RT info:eu-repo/semantics/article
T1 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
A1 Castro Reigía, David
A1 García, Iker
A1 Sanllorente Méndez, Silvia
A1 Ortiz Fernández, Mª Cruz
A1 Sarabia Peinador, Luis Antonio
K1 PLS Calibration
K1 NIR Spectroscopy
K1 Contaminants
K1 Food adulterants
K1 Capability of detection
K1 Minimun detectable concentration
K1 False positive
K1 False negative
K1 False compliance
K1 False non-compliance
K1 Química analítica
K1 Chemistry, Analytic
K1 Alimentos
K1 Food
AB 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.
PB MDPI
YR 2025
FD 2025-04
LK http://hdl.handle.net/10259/10459
UL http://hdl.handle.net/10259/10459
LA eng
NO The authors thank Lugar daVeiga S.L.L., Lácteos De Moeche S.L., and Fundación CITOLIVA/INOLEO for allowing the measurements in their facilities and the entities for the financial support.
DS Repositorio Institucional de la Universidad de Burgos
RD 15-may-2025