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Methods for Improving Prediction Ability of Model

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Chemometric Methods in Analytical Spectroscopy Technology

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Abstract

Model prediction ability mainly refers to the performance of robustness and accuracy. They are unified in some cases but contradictory in others. For example, for liquid samples, the conditions of spectroscopic acquisition (such as temperature and pressure) can be strictly controlled. A quantitative calibration model can be established accordingly. The model has high prediction accuracy for samples within the bounds under the same conditions. However, if the spectral acquisition conditions change moderately, the prediction accuracy of the model will become significantly worse. A hybrid calibration model can be established on the spectra collected under different conditions to improve the robustness (or adaptability) of the model. Yet, the model accuracy would be decreased in this case. In practice, it is often necessary to seek a balance between robustness and accuracy.

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  1. School of Chemical Engineering and Technology, Tiangong University, Tianjin, China

    Xihui Bian

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Bian, X. (2022). Methods for Improving Prediction Ability of Model. In: Chemometric Methods in Analytical Spectroscopy Technology. Springer, Singapore. https://doi.org/10.1007/978-981-19-1625-0_14

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