期刊
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
卷 233, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.chemolab.2022.104748
关键词
Data fusion; Multi -spectral techniques; Multi -analytical platforms; Class modeling; Chemometrics tools; Authentication
This study proposes a new method for authentication that combines fingerprint analytical techniques with multivariate methods of one-class classifiers. The method utilizes common dimension analysis and dual data-driven to calculate misclassification errors and determine their cut-off levels. The results demonstrate that this method improves the quality and efficiency of the authentication model compared to traditional methods, with the mid-level data fusion approach providing clearer separation between target and non-target classes. Further applications are being conducted to confirm the reliability of the method with other matrices and analytical techniques.
An attractive alternative to solve authentication challenges is the combination of fingerprint analytical tech-niques with multivariate methods of one-class classifiers (OCC). In this proof of concept, we propose a novel multiblock method for OCC that emerged from the association of the common dimension (ComDim) analysis with the dual data-driven, which allows to calculate errors of misclassification based on the orthogonal and score distances with a subsequent determination of their cut-off levels. The applicability of data-driven - common dimension (DD-ComDim) analysis was verified for the authentication of diesel S10 (10 ppm of sulfur) against S500 (500 ppm of sulfur) using two low-field 1H NMR datasets: medium-resolution (MR-NMR) and time-domain NMR relaxometry (TD-NMR). The performance of the DD-ComDim (mid-level data fusion) was compared with data-driven - soft independent modeling of class analogy (DD-SIMCA) for each data set separately and concat-enated (low-level data fusion). The results suggest that this novel method can improve the quality and efficiency of the model in authentication when compared to a traditional method. In addition, it demonstrated that the threshold of separation between target and non-target classes was more evident with the mid-level data fusion approach. Other applications are currently underway, intending to verify the method's applicability with other matrices and analytical techniques to confirm the reliability of the DD-ComDim.
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