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Journal Articles Analytica Chimica Acta Year : 2022

Unsupervised dynamic orthogonal projection. An efficient approach to calibration transfer without standard samples

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Abstract

Calibration transfer has been traditionally performed in the context of transferring models between instruments using standard samples. Recently, new methodologies and applications have shown that transfer techniques can be adopted to achieve calibration transfer between other types of domains, such as product form, variant or seasonality. In addition, to achieving a higher efficiency for calibration transfer, it is desirable to perform the transfer without the need for standard samples or new reference analyses. Therefore, we propose a method for unsupervised calibration transfer based on the orthogonalization for structural differences between domains. The method has been successfully applied to one simulated dataset and two real datasets. In the studied cases, the proposed methodology allowed to achieve a successful transfer of calibration models and enabled the interpretation of the interferences responsible for the degradation of the original calibration models when transferred to the new domain.
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Dates and versions

hal-03799908 , version 1 (06-10-2022)

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Valeria Fonseca Diaz, Jean-Michel Roger, Wouter Saeys. Unsupervised dynamic orthogonal projection. An efficient approach to calibration transfer without standard samples. Analytica Chimica Acta, 2022, 1225, pp.340154. ⟨10.1016/j.aca.2022.340154⟩. ⟨hal-03799908⟩
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