2026-04-17T12:07:35Zhttps://riubu.ubu.es/oai/request

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

00925njm 22002777a 4500 dc Castro Reigía, David author García, Iker author Sanllorente Méndez, Silvia author Ortiz Fernández, Mª Cruz author Sarabia Peinador, Luis Antonio author 2025-04 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. http://hdl.handle.net/10259/10459 10.3390/app15094808 2076-3417 PLS Calibration NIR Spectroscopy Contaminants Food adulterants Capability of detection Minimun detectable concentration False positive False negative False compliance False non-compliance 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