We are excited to announce that effective immediately, our new Netherlands-based NIR service center is open for business.
The NIR Community from ASD Inc., a Malvern Panalytical company
Developed in cooperation with FPInnovations, ASD produced the industry’s first at-line analyzer for simultaneous measurement of Kappa number and brightness of wet pulp. The QualitySpec® K-B analyzer allows fiber-line testers and operators to take multiple Kappa number and pulp brightness measurements per hour from multiple process locations. As part of a mill’s process control strategy, this more effective method of measurement minimizes Kappa number variation, chemical costs and potential off-grade. The net results are lower raw material and fuel costs, and higher yield of more uniform pulp.
The QualitySpec 7000 full-range spectral analyzer is one of the only devices that can measure multiple material constituents, over a moving conveyor belt, and immediately leverage that data to make production adjustments in real time. By reducing even small amounts of additives or wasted material, manufacturers can save hundreds of thousands, and sometimes millions of dollars each year.
By joining the Encompass Partner Program, customers can quickly locate, source and integrate complementary components and connectivity solutions that best solve application challenges, and dramatically increases the exposure of ASD Inc. to new customers and market sectors.
ASD is focused on expanding to new customers and market sectors and is looking forward to partnering with Rockwell Automation. Read the official press release.
Today I'm going to talk about how to go about developing a chemometric model based on ground samples. We make certain spectral considerations in this process, namely that there are multiple components in a sample and many physical phenomenon creating the spectra.
NIR is a secondary method of sample analysis, meaning that you must have a reference for the spectrum produced by NIR. The reference method must be well controlled with the lowest possible error and must be referenced to a high quality assay. To do this, we test assays with blind samples to document the Standard Error of Laboratory (SEL) and perturb reference data with added noise to understand the effect on the results. Then, we must solve spectral collection issues such as the need to validate the ASD systems with wavelength standards under controlled conditions (temperature, moisture, particle size, etc.) using the same sample presentation methods.
The number of samples needed for test and calibration varies, but we typically start with 20% of the samples for this phase. For a feasibility study we like to see a sample population of 60-90 and for a starting model a population of 120-180. For a production model the sample number is typically greater than 180. Calibration and validation sets must contain the diversity, both spectral and compositional, that we would expect to encounter in routine samples. Samples can be calibrated and validated as long as the range of composition is provided, the samples are scanned in the same form and the samples are composed of natural blends.
A test set needs to be representative (not collected later), it should use the same range of composition and is should be collected in a simple way. Then, the samples are ranked and every 5th sample is withheld for further testing.
Commercial chemometric programs can be used to create multivariate models. The key to creation of a good multivariate model is to understand the statistics, especially Standard Error of Cross Validation (SECV), as R-squared can be misleading. It’s possible to use R-squared in this process, but SECV is more accurate. In a probationary use of the model, we monitor early results and cross check with reference methods to validate, but we don’t use a different lab or lab technique as such a change could introduce error in the model.
After the probationary period, we review the model, watching for high residuals or samples at the extreme limits of compositions. If you change labs, you need to re-validate. After the probationary period we define appropriate criteria for revision of the model (e.g., a time or validate error criterion).
While multivariate models can be quantitative, they require careful calibration and test sets and proper monitoring to ensure the model is well maintained. Good NIR models do work; they just require a little more effort on our part to succeed.