The purpose of this work is to develop a tool to search for a gradient profile with ternary or binary mixtures in liquid chromatography, that can provide well-resolved chromatograms in the shortest time for multianalyte analysis. This approach is based exclusively on experimental data and does not require a retention time model of the compounds to be separated. The methodology has been applied for the quantification of four primary aromatic amines (PAAs) using HPLC with fluorescence detector (FLD). Aniline (ANL), 2,4-diaminotoluene (TDA), 4,4'-methylenedianiline (MDA) and 2-aminobiphenyl (ABP) have been selected since their importance in food contact materials (FCM). In order to achieve that, partial least squares (PLS) models have been fitted to relate CMP (control method parameters) and CQA (critical quality attributes). Specifically, PLS models have been fitted using 30 experiments for each one of the four CQA (resolution between peaks and total elution time), considering 33 predictor variables (the composition of the methanol and acetonitrile in the mobile phase and the time of each one of the 11 isocratic segments of the gradient). These models have been used to predict new candidate gradients, and then, some of those predictions (the ones with resolutions above 1.5, in absolute value, and final time lower than 20 min) have been experimentally validated. Detection capability of the method has been evaluated obtaining 1.8, 189.4, 28.8 and 3.0 µg L^-1 for ANL, TDA, MDA and ABP, respectively. Finally, the application of chemometric tools like PARAFAC2 allowed the accurate quantification of ANL, TDA, MDA and ABP in paper napkins in the presence of other interfering substances coextracted in the sample preparation process. ANL has been detected in the three napkins analysed in quantities between 33.5 and 619.3 µg L^-1, while TDA is present in only two napkins in quantities between 725.9 and 1908 µg L^-1. In every case, the amount of PAAs found, exceeded the migration limits established in European regulations.

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.