brief Review” Biosensors LINK

“Rapid monitoring approaches for concentration process of lanqin oral solution by near-infrared spectroscopy and chemometric models.” LINK

“Establishment of Near-Infrared Prediction Models for Toluene Insoluble Substance and Other 3 Indexes of Medium Temperature Coal Pitch” LINK

“Modified selfadaptive model for improving the prediction accuracy of soil organic matter using laserinduced breakdown spectroscopy” LINK

“A Comparative Study of PLSR and SVM-R with Various Preprocessing Techniques for the Quantitative Determination of Soluble Solids Content of Hardy Kiwi Fruit by a Portable Vis/NIR Spectrometer.” LINK

“A Brief History of Whiskey Adulteration and the Role of Spectroscopy Combined with Chemometrics in the Detection of Modern Whiskey Fraud” LINK




Research on Spectroscopy

“Continuous and real-time indoor and outdoor methane sensing with portable optical sensor using rapidly pulsed IR LEDs.” LINK




Equipment for Spectroscopy

“OpenRAMAN: Open-Source Raman Spectrometer in 1 day” Raman Spectroscopy 3Dprinting LINK

“Nondestructive authentication of the regional and geographical origin of cocoa beans by using a handheld NIR spectrometer and multivariate algorithm.” LINK




Future topics in Spectroscopy

“Artificial Intelligence-as-a-Service Market-Growth, Trends, and Forecast (2020-2025)” ArtificialIntelligence AIaaS MarketGrowth LINK




Process Control and NIR Sensors

“Real Time Monitoring of Quality Attributes by Inline Fourier Transform Infrared Spectroscopic Sensors at Ultrafiltration and Diafiltration of Bioprocess” LINK




Agriculture NIR-Spectroscopy Usage

“Application of advanced molecular spectroscopy and modern evaluation techniques in canola molecular structure and nutrition property research” LINK

“Sensors, Vol. 20, Pages 4645: Wavelength Selection FOR Rapid Identification of Different Particle Size Fractions of Milk Powder Using Hyperspectral Imaging” LINK

“Role of Near Infrared Spectroscopy in Agriculture” LINK

“Comparison between synthetic and rosemary-based antioxidants for the deep frying of French fries in refined soybean oils evaluated by chemical and non-destructive …” LINK




Forestry and Wood Industry NIR Usage

“Phenotypic techniques and applications in fruit trees: a review.” LINK




Food & Feed Industry NIR Usage

“Rapid screening of DON contamination in whole wheat meals by Vis/NIR spectroscopy and computer vision coupling technology” LINK





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Spectroscopy and Chemometrics News Weekly #38, 2020

NIR Calibration-Model Services

Safe Cost and Time with new NIR Model Development and Maintenance in the Analytical Testing Laboratory. | NIRS NIR instruments Spectrometer Infrared Spectroscopy Food Feed Dairy Sample Analysis Analytical Testing Laboratory LINK

Kosten- und Zeitersparnis durch die Entwicklung und Wartung neuer NIR-Modelle im analytischen Prüflabor. | NIRS NIR Instrumente Spektrometer Infrarot Spektroskopie Lebensmittel Futtermittel Molkerei Probe Analyse LINK

Spectroscopy and Chemometrics News Weekly 37, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 37, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 37, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

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

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




Near-Infrared Spectroscopy (NIRS)

“Age estimation of red snapper (Lutjanus campechanus) using FT-NIR spectroscopy: feasibility of application to production ageing for management” LINK

“Sensing Botrytis cinerea in Tomato Using Visible/Near-Infrared (VIS/NIR) Spectroscopy” LINK

“Intelligent evaluation of storage period of green tea based on VNIR hyperspectral imaging combined with chemometric analysis” LINK

“Applied Sciences, Vol. 10, Pages 5498: NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Identify Shrimp Freshness” LINK

“NON-DESTRUCTIVE IDENTIFICATION OF INTERNAL WATERCORE IN APPLES BASED ON ONLINE VIS/NIR SPECTROSCOPY” LINK

“Qualidade da madeira de Eucalyptus benthamii para produção de celulose por espectroscopia no infravermelho próximo (NIRS)” LINK

“Multilevel LASSO-based NIR temperature-correction modeling for viscosity measurement of bisphenol-A.” LINK

“High-precision identification of the actual storage periods of edible oil by FT-NIR spectroscopy combined with chemometric methods.” | LINK




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

“Stingless Bee Honey Classification Using Near Infrared Light Coupled With Artificial Neural Network” LINK

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

“Classification of fish species from different ecosystems using the near infrared diffuse reflectance spectra of otoliths” LINK

“Non-Destructive Identification and Estimation of Granulation in Honey Pomelo Using Visible and Near-Infrared Transmittance Spectroscopy Combined with Machine Vision Technology” LINK

“Prediction of Soil Organic Carbon in a New Target Area by Near-Infrared Spectroscopy: Comparison of the Effects of Spiking in Different Scale Soil Spectral Libraries” Sensors LINK

“Application of Detrended Fluctuation Analysis and Yield Stability Index to Evaluate Near Infrared Spectra of Green and Roasted Coffee Samples” LINK

“A Paper-Based Near-Infrared Optical Biosensor for Quantitative Detection of Protease Activity Using Peptide-Encapsulated SWCNTs” Sensors LINK

“NEAR INFRARED SPECTROSCOPY FOR SEPARATION OF TAUARI WOOD IN BRAZILIAN AMAZON NATIVE FOREST.” Journal of Tropical Forest Science, 2020 LINK

“Classification of Wood Species Frequently Used for Modern and Ancient Buildings Utilizing Near-Infrared Spectroscopy with Multivariate Analysis and Enhancement of Its Generalization Performance” LINK

“Viability of near infrared spectroscopy for a rapid analysis of the bioactive compounds in intact cocoa bean husk” LINK

“The use of infrared spectroscopy and machine learning tools for detection of Meloidogyne infestations” LINK




Chemometrics and Machine Learning

“Rapid monitoring approaches for concentration process of lanqin oral solution by near-infrared spectroscopy and chemometric models” LINK

“Real-time quantification of low-dose cohesive formulations within a sampling interface for flowing powders” LINK

“Development of a novel wavelength selection method VCPA-PLS for robust quantification of soluble solids in tomato by on-line diffuse reflectance NIR” LINK

“Green Analytical Methods of Antimalarial Artemether-Lumefantrine Analysis for Falsification Detection Using a Low-Cost Handled NIR Spectrometer with DD-SIMCA and Drug Quantification by HPLC.” LINK




Optics for Spectroscopy

“Sensors, Vol. 20, Pages 4379: A Novel Approach to Using Spectral Imaging to Classify Dyes in Colored Fibers” LINK

“Remote Sensing, Vol. 12, Pages 2502: Vegetation Detection Using Deep Learning and Conventional Methods” LINK




Research on Spectroscopy

“Effect of Mg-doping ZnO nanoparticles on detection of low ethanol concentrations” LINK

“Modern Methods for Assessing the Quality of Bee Honey and Botanical Origin Identification” LINK




Agriculture NIR-Spectroscopy Usage

“Extended multiplicative signal correction to improve prediction accuracy of protein content in weathered sorghum grain samples” LINK

“Soybean seed vigor discrimination by infrared spectroscopy and machine learning algorithms” LINK

“Comparison between synthetic and rosemary-based antioxidants for the deep frying of French fries in refined soybean oils evaluated by chemical and non-destructive rapid methods.” LINK

“Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review” SmartCities LINK




Horticulture NIR-Spectroscopy Applications

“Prediction of dry matter content of recently harvested Hass avocado fruits using hyperspectral imaging” LINK




Forestry and Wood Industry NIR Usage

“Phenotypic techniques and applications in fruit trees: a review” LINK




Other

“IBM publishes its quantum roadmap, says it will have a 1,000-qubit machine in 2023” LINK





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NIR Analysis in Laboratory and Laboratories – aka NIR Labs and NIR testing


Do you have a NIR spectrometer in your Lab?

How many other analytics you do in the Lab could be done faster and cheaper with NIR?

Is this possible and precise enough?

Try, we have the solution for you!
You have the NIR, scan the samples, you have the lab values and the spectra, we calibrate for you!

To see if the application is possible and how precise it can be due to knowledge based intensive model optimizations.

We do the NIR feasibility study with data for you. Fixed prices

NIR has huge application potentials and it’s a Green analytical method, that’s fast and easy to use. And has today the possibility to scale out with inexpensive mobile NIR spectrometers.

Bring the Lab to the sample. To avoid sample transport and get immediate results for decision at the place or in the process.

Just try the NIR application, use the NIR daily, collect data in parallel, we develop, optimize and maintain the calibration models for you.

How do you think?

Start Calibrate


What is possible today with NIR?
The number of different Applications exploded in the last 2-3 years!
See NIR research papers news daily on @CalibModel or the 7-day summariesNIR News Weekly” here.

Spectroscopy and Chemometrics News Weekly #37, 2020

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 36, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 36, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 36, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

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

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




Near-Infrared Spectroscopy (NIRS)

“NIR Spectroscopic Techniques for Quality and Process Control in the Meat Industry” LINK

“Estimating coefficient of linear extensibility using Vis–NIR reflectance spectral data: Comparison of model validation approaches” LINK

“NIR spectroscopy and chemometric tools to identify high content of deoxynivalenol in barley” LINK

“Combining multivariate method and spectral variable selection for soil total nitrogen estimation by Vis–NIR spectroscopy” LINK

“Multi-task deep learning of near infrared spectra for improved grain quality trait predictions” LINK

“Multi-factor Fusion Models for Soluble Solid Content Detection in Pear (Pyrus bretschneideri ‘Ya’) Using Vis/NIR Online Half-transmittance Technique” LINK

“Determining regression equations for predicting the metabolic energy values of barley-producing cultivars in Iran and comparing the results with the results of NIRS method and cultivars …” LINK

“Using Near-Infrared Reflectance Spectroscopy (NIRS) to Predict Glucobrassicin Concentrations in Cabbage and Brussels Sprout Leaf Tissue” LINK

“Near-Infrared Spectroscopy for Analyzing Changes of Pulp Color of Kiwifruit in Different Depths” LINK

“Novel NIR modeling design and assignment in process quality control of Honeysuckle flower by QbD” LINK




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

“Near-infrared spectroscopy to determine cold-flow improver concentrations in diesel fuel” LINK

“Improving spatial synchronization between X-ray and near-infrared spectra information to predict wood density profiles” LINK

“Functional principal component analysis for near-infrared spectral data: a case study on Tricholoma matsutakeis” LINK

“Midinfrared spectroscopy as a tool for realtime monitoring of ethanol absorption in glycols” LINK

“Inline characterization of dispersion uniformity evolution during a twinscrew blending extrusion based on nearinfrared spectroscopy” LINK

“Development of Fourier Transform near Infrared Spectroscopy Methods for the Rapid Quantification of Starch and Cellulose in Mozzarella and Other Italian-Type CHEESES” LINK

“Prediction of Anthocyanin Content in Three Types of Blueberry Pomace by Near-Infrared Spectroscopy” LINK

“Sweetness Detection and Grading of Peaches and Nectarines by Combining Short-and Long-Wave Fourier-Transform Near-Infrared Spectroscopy” LINK




Spectral Imaging

“Use of UAS Multispectral Imagery at Different Physiological Stages for Yield Prediction and Input Resource Optimization in Corn” Remote Sensing LINK




Chemometrics and Machine Learning

“Combination of visible reflectance and acoustic response to improve nondestructive assessment of maturity and indirect prediction of internal quality of redfleshed pomelo” LINK

“Green Analytical Methods of Antimalarial Artemether-Lumefantrine Analysis for Falsification Detection Using a LowCost Handled NIR Spectrometer with DD-SIMCA and Drug Quantification by HPLC” LINK

“Data fusion of UPLC data, NIR spectra and physicochemical parameters with chemometrics as an alternative to evaluating kombucha fermentation” LINK

“Effect of physicochemical factors and use of milk powder on milk rennet-coagulation: Process understanding by near infrared spectroscopy and chemometrics” LINK

“A Digital Approach to Model Quality and Sensory Traits of Beers Fermented under Sonication Based on Chemical Fingerprinting” LINK

“Latent Variable Graphical Modeling for High Dimensional Data Analysis” LINK




Equipment for Spectroscopy

“Evaluation of Salmon, Tuna, and Beef Freshness Using a Portable Spectrometer” Sensors LINK

“Developing a soil spectral library using a low-cost NIR spectrometer for precision fertilization in Indonesia” LINK

“Compact Solid Etalon Computational Spectrometer: FY19 Optical Systems Technology Line-Supported Program” LINK




Agriculture NIR-Spectroscopy Usage

“Detection of Melamine Adulteration in Milk Powder by Using Optical Spectroscopy Technologies in the Last Decade—a Review” LINK




Horticulture NIR-Spectroscopy Applications

“Accurate non-destructive prediction of peach fruit internal quality and physiological maturity with a single scan using near infrared spectroscopy” LINK

“Recent Advances in Spectral Analysis Techniques for Non-Destructive Detection of Internal Quality in Watermelon and Muskmelon: A Review” “光谱分析在西甜瓜内部品质无损检测中的研究进展” LINK




Food & Feed Industry NIR Usage

“Detection of fraud in highquality rice by nearinfrared spectroscopy” LINK

“Detecting food fraud in extra virgin olive oil using a prototype portable hyphenated photonics sensor” LINK

“Nondestructive detection of sunset yellow in cream based on near-infrared spectroscopy and interval random forest” LINK




Other

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

“The Effect of Monomers on the Recognition Properties of Molecularly Imprinted Beads for Proto-hypericin and Proto-pseudohypericin” | FLOREA GAVRILA 1 20.pdf LINK





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Spectroscopy and Chemometrics News Weekly #33, 2020

NIR Calibration-Model Services

Spectroscopy and Chemometrics News Weekly 32, 2020 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 32, 2020 | NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK

Spettroscopia e Chemiometria Weekly News 32, 2020 | NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK

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

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




Near-Infrared Spectroscopy (NIRS)

“Integrated soluble solid and nitrate content assessment of spinach plants using portable NIRS sensors along the supply chain” LINK

“Evaluation of Near Infrared Spectroscopy (NIRS) and Remote Sensing (RS) for Estimating Pasture Quality in Mediterranean Montado Ecosystem” LINK

“Evaluation of Homogeneity in Drug Seizures Using Near-Infrared (NIR) Hyperspectral Imaging and Principal Component Analysis (PCA)”LINK

“FT-NIRS Coupled with PLS Regression as a Complement to HPLC Routine Analysis of Caffeine in Tea Samples” Foods LINK




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

“Model based optimization of transflection near infrared spectroscopy as a process analytical tool in a continuous flash pasteurizer” LINK

“EXPRESS: Monitoring Polyurethane Foaming Reactions Using Near-Infrared Hyperspectral Imaging” LINK

“Near-infrared spectroscopy for monitoring free jejunal flap.” LINK

“Real-Time and Online Inspection of Multiple Pork Quality Parameters Using Dual-Band Visible/N ear-Infrared Spectroscopy” LINK

“An approach to quantify natural durability of Eucalyptus bosistoana by near infrared spectroscopy for genetic selection” LINK

“Rapid detection of green pea adulteration in ground pistachio nuts using near and mid-infrared spectroscopy” LINK

“Non‐invasive quality analysis of thawed tuna using near infrared spectroscopy with baseline correction” LINK




Raman Spectroscopy

“Low-Content Quantitation in Entecavir Tablets Using 1064 nm Raman Spectroscopy” LINK




Hyperspectral Imaging (HSI)

“Detecting defects on cheese using hyperspectral image analysisLINK

“Non-destructive discrimination of the variety of sweet maize seeds based on hyperspectral image coupled with wavelength selection algorithm” LINK

“Hyperspectral Imaging from a Multipurpose Floating Platform to Estimate Chlorophyll-a Concentrations in Irrigation Pond Water” Remote Sensing LINK

“Alfalfa Yield Prediction Using UAV-Based Hyperspectral Imagery and Ensemble Learning” LINK




Chemometrics and Machine Learning

“Maintaining the predictive abilities of near-infrared spectroscopy models for the determination of multi-parameters in White Paeony Root” LINK

“Machine Learning Modeling of Wine Sensory Profiles and Color of Vertical Vintages of Pinot Noir Based on Chemical Fingerprinting, Weather and Management Data” Sensors LINK

“Authentication of the Geographical Origin of “Vallerano” Chestnut by Near Infrared Spectroscopy Coupled with Chemometrics” LINK




Agriculture NIR-Spectroscopy Usage

“Relationship between chemical composition and standardized ileal digestible amino acid contents of corn grain in broiler chickens” LINK

“NIR spectroscopy and management of bioactive components, antioxidant activity, and macronutrients in fruits” LINK

“Determination of mechanical properties of whey protein films during accelerated aging: Application of FTIR profiles and chemometric tools” LINK

“A portable IoT NIR spectroscopic system to analyze the quality of dairy farm forage” LINK

“Exploring Relevant Wavelength Regions for Estimating Soil Total Carbon Contents of Rice Fields in Madagascar from Vis-NIR Spectra with Sequential Application of …” LINK

“Applied Sciences, Vol. 10, Pages 4345: Observation of Potential Contaminants in Processed Biomass Using Fourier Transform Infrared Spectroscopy” LINK

“Animals, Vol. 10, Pages 1095: Comparison of Fatty Acid Proportions Determined by Mid-Infrared Spectroscopy and Gas Chromatography in Bulk and Individual Milk Samples” LINK

“Manipulation of Fruit Dry Matter via Seasonal Pruning and Its Relationship to dAnjou Pear Yield and Fruit Quality” Agronomy LINK




Forestry and Wood Industry NIR Usage

“The Effect of Construction and Demolition Waste Plastic Fractions on Wood-Polymer Composite Properties” LINK




Food & Feed Industry NIR Usage

“Analysis of sorghum content in corn–sorghum flour bioethanol feedstock by near infrared spectroscopy” LINK

“Quantitative detection of fatty acid value during storage of wheat flour based on a portable near-infrared (NIR) spectroscopy system” LINK

“Integration of spectra and image features of Vis/NIR hyperspectral imaging for prediction of deoxynivalenol contamination in whole wheat flour” LINK

“Ongoing Research on Microgreens: Nutritional Properties, Shelf-Life, Sustainable Production, Innovative Growing and Processing Approaches” Foods LINK




Pharma Industry NIR Usage

“Direct Catalytic Fuel Cell Device Coupled to Chemometric Methods to Detect Organic Compounds of Pharmaceutical and Biomedical Interest” Sensors LINK




Laboratory and NIR-Spectroscopy

“Application of infrared microscopy and alternating least squares to the forensic analysis of automotive paint chips” LINK




Other

“Phenotypic plasticity and nonstructural carbohydrates in annual growth rings of the australian red cedar clones in contrasting enviroments” 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