RT info:eu-repo/semantics/article
T1 PLS class modelling using error correction output code matrices, entropy and NIR spectroscopy to detect deficiencies in pastry doughs
A1 Castro Reigía, David
A1 Ortiz Fernández, Mª Cruz
A1 Sanllorente Méndez, Silvia
A1 García, Inmaculada
A1 Sarabia Peinador, Luis Antonio
K1 Sensitivity and specificity
K1 DMCEN
K1 PLS2-Class modelling
K1 Process control
K1 Kneading
K1 Rolling
K1 Química
K1 Chemistry
K1 Química analítica
K1 Chemistry, Analytic
K1 Alimentos
K1 Food
K1 Investigación operativa
K1 Operations research
AB 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.
PB Elsevier
SN 0169-7439
YR 2024
FD 2024-03
LK http://hdl.handle.net/10259/9959
UL http://hdl.handle.net/10259/9959
LA eng
NO 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.
DS Repositorio Institucional de la Universidad de Burgos
RD 18-ene-2025