Validation requirements of screening analytical methods based on scenario-specified applicability indicators

Antibiotic resistance in the aquatic environment: Analytical techniques and interactive impact of emerging contaminants

Antibiotic pollution is becoming an increasingly severe threat globally. Antibiotics have emerged as a new class of environmental pollutants due to their expanding usage and indiscriminate application in animal husbandry as growth boosters. Contamination of aquatic ecosystems by antibiotics can have a variety of negative impacts on the microbial flora of these water bodies, as well as lead to the development and spread of antibiotic-resistant genes. Various strategies for removing antibiotics from aqueous systems and environments have been developed. Many of these approaches, however, are constrained by their high operating costs and the generation of secondary pollutants. This review aims to summarize research on the distribution and effects of antibiotics in aquatic environments, their interaction with other emerging contaminants, and their remediation strategy. The ecological risks associated with antibiotics in aquatic ecosystems and the need for more effective monitoring and detection system are also highlighted.

Chromatographic Fingerprinting and Food Identity/Quality: Potentials and Challenges

Chromatograms are a valuable source of information about the chemical composition of the food being analyzed. Sometimes, this information is not explicit and appears in a hidden or not obvious way. Thus, the use of chemometric tools and data-mining methods to extract it is required. The fingerprint provided by a chromatogram offers the possibility to perform both identity and quality testing of foodstuffs. This perspective is aimed at providing an updated opinion of chromatographic fingerprinting methodology in the field of food authentication. Furthermore, the limitations, its absence in official analytical methods, and the future directions of this methodology are discussed.

PLS-DA vs sparse PLS-DA in food traceability. A case study: Authentication of avocado samples

Conventional and sparse partial least squares-discriminant analysis (PLS-DA and sPLS-DA) have been successfully tested in order to authenticate avocado samples in terms of three different geographical origins and six kinds of cultivar. For this, lipid chromatographic fingerprints of different avocado fruits have been acquired using gas chromatography coupled with flame ionization detector (GC-FID) and employed for building classification models. In addition, classification models concatenating strategy has been applied, which has proved to be successful to resolve multiclass problems in food authentication. Finally, fine performance metrics around of 0.95 were obtained for both multivariate classification methods.