Determination of pollutants, antibiotics, and drugs in surface water in Italy as required by the third EU Water Framework Directive Watch List: method development, validation, and assessment.

In this paper, we report a study concerning the quantification of new emerging pollutants in water as a request from the third European Watch List mechanism. The EU Watch List compound was investigated by an internal method that was validated in terms of detection limits, linearities, accuracy, and precision in accordance with quality assurance criteria, and it was used to monitor several rivers from 11 Italian regions. The methodology developed was satisfactorily validated from 5 to 500 ng L-1 for the emerging pollutants studied, and it was applied to different river waters sampled in Italy, revealing the presence of drugs and antibiotics. Rivers were monitored for 2 years by two different campaigns conducted in 2021 and 2022. A total of 19 emerging pollutants were investigated on 45 samples. The most detected analytes were O-desmethylvenlafaxine and venlafaxine. About azole compounds, sulfamethoxazole, fluconazole, and Miconazole were found. About antibiotics, ciprofloxacin and amoxicillin were found in three and one samples, respectively. Moreover, statistical analyses have found a significant correlation between O-desmethylvenlafaxine with venlafaxine, sulfamethoxazole with venlafaxine, and fluconazole with venlafaxine.

Dissipation dynamics and comparative dietary exposure assessment of mefentrifluconazole in rice.

A fast and sensitive analytical method based on UHPLC coupled with tandem mass spectrometry was established to investigate the dissipation and final residual amounts of mefentrifluconazole in rice, and dietary risk to consumers was evaluated. The method provided good linearity (R2 ≥ 0.9979), accuracy (recovery range, 79.0-101.5%), precision (relative standard deviation range, 1.3-13.9%), and sensitivity (limit of quantification, 0.005 mg/kg). The dissipation dynamics of mefentrifluconazole in rice followed first-order kinetics, with half-lives of 2.8-16.6 days. The final residues of mefentrifluconazole in various samples of harvested brown rice ranged from less than the limit of quantification to 0.092 mg/kg, the latter value being higher than the maximum residue limit recommended by the European Union. Comparative dietary exposure of mefentrifluconazole was assessed using field data and Chinese dietary patterns for different genders, regions, and age data. Although the results showed acceptable levels of risk for both acute exposure (the percentage of the acute reference dose ≤ 0.7483%) and chronic dietary intake (the percentage of acceptable daily intake ≤ 31.8516%), more studies of children are needed because they are at higher risk than other groups. This work provides the necessary data for registering and establishing the maximum residue limit for mefentrifluconazole in rice in China and reveals the potential risks to different groups of long-term application of mefentrifluconazole to rice and other crops.

Detection Method of Falsified Medicines by Using a Low-Cost Raman Scattering Spectrometer Combined with Soft Independent Modeling of Class Analogy and Partial Least Squares Discriminant Analysis.

There are many reports of falsified medicines that may cause harm to patients. A rapid and simple method of identifying falsified medicines that could be used in the field is required. Although Raman scattering spectroscopy has become popular as a non-destructive analysis, few validation experiments on falsified medicines that are actually distributed on the market have been conducted. In this study, we validated a discriminant analysis using an ultra-compact, portable, and low-cost Raman scattering spectrometer combined with multivariate analysis. The medicines were three types of erectile dysfunction therapeutic tablet and one type of antifungal tablet: tadalafil (Cialis), vardenafil hydrochloride (Levitra), sildenafil citrate (Viagra), and fluconazole (Diflucan), which is sometimes advertised as female Viagra. For each medicine, the authentic standard product and products obtained by personal import via the internet (genuine or falsified) were used. Discriminant analyses were performed on the Raman spectra combined with soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA). It was possible to identify all falsified samples by SIMCA using the standard product model for all four products. Using the PLS-DA using the PLS models of the four standard products, falsified Levitra and Diflucan samples were classified correctly, although some falsified Cialis and all Viagra samples also belonged to the standard class. In this study, SIMCA might be more suitable than PLS-DA for identifying falsified medicines. A spectroscopic module that combines the low-cost Raman scattering spectroscopy with SIMCA might contribute to the rapid identification of falsified medicines in the field.

Usefulness of capability indices in the framework of analytical methods validation.

Analytical methods capability evaluation can be a useful methodology to assess the fitness of purpose of these methods for their future routine application. However, care on how to compute the capability indices have to be made. Indeed, the commonly used formulas to compute capability indices such as Cpk, will highly overestimate the true capability of the methods. Especially during methods validation or transfer, there are only few experiments performed and, using in these situations the commonly applied capability indices to declare a method as valid or as transferable to a receiving laboratory will conduct to inadequate decisions. In this work, an improved capability index, namely Cpk-tol and the corresponding estimator of proportion of non-conforming results (π(Cpk-tol)) have been proposed. Through Monte-Carlo simulations, they have been shown to greatly increase the estimation of analytical methods capability in particular in low sample size situations as encountered during methods validation or transfer. Additionally, the usefulness of this capability index has been illustrated through several case studies covering applications commonly encountered in the pharmaceutical industry. Finally a methodology to determine the optimal sample size required to validate analytical methods is also given using the proposed capability metric.