Lucía Pareja

It is with great pleasure that we present this Special Issue of the Brazilian Journal of Analytical Chemistry (BrJAC), dedicated to the 8th Uruguayan Congress of Analytical Chemistry (CUQA 8), held in Montevideo, Uruguay, on October 13–15, 2024. This Special Issue celebrates the vitality, diversity, and scientific maturity of the Analytical Chemistry community in Uruguay and the Southern Cone. This Special Issue opens with an engaging interview with Prof. Eduardo Dellacassa, who offers a rich and personal reflection on his scientific trajectory, from his rural childhood to becoming one of the most influential Latin American researchers in natural products, metabolomics, and analytical applications in food and pharmaceutical sciences. His reflections on the evolving landscape of analytical chemistry—particularly the need for “evolutionary analytics” as an open and flexible concept—provide a thought-provoking perspective for readers. Complementing this piece, the Point of View article by Prof. Eduardo Méndez addresses key conceptual and methodological aspects of nanometrology, underscoring its growing importance in ensuring measurement quality at the nanoscale. Additionally, the Letter authored by Prof. Ignacio Machado and Prof. Lucía Pareja, titled “Artificial Intelligence: A Transformative Ally in Analytical Chemistry,” highlights the expanding role of AI-driven tools in method development, data treatment, and decision-making, and discusses how these technologies are reshaping analytical practice in academic and applied settings. The research articles featured in this Special Issue represent the scientific breadth showcased during CUQA’s 8th edition and illustrate the multidisciplinary character of contemporary analytical chemistry. These contributions include: • “Incidence study of two UV filters (octocrylene and octinoxate) and the synthetic fragrance galaxolide in commercial yellow clam (Amarilladesma mactroides)” – an environmental analytical study addressing contaminants of emerging concern in coastal ecosystems. • “Risk-Based Selection of Active Pharmaceutical Ingredients in the Development of HPLC and NIR Methods for Pediatric Preparations” – an applied pharmaco-analytical work integrating quality-by-design principles with spectroscopic and chromatographic method development. • “Development of methods for selenium determination in fish: tools for ecotoxicological studies” – a contribution that highlights the importance of trace-element analysis for environmental and toxicological monitoring. • “Low-cost particulate matter sensor in indoor and external classroom environments – an innovative approach that demonstrates the relevance of accessible analytical technologies for public health and educational settings”. • “Evaluation of Calendula officinalis extract as a functionalization agent for gold nanoparticles” – a study at the interface of nanotechnology and bioanalytical chemistry, exploring sustainable routes for nanoparticle functionalization. Together, these articles reflect the scientific rigor and creativity of researchers working in Uruguay and the region. They also underscore the commitment of CUQA to fostering high-quality research, methodological innovation, and meaningful applications across environmental, pharmaceutical, food, and bioanalytical chemistry. We extend our sincere appreciation to all authors for their valuable contributions and to all reviewers for generously offering their time and expertise to ensure the scientific quality of this collection. We are also grateful to the CUQA 8 Organizing and Scientific Committees for their dedication, and to BrJAC for supporting this Special Issue devoted to strengthening regional analytical chemistry. We wish all readers an inspiring and enriching reading experience.

The scientific landscape is undergoing a profound transformation driven by artificial intelligence (AI). While its influence is already evident in disciplines such as bioinformatics, materials science, and drug discovery, analytical chemistry has only now begun to fully embrace its potential.1 Yet, few areas could benefit more from the structured, data-rich nature of AI than the analytical sciences themselves. Analytical chemistry has always been the discipline of signals, patterns, and interpretation. In that sense, analytical chemistry could be regarded as a conceptual precursor to machine learning thinking, long before the modern algorithms existed. AI provides an unprecedented opportunity to enhance analytical workflows, at every stage, from experimental design and method development to data processing, interpretation and decision-making.2 Machine learning models can optimize instrumental conditions, uncover hidden correlations between parameters, and automate complex calibration or validation procedures. For several years now, the trend has been toward the use of miniaturized and more environmentally friendly methodologies, and in this regard the development of AI can be of great help to improve existing greenness-assessment algorithms, providing smarter, more sustainable analytic protocols with lower sample and solvent consumption. In spectroscopic and chromatographic analyses, AI algorithms are increasingly capable of distinguishing genuine analytical signals from background noise or matrix interferences, enabling faster and more reliable quantification and identification.2 Beyond improving performance, AI is redefining the very role of the analytical chemist — from manual operator to data curator and critical interpreter. AI may also facilitate structural elucidation of unknown compounds, possibly offering a cost-effective alternative to expensive commercial spectral libraries or extensive manual interpretation workflows. However, this transformation brings certain challenges. AI systems must be transparent, explainable, and validated according to the same rigorous standards that govern traditional analytical methods.3 The “black box” problem remains one of the greatest barriers to trust and acceptance. It is essential that machine-learned models complement, rather than replace, human expertise — that they become partners in analytical reasoning, not substitutes for it. This balance between automation and understanding is central to the spirit of analytical chemistry. The adoption of AI also requires rethinking education and training. Future analytical chemists will need to navigate not only spectral lines and chromatograms but also fluency in algorithms, datasets, and validation metrics1,3 Integrating AI literacy and data science skills into analytical chemistry curricula is no longer optional: it is a prerequisite for keeping the discipline relevant and forward-looking. Those who understand how to merge chemical intuition with computational power will lead the next generation of analytical breakthroughs. The essence of analytical chemistry has always been about transforming raw data into knowledge. In this new era, artificial intelligence emerges not just as a tool, but as a collaborator in that pursuit. The challenge —and opportunity— lies in ensuring that as machines learn to think, we do not lose our capacity to question. The analytical chemist of tomorrow will not merely measure, they will also design, model, predict, and interpret. Embracing AI is not the end of analytical chemistry as we know it—it is its most exciting reinvention.

The instrumental development of tandem mass spectrometers fosters the actual trend in the trace analysis of organic compounds to the development of methods that allow the analysis of contaminants of the most diverse origin in a single analytical sample. The multiclass methods are aligned with the Who's One Health initiative while accomplishing the concepts of green chemistry. However, there are few reports of wide scope multiclass methods for the analysis of contaminants in this matrix. In this work, a method for the simultaneous determination of 62 compounds in bovine fat, was developed and validated following DG-SANTE/11813/2021 guidance. Liquid nitrogen milled fat was extracted with toluene and acetonitrile. Then a clean-up in a cryogenic bath, followed by dispersive solid phase extraction was performed. Residue determination was done using liquid chromatography for 44 compounds and gas chromatography for 18 compounds, both coupled to tandem mass spectrometry in MRM mode. The method was first developed and validated for two ectoparasiticides, then the scope was expanded for the analysis of 13 veterinary drugs and 49 pesticides. Recovery percentages were in the range of 60–134%, high matrix effect was observed in 50% of the scope of the method. Most compounds presented limits of quantification of 10 μg kg⁻¹ in compliance with international requirements. The method was applied to monitor 49 commercial samples to evaluate its performance. Eighty percent of samples contained ethion and 10% had fluazuron, both within MRLs, highlighting the need for proper withdrawal times.

Ramularia leaf spot (RLS) is primarily managed by foliar fungicide spraying, which can result in residues in the grain. In recent years, increasing attention has been paid to the risks these residues pose to consumers. This study aimed to evaluate the efficacy of fungicide management used for RLS control and their fungicide residues in barley grain. Four different alternatives of fungicide mixtures: fluxapyroxad + pyraclostrobin + epoxiconazole; pyraclostrobin + epoxiconazole + chlorothalonil; prothioconazole + trifloxystrobin, and isopyrazam + azoxystrobin, in three spray timings: GS33, GS47 and GS33+GS47, were evaluated in five field experiments. An untreated and a fully protected treatment were included. Fungicide residues, disease severity, control efficacy, area under the disease progress curve, and plump grain yield were calculated. All fungicide strategies adhered to food safety, complying with the Maximum Residue Limits established by Codex and the European Union. Effectiveness varied based on RLS development, application time, and number of applications. Fungicide treatments applied at GS33+GS47 were the most effective as the highest severity levels were observed at the stage after GS47 under the conditions studied. Plump grain yield only showed minimal differences in the late epidemic. The study emphasized the low risk of fungicide presence in grains, favoring efficacy when selecting RLS management options. Any changes in management or regulations should be carefully reviewed to maintain findings. The research underscored the compatibility of recommended fungicide treatments with food safety standards, highlighting the balance between disease control efficacy and consumer safety.

Decontamination procedures are needed for ready-to-eat foods, especially vegetables. In this work, we focused on the effects of four cleaning solutions and ultrasound baths on the amount of pesticide residue left on lettuces. Five pesticides were applied to lettuces grown in controlled conditions. The residues were analyzed with an acetate QuEChERS method and a HPLC-MS/MS system. All the tested methods resulted in diminution of residues without significant differences among them. Out of 16 pesticides analyzed on commercial samples, only five were found on seven of the samples, without exceeding the MRLs.