2026-04-17T12:07:59Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/104592025-05-14T00:05:09Zcom_10259_4249com_10259_5086com_10259_2604col_10259_4250

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 Castro Reigía, David García, Iker Sanllorente Méndez, Silvia Ortiz Fernández, Mª Cruz Sarabia Peinador, Luis Antonio PLS Calibration NIR Spectroscopy Contaminants Food adulterants Capability of detection Minimun detectable concentration False positive False negative False compliance False non-compliance Química analítica Alimentos Chemistry, Analytic Food 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. 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. 2025-05-13T06:24:37Z 2025-05-13T06:24:37Z 2025-04 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://hdl.handle.net/10259/10459 10.3390/app15094808 2076-3417 eng Applied Sciences. 2025, V. 15, n. 9, p. 4808 https://doi.org/10.3390/app15094808 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess application/pdf MDPI https://riubu.ubu.es/bitstream/10259/10459/4/Castro-applsci_2025.pdf.jpg Hispana TEXT http://creativecommons.org/licenses/by/4.0/ RIUBU. Repositorio Institucional de la Universidad de Burgos http://hdl.handle.net/10259/10459