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

Diaz, Valeria Fonseca; Roger, Jean‐Michel; Saeys, Wouter

doi:10.1016/j.aca.2022.340154

Cited by 12 publications

(3 citation statements)

References 19 publications

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“…An unsupervised version of DOP has recently been published [70]. It proposes the creation of virtual standards based solely on two spectra matrices of the source and target domains.…”

Section: Robust Modeling: Orthogonalizationmentioning

confidence: 99%

Cloning instruments, model maintenance and calibration transfer

Roger,

Fonseca-Diaz,

Nikzad-Langerodi

2024

Preprint

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Most literature on the application of Non-Destructive Spectral Sensors (NDSS) reports proofs of concept limited to model calculation (calibration) and its application on a so-called independent data set (validation, or test). However, developing NDSS also requires proving that the performance obtained during this first validation remains valid when conditions change. This generic problem is referred to as robustness in chemometrics. When the measurement conditions change, the measured spectrum is subject to a deviation. The reproducibility of the model, and thus of the sensor, with respect to this deviation, defines its robustness. The application of NDSS involves a large number of processes, and thus deviation sources. Instrument cloning, between laboratory instruments or from a benchtop to an online device, is certainly the most concerning issue for deploying NDSS-based applications. This problem has been studied for many years in chemometrics, under the paradigm of calibration transfer, through geometric corrections of spectra, spectral spaces, or calibration model corrections. The same problem has been addressed in the machine learning community under the domain adaptation paradigm. Although all these issues have been addressed separately over the last twenty years, they all fall under the same topic, i.e., model maintenance under dataset shift. This paper aims to provide a vocabulary of concepts for formalizing the calibration model maintenance problem, reviewing recent developments on the subject, and categorizing prior work according to the proposed concepts.

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“…An unsupervised version of DOP has recently been published [70]. It proposes the creation of virtual standards based solely on two spectra matrices of the source and target domains.…”

Section: Robust Modeling: Orthogonalizationmentioning

confidence: 99%

Cloning instruments, model maintenance and calibration transfer

Roger,

Fonseca-Diaz,

Nikzad-Langerodi

2024

Preprint

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“…However, in practical applications, when the detection conditions, detection environment or equipment change, the original model cannot adapt to the new application scenario, and its predictive ability of unknown samples decreases. Model transfer can effectively solve the above model inapplicability problem, avoid duplicate modeling, realize the sharing of data resources, and reduce the manpower and resources required for modeling and model management, which is of great significance to promote the utilization of NIR technology in the Pulp & Paper Industry [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Application of SWSRA-DS algorithm in improving the model transfer for near infrared analysis of pulpwood holocellulose

Wang,

Hu,

Liu

et al. 2023

Infrared Physics & Technology

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“…In this context, efforts were devoted to the development of analytical solutions that allow to move the knowledge acquired from one site or data source to another where our interest is focused, such as in the activities of scale-up, product transfer, and process monitoring. , A variety of model transfer approaches were developed for handling specifically spectroscopic data. − Techniques such as partial least-squares (PLS) model inversion, Joint-Y PLS, calibration/model transfer, ,, and, more recently, transfer learning ,,, and domain adaptation offer different paths to achieve the aforementioned goals. These methods address partially the aforementioned challenge of connecting multiple sites, but their objective is not to provide a global management platform but instead a way to transfer existing knowledge to a new context of interest.…”

Section: Introductionmentioning

confidence: 99%

A Federated Classification Approach of Waste Lubricant Oils in Geographically Distributed Laboratories

Reis

Strelet

Sansana

et al. 2022

Ind. Eng. Chem. Res.

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Decision making and operational management of multiple identical units, equipment, or plants (sources) that are geographically distributed are becoming increasingly common. Instead of developing a classifier for each source, with much less data and representativeness of the entire population, this work presents a unique centralized classifier using a federated approach. The centralized classifier can take into account all data collected from multiple distributed sources and accommodate their local and specific data structures and correlation patterns. The federated approach has the built-in capability of expansion, allowing for the inclusion of more sources, without the need for retraining. Therefore, new sources may immediately benefit from the existing unified classification model as soon as they are “connected,” which only requires a projection operation. The proposed federated approach is applied to a real case study of predicting an important property (coagulation) of waste lubricant oil (WLO) in several locations of the recovery network. The coagulation behavior determines if WLO can be regenerated and used to produce again base oil. Fourier transform infrared spectra are collected at different sources (laboratories) and combined using the federated framework, leading to high classification accuracy and a more generalizable model.

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

Cited by 12 publications

References 19 publications

Cloning instruments, model maintenance and calibration transfer

Cloning instruments, model maintenance and calibration transfer

Application of SWSRA-DS algorithm in improving the model transfer for near infrared analysis of pulpwood holocellulose

A Federated Classification Approach of Waste Lubricant Oils in Geographically Distributed Laboratories

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