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To test the predictive power, PLS and PLP models were calibrated with the calibration data set composed by 50% of data points and then simulated on the validation data set composed by the remaining 50% measured points.
The success of a PLS calibration relies largely on the representativeness of the calibration data set.
A calibration data set with 79 soil samples from 16 locations in Denmark, comprising both topsoil and subsoil horizons, was selected from the literature.
The algorithm is based on the concept of 'data depth', i.e. the location of new data in relation to the convex hull of the calibration data set.
We focus on the 89 of the 100 stories in the calibration data set that were promoted within 24 hours of submission.
This may relate to the longer stress application used in laboratory compression tests for these data compared to the other calibration data set and to the procedure used in this study.
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In the presence of unresolved model processes and noise, hindcasting longer (5 + years) data sets required longer (5 + years) calibration data sets, particularly when sampling intervals exceeded 60 days.
The best models gave standard error of prediction values of 6.6 g/100 g for yield of whey and 0.05 g/100 g for fat in whey, as compared with 4.4 and 0.013 g/100 g, respectively, for the calibration data sets.
Previous work makes use either of synthetic simulation data, of correlation measures in real-world chip applications or of special calibration data sets to judge the quality of different expression scores [ 14- 19].
The spectroscopic data were evaluated by Partial Least Squares (PLS) using the HPLC results as the calibration concentration data set.
A subset of data is used for calibration, whereas another data set corresponding to a load of 150 L is used for model validation.
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