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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/10459

    Título
    Detection of Nutrients and Contaminants in the Agri-Food Industry Evaluating the Probabilities of False Compliance and False Non-Compliance Through PLS Models and NIR Spectroscopy
    Autor
    Castro Reigía, David
    García, Iker
    Sanllorente Méndez, SilviaAutoridad UBU Orcid
    Ortiz Fernández, Mª CruzAutoridad UBU Orcid
    Sarabia Peinador, Luis AntonioAutoridad UBU Orcid
    Publicado en
    Applied Sciences. 2025, V. 15, n. 9, p. 4808
    Editorial
    MDPI
    Fecha de publicación
    2025-04
    DOI
    10.3390/app15094808
    Resumen
    NIR spectroscopy has become one of the most prominent techniques in the food industry due to its easy and fast use. Coupled with PLS, it is a well-established method for determining nutrients, contaminants, or adulterants in foods. Nevertheless, it is not common when calculating the capability of detection or discrimination given a target/permitted value, providing probabilities of false non-compliance (α) or false compliance (β). That is exactly the main purpose of this work, where a single procedure using the accuracy line to evaluate these figures of merit by generalizing ISO 11843 when using NIR-PLS in real scenarios in agri-food industries is shown. Nevertheless, it is a completely general procedure and can be used in any analytical context in which a PLS calibration is applied. As an example of its versatility, several analytical determinations were performed using different common food matrices in the agri-food industry (butter, flour, milk, yogurt, oil, and olives) for the quantification of protein, fat, salt, and two agrochemicals. Some results were a detection capability of 5.2% of fat in milk, 1.20 mg kg−1 for diflufenican, and 2.34 mg kg−1 for piretrin in olives when maximum limits were established at 5%, 0.6 mg kg−1, and 0.5 mg kg−1 respectively. Also, 1.02% for salt in butter and 11.45%, 3.78%, and 2.65% for protein in flour, milk, and yogurt, respectively, were obtained when minimum limits were established at 1.2%, 12%, 4%, and 3% respectively. In all cases α = β = 0.05.
    Palabras clave
    PLS Calibration
    NIR Spectroscopy
    Contaminants
    Food adulterants
    Capability of detection
    Minimun detectable concentration
    False positive
    False negative
    False compliance
    False non-compliance
    Materia
    Química analítica
    Chemistry, Analytic
    Alimentos
    Food
    URI
    http://hdl.handle.net/10259/10459
    Versión del editor
    https://doi.org/10.3390/app15094808
    Aparece en las colecciones
    • Artículos Q&C
    Atribución 4.0 Internacional
    Documento(s) sujeto(s) a una licencia Creative Commons Atribución 4.0 Internacional
    Ficheros en este ítem
    Nombre:
    Castro-applsci_2025.pdf
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    2.017Mb
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