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
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 de...
| Authors: | , , , , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2025 |
| Country: | España |
| Institution: | Universidad de Burgos (UBU) |
| Repository: | Repositorio Institucional de la Universidad de Burgos (RIUBU) |
| OAI Identifier: | oai:riubu.ubu.es:10259/10459 |
| Online Access: | http://hdl.handle.net/10259/10459 |
| Access Level: | Open access |
| Keyword: | 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 |
| Summary: | 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. |
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