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
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
Entity: Universitat Rovira i Virgili
Journal publication year: 2021
Publication Type: Journal Publications
Repositori URV