NIR model optimization – NIR Calibration Model

April 15, 2019

Spectroscopy and Chemometrics News Weekly #15, 2019Spektroskopie und Chemometrie Neuigkeiten Wöchentlich #15, 2019Spettroscopia e Chemiometria Weekly News #15, 2019

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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

CalibrationModel.com

Chemometrics

Near Infrared

Infrared

Agriculture

Other

CalibrationModel.com

Chemometrics

Near Infrared

Infrared

Agriculture

Other

How to develop near-infrared spectroscopy calibrations in the 21st Century? / Wie werden Nahinfrarotspektroskopie Kalibrierungen im 21. Jahrhundert entwickelt?

The Problem

Calibration modeling is a complex and very important part of NIR spectroscopy, especially for quantitative analysis. If the model is badly designed the best instrument precision and highest data quality does not help getting good and robust measurement results. And NIR Spectroscopy requires periodically recalibration and validation.

How are NIR models built today?

In a typical usage in industry, a single person is responsible to develop the models (see survey). He or she uses a Chemometric software that has a click-and-wait working process to adjust all the possible settings for the used algorithms in dialogs and wait for calculations and graphics and then to think about the next modeling steps and the time is limited to do so. Do we expect to find the best use-able or optimal model that way? How to develop near-infrared spectroscopy calibrations in the 21st Century?

Our Solution

Why not put all the knowledge a good model builder is using into software and let the machines do the possibilities of calculations and presenting the result? Designing the software that way, that the domain knowledge is built-in, not just only the algorithms for machine learning and make it possible to scale the calculations to multi-core computers and up to cloud servers. Extend the Chemometric Software with the Domain Knowledge and make as much computer power available as needed.

As it was since the beginning

User → Chemometric Software → one Computer → some results to choose from

==> User's time needed to click-and-wait for creating results

Our Solution

User → (Domain Knowledge → automatized Chemometric Software) → many Computers → the best models

==> User's time used to study the best models and reasoning about his product / process

Note that the “Domain Knowledge” here does perfectly support the User's product and process knowledge to get the things done right and efficient.

Scaling at three layers

Knowledge : use the domain knowledge to drive the Chemometric Software
Chemometric Software : support many machine learning algorithms and data pre-processings and make it automatic
Computer : support multi-core calculations and scale it to the cloud

The hard part in doing this, is of course the aggregation of the needed domain knowledge and transform it into software. The Domain Knowledge for building Chemometric NIR Spectroscopic models is well known and it's huge and spreads multiple disciplines. Knowledge-driven software for computing helps to find the gold needle in the haystacks. It's all about scaling that makes it possible. See Proof of Concept.

New possibilities

NIR users can get help working more efficient and getting better models.
New types of applications for NIR can be discovered.
Evaluation of NIR Applications to replace conventional analytical methods.
Hopeless calibrations development efforts can be re-started.
Higher model accuracy and robustness can be delivered.
Automate the experimental data part of your application study.
Person independent optimization will show new solutions, because it's not limited by a single mindset => combining all the aggregated knowledge and its combinations.
Software independent optimization will show new solutions, because none of vendor specific limitations and missing algorithms are present => combining all open available algorithms and there permutations.
Computing service is included.

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