Milk is one of the most widely consumed foods in the world, despite the increasing consumption of plant-based alternatives. Although rich in nutrients and believed by consumers to be free of undesirable contaminants, milk, whether of animal or plant origin, is not always free from residues of chemical substances, including veterinary medicines. For instance, in intensive livestock production, antibiotics are often used to treat animals or, illicitly, to improve their growth performance, which can lead to their presence in the final food. Additionally, the continuous use of veterinary drugs in intensive animal production can lead to their occurrence in agricultural soils and therefore are absorbed by plants as another source of entering the food chain. An effective and accurate multi-detection quantitative screening method to analyze 89 antibiotics in milk was optimized by ultra-high-performance liquid chromatography coupled with a time-of-flight detector (UHPLC-ToF-MS) and further validated in accordance with the Commission Implementing Regulation (CIR) 808/2021 and the International Council for Harmonization (ICH) guidelines on the validation of analytical procedures. Apart from the specific parameters required by CIR 808/2021, the aim was to access the lower limits of the method, limits of detection (LoD) and quantification (LoQ), regardless of the maximum residue limits (MRLs) defined in the legislation. The method was then applied in the analysis of 32 supermarket samples, resulting in four positive findings, including one plant-based sample. The antibiotics found were from the macrolides and sulphonamides families. Nevertheless, the concentrations detected were below the established maximum residue level (MRL).

Anthropogenic chemical contamination represents a key stressor of natural environments with pharmaceuticals comprising a particular group of emerging pollutants with the potential to induce biological responses in non-target organisms. Therefore, an analytical method based on ultra-high-performance liquid chromatography coupled to time-of-flight tandem mass spectrometry (UHPLC-TOF-MS) was developed for estuarine and seawaters, targeting 63 globally used pharmaceuticals (including amoxicillin, ciprofloxacin, sulfamethoxazole, trimethoprim and venlafaxine included in the Surface Water Watch List) from 8 therapeutic groups: antibiotics, analgesic, NSAIDs, antidepressants, β-blockers, lipid regulators, anticonvulsants and antihypertensive drugs. The method presents high selectivity and sensitivity, with the limits of detection ranging from 0.01 to 8.92 ng/L and the limits of quantification from 0.02 to 29.73 ng/L. Considering precision, the highest value was achieved for amoxicillin (20.9%) and the lower for ofloxacin (2.6%), while recoveries ranged from 80.6 to 112.6%. Overall, the quantification method was highly efficient for multi-residues quantification in such complex environmental samples.

Maize value chains represent invaluable end-consumer products in food systems worldwide. Mycotoxin contamination along these chains causes severe economic and health impacts from the plant, animal and human safety points of view. This work aimed to develop a single standardized methodology that fulfilled extraction and detection procedures for 22 mycotoxins in maize chain matrices. The main goal concerned the validation of a QuEChERS-based ultra-high-performance liquid chromatography coupled to the tandem mass spectrometry (UHPLC-MS/MS) method in compliance with established performance criteria for mycotoxin determination. Validation parameters encompassing specificity/selectivity, linearity, precision, recovery, Limits of Detection (LOD) and Limits of Quantification (LOQ) were evaluated, and acceptable data were found for all the mycotoxins in the matrices under study, namely, seeds, flowering plants, silage and feed. The applied method presented LODs and LOQs lower than 40.3 and 42.1 ng g⁻¹, respectively, and recoveries ranging from 80.7 to 118.1%, with precision values below 20.5%. A first-time full analytical procedure in a multi-matrix and multi-analyte approach was successfully validated, representing a valuable control tool for mycotoxin monitoring in maize chains. This approach will ultimately allow a response to the need for integrated risk assessments encompassing full, comprehensive analysis of whole food chains in compliance with the maximum levels established in European regulations, and the establishment of accurate solutions in each chain-specific critical point, helping to provide more sustainable, safer and healthy food systems.

Fungal and mycotoxin control at a primary stage in the food chain is crucial to maintaining the nutritional quality of animal feed. The control of fungal and mycotoxins is one of the essential points that a good biosecurity program must establish to ensure the safe feeding and protection of animal and human health. Acquiring a comprehensive understanding of the role of mycotoxins is vital to identifying breaches of this control and enabling the performance of proper risk assessments and accurate risk management strategies. This study focused on the identification of regulated and emerging mycotoxins in agricultural fields and dairy farms through an analytical methodology by ultra high-performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-QTrap-MS/MS). This allowed us to identify a Portuguese mycotoxin profile in the maize value chain for the first time. Regarding our occurrence data, mycotoxins were identified in all samples, namely seeds, flowering plants, grain and forage at harvest, maize silage, and animal feed. FBs and ENNB were highly present in all stages of the production cycle. This work offers an initial insight into a full screening of regulated and emerging mycotoxins within an important agricultural commodity. The high occurrence of these compounds points to the need to perform occurrence surveys from an integrative perspective to protect consumers’ health, especially within food chains that provide various important staple foods worldwide.

The growth of the world population forces a more outstanding and more efficient food production, forcing agribusiness into the race for greater productivity. Thus, agrochemicals as a tool for increasing and defending production have become more critical with each harvest. This work presents an ontology that describes the knowledge involved in the need for Agrochemical Pervasive Traceability Model (APTM). This proposed ontology is called ontology for pervasive traceability of agrochemicals (OntoPTA). We present classes and their relationships in a hierarchical way and a visualization from the OWL ontology language. This ontology fills the gap in the understanding and modeling of this type of agribusiness process. This modeling helps farm administrators and software developers to perform better analysis for the development, use and maintenance of systems in agribusiness.