Spectroscopy and Chemometrics News Weekly #15, 2019

CalibrationModel.com

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

Spectroscopy and Chemometrics News Weekly 14, 2019 | NIRS NIR Spectroscopy Chemometrics analysis Spectrometer Spectrometric Analytical Sensors QC Lab Labs Laboratories Laboratory LabServices Quality Checking FoodLab Foodtesting LINK

Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 14, 2019 | NIRS NIR Spektroskopie Chemometrie Spektrometer Sensor Nahinfrarot Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse Qualitätslabor Labor LINK

Spettroscopia e Chemiometria Weekly News 14, 2019 | NIRS Spettroscopia Chemiometria analisi chimica Spettrale Spettrometro Sensore Attrezzatura analitica Laboratorio analisi prova qualità LabManager LINK

This week’s NIR news Weekly is sponsored by YourCompanyNameHere – BestNIRinstruments. Check out their product page … link




Chemometrics

“Evaluation of pork freshness using two-dimensional correlation visible/near-infrared spectroscopy combined with support vector machine.” LINK

“Rapid analysis of the Tanreqing injection by near-infrared spectroscopy combined with least squares support vector machine and Gaussian process modeling techniques” LINK

“NIR Spectroscopy Coupled Chemometric Algorithms for Rapid Antioxidants Activity Assessment of Chinese Dates (Zizyphus Jujuba Mill.)” LINK

“Near infrared system coupled chemometric algorithms for the variable selection and prediction of baicalin in three different processes” LINK

“Innovative and rapid analysis for rice authenticity using hand-held NIR spectrometry and chemometrics” LINK

“Predictions of fruit shelf life and quality after ripening: Are quality traits measured at harvest reliable indicators?” LINK

“Hierarchical Multi-Scale Convolutional Neural Networks for Hyperspectral Image Classification” remote sensing Sensors LINK

“Prediction of soil salinity with soil-reflected spectra: A comparison of two regression methods” LINK

“Rapid Determination of Green Tea Origins by Near-Infrared Spectroscopy and Multi-Wavelength Statistical Discriminant Analysis” LINK




Near Infrared

Engineers are making their mark on biotech by showing that near-infrared light can trigger the release of CRISPR-Cas9 to slow tumor growth | inovation Engineering LINK

“NIRS quantification of lake sediment composition by multiple regression using end-member spectra” LINK

“Research and analysis of cadmium residue in tomato leaves based on WT-LSSVR and Vis-NIR hyperspectral imaging.” LINK

“Nontargeted Analytical Methods as a Powerful Tool for the Authentication of Spices and Herbs: A Review” NIRS LINK

“Exploratory data assessment of fecal NIRS from small ruminants: toward a global model to Brazilian Northeastern rangelands.” LINK

“Predição do teor de matéria seca e da proteína bruta do Capim-tanzânia por meio da espectroscopia NIR.” LINK

“Compositional analysis of semolina with added fibers by near infrared spectroscopy (NIR)” LINK

“Exploratory analysis of fecal NIR spectra similarity of goats and sheep grazing Brazilian Northeastern rangelands.” LINK

“Feasibility Study on Identification of Mercury Element From An Unknown Substance Using Visible-NIR Spectroscopy and SVM Classifier” LINK




Infrared

“Rapid and non-destructive analysis for the identification of multi-grain rice seeds with near-infrared spectroscopy” LINK

“Rapid nondestructive detection of multiple quality parameters of fresh purple sweet potato based on visible near infrared spectroscopy.” LINK

“LOCAL regression applied to a citrus multispecies library to assess chemical quality parameters using near infrared spectroscopy” LINK

“Review of Anti-counterfeiting of Prints Based on Infrared Spectroscopy” LINK

“Near infrared and hyperspectral studies of archaeological stratigraphy and statistical considerations” LINK




Agriculture

“Estimating the Composition of Food Nutrients from Hyperspectral Signals Based on Deep Neural Networks” Sensors LINK

“Short communication: Effects of drying and analytical methods on nitrogen concentrations of feeds, feces, milk, and urine of dairy cows” LINK

“Espectroscopia óptica para detecção de resíduos de antibióticos em leite” “Optical spectroscopy for the detection of residues of antibiotics in milk” LINK




Other

” Spectral sensing to build picture of natural disasters” LINK

“Determination of oil content and fatty acids profile in sunflower seeds through near infra-red spectroscopy under various treatments of potassium nitrate, zinc sulphate and gibberellic acid” LINK

“БЫСТРОЕ ОПРЕДЕЛЕНИЕ ПРОИСХОЖДЕНИЯ ЗЕЛЕНОГО ЧАЯ С ПОМОЩЬЮ СПЕКТРОСКОПИИ БЛИЖНЕЙ ИК ОБЛАСТИ И МНОГОВОЛНОВОГО …” LINK

“Pin-sized sensor could bring chemical ID to smartphone-sized devices” LINK

“Ubiquitous spectral sensing with FT-IR MEMS chip” LINK





Procedures for NIR calibration – Creation of NIRS spectroscopy calibration curves

Do you know the effect that you prefer to try out their favorite data pretreatments in combination and often try the same wavelength selections based spectra of the visualized?

You try as six to ten combinations until one of them selects his favorite calibration model, to then continue to optimize. Since then suddenly fall to outliers, because it goes in depth, so is familiar with the data, we know now the spectra of numbers of outliers and is familiar with the extreme values.

Now, the focus is on the major components (principal components, Latent Variables, factors) and makes sure not to over-fit and under-fit not to. The whole takes a few hours and finally one is content with the model found.

So what would happen if you all in the beginning tried variants found outliers removed and re-evaluated and compared? The results would be better than that of the previous model choice? One does not try out? Because it is cumbersome and takes hours again?

We have developed a software which simplifies this so that also the number of model variations can be increased as desired. The variants generation is automated with an intelligent control system, as well as the optimization and comparing the models and finally the final selection of the best calibration model.

Our software includes all the usual known data pretreatment methods (data pre-processing) and can combine them useful. Since many Preteatments are directly dependent on the wavelength selection, such as the normalization the determined within a wavelength range of the scaling factors to normalize the spectra so that pretreatments with the wavelength ranges may be combined. So a variety of settings sensible model comes together that are all calculated and optimized. For the automatic selection of the relevant wavelength ranges, different methods are used, which are based on the spectral intensities. Thus, for example, regions with total absorption is not used, and often interfering water bands removed or retained.

Over all the calculated model variations as a summary outlier analysis can be made. Are there any new outliers (hidden outlier) discovered, all previous models can be automatically recalculated, optimized and compared without these outliers.

From this great number of calculated models with the statistical quality reviews (prediction performance) the optimum calibration can now be selected. For this purpose, not simply sorting by the prediction error (prediction error, SEP RMSEP) or the coefficient of determination (coefficient of determination r2), but by several statistical and test values are used jointly toward the final assessment of optimal calibration.

Thus we have created a platform that allows the highly automated work what a man can never do with a commercial software.

We therefore offer the largest number of matched to your application problem modeling calculations and choose the best calibration for you!

This means that our results are faster, more accurate, robust and objective basis (person independent) and quite easy for you to apply.

You have the full control of the models supplied by us, because we provide a clearly structured and detailed blueprint of the complete calibration, with all settings and parameters, with all necessary statistical characteristics and graphics.

Using this blueprint, you can adjust the quantitative calibration model itself in the software you use, understand and compare. You have everything under control form model creation, model validation and model refinement.

Your privacy is very important to us. The NIR data that you briefly provide us for the custom calibration development will remain of course your property. Your NIR data will be deleted after the job with us.

Interested, then do not hesitate to contact us.