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Calibration transfer via filter learning. | LitMetric

Calibration transfer via filter learning.

Anal Chim Acta

College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, 325035, PR China.

Published: April 2024

AI Article Synopsis

  • Calibration transfer in analytical chemistry typically needs multiple reference samples for accuracy, but new methods are emerging to streamline this process.
  • The proposed method uses a master instrument's calibration database and only one known spectrum from a slave instrument, employing a multivariate Gaussian kernel to create a simulated counterpart.
  • This approach simplifies the transfer process and allows for unsupervised calibration, making it a promising alternative to traditional methods that require more extensive reference samples.

Article Abstract

Background: Calibration transfer is an essential activity in analytical chemistry in order to avoid a complete recalibration. Currently, the most popular calibration transfer methods, such as piecewise direct standardization and dynamic orthogonal projection, require a certain amount of standard or reference samples to guarantee their effectiveness. To achieve higher efficiency, it is desirable to perform the transfer with as few reference samples as possible.

Results: To this end, we propose a new calibration transfer method by using a calibration database from a master instrument (source domain) and only one spectrum with known properties from a slave instrument (target domain). We first generate a counterpart of this spectrum in the source domain by a multivariate Gaussian kernel. Then, we train a filter to make the response function of the slave instrument equivalent to that of the master instrument. To avoid the need for labels from the target domain, we also propose an unsupervised way to implement our method. Compared with several state-of-the-art methods, the results on one simulated dataset and two real-world datasets demonstrate the effectiveness of our method.

Significance: Traditionally, the demand for certain amounts of reference samples during calibration transfer is cumbersome. Our approach, which requires only one reference sample, makes the transfer process simple and fast. In addition, we provide an alternative for performing unsupervised calibration transfer. As such, the proposed method is a promising tool for calibration transfer.

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Source
http://dx.doi.org/10.1016/j.aca.2024.342404DOI Listing

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