Código de projecto 3: PID2019-106862RB-I00/AEI/10.13039/501100011033, PDC2021-120921-I00
Autor según el artículo: Ezenarro, J.; Riu, J.; Ahmed, H.J.; Busto, O.; Giussani, B.; Boqué, R.
Departamento: Química Analítica i Química Orgànica
e-ISSN: 1873-3573
Autor/es de la URV: Ezenarro Garate, Jokin / Riu Rusell, Jordi / Ahmed, Hawbeer Jamal / Busto Busto, Olga / Boqué Martí, Ricard
Código de proyecto: PID2019-104269RR-C33 / MICIU / AEI / 10.13039/501100011033
Resumen: Near-infrared (NIR) spectroscopy is a well-established analytical technique that has been used in many applications over the years. Due to the advancements in the semiconductor industry, NIR instruments have evolved from benchtop instruments to miniaturised portable devices. The miniaturised NIR instruments have gained more interest in recent years because of the fast and robust measurements they provide with almost no sample pretreatments. However, due to the very different configurations and characteristics of these instruments, they need a dedicated optimization of the measurement conditions, which is crucial for obtaining reliable results. To comprehensively grasp the capabilities and potentials offered by these sensors, it is imperative to examine errors that can affect the raw data, which is a facet frequently overlooked. In this study, measurement error covariance and correlation matrices were calculated and then visually inspected to gain insight into the error structures associated with the devices, and to find the optimal preprocessing technique that may result in the improvement of the models built. This strategy was applied to the classification of sweet and bitter almonds, which were measured with the three portable low-cost NIR devices (SCiO, FlameNIR+ and NeoSpectra Micro Development Kit) after removing the shelled, since their classification is of utmost importance for the almond industry. The results showed that bitter almonds can be classified from sweet almonds using any of the instruments after selecting the optimal preprocessing, obtained through inspection of covariance and correlation matrices. Measurements obtained with FlameNIR + device provided the best classification models with an accuracy of 98 %. The chosen strategy provides new insight into the performance characterization of the fast-growing miniaturised NIR instruments.
Acción del programa de financiación 2: Departament de Recerca i Universitats, Generalitat de Catalunya
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: jokin.ezenarro@urv.cat
ISSN: 0039-9140
Codigo del proyecto 2: ref.2021 SGR 00705
Programa de financiación 2: Chemometrics and Sensorics for Analytical Solutions (CHEMOSENS)
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://www.sciencedirect.com/science/article/pii/S0039914024006507
Programa de financiación: Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i y de I+D+i Orientada a los Retos de la Sociedad. Proyectos de I+D+i Retos Investigación 2017-2020
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
Programa de financiación 3: Spanish Ministry of Science, Innovation and Universities (MICIU) and the State Research Agency (AEI)
Acrónimo: ALLFRUIT4ALL
DOI del artículo: 10.1016/j.talanta.2024.126271
Año de publicación de la revista: 2024
Acción del progama de financiación: Ciencias y tecnologías de alimentos
Tipo de publicación: Journal Publications
Repositori URV