Articles producció científica> Química Analítica i Química Orgànica

Measurement errors and implications for preprocessing in miniaturised near-infrared spectrometers: Classification of sweet and bitter almonds as a case of study

  • Identification data

    Identifier: imarina:9368380
    Handle: https://hdl.handle.net/20.500.11797/imarina9368380
    Authors:
    Ezenarro, J.Riu, J.Ahmed, H.J.Busto, O.Giussani, B.Boqué, R.
    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 i

  • Others:

    Project code 3: PID2019-106862RB-I00/AEI/10.13039/501100011033, PDC2021-120921-I00
    Author, as appears in the article.: Ezenarro, J.; Riu, J.; Ahmed, H.J.; Busto, O.; Giussani, B.; Boqué, R.
    Department: Química Analítica i Química Orgànica
    e-ISSN: 1873-3573
    URV's Author/s: Ezenarro Garate, Jokin / Riu Rusell, Jordi / Ahmed, Hawbeer Jamal / Busto Busto, Olga / Boqué Martí, Ricard
    Project code: PID2019-104269RR-C33 / MICIU / AEI / 10.13039/501100011033
    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.
    Program founding action 2: Departament de Recerca i Universitats, Generalitat de Catalunya
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: jokin.ezenarro@urv.cat
    ISSN: 0039-9140
    Project code 2: ref.2021 SGR 00705
    Founding program 2: Chemometrics and Sensorics for Analytical Solutions (CHEMOSENS)
    Papper version: info:eu-repo/semantics/publishedVersion
    Funding program: 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
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Founding program 3: Spanish Ministry of Science, Innovation and Universities (MICIU) and the State Research Agency (AEI)
    Acronym: ALLFRUIT4ALL
    Journal publication year: 2024
    Funding program action: Ciencias y tecnologías de alimentos
    Publication Type: Journal Publications

  • Keywords:

    Preprocessing Error covariance matrices Correlation error Variability sources Near-infrared (NIR) Discriminant analysis
    0039-9140

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