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• W3210123508 endingPage "339205" @default.
• W3210123508 startingPage "339205" @default.
• W3210123508 abstract "When fourier transform infrared spectroscopy (FTIR) techniques combined with multivariate calibration are used to measure the key process features or analyte concentrations during batch process, model adaption is indispensable for maintaining the predictability of a primary calibration model in new secondary batches. Many model adaption methods conforming to the actual application scenario of batch process have been proposed. Here we report on a novel standard-free model adaption method without reference measurement called variable selection strategy with self-organizing maps (VSSOM). It uses self-organizing maps (SOM) to classify the whole spectral variables into multiple classes according to the spectra from primary batch and secondary batch, respectively; and the corresponding primary feature subsets and secondary feature subsets are formed firstly. Secondly, candidate feature subsets without empty elements are generated by operating intersection between any primary feature subsets and any secondary feature subsets. Thirdly, the candidate feature subset with minimum root mean square error of cross-validation (RMSECV) for the primary calibration set is selected as the optimal feature subset. In this manner, the optimal feature subset can be identified from the candidate feature subsets. In other words, VSSOM aims to create a stable and consistent feature subset across different batches provided that it selects better features within the intersection sets between primary feature subsets and any secondary feature subsets. Two batch process datasets (γ-polyglutamic acid fermentation and paeoniflorin extraction) are presented for comparing the VSSOM method with No transfer partial least squares (PLS), boxcar signal transfer (BST), successive projection algorithm (SPA), transfer component analysis (TCA) and domain-invariant iterative partial least squares (DIPALS). Experimental results show that VSSOM has superior performance and comparable prediction performance in all the scenarios." @default.
• W3210123508 created "2021-11-08" @default.
• W3210123508 creator A5007267853 @default.
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• W3210123508 date "2021-12-01" @default.
• W3210123508 modified "2023-10-14" @default.
• W3210123508 title "Self-organizing maps-based generalized feature set selection for model adaption without reference data for batch process" @default.
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• W3210123508 doi "https://doi.org/10.1016/j.aca.2021.339205" @default.
• W3210123508 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34794558" @default.
• W3210123508 hasPublicationYear "2021" @default.
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