Repositori institucional URV
| Autor/es de la URV: | Ezenarro Garate, Jokin / Riu Rusell, Jordi / Ahmed, Hawbeer Jamal / Busto Busto, Olga / Boqué Martí, Ricard |
| Autor según el artículo: | Ezenarro, J.; Riu, J.; Ahmed, H.J.; Busto, O.; Giussani, B.; Boqué, R. |
| Direcció de correo del autor: | jokin.ezenarro@urv.cat |
| Año de publicación de la revista: | 2024 |
| Tipo de publicación: | Journal Publications |
| ISSN: | 0039-9140 |
| e-ISSN: | 1873-3573 |
| 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. |
| DOI del artículo: | 10.1016/j.talanta.2024.126271 |
| Enlace a la fuente original: | https://www.sciencedirect.com/science/article/pii/S0039914024006507 |
| Versión del articulo depositado: | info:eu-repo/semantics/publishedVersion |
| Acceso a la licencia de uso: | https://creativecommons.org/licenses/by/3.0/es/ |
| Departamento: | Química Analítica i Química Orgànica |
| URL Documento de licencia: | https://repositori.urv.cat/ca/proteccio-de-dades/ |
| 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 |
| Acción del progama de financiación: | Ciencias y tecnologías de alimentos |
| Acrónimo: | ALLFRUIT4ALL |
| Código de proyecto: | PID2019-104269RR-C33 / MICIU / AEI / 10.13039/501100011033 |
| Programa de financiación 2: | Chemometrics and Sensorics for Analytical Solutions (CHEMOSENS) |
| Acción del programa de financiación 2: | Departament de Recerca i Universitats, Generalitat de Catalunya |
| Codigo del proyecto 2: | ref.2021 SGR 00705 |
| Programa de financiación 3: | Spanish Ministry of Science, Innovation and Universities (MICIU) and the State Research Agency (AEI) |
| Código de projecto 3: | PID2019-106862RB-I00/AEI/10.13039/501100011033, PDC2021-120921-I00 |
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