An easy, selective, and sensitive method has been developed for the determination of enrofloxacin (ENR) and its main active metabolite, ciprofloxacin (CIP), in raw bovine milk using CE with UV detection at 268 nm. Milk samples were prepared by a clean-up/extraction procedure based on protein precipitation with hydrochloride acid followed by being defatted by centrifugation and SPE using a hydrophilic-lipophilic balance cartridge. Optimum separation was obtained using a 50 mM phosphoric acid at pH 8.4 and the total electrophoretic run time was 6 min. Sample preparation by this method yielded clean extracts with quantitative and consistent mean recoveries from 89 to 97% for CIP and from 93 to 98% for ENR. LODs obtained were lower to the maximum residue limits for these fluoroquinolones. The precision of the ensuing method is acceptable; thus, the RSD for peak area and migration time was less than 8.5 and 0.5% for CIP and 9.9 and 0.9% for ENR, respectively. The results showed that the proposed method was efficient showing good recoveries, sensitivity, and precision for the studied compounds and could be satisfactorily applied in routine analysis for the monitoring of ENR and CIP residues in milk, due to its ruggedness and feasibility demonstrated.

CE has generated considerable interest in the research community since instruments were introduced by different trading companies in the 1990s. Nowadays, CE is popular due to its simplicity, speed, highly efficient separations and minimal solvent and reagent consumption; it can also be included as a useful technique in the nanotechnology field and it covers a wide range of specific applications in different fields (chemical, pharmaceutical, genetic, clinical, food and environmental). CE has been very well evaluated in research laboratories for several years, and different new approaches to improve sensitivity (one of the main drawbacks of CE) and robustness have been proposed. However, this technique is still not well accepted in routine laboratories for food analysis. Researching in data bases, it is easy to find several electrophoretic methods to determine different groups of analytes and sometimes they are compared in terms of sensitivity, selectivity, precision and applicability with other separation techniques. Although these papers frequently prove the potential of this methodology in spiked samples, it is not common to find a discussion of the well-known complexity of the matrices to extract analytes from the sample and/or to study the interferences in the target analytes. Summarizing, the majority of CE scientific papers focus primarily on the effects upon the separation of the analytes while ignoring their behavior if these analytes are presented in real samples.

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Recently, the olive oil industry has been the subject of harsh criticism for false labeling and even adulterating olive oils. This situation in which both the industry and the population are affected leads to an urgent need to increase controls to avoid fraudulent activities around this precious product. The aim of this work is to propose a new analytical platform by coupling electrospray ionization (ESI), differential mobility analysis (DMA), and mass spectrometry (MS) for the analysis of olive oils based on the information obtained from the chemical fingerprint (nontargeted analyses). Regarding the sample preparation, two approaches were proposed: (i) sample dilution and (ii) liquid−liquid extraction (LLE). To demonstrate the feasibility of the method, 30 olive oil samples in 3 different categories were analyzed, using 21 of them to elaborate the classification model and the remaining 9 to test it (blind samples). To develop the prediction model, principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used. The overall success rate of the classification to differentiate among extra virgin olive oil (EVOO), virgin olive oil (VOO), and lampante olive oil (LOO) was 89% for the LLE samples and 67% for the diluted samples. However, combining both methods, the ability to differentiate EVOO from lower-quality oils (VOO and LOO) and the edible oils (EVOO and VOO) from nonedible oil (LOO) was 100%. The results show that ESI-DMA-MS can become an effective tool for the olive oil sector.

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