Background: Veterinary drugs are widely used in animals to prevent diseases and are a complex set of drugs with very different chemical properties. Multiclass and multi-residue methods for simultaneous detection of residues from veterinary drugs and contaminants in urine are very rare or non-existent. Therefore, the aim of this study was to develop and validate a sensitive and reliable quantitative LC-MS/MS method for simultaneous determination of a wide range of veterinary drug and pesticide residues and mycotoxins in bovine urine. This involved 42 veterinary drug residues (4 thyreostats, 6 anabolic hormones, 2 lactones, 10 beta agonists, 15 antibiotics, 5 sulphonamides), 28 pesticides and 2 mycotoxins. Stable isotopically labelled internal standards were used to facilitate effective quantification of the analytes. Analysis was performed in both positive and negative ionization modes with multiple reaction monitoring transitions over a period of 12 min. Results: The parameters validated included linearity, limit of detection (LOD), limit of quantification (LOQ), detection capability (CCβ), decision limit (CCα), stability, accuracy and precision. The process followed guidelines of the regulation 2021/808/EC. The calibration curves were linear with coefficient of correlation (R²) from 0.991 to 0.999. The LODs were from 0.01 to 2.71 µg/L, while the LOQs were from 0.05 to 7.52 µg/L. The CCα and CCβ were in range 0.05–12.11 µg/L and 0.08–15.16 µg/L. In addition, the average recoveries of the spiked urine samples were from 71.0 to 117.0% and coefficient of variation (CV) < 21.38% (intraday and interday). Conclusion: A new isotopic LC-MS/MS method has been developed, validated and applied for identification and quantification of 72 residues of veterinary drugs and pesticides and other contaminants such as mycotoxins in bovine urine. The most appropriated sample preparation procedures involved sodium acetate buffer, enzymatic hydrolysis using β-glucuronidase and cleanup solid phase extraction with OASIS SPE cartridges. The parameters were satisfactorily validated fulfilling requirements under Regulation 2021/808/EC. Consequently, the method could be used in routine analysis of bovine urine samples for simultaneous detection of veterinary drug and pesticide residues as well as contaminants such as mycotoxins.

A different, reliable, and cost-effective strategy for the analysis of aflatoxins, ochratoxin A, and zearalenone in corn-based foods was proposed, including one multi-toxin immunoaffinity column (IAC) sample preparation and three different high-performance liquid chromatography fluorescence detection methods. The analytical procedures were tested and verified, keeping in mind their occurrence at trace levels in corn-based foods. With the validation of the proposed multi-toxin IAC methodology and comparison of the performance characteristics with methods using a single-toxin IAC, we confirmed the reliability of the multi-toxin IAC procedure versus the single-toxin IAC. The methods were validated by revealing satisfactory performance characteristics; for example, the obtained values of limit of detection were significantly lower than the maximum limits for all mycotoxins of concern. In addition, the recovery values were between 70.9 % and 106.1 % for all mycotoxins of interest, with precision values lower than 10.5 %.

Commensal Escherichia coli has the potential to easily acquire resistance to a broad range of antimicrobials, making it a reservoir for its transfer to other microorganisms, including pathogens. The aim of this study was to determine the prevalence of resistant commensal Escherichia coli isolated from dairy cows’ feces. Phenotypic resistance profiles and categorization were determined by minimum inhibitory concentration (MIC) testing with the broth microdilution method, while the PCR method was used to determine the presence of resistant genes. Out of 159 commensal E. coli isolates, 39 (24.5%) were confirmed to have resistance. According to the MIC values, 37 (97.3%) and 1 (2.7%) isolate were phenotypically categorized as ESBL and ESBL/AmpC, respectively. All isolates showed resistance to ampicillin, while 97.4%, 56.4%, and 36% showed resistance to cefotaxime, ciprofloxacine, and azitromycine, respectively. Not all isolates that showed phenotypic resistance were found to be carrying the corresponding gene. The most prevalent resistant genes were gyrA, tetA, sul2, and tetB, which were present in 61.5%, 64%, 54%, and 49% of the isolates, respectively. The results clearly indicate that, besides their resistance to multiple antimicrobials, the commensal E. coli isolates did not necessarily carry any genes conferring resistance to that particular antimicrobial.

Screening and confirmatory methods for Aflatoxin M1 (AFM1) analysis were optimized and studied regarding the European Decision 2002/657/EC requirements. The recoveries of AFM1 from the spiked samples at levels between 0.025 and 0.075 µg kg-1 were 99.6-110.3 % with obtained precision 10.5-13.4 %, confirming the immunochemical method precision and reliability for intensive surveillance studies. According to the regulatory requirements, for confirmation of the obtained positives from the screening, the reference high-performance liquid chromatographic method was evaluated in reference to the performance criteria. The obtained between-day recovery was in the range 67.2-72.8 %, and the precision range 5.4-9.1 %. 205 positives from the screening were subjected to confirmatory analysis. The comparison of the data obtained from the immunoassay and chromatographic method (R2=0.764) revealed a slight overestimation of the screening method, but no cases of false-negative determinations occurred.

The Commission Delegated Regulation (EU) 2024/1229 establishes maximum limits (MLs) for the cross-contamination of antimicrobial substances in feed intended for food-producing animals. This study presents the development and in-house validation of two multi-class analytical methods for detecting antimicrobial substances in poultry feed at cross-contamination levels, using ultra-high-performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS). Methods with and without solid-phase extraction (SPE) purification were developed for 26 antimicrobial substances across eight classes, covering a concentration range of 5–300 µg kg–1. The established methods were validated for parameters including selectivity, limit of quantification, within- and between-day precision, accuracy, decision limit, matrix effect, and short-term analyte stability in the corresponding extracts. Both methods met the required performance criteria for detecting antimicrobial substances at cross-contamination levels, except for reduced sensitivity to lincomycin in the SPE method. The between-day precision was below 18 % and 19 %, with recoveries ranging from 95 to 103 %, for the method without SPE, and from 88 % to 101 % for the method with SPE, respectively. The analytes remained stable after two days of storage in the dark under two temperature regimes. These methods were applied to 39 poultry feed samples, revealing that approximately 41 % contained one or more antimicrobial substances, some of which exceeded the cross-contamination MLs. This study underscores the importance of simple, rapid, sensitive, and reliable analytical methods for controlling the presence of antimicrobials at cross-contamination levels. Such methods enable stringent control to prevent uncontrolled antimicrobial use in poultry production systems, thereby mitigating the development of antimicrobial resistance.

Milk and dairy products are highly vulnerable to Economically Motivated Adulteration (EMA), particularly through the substitution of milk fat with cheaper non-milk fats. This paper presents a comprehensive review of analytical approaches used for detecting vegetable oils (e.g., palm, coconut, sunflower) and animal-origin fats such as pork lard and bovine tallow, as well as the fraudulent mixing of milk from different species. Methods for lipid extraction are examined, including traditional gravimetric procedures such as the Röse–Gottlieb method and high-efficiency alternatives such as Accelerated Solvent Extraction and supercritical fluid extraction. Analytical strategies for fraud detection are evaluated, demonstrating that while fatty acid profiling is widely applied, its sensitivity is limited by natural variability. Greater discriminatory power can often be achieved through triacylglycerol analysis combined with mathematical models such as the Precht formulae (which generate S-values), although performance varies depending on the adulterant matrix, adulteration level, reference population, and analytical protocol. Similarly, sterol profiling, particularly the detection of phytosterols like β-sitosterol, is a valuable marker for vegetable oil adulteration but does not provide an equivalent solution for detecting animal fat adulteration. The potential of rapid, non-destructive screening tools, including Fourier-transform infrared and Raman spectroscopy supported by chemometrics, is also assessed. A central analytical challenge in detecting such fraud lies in the complexity and variability of milk fat composition, which hinders any single analytical method from universally identifying all forms of non-milk fat adulteration; therefore, a tiered strategy combining rapid screening tools with high-resolution confirmatory methods is preferable. Future perspectives highlight the increasing importance of green analytical approaches, artificial intelligence, and portable detection systems for enhancing verification within the global dairy supply chain. However, the effectiveness of AI and chemometric methods depends heavily on the availability of representative training datasets and rigorous external validation to avoid overfitting and ensure reliable application across diverse samples.