2026-04-17T14:00:50Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/99592025-01-20T07:58:53Zcom_10259_4249com_10259_5086com_10259_2604col_10259_4250

Castro Reigía, David 2020c09d-babf-44bd-ba2a-79fa68eb2eb6 600 Ortiz Fernández, Mª Cruz 406 600 0000-0002-4751-8929 Sanllorente Méndez, Silvia 522 600 0000-0001-8693-8806 García Esteban-Barcina, Iker 66a45463-223d-408e-8ad8-1013f518fa03 600 Sarabia Peinador, Luis Antonio 534 600 0000-0002-5936-7929 2025-01-17T13:57:17Z 2025-01-17T13:57:17Z 2024-03 0169-7439 http://hdl.handle.net/10259/9959 10.1016/j.chemolab.2024.105092 Biscuits are a highly demanded product worldwide. Its success makes their manufacture process a challenging task, needing new strategies to maintain the high production levels and a high-quality standard. This is determined by two key processes: the kneading and the rolling. This manuscript aims to reflect the improvements that the application of a novel soft multiclass compliant classification method (PLS2-CM) entails regarding the traditional chemometric class modelling. With this new approach, the intention is to detect possible deficiencies in biscuit doughs (excess of water, lack of water or little kneading time) during both industrial processes by using NIR spectroscopy. In PLS2-CM, the coding of the classes is done using an Error Correcting Code Matrix (ECOC), which implies to employ several binary learners so that their number and structure are not predetermined beforehand but are function of the data set to be modelled. The optimization criterion in PLS2-CM is the sensitivity and specificity matrix evaluated by the Diagonal Modified Confusion Entropy (DMCEN), a new index inspired by the Shannon's entropy that is more sensitive to changes in the elements of that matrix than the usual total efficiency. The results obtained according to this index are better with this new soft classification method than the ones obtained when using other soft class modelling techniques such as soft independent modelling of class analogy (SIMCA) or unequal dispersed classes (UNEQ). In this work it is shown that it is possible to completely distinguish a correct kneaded dough from another defective one with a specificity equal to 1 during the kneading process, but the class corresponding with water deficit dough, accepts a very high percentage (80 % in training and 92 % in prediction) of the excess-water dough spectra. Despite that, after the fermentation and during the rolling process, the same doughs are modelled with complete sensitivity and specificity in prediction (100 %), which indicates that the physico-chemical changes produced during the fermentation are decisive to characterize the absence of defects in biscuit doughs kneading by NIR spectroscopy. This work has been funded by the Ministerio de Comercio, Industria y Turismo under Project PHOTONICS4BAKERY (AEI-010500-2021b-111) Junta Castilla y León, Consejería de Educación under Project BU052P20 cofinanced with Regional European Funds. application/pdf eng Elsevier Chemometrics and Intelligent Laboratory Systems. 2024, V. 246, 105092 https://doi.org/10.1016/j.chemolab.2024.105092 Atribución-NoComercial 4.0 Internacional http://creativecommons.org/licenses/by-nc/4.0/ info:eu-repo/semantics/openAccess Sensitivity and specificity DMCEN PLS2-Class modelling Process control Kneading Rolling Química Química analítica Alimentos Investigación operativa Chemistry Chemistry, Analytic Food Operations research PLS class modelling using error correction output code matrices, entropy and NIR spectroscopy to detect deficiencies in pastry doughs info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Chemometrics and Intelligent Laboratory Systems 246 105092 THUMBNAIL Castro-cils_2024.pdf.jpg Castro-cils_2024.pdf.jpg IM Thumbnail image/jpeg 4794 https://riubu.ubu.es/bitstream/10259/9959/4/Castro-cils_2024.pdf.jpg b9b7f782b7f65d62d3b9136a05925421 MD5 4 LICENSE license.txt license.txt text/plain; charset=utf-8 998 https://riubu.ubu.es/bitstream/10259/9959/3/license.txt aa90105e1c90a1b4fe810649aa967b7d MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 914 https://riubu.ubu.es/bitstream/10259/9959/2/license_rdf 24013099e9e6abb1575dc6ce0855efd5 MD5 2 ORIGINAL Castro-cils_2024.pdf Castro-cils_2024.pdf application/pdf 1932068 https://riubu.ubu.es/bitstream/10259/9959/1/Castro-cils_2024.pdf 306dae78dda550c0509373b57d3b2375 MD5 1 10259/9959 oai:riubu.ubu.es:10259/9959 2025-01-20 08:58:53.664 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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