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.
Meet us at the NIR 2013 – 16th International Conference on Near Infrared Spectroscopy (ICNIRS 2013) in Montpeiller, France , 2-7 June 2013. If you are interested in analysis and optimization of your data during the conference, please take a JCAMP export of your data on a USB-memory stick with you. We will also present a poster P129 : ‘A novel intelligent knowledge-based Chemometric Software Framework for quantitative NIR Calibration Modeling‘ by Roman Bossart some links : La Grande Motte Panorama, WebCam, Weather
5. Calibration Precision
What do you believe, can NIR calibration models be more precise than reference values?
6. Calibration Maintenance
How often do you update your quantitative calibrations per year?
7. Quantitative Calibrations
How many quantitative (%) calibrations do you have in use?
8. Quantitative Parameters
In all your quantitative calibrations, how many parameters (properties) you have in total?
9. Qualitative Calibrations
How many qualitative (identification) calibrations do you have in use?
Please vote and see the assessments below.
Part 1, Part 3
Part 1, Part 3
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)
- Extended Multiplicative Signal Correction (EMSC)
- Orthogonal Signal Correction (OSC)
- Dynamic Orthogonal Projection (DOP)
- Error Removal by Orthogonal Subtraction (EROS)
- External Parameter Orthogonalization (EPO)
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.