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Model performance was comprehensively assessed based on the ability to predict acetaminophen concentrations in multiple prediction sets.
The predicted protein pairs from different prediction sets related to complexes are about one-third (see Table 2).
NIR method was validated on the full calibration range with external prediction sets, using the accuracy profile approach.
Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets.
Based on the various divisions in the calibration and prediction sets, an effective modeling approach with good stability was proposed.
Various experimental data with different temperature and fuel compositions are used to verify the obtained GP model for training and prediction sets in Fig. 4a, b, respectively.
As can be seen from this figure, the square of the correlation coefficient (R2) of 0.97 and 0.95 is obtained for training and prediction sets, respectively.
Open image in new window Fig. 3 Comparison of the actual values versus the evolved optimum GP model for both training and prediction sets.
A rigorous process based on the various divisions of calibration and prediction sets was performed to achieve modeling optimization with stability.
But what happens if this prediction sets up a fear and does not come to pass?
It is best to regard the various prediction sets as complementary.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com