Autor según el artículo: Rovira, Gloria; Miaw, Carolina Sheng Whei; Martins, Mario Lucio Campos; Sena, Marcelo Martins; de Souza, Scheilla Vitorino Carvalho; Ruisanchez, Itziar; Callao, M Pilar
Departamento: Química Analítica i Química Orgànica
Autor/es de la URV: Callao Lasmarias, María Pilar / Rovira Garrido, Glòria / Ruisánchez Capelastegui, María Iciar
Palabras clave: Untargeted chemometrics Roc curve One-class simca Nir Multivariate data-analysis High-level data fusion Atr-ftir validation roc curve raman one -class simca nir midinfrared spectroscopy high-level data fusion food classification authentication atr-ftir
Resumen: An untargeted strategy was developed to determine cashew nuts adulteration with Brazilian nuts, pecan nuts, macadamia nuts and peanuts. A one-class SIMCA model was developed for the cashew non-adulterated samples by means of two spectroscopic techniques: Near-Infrared (NIR) and Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR). Receiver operating characteristic (ROC) curves have been proved to be useful to optimize class limits, both for the NIR and ATR-FTIR models, allowing to balance the values of the performance parameters. An increase in the sensitivity of the training and test set has been obtained from 79% with NIR and 85% with ATR-FTIR to 93% in both cases. As a result, the specificity has slightly decreased from 100% with NIR and a range of 90–98% with ATR-FTIR to a range of 82–98% and 84–96%, respectively. The implementation of high-level data fusion to the classification results obtained from NIR and ATR-FTIR, considering the limit value optimized by ROC curves, allowed the improvement of the performance parameters of the untargeted strategy. Obtaining sensitivity values for the training and test set of 100% and 93%, respectively. Specificity values of 100% were obtained for the detection of Brazilian nuts, macadamia nuts and peanuts, while for pecans it was 98%.
Áreas temáticas: Spectroscopy Saúde coletiva Química Odontología Nutrição Medicina ii Medicina i Materiais Interdisciplinar Geociências Farmacia Engenharias iv Engenharias iii Engenharias ii Engenharias i Enfermagem Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência de alimentos Chemistry, analytical Biotecnología Biodiversidade Astronomia / física Analytical chemistry Administração pública e de empresas, ciências contábeis e turismo
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: mariapilar.callao@urv.cat gloria.rovira@urv.cat itziar.ruisanchez@urv.cat
Identificador del autor: 0000-0003-2691-329X 0000-0002-7097-3583
Fecha de alta del registro: 2024-10-12
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://www.sciencedirect.com/science/article/abs/pii/S0026265X22006440
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Microchemical Journal. 181 107816-
Referencia de l'ítem segons les normes APA: Rovira, Gloria; Miaw, Carolina Sheng Whei; Martins, Mario Lucio Campos; Sena, Marcelo Martins; de Souza, Scheilla Vitorino Carvalho; Ruisanchez, Itzia (2022). In-depth chemometric strategy to detect up to four adulterants in cashew nuts by IR spectroscopic techniques. Microchemical Journal, 181(), 107816-. DOI: 10.1016/j.microc.2022.107816
DOI del artículo: 10.1016/j.microc.2022.107816
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2022
Tipo de publicación: Journal Publications
Repositori URV