Graph‐based calibration transfer

  title={Graph‐based calibration transfer},
  author={Ramin Nikzad‐Langerodi and Florian Sobieczky},
  journal={Journal of Chemometrics},
The problem of transferring calibrations from a primary to a secondary instrument, that is, calibration transfer (CT), has been a matter of considerable research in chemometrics over the past decades. Current state‐of‐the‐art (SoA) methods like (piecewise) direct standardization perform well when suitable transfer standards are available. However, stable calibration standards that share similar (spectral) features with the calibration samples are not always available. Towards enabling CT with… 
5 Citations
Domain adaptation for regression under Beer-Lambert's law
A novel algorithm is proposed that identifies a low-dimensional subspace aiming at the following two objectives: (i) the projections of the source domain samples are informative w.r.t. the output variable and (ii) the projected domain-specific input samples have a small covariance difference.
A chemometrician's guide to transfer learning
Transfer learning (TL), the sub‐discipline of machine learning devoted to learning from different domains, has gained increasing attention over the past decade. With the current contribution, we aim
Are standard sample measurements still needed to transfer multivariate calibration models between near-infrared spectrometers? The answer is not always
It is concluded that CT approaches that do not rely on standard sample measurements hold promise to help making calibration models sharable between similar analytical devices and to increase the applicability of CT to real-world problems in the analytical sciences.
CT-GUI: A graphical user interface to perform calibration transfer for multivariate calibrations
  • Puneet Mishra
  • Computer Science
    Chemometrics and Intelligent Laboratory Systems
  • 2021
A new MATLAB based graphical user interface integrating several multivariate calibration transfer approaches is presented and the toolbox is shown to be a push-button toolbox that even non-expert can use to achieve complex calibration transfer tasks.
Deep calibration transfer: Transferring deep learning models between infrared spectroscopy instruments
The main benefit of the deep CT is that it is a standard free approach and does not require any standard sample measurements to transferDL models between instruments, which can support a widespread sharing of chemometric DL models between the scientific practitioners.


Standard-free calibration transfer - An evaluation of different techniques☆
This contribution introduces, discusses and evaluates a wide-ranging subset of transfer approaches available in chemometrics and the field of machine learning, where they focus on techniques applicable in situations where transfer standards cannot be provided and only few reference measurements are available for the new setting.
Calibration Maintenance and Transfer Using Tikhonov Regularization Approaches
Harmonious (bias/variance tradeoff) and parsimonious (effective rank) considerations for TR are compared with the same TR format applied to partial least squares (PLS), showing that both approaches are viable solutions to the calibration maintenance and transfer problems.
Domain-Invariant Partial-Least-Squares Regression.
Di-PLS regression is introduced, which extends ordinary PLS by a domain regularizer in order to align the source and target distributions in the latent-variable space and shows that a domain-invariant weight vector can be derived in closed form, which allows the integration of (partially) labeled data from the sources and target domains as well as entirely unlabeled data fromThe latter.
Transfer of multivariate calibration models: a review
An overview of the different methods used for calibration transfer and a critical assessment of their validity and applicability is presented, focusing on methods for transfer of near-infrared (NIR) spectra.
Improving the transfer of near infrared prediction models by orthogonal methods
Abstract Eight calibration transfer methods based on the removal of orthogonal signal were compared for the standardization of whole soybean protein and oil models. Dynamic orthogonal projection
Penalized eigendecompositions: motivations from domain adaptation for calibration transfer
This work reports on penalty‐based eigendecompositions, a class of domain adaptation methods that has its motivational roots in linear discriminant analysis and compares these approaches against chemometrics‐based approaches using several benchmark Chemometrics data sets.
Calibration transfer between NIR spectrometers: New proposals and a comparative study
Two novel methods to perform calibration transfer between NIR spectrometers are proposed, based on trimmed scores regression and joint‐Y partial least squares regression, which permit to exploit the specific relationships between instruments for imputing new unmeasured spectra.
Maintaining the predictive abilities of multivariate calibration models by spectral space transformation.
Results show that SST can achieve satisfactory analyte predictions from spectroscopic measurements subject to spectrometer/probe alteration, when only a few standardization samples are used, and has the advantages of implementation simplicity, wider applicability and better performance in terms of predictive accuracy.
Dual-Domain Calibration Transfer Using Orthogonal Projection
We report the use of dual-domain regression models, which were built utilizing a wavelet prism decomposition and paired with transfer by orthogonal projection, for the calibration transfer of
An evaluation of orthogonal signal correction applied to calibration transfer of near infrared spectra
Orthogonal signal correction (OSC) is a technique for pre-processing of, for example, NIR-spectra before they are subjected to a multivariate calibration. With OSC the X-matrix is corrected by a