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Spectroscopic fingerprinting and chemometrics for the discrimination of Italian Emmer landraces

  • Identification data

    Identifier: imarina:9225571
    Authors:
    Foschi, MartinaBiancolillo, AlessandraVellozzi, SimonaMarini, FedericoD'Archivio, Angelo AntonioBoque, Ricard
    Abstract:
    Emmer is a traditional Italian wheat species attracting growing attention for the high-nutritive and dietary value. The growth of emmer consumption and the recent spreading even in areas where production was not traditional pose a risk to biodiversity and to the geographical identities. Thus, the present work aims to develop a nondestructive and routine-compatible method to discriminate three Italian landraces and lay the basis for a possible authentication method. One-hundred and forty-seven emmer samples, harvested in 2019 in three traditional production areas (Garfagnana, Monteleone di Spoleto, Gran Sasso and Monti della Laga National Park), were investigated by Mid-Infrared (MIR) and Near-Infrared (NIR) spectroscopy. Two different approaches of multiclass Partial Least Squares-Discriminant Analysis (PLS-DA) were applied on the collected fingerprinting profiles. Eventually, Data-Fusion strategies have been employed to combine the different information sources and classify the samples according to the geographical origin. The most accurate predictions were provided by the Sequential and Orthogonalized-Partial Least Squares-Discriminant Analysis (SO-PLS-DA) model, which misclassified only one test sample over 44 (in external validation). Finally, a chemical interpretation of the most discriminant variables was performed.
  • Others:

    Author, as appears in the article.: Foschi, Martina; Biancolillo, Alessandra; Vellozzi, Simona; Marini, Federico; D'Archivio, Angelo Antonio; Boque, Ricard;
    Department: Química Analítica i Química Orgànica
    URV's Author/s: Boqué Martí, Ricard
    Keywords: Wheat Turgidum ssp dicoccum Triticum-dicoccon schrank So-pls Quality Prediction Partial least-squares Partial least squares regression Nonhuman Near infrared spectroscopy National park Multi-block Major clinical study Landrace Infrared reflectance spectroscopy Infrared Human tissue Geographical origin Emmer Discriminant analysis Data-fusion Data fusion Classification Chemometrics Chemical-composition Article
    Abstract: Emmer is a traditional Italian wheat species attracting growing attention for the high-nutritive and dietary value. The growth of emmer consumption and the recent spreading even in areas where production was not traditional pose a risk to biodiversity and to the geographical identities. Thus, the present work aims to develop a nondestructive and routine-compatible method to discriminate three Italian landraces and lay the basis for a possible authentication method. One-hundred and forty-seven emmer samples, harvested in 2019 in three traditional production areas (Garfagnana, Monteleone di Spoleto, Gran Sasso and Monti della Laga National Park), were investigated by Mid-Infrared (MIR) and Near-Infrared (NIR) spectroscopy. Two different approaches of multiclass Partial Least Squares-Discriminant Analysis (PLS-DA) were applied on the collected fingerprinting profiles. Eventually, Data-Fusion strategies have been employed to combine the different information sources and classify the samples according to the geographical origin. The most accurate predictions were provided by the Sequential and Orthogonalized-Partial Least Squares-Discriminant Analysis (SO-PLS-DA) model, which misclassified only one test sample over 44 (in external validation). Finally, a chemical interpretation of the most discriminant variables was performed.
    Thematic Areas: Statistics & probability Spectroscopy Software Robotics & automatic control Química Process chemistry and technology Mathematics, interdisciplinary applications Matemática / probabilidade e estatística Interdisciplinar Instruments & instrumentation Farmacia Engenharias iv Engenharias iii Engenharias ii Computer science, artificial intelligence Computer science applications Ciências ambientais Ciências agrárias i Ciência de alimentos Ciência da computação Chemistry, analytical Biotecnología Automation & control systems Analytical chemistry
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: ricard.boque@urv.cat
    Author identifier: 0000-0001-7311-4824
    Record's date: 2024-07-27
    Papper version: info:eu-repo/semantics/acceptedVersion
    Link to the original source: https://www.sciencedirect.com/science/article/abs/pii/S0169743921001167
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Chemometrics And Intelligent Laboratory Systems. 215
    APA: Foschi, Martina; Biancolillo, Alessandra; Vellozzi, Simona; Marini, Federico; D'Archivio, Angelo Antonio; Boque, Ricard; (2021). Spectroscopic fingerprinting and chemometrics for the discrimination of Italian Emmer landraces. Chemometrics And Intelligent Laboratory Systems, 215(), -. DOI: 10.1016/j.chemolab.2021.104348
    Article's DOI: 10.1016/j.chemolab.2021.104348
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2021
    Publication Type: Journal Publications
  • Keywords:

    Analytical Chemistry,Automation & Control Systems,Chemistry, Analytical,Computer Science Applications,Computer Science, Artificial Intelligence,Instruments & Instrumentation,Mathematics, Interdisciplinary Applications,Process Chemistry and Technology,Robotics & Automatic Control,Software,Spectroscopy,Statistics & Probability
    Wheat
    Turgidum ssp dicoccum
    Triticum-dicoccon schrank
    So-pls
    Quality
    Prediction
    Partial least-squares
    Partial least squares regression
    Nonhuman
    Near infrared spectroscopy
    National park
    Multi-block
    Major clinical study
    Landrace
    Infrared reflectance spectroscopy
    Infrared
    Human tissue
    Geographical origin
    Emmer
    Discriminant analysis
    Data-fusion
    Data fusion
    Classification
    Chemometrics
    Chemical-composition
    Article
    Statistics & probability
    Spectroscopy
    Software
    Robotics & automatic control
    Química
    Process chemistry and technology
    Mathematics, interdisciplinary applications
    Matemática / probabilidade e estatística
    Interdisciplinar
    Instruments & instrumentation
    Farmacia
    Engenharias iv
    Engenharias iii
    Engenharias ii
    Computer science, artificial intelligence
    Computer science applications
    Ciências ambientais
    Ciências agrárias i
    Ciência de alimentos
    Ciência da computação
    Chemistry, analytical
    Biotecnología
    Automation & control systems
    Analytical chemistry
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