Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants that tend to accumulate in solid matrices such as sewage sludge, raising concerns regarding their fate and potential ecological risks. This study aimed to develop and validate a robust analytical method for the accurate determination of PFAS in dehydrated sludge. A liquid chromatography–tandem mass spectrometry (LC–MS/MS) method was optimized for 28 PFAS, including perfluoroalkyl carboxylic acids (PFCAs) and sulfonic acids (PFSAs). Solid–liquid extraction with basic methanol was followed by cleanup using a cartridge packed with ferrite and sodium sulfate to remove moisture and particulate interferences. Chromatographic separation was performed with an Avantor® ACE® PFAS Delay column coupled to an Agilent triple quadrupole MS operating in negative electrospray ionization mode. The method achieved excellent sensitivity (MDL < 0.02 µg/g dry weight for most compounds), satisfactory precision (RSD < 15%), and recoveries between 80–118%. Optimization of mobile phase additives, gradient conditions, and MS parameters enhanced chromatographic resolution and signal-to-noise ratio. The validated method demonstrates high reliability for PFAS determination in complex solid matrices and can be applied as a valuable tool for environmental monitoring and risk assessment of sludge management practices.

A high-performance liquid chromatography method coupled with diode array detection (HPLC-DAD) was developed for simultaneous quantification of andrographolide (AG) and 14-deoxy-11,12-didehydroandrographolide (DDAG) in rat plasma. A salt-assisted liquid–liquid extraction (SALLE) procedure was optimized, with MgSO4 yielding the highest extraction efficiency (>90% for both AG and DDAG), outperforming conventional solvent extraction, and being comparable to solid-phase extraction. The method exhibited acceptable linearity (125–2000 ng/mL, R2 > 0.99), with low limits of detection and quantification of 60 and 70 ng/mL for AG and 201 and 234 ng/mL for DDAG, respectively, while adhering to the ICH M10 criteria for accuracy, precision, and stability under various storage conditions. Stability testing of the prepared samples demonstrated that >99% AG and 95% DDAG were retained when stored at low temperatures, specifically below 4 °C. The developed method was successfully applied in a pharmacokinetic study following oral administration of Andrographis paniculata extract (containing AG 7.5 mg/kg) to healthy Wistar rats. The SALLE-HPLC-DAD method developed herein enables selective AG quantification without significant matrix interference. In conclusion, this study introduces an alternative sample preparation and analytical method that is fast, cost-effective, and reliable, making it suitable for pharmacokinetic studies of the principal biomarker of Andrographis paniculata.

In modern circular-economy value chains, wood is frequently processed into fines, chips, or powders—forms in which anatomical features are no longer visible, rendering traditional visual identification methods ineffective. This study introduces a rapid, non-destructive attenuated total reflection–Fourier transform infrared (ATR-FTIR) spectroscopy approach, combined with chemometric modeling, to address this challenge by enabling both the classification and compositional profiling of processed wood fractions. Using full-spectrum ATR-FTIR data, partial least squares discriminant analysis (PLS-DA) models achieved high-accuracy classification of wood by type, species, and provenance, with sensitivity and specificity reaching up to 1.00. In addition, PLS and backward interval BiPLS models predicted total lignin, acid-soluble lignin, and extractives with strong performance (R2 > 0.90, RPD > 2). Interval selection further enhanced prediction accuracy by reducing RMSEP by up to 30%, improving model stability for real-world application. By replacing slow, reagent-intensive wet chemistry with a rapid, green, and scalable technique, the presented methodology provides a valuable tool for authentication, quality control, and resource optimization when dealing with mechanically processed or recycled wood.

In this study, an integrated analytical method coupling ultra-high-performance liquid chromatography–photodiode array detection–quadrupole time-of-flight mass spectrometry with total antioxidant capacity determination (UHPLC-PDA-Q-TOF/MS-TACD) was developed for the rapid screening and identification of antioxidants in complex natural extracts. The system enables simultaneous chromatographic separation, mass spectrometric characterization, and on-line activity assessment by combining 1,1-diphenyl-2-picrylhydrazyl radical (DPPH) and ferric ion reducing antioxidant power (FRAP) assays. When applied to Rosa rugosa samples from five different origins, the approach efficiently separated the extract and successfully localized antioxidants directly from the chromatographic profile. A total of 86 compounds were identified, including flavonoids, tannins, and phenolic acids. Among them, 60 exhibited free radical scavenging capacity and 59 showed reducing activity. Activity verification experiments showed that all seven compounds exhibited good antioxidant activity. The IC50 values of gallic acid, ellagic acid, quercetin 3-O-rhamnoside, and rutin were 0.019, 0.025, 0.043, and 0.046 mM, respectively, which were significantly better than the positive control (vitamin C). This method provides methodological and technical support for the rapid discovery of antioxidant components in complex natural products.

Wine phenolics serve as robust chemical signatures correlated to grape variety, processing, and regional identity. This study explores the potential of machine learning algorithms, combined with the phenolic profiles of Albanian wines, to classify them according to grape variety. Geographic origin analysis was conducted as a preliminary exploration. The dataset of phenolic compounds included white and red wines, spanning the 2017 to 2021 vintages. Using five supervised algorithms—Support Vector Machine (SVM), Random Forest, XGBoost, Logistic Regression, and K-Nearest Neighbors—a high classification accuracy was achieved, with SVM reaching 100% under Leave-One-Out Cross-Validation (LOOCV). To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) and stratified cross-validation were applied. Random Forest feature importance consistently highlighted trans-Fertaric acid and Procyanidin B3 as dominant discriminants. Parallel coordinates plots demonstrated clear varietal patterns driven by phenolic differences, while PCA and hierarchical clustering confirmed unsupervised grouping consistent with wine type and maceration level. Permutation testing (1000 iterations) confirmed the non-randomness of model performance. These findings show that a small set of phenolic markers can offer high classification accuracy, supporting chemically based wine authentication. Although the dataset is relatively small, thorough cross-validation, non-redundant modeling, and chemical interpretability provide a solid foundation for scalable methods. Future work will expand the dataset and explore sensor-based phenolic measurement to enable rapid authentication in wine.

As one of the world’s most widely used packaging materials, paper obtains its properties from its major component: wood. Variations in the species of wood result in variations in the paper’s mechanical properties. The pulp and paper production industry is known to be a polluting industry and a consumer of a large amount of energy but remains an essential heavy industry globally. Paper production, based largely on the kraft process, is mainly intended for the food packaging sector and, thus, is associated with contamination risks. The lack of standardized regulations and the different analytical techniques used make information on the subject complex, particularly for inorganic elements where little information is available in the literature. Most research in this field is based on sample preparation using mineralization via acid digestion to obtain a liquid and homogeneous matrix, mainly with a HNO3/H2O2 mixture. The most commonly used techniques are Atomic Absorption Spectrometry (AAS), Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES), and Inductively Coupled Plasma Mass Spectrometry (ICP-MS), each with its advantages and disadvantages, which complicates the use of these tech-niques for routine analyses on an industrial site. In the same field of inorganic compound analysis, Microwave Plasma Atomic Emission Spectrometry (MP-AES) has become a real alternative to techniques such as AAS or ICP-AES. This technique has been used in several studies in the food and environmental fields. This publication aims to examine, for the first time, the state of the art regarding the analysis of inorganic elements in food packaging and different matrices using MP-AES. The entire manufacturing process is studied to identify possible sources of inorganic contaminants. Various analytical techniques used in the field are also presented, as well as research conducted with MP-AES to highlight the potential benefits of this technique in the field.

(1) Background: Caffeine is one of the most widely consumed psychoactive substances. Its safety profile and short half-life make it an ideal drug model for studying the pharmacokinetics of caffeine. This study aimed to develop a method for determination of caffeine in a small volume of saliva (200 µL). (2) Methods: Solid-phase extraction was employed to isolate caffeine from saliva, followed by quantitative analysis using liquid chromatography coupled with diode-array detection. Chromatographic separation was achieved on a C18 column, using a gradient mobile phase of acetonitrile and 0.1% formic acid. (3) Results: The method was validated for selectivity, linearity, precision, and accuracy. Linearity was established over the range of 10–10,000 ng/mL (R2 = 0.995). The coefficients of variation for intra- and inter-day precision for the three tested caffeine concentrations did not exceed 12.11%. Recovery from spiked saliva samples exceeded 90.53%. The developed method was applied to preliminary studies to follow the pharmacokinetics of caffeine in saliva. The concentration of the substance was studied in the saliva obtained from a volunteer after espresso consumption. (4) Conclusions: The developed method will offer a reliable approach for non-invasive caffeine monitoring in clinical and research applications.

Significant depletion of natural resources, coupled with increased environmental pollution resulting from the constant evolution of global industrialization, poses a considerable problem. Therefore, it is unsurprising that sustainable “green” chemistry and technology are gathering the worldwide scientific community, whose common goal is to find applicable solutions for the abovementioned problems. This paper combined the ultrasonic extraction method (a form of “green” technology) with natural deep eutectic solvents (NADESs, a type of “green” solvent) for the production of extracts from an industrial by-product (discarded waste wild apple dust). Waste wild apple dust was pretreated with different NADESs in order to explore the pretreatment benefits regarding ultrasonic extraction of bioactive compounds. Among all solvents used, aqueous propylene glycol was chosen as the best system, which, combined with Reline NADES pretreatment, provided the highest TPC and TFC values, together with the best antioxidant activities. UHPLC-DAD-MS analyses of extracts revealed the presence of natural organic acids, quercetin and kaempferol derivatives, tannins, and flavones. Following this procedure, valorization of agro-industrial apple herbal waste resulted in obtaining extracts with high potential for utilization in different industrial branches (food and pharmaceutical industries), contributing to both cleaner production and reduced environmental impact.

Pesticide residue violations continue to challenge the compliance of Turkish horticultural exports with European Union food safety regulations. This study examined 1138 RASFF alerts (1660 detections) issued between 2020 and 2025 using statistical enrichment, time-series modeling, and unsupervised machine learning. Pepper was the most frequently rejected commodity, with strong enrichments of formetanate (71-fold), pyridaben (35-fold), and acetamiprid (5-fold). Notably, chlorpyrifos and chlorpyrifos-methyl remained among the most commonly detected residues despite EU bans, suggesting continued use of stockpiled or illicit products and prolonged environmental persistence. Rejections peaked during winter and spring, particularly for citrus and greenhouse-grown crops. Clustering and association rule mining revealed modular commodity–pesticide structures and recurrent co-detection patterns. Anomaly detection further identified discrete periods of irregular contamination. Overall, the results indicate that violations are seasonally patterned and structurally embedded. Targeted monitoring aligned with crop calendars and stricter enforcement of legacy pesticide phase-outs could significantly improve compliance and reduce export rejections.

Fundamental research, exploration, extraction, and metallurgical studies of rare earth elements (REEs) require the use of analytical techniques. Recently, emerging developments of analytical instrumentation for REEs have taken place, with some of them having shrunk in size, becoming handheld devices. The Flame and Graphite Furnace AAS, ICP-OES, and MP-AES are standard laboratory techniques used for the analysis of REEs. ICP-MS, ICP-MS/MS, ICP-TOF-MS, HR-ICP-MS, MH-ICP-MS, and MC-ICP-MS are popular techniques for REE analysis thanks to their ultrahigh sensitivity, minimal interference effects, and broad applicability. The INAA, XRF, LIBS, and LA-based ICP-MS techniques are widely employed for the direct analysis of solid samples. The TIMS, SIMS, and SHRIMP are common techniques used for dating isotopic REE deposits. The portable XRF, LIBS, and Raman spectrometer devices can perform on-the-spot in situ analysis, which may help make speedy decisions in the exploration study of REEs. Currently, hyperspectral remote sensing platforms, such as handheld, drone, and satellite-based devices, are preferred for the exploration of REEs due to their cost-effectiveness, which enables the coverage of large areas in a limited amount of time. The use of microanalytical sensors installed on remotely operated vehicles has been successfully applied in analyzing rich REE-bearing deposits in the deep sea. In general, this review provides in-depth information on all essential aspects, from analytical instruments to cutting-edge developments in the analysis of REE-bearing resources.