Representative mass reduction in the laboratory: riffle splitting galore (with or without errors)

Representative mass reduction in the laboratory: riffle splitting galore (with or without errors)

Investigation of paper collages by near infrared imaging techniques

Investigation of paper collages by near infrared imaging techniques

LOCAL Prediction with near Infrared Multi-Product Databases - Feb 02, 2017

This month I am posting about LOCAL calibrations. Here you have a paper you can download to know more about this type of calibrations.

LOCAL Prediction with near Infrared Multi-Product Databases - Feb 02, 2017

It´s free

Win ISI help in the software show some concepts about how the LOCAL works. As Marc says in a comment, there are not so much info and this Help is important. We will continue, because I have some info about how the algorithm for weighted average works so hope to give you more information in the next days.

LOCAL Optimization

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Background

LOCAL™ is a patented calibration technique developed by Infrasoft International LLC. For information on LOCAL and how it works, please see the LOCAL discussion topic.

WINISI 4™ introduces an enhanced version of LOCAL that will allow calibration originators to easily optimize the regression parameters for LOCAL databases.

Overview

LOCAL regressions are optimized using a number of calibration parameters. One of the most important parameters in the LOCAL regression is the maximum and minimum number of PLS factors used. The enhanced version of LOCAL (available for WIn ISI 4) can automatically determine the optimal values to use for the maximum and minimum number of PLS factors.

Details

LOCAL calibrations work by creating temporary custom calibrations for each unknown sample. The calibrations are made from a small sample set chosen by selecting spectrally similar samples from a larger calibration library. The spectrally similar samples are then regressed using PLS. The regression is performed for the each of the factors specified in the range described by the Minimum number of factors and the Maximum number of factors. The final predicted value is the weighted average of all predictions over the range of factors. (This point is important because several predictions are done, one for every number of factors in the range of number of factors choosen between minimun and maximum. At the same time several GHs and NHs are calculated in that range based on the PLS scores as PL1 makes in the option Create a Score file from a Spectra file used in Win ISI with the option PL1)

The LOCAL program in WinISI III worked by computing a weighted average of predicted values from PLS prediction models that vary the number of PLS factors. The user specified in advance the minimum and maximum number of PLS factors to include in the weighted average. To evaluate a different combination of Minimum and Maximum number of factors, a completely new regression needed to be performed.

The new LOCAL analysis program stores all the data needed to compute a weighted average with any minimum and maximum number of PLS factors for each sample in the test file. As a post-processing step, the program evaluates all possible minimum and maximum pairs to determine the set with the smallest prediction error on the test file. Thus the LOCAL analysis program might evaluate a 4 - 30 factor range and output an optimal range of 4 - 15 factors for best performance. The Min / Max range of 4 - 15 would then be entered into ISIScan™.

To consider when developing LOCAL Calibrations (GHs and STD)

I see that to many of you likes the LOCAL concept, so why not to try to get deeper into it. I have some questions and hope to give you answers as far as I know and understand.

To give from this Blog thanks to Mark Westerhaus for answering my mails when I have some doubts.

First we have to say that when a sample is analyzed a certain number of samples are selected for every constituent  and the "GH reported for each constituent is an average of several H values, using the same weights and range of factors used for the final predicted value" MW.

"Each H value is computed during a PLS calibration using the PLS scores, and is equivalent to using a PL1 file" MW .
 In the LOCAL database there can be several products quite different from the others but this is not the purpose of the LOCAL. The idea is to have a library of closely related product and in this case we should be able to find a common standardization to all of them.