Spectroscopy and Chemometrics News Weekly #38-#42, 2017


Near Infrared

UV-VIS, NIR, FTIR, Raman, fluorescence, XRF brands with recognized file formats by optical spectroscopy software http… LINK!


“Shrinking FT-NIR Spectrometers Empower Consumers, Businesses” | NIRS sensors LINK


Feasibility of near infrared transmittance spectroscopy to predict cheese ripeness |(17)30860-3/abstract NIRS predict cheese ripeness LINK


John Deere HarvestLab 3000 uses NIR Spectroscopy to evaluate constituent characteristics such as protein, NDF, ADF LINK


Near-Infrared Spectroscopy of Limestone Ore for CaO Estimation under Dry and Wet Conditions LINK



Equipment

“Infrared spectrometers: NIR and MIR compared” | Infrared spectrometer LINK


“Shrinking FT-NIR Spectrometers Empower Consumers, Businesses” NeoSpectraMicro spectral sensors technology https://… LINK!


How to Design a Spectrometer | grating spectrograph Spectrometer engineering optical absorption LINK

Equipment

Real-Time Food Authentication Using a Miniature Mass Spectrometer LINK



Agriculture

Non-destructive technique for determining the viability of soybean (Glycine max) seeds using FT-NIR spectroscopy. LINK


Mid- and near-IR spectroscopy diagnostic tools developed under EU project LINK


“Supercontinuum Lasers Lead to Better Bread and Beer” | barley grains beta-glucan seed sorting LINK


Fluorescence and Reflectance Sensor Comparison in Winter Wheat LINK



Food & Feed

“Food Safety for the 21st Century” | FSMA spectroscopy blockchain LINK



Laboratory

Development and Bioanalytical Applications of a White Light Reflectance Spectroscopy Label-Free Sensing Platform LINK



Environment

Spectroscopy Could Simplify Soil Texture Analysis LINK


Effects of Moisture & Particle Size on Quantitative Determination of TOC in Soils Using NIR Spectroscopy LINK


A global spectral library to characterize the world’s soil LINK



Chemometrics

Feasibility of near infrared transmittance spectroscopy to predict cheese ripeness. LINK


Chemometric compositional analysis of phenolic compounds in fermenting samples & wines using infrared spectroscopy. LINK


… Chemometric tactics for spectral data compression combined with likelihood ratio approach. LINK


Data Augmentation of Spectral Data for Convolutional Neural Network (CNN) Based Deep Chemometrics | DeepLearning LINK


First revolutionary NIR seed calibration robot makes predicting quality seeds easy, debuts at seedme… LINK!


Chemometrics: Data Driven Extraction for Science, 2nd Edition LINK


Near Infrared Spectrometry for Rapid Non-Invasive Modelling of Aspergillus-Contaminated Maturing Kernels of Maize LINK




Selecting the number of factors in principal component analysis by permutation testing—Numerical & practical aspects LINK


“Calibration Transfer Chemometrics, Part I: Review of the Subject” | NIRS Spectroscopy Chemometrics LINK


Extraction optimization and pixel-based chemometric analysis of semi-volatile organic compounds in groundwater LINK



CalibrationModel.com

Spectroscopy and Chemometrics News Weekly 31-37, 2017 | Molecular Spectroscopy NIRS Chemometrics Sensors LINK


Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 31-37, 2017 | NIRS Spektroskopie Chemometrie Sensor LINK


Spettroscopia e Chemiometria Weekly News 31-37, 2017 | NIRS Spettroscopia Chemiometria sensore LINK




Summary of the NIR Chemometric survey polls

Summary of the NIR Chemometric survey polls (as of end of Sept. 2013)

The interesting finding is that most of the answers fit the following pattern. The most companies that use NIR have one NIR Instrument and only one employee that is able to develop NIR calibrations. For that the most common off-the-shelf chemometrics program is used and spent 2 hours or over a month and therefore gets no calibration training about the complex topics like Chemometrics and NIR Spectroscopy or only once (introduction). The calibration maintenance ranges from never to 3 times a year. Interestingly, there was no one who uses portable NIR instruments. We continue our surveys, for the discovery of new trends. Conclusion Seeing this picture, we think that there is huge potential to improve the calibrations. Advanced knowledge can help individuals to build the calibrations with best practices and improve their models accuracy and reliability. Once the decision and investment in NIR technology is done, you should get the best out of your data, because this extra NIR performance can be given by calibration optimization. We offer this as an easy to use and independent service.

NIR Spectroscopy and Chemometric surveys, polls and assessments


1. Calibration Developers
How many persons in your company are able to develop a NIR Calibration?


2. Calibration Development
How much time do you spend to develop a calibration model?


3. Chemometric Software / Spectroscopy Software
Which Chemometric Software are you using for NIR?


4. NIR Spectrometer Brand
Which NIR Spectrometer Brand do you use?


Please vote and see the assessments below.

Part 2, Part 3
Calibration Developers
How many persons in your company are able to develop a NIR Calibration?
Calibration Development
How much time do you spend to develop a calibration model?
Chemometric Software
Which Chemometric Software are you using for NIR?
NIR Spectrometer Brand
Which NIR Spectrometer Brand do you use?


Part 2, Part 3

Recent advanced chemometric methods

You are searching for recent advanced chemometric methods to get better calibration models for NIR? Methods and algorithms like:
  • Artificial Neural Networks (ANN)
  • General Regression Neural Networks (GR-NN)
  • RBF Neural Networks (RBF-NN)
  • Support Vector Machines (SVM)
  • Multiway Partial Least Squares (MPLS),
  • Orthogonal PLS (OPLS), (O-PLS), OPLS-AA, OPLS-ANN
  • R-PLS, UVE-PLS, RUVE-PLS, LOCAL PLS
  • Hierarchical Kernel Partial Least Squares (HKPLS)
  • Random Forest (RF)
  • etc.
and data pre-processing methods like
  • Extended Multiplicative Signal Correction (EMSC)
  • Orthogonal Signal Correction (OSC)
  • Dynamic Orthogonal Projection (DOP)
  • Error Removal by Orthogonal Subtraction (EROS)
  • External Parameter Orthogonalization (EPO)
  • etc.
that are partly available as modules for software packages like Matlab, Octave, R-Project, etc. Why invest a lot of time and money with new tools? Have you tried it really hard to optimize your calibrations with standard chemometrics methods like Partial Least Squares (PLS), Principal Component Regression (PCR) and Multiple Linear Regression (MLR) which are available in all chemometric software packages? Are you sure you have tried all the good rules and optimization possibilities? Get it done right with the compatible standard methods, we are specialized in optimization and development of NIR calibrations, let us help you, give us a try!

What is NIR-Spectroscopy? (simple explanation, simply explained)

In the most cases a simple Halogen lamp emits light including the near infrared (NIR) spectrum (harmless radiation) to the sample/probe and the reflected light is measured. The light loses some energy on-and-in the sample depending on its physical and chemical (molecular) structure. The missing part of the light is treated as a fingerprint of the sample that is mathematically analyzed with prefabricated NIR calibration models (built with chemometric methods), based on trained known samples. That makes it possible to simultaneous analyze multiple physical- and chemical-properties (constituent, ingredient, analyte) within a few seconds and is non-destructive to samples.

The Ghost Calibrator

To explain our service in an other way, I use an analogy between a book and a calibration. Building good calibrations is like writing a good book (a bestseller). You can write in a foreign language (chemometrics) with a high sophisticated word-processor (the chemometric software) that has a grammar checker (an outlier detection). Due to the complexity of the language (chemometrics) and the difficulty of the chosen book topic (the data) and the incomplete automatic grammar checker, you can never be sure if the grammar is correct and may not lead to misunderstanding (bad prediction performance). So the best way is to let a native language speaker check and correct the text. In that way (the analogy), you can see us even as a ghostwriter (a ghost calibration developer, a ghostcalibrator) that helps you, writing the book (with long year experience, consolidated knowledge, time saving, a lot of benefit). The analogy fits very well, because you can define the topic of the book (with your data). Finally you own the calibration and you have the full insight in how it is done. You have it under full control.