Author, as appears in the article.: Ezenarro, Jokin; Riu, Jordi; Ahmed, Hawbeer Jamal; Busto, Olga; Giussani, Barbara; Boque, Ricard
Department: Química Analítica i Química Orgànica
URV's Author/s: Boqué Martí, Ricard / Busto Busto, Olga / EZENARRO GARATE, JOKIN / Riu Rusell, Jordi
Keywords: Variability sources Spectroscopy Spectr Preprocessing Near-infrared (nir) Error covariance matrices Discriminant analysis Discriminant analysi Correlation error
Abstract: 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.
Thematic Areas: Zootecnia / recursos pesqueiros Spectroscopy Saúde coletiva Química Nutrição Medicina veterinaria Medicina iii Medicina ii Medicina i Materiais Interdisciplinar Geociências General medicine General chemistry Farmacia Ensino Engenharias iv Engenharias iii Engenharias ii Engenharias i Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência de alimentos Chemistry, analytical Chemistry (miscellaneous) Biotecnología Biodiversidade Biochemistry Astronomia / física Analytical chemistry Administração pública e de empresas, ciências contábeis e turismo
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: jokin.ezenarro@urv.cat jokin.ezenarro@urv.cat jordi.riu@urv.cat ricard.boque@urv.cat olga.busto@urv.cat
Author identifier: 0000-0001-9234-7877 0000-0001-9234-7877 0000-0001-5823-9223 0000-0001-7311-4824 0000-0002-2318-6800
Record's date: 2025-03-22
Paper version: info:eu-repo/semantics/publishedVersion
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Paper original source: Talanta. 276 126271-
APA: Ezenarro, Jokin; Riu, Jordi; Ahmed, Hawbeer Jamal; Busto, Olga; Giussani, Barbara; Boque, Ricard (2024). Measurement errors and implications for preprocessing in miniaturised near-infrared spectrometers: Classification of sweet and bitter almonds as a case of study. Talanta, 276(), 126271-. DOI: 10.1016/j.talanta.2024.126271
Entity: Universitat Rovira i Virgili
Journal publication year: 2024
Publication Type: Journal Publications
Repositori URV