Spectroscopy and Chemometrics News Weekly #1, 2020

CalibrationModel.com

Five Mistakes to avoid on Digitalization in NIR-Spectroscopy – that Lab Managers, Executives and CEOs must know! NIRSpectroscopy NIRS Sensors NearInfrared Analyzers DigitalTransformation QualityControl foodtech machinelearning AI datascience LINK

SAFE COST IN MAINTAINING NIR-SPECTROSCOPY METHODS | NIRSpectroscopy NIRS Spectroscopy DigitalTransformation Analysis Lab Laboratory Application Quantitative Analysis Methods Measurements Analytical Parameters Spectrometer Quality Accuracy LINK

Do you develop NIR / NIRS calibrations by yourself? Can you sell it? No? Buy it! Digitalization LabManager LabAutomation CEO Digitalisation Spectroscopy AutoML LowCost lowerCost SaveMoney SaveTime Efficiency Effectivity LINK

5 Fehler, die es bei der Digitalisierung in der NIR-Spektroskopie zu vermeiden gilt – das müssen Labormanager, Führungskräfte und CEOs wissen! NIRSpectroscopy NIRS Sensor NearInfrared Analyzers DigitalTransformation foodtech machinelearning LINK

Spectroscopy & Chemometrics News Weekly 52, 2019 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Food Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality Check LINK

Spectroscopy & Chemometrics News Weekly 51, 2019 | NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory AI Software IoT Sensors QA QC Testing Quality LINK

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

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Near Infrared

“Estimation and classification of popping expansion capacity in popcorn breeding programs using NIR spectroscopy” LINK

“Simultaneous detection of quality and safety in spinach plants using a new generation of NIRS sensors” LINK

“Identification of Genuine and Adulterated Pinellia ternata by Mid-Infrared (MIR) and Near-Infrared (NIR) Spectroscopy with Partial Least Squares – Discriminant …” LINK

“Monitoring coffee roasting cracks and predicting with in situ near-infrared spectroscopy” LINK

“Assessment of a soil fertility index using visible and near-infrared spectroscopy in the rice paddy region of southern China” LINK

“Conversion of the Felixton Mill laboratory from conventional to NIRS analysis.” LINK

“Automatic cancer discrimination based on near-infrared spectrum and class-modeling technique” LINK

“Food powders classification using handheld Near-Infrared Spectroscopy and Support Vector Machine” LINK

“Development and validation of a method for separation of pregabalin and gabapentin capsules using Near Infrared hyperspectral imaging” LINK

“Field-resolved infrared spectroscopy of biological systems” Nature LINK

“Infrared spectroscopy finally sees the light” nature LINK

“Journal Highlight: Estimation of protein and fatty acid composition in shellintact cottonseed by near Infrared reflectance spectroscopy” LINK

” Visible/near-infrared Spectroscopy as a Novel Technology for Nondestructive Detection of Escherichia coli ATCC 8739 in Lettuce Samples” LINK

“The model updating based on near infrared spectroscopy for the sex identification of silkworm pupae from different varieties by a semi-supervised learning with pre …” LINK

“… Study on the Determination of ppm-Level Concentration of Histamine in Tuna Fish Using a Dry Extract System for Infrared Coupled with Near-Infrared Spectroscopy” LINK




Raman

“Transmission Raman Spectroscopic Quantification of Active Pharmaceutical Ingredient in Coated Tablets of Hot-Melt Extruded Amorphous Solid Dispersion” LINK




Hyperspectral

“Plastic waste monitoring and recycling by hyperspectral imaging technology” LINK

“Comparison of Ink Classification Capabilities of Classic Hyperspectral Similarity Features” LINK




Chemometrics

“DEVELOPING NEAR INFRARED SPECTROSCOPIC MODELS FOR PREDICTING DENSITY OF Eucalyptus WOOD BASED ON INDIRECT MEASUREMENT” LINK

“Development of Partial Least Square (PLS) Prediction Model to Measure the Ripeness of Oil Palm Fresh Fruit Bunch (FFB) by Using NIR Spectroscopy” LINK




Facts

“Bombs and cocaine: detecting nefarious nitrogen sources using remote sensing and machine learning” LINK




Agriculture

“Laboratory-based hyperspectral image analysis for the classification of soil texture” LINK

“Research on simultaneous detection of SSC and FI of blueberry based on hyperspectral imaging combined MS-SPA” LINK

“Polymers, Vol. 12, Pages 78: Insight into the Intermolecular Interaction and Free Radical Polymerizability of Methacrylates in Supercritical Carbon Dioxide” LINK

“Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging.” LINK




Other

“绿泥石矿物近红外光谱吸收谱带的位移机理与控制机制研究” LINK

“Spektroskopie – Unverwechselbarer molekularer Fingerabdruck” LINK





Five Mistakes to avoid on Digitalization in NIR-Spectroscopy – that Lab Managers, Executives and CEOs must know!

The fast and non-destructive NIR-Spectroscopy analysis method is based on predictive models that are built upon spectral and Lab reference data.

Developing such models with Chemometric techniques or Machine Learning Algorithms can now be fully automatized with many advantages.

If your company uses NIR-Spectroscopy, do you know if your company yet tried this calibration service? And if not, why not?

Here are some possible reasons why such services are blocked in your company.

With the knowledge where and why it may be blocked, you are able to overcome the barriers and go for innovation.

Misbeliefs of People that may block Advanced Services (AI and Automatic Machine Learning)


5 Mistakes to avoid on Digitalization in NIR-Spectroscopy
Misbeliefs People Remove barriers
1 You need to program or scripting with data modeling tools (R, Phyton, TensorFlow, Matlab, sas, SPSS, H2O, scikit, …..) The opinion that only PhD (statisticians, data scientists) are smart enough to create models. Give the service a try, and compare! There are free trials for calibration development.
2 We have done it always manually by hand. We must see the plots and interpret the stats in every step.
Job protectionism : “My job will dramatically change if that will work, so I will never prepare real-life data to give someone a possibility to try such a service.”
AI / ML haters (NIR-Specialists, NIR-Experts) don’t belive in automated calibration. Give the service a try, and compare! You will see plots and stats! There are free trials for calibration development.
3 Against any new hardware, software or service because of supposed new problems. Preserving ones – the permission gate keepers (IT) Give it a try, without an account, download the free NIR-Predictor software (full version, no limitations) with included demo data, it installs and runs without Administrator priviledge!
4 Ensures that new methodologies follow the rules. Any new thing will give extra work for them. Compliance and Regulation officers. Get access to the detailed model blueprint (ISO 12099) – there is no vendor lock-in or black-box model. You can even anonymize your data for Knowledge Protection – see JCAMP-Anonymizer.
5 Our employees are open for new things. Our employees do not block. Managers, executives, board level. Remove the Barriers to Innovation!


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