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Mid-infrared spectroscopy and machine learning as a complementary tool for sensory quality assessment of roasted cocoa-based products

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Mid-infrared spectroscopy and machine learning as a complementary tool for sensory quality assessment of roasted cocoa-based products

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dc.contributor.author Collazos-Escobar, Gentil Andres es_ES
dc.contributor.author Barrios-Rodríguez, Yeison Fernando es_ES
dc.contributor.author Bahamón-Monje, Andrés F. es_ES
dc.contributor.author Gutiérrez-Guzmán, Nelson es_ES
dc.date.accessioned 2025-02-26T19:09:37Z
dc.date.available 2025-02-26T19:09:37Z
dc.date.issued 2024-09 es_ES
dc.identifier.issn 1350-4495 es_ES
dc.identifier.uri http://hdl.handle.net/10251/214861
dc.description.abstract [EN] Monitoring sensory quality in cocoa-based products is time-consuming and requires expert panelists. Integrating Mid-infrared (MIR) spectroscopy and chemometric models is a promising tool for real-time quality inspection. This study evaluated machine learning (ML) models based on the latent relationship between spectral and sensory information to predict the overall quality of roasted cocoa. Fifty-four roasted cocoa samples were analyzed using ATR¿FTIR in the 4000¿650¿cm¿1 range and sensory evaluated by four trained panelists. Spectral data were preprocessed using Multiplicative Scatter Correction (MSC) and combined with sensory data. Subsequently, the block-scale Principal Component Analysis (PCA) was performed. Secondly, a PCA was calibrated only on the spectral data to obtain uncorrelated regressors as input to the supervised ML techniques. Supported Vector Machine Regression Model (SVMR) and the Random Forest Regression Model (RFR) were used to predict the overall quality of roasted cocoa samples. The training (75¿%) and validation (25¿%) of the ML techniques were performed 1000 times, and the hyperparameters optimization of each method was assessed via multifactor Analysis of Variance (ANOVA). According to the tasting panel results, the cocoa beans from different growing areas, initially appeared to have similar sensory characteristics. However, using PCA, a distinction was identified in the northern beans. The SVMR and RFR models demonstrated an outstanding ability to describe the overall quality of roasted cocoa samples. Further, the statistical results revealed the potential of MIR coupled with SVMR as a reliable and robust tool for the rapid (CT¿<¿0.02¿s) and accurate prediction (MRE¿<¿2¿%, R2¿>¿99.9¿%) of the overall quality of roasted cocoa-based products. This work demonstrates that it is possible to implement artificial intelligence tools to support decisions in cocoa evaluation, ensuring compliance with quality standards and allowing segmentation according to origin and characteristics. es_ES
dc.description.sponsorship The authors sincerely thank the Centro Surcolombiano de Inves-tigacion en Cafe (CESURCAFE) from the Universidad Surcolombiana for their invaluable support, which was essential for completing this work. This study was supported by the funding for open access charge: Universitat Politecnica de Valencia. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Infrared Physics & Technology es_ES
dc.rights Reconocimiento - No comercial (by-nc) es_ES
dc.subject Mid-infrared es_ES
dc.subject Functional groups es_ES
dc.subject Quality monitoring es_ES
dc.subject Non-destructive testing es_ES
dc.subject Machine learning es_ES
dc.subject Artificial intelligence es_ES
dc.subject Optimization es_ES
dc.title Mid-infrared spectroscopy and machine learning as a complementary tool for sensory quality assessment of roasted cocoa-based products es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.infrared.2024.105482 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Tecnología de Alimentos - Departament de Tecnologia d'Aliments es_ES
dc.description.bibliographicCitation Collazos-Escobar, GA.; Barrios-Rodríguez, YF.; Bahamón-Monje, AF.; Gutiérrez-Guzmán, N. (2024). Mid-infrared spectroscopy and machine learning as a complementary tool for sensory quality assessment of roasted cocoa-based products. Infrared Physics & Technology. 141. https://doi.org/10.1016/j.infrared.2024.105482 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.infrared.2024.105482 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 141 es_ES
dc.relation.pasarela S\524108 es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.subject.ods 02.- Poner fin al hambre, conseguir la seguridad alimentaria y una mejor nutrición, y promover la agricultura sostenible es_ES
dc.subject.ods 09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación es_ES
upv.costeAPC 2650 es_ES


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