Search Results (9,770)

Background/Objectives: Fluoroquinolones (FQNs) are widely prescribed broad-spectrum antibiotics but are associated with central and peripheral nervous system adverse reactions (ARs). Regulatory agencies have issued multiple safety warnings regarding their neuropsychiatric effects; however, large-scale, comparative evaluations across individual FQNs remain limited. This study aimed to comprehensively characterise and compare neuropsychiatric profiles associated with Ciprofloxacin, Levofloxacin, and Moxifloxacin using pharmacovigilance data. Methods: A retrospective pharmacovigilance study was conducted using reports from the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) between 2010 and 2025. Neuropsychiatric ARs were identified using MedDRA terms, including neurological and psychiatric manifestations. Reporting trends, demographic characteristics, and event frequencies were descriptively analysed. Signal detection was performed using the Information Component (IC), Proportional Reporting Ratio (PRR), and Reporting Odds Ratio (ROR). Results: A total of 95,968 individual case safety reports involving neuropsychiatric ARs were included. Levofloxacin accounted for the highest number of reported events, followed by Ciprofloxacin and Moxifloxacin. Disproportionality analyses identified peripheral neuropathy as the strongest neurological signal for Levofloxacin and Moxifloxacin, while Ciprofloxacin showed stronger central nervous system associations. Psychiatric ARs were drug-specific, with anxiety predominating for Ciprofloxacin and Moxifloxacin, and insomnia for Levofloxacin. All major signals were statistically robust (IC025 > 0), confirming distinct compound-specific neuropsychiatric risk profiles. Conclusions: The large-scale 16-year analysis demonstrates distinct, drug-specific neuropsychiatric risk profiles. The available evidence supports a non-interchangeable safety profile among FQNs and emphasises the importance of drug-specific risk–benefit assessment in clinical practice. Full article

Polyethylene (PE) microplastics are increasingly recognized as a critical environmental and food-safety concern; however, routine monitoring remains limited by conventional methods that are labor-intensive, time-consuming, and difficult to translate into rapid, on-site screening. Here, we report a machine learning-guided electrochemical fingerprinting platform for rapid PE microplastic detection using a chitosan–PE interfacial film coupled with electrochemical impedance spectroscopy (EIS) and coulometry. The platform generated concentration-dependent electrical fingerprints in artificial ocean water, captured through Bode, Nyquist, and charge–time responses. Quantification was achieved across 1–256 ng/mL with strong linearity (R² = 0.976) and an ultralow LoD of 0.1 ng/mL, demonstrating high analytical sensitivity. Practical applicability was validated through spike–recovery in ocean water (R² = 0.967) and shrimp-derived matrices with matrix-matched normalization, yielding recoveries of 90–105% across low, mid, and high spike levels. Under the tested particle set, PE produced stronger responses than non-target polypropylene (PP) and polystyrene (PS), supporting empirical polymer discrimination. Machine learning classification using impedance-derived features achieved an AUC = 0.98, with 100% correct identification of Low and 95.24% correct identification of High samples. Overall, this electrochemical–ML framework enables rapid, sensitive, and matrix-tolerant PE microplastic screening in environmental water and seafood-related matrices, offering a promising pathway toward portable microplastic monitoring. Full article

Lactobacillus species are widely used in various food products, including conventional food products, dairy products, and health food products. To achieve the desired functional properties, manufacturers commonly incorporate two or more distinct Lactobacillus species during production. In this study, a multiplex PCR detection method was developed for four Lactobacillus species commonly used in food based on TaqMan real-time fluorescent PCR technology, enabling the efficient and rapid identification of multiple Lactobacillus strains in food matrices. The research team selected and validated four representative species—Lactobacillus rhamnosus, Lactobacillus plantarum, Lactobacillus acidophilus, and Lactobacillus paracasei—as targets for the multiplex PCR assay, designing specific primer–probe combinations for each. The accuracy and reliability of the detection method were rigorously evaluated through a series of validation experiments, including the assessment of primer–probe specificity, optimization of fluorescent signal labeling chemistries, determination of the limits of detection for individual strains, evaluation of the method’s repeatability, and analysis of commercial food samples. The results demonstrated that the selected primer–probe sets exhibited no cross-reactivity in the multiplex system and specifically amplified their target Lactobacillus species, with no amplification observed for non-target strains. The established method achieved a minimum LOD for L. acidophilus of 10² CFU/g and showed high repeatability across replicates. Furthermore, the successful detection of labeled Lactobacillus strains in commercial products confirmed the method’s practical applicability. Therefore, the developed multiplex real-time PCR assay provides a reliable, sensitive, and high-throughput tool for the simultaneous detection of multiple Lactobacillus species in complex food products and holds potential for application in quality control, product authentication, and regulatory compliance monitoring. Full article

Bivalve mollusks efficiently bioaccumulate human enteric viruses, posing significant food safety risks. This study assessed the prevalence of Norovirus (NoV GI and NoV GII), Hepatitis A virus (HAV), and Hepatitis E virus (HEV) in 59 samples of live mussels (Mytilus galloprovincialis) collected from the Bulgarian Black Sea coast between July 2022 and July 2023. Viral detection was performed using one-step real-time reverse transcription-polymerase chain reaction (RT-qPCR) following ISO 15216-2 standards, with a mean extraction efficiency of 4.06%. Norovirus GII was the most prevalent pathogen, with detection peaks following intense rainfall events in July 2023. In contrast, all samples tested negative for HAV and HEV. The analysis showed no significant correlation between E. coli contamination levels and the presence of NoV (Mann–Whitney U test, p = 0.565). The viral RNA was detected in several samples that otherwise complied with regulatory bacterial standards for direct consumption (≤230 MPN/100 g). In conclusion, within the limitations of the evaluated sample size and the specific geographically unbalanced sampling design, NoV GII was the predominant genogroup detected. These results suggest that current bacterial indicators may be insufficient to ensure viral safety in these products. In this regard, national control authorities need to undertake timely policies and measures for better and adequate surveillance, control and prevention of viruses in the different parts of the food chain. Full article

The presented study aimed to fully characterise berry powders derived from raspberry, blackberry and strawberry (RB, BB, SB) as well as raspberry and blackberry seed powders (RBS, BBS) in terms of proximate composition, the individual profile of minerals, sugars, organic and fatty acids, and phenolic and volatile compounds. Additionally, testing of powders’ colour and antioxidant activity, as well as spectroscopic analysis, were also performed. Higher total and individual sugars, organic and phenolic acids, flavonols and anthocyanins content distinguished berry powders from the seed powders. Individually, RB contained significant amounts of citric and chlorogenic acids, BB was superior in cyanidin-3-O-glucoside and quercetin-3-O-rutinoside content, while SB was characterised by high sucrose, fructose, omega-3, and mineral (Ca, Mg, Fe) content. Berry seed powders exhibited remarkable TDF content, beneficial PUFA/SFA ratio, lighter colour, higher individual flavan-3-ols quantity, TPC and DPPH activity compared to berry powders. Mentioned discrepancies between berry and berry seed powders on a compositional level were also visible on ATR-FTIR spectra across all detected regions reflecting bonds attributed to cellulose, lipids, phenols and sugars. Pleasant, predominantly green, fruity and floral aromas were associated with berry powders, whilst additional herbal notes were characteristic of berry seed powders, all derived from the alcohols, aldehydes, esters and ketones as paramount volatile compounds. All examined powders can bear a nutritional claim of “high in” fibre (20.47–65.33%) and Mg (114.52–128.70 mg/100 g), enabling the design of food products packed with nutrients and bioactives while simultaneously reducing fresh fruit and fruit-processing waste. Full article

Acorns (Quercus spp.) are an underutilized forest resource with recognized nutritional and bioactive potential, making them promising candidates for the development of sustainable plant-based functional foods. This study aimed to valorize acorns through the formulation of two novel acorn-based products, a plant-based beverage, and a pudding, and to assess their nutritional properties, sensory acceptability, and, for the beverage, refrigerated shelf-life stability. The beverage was optimized as a neutral-flavored milk alternative, using sodium alginate as a natural clean-label stabilizer to enhance emulsion stability and physicochemical properties. The final formulation exhibited low energy density and a lipid profile rich in monounsaturated fatty acids, contributing to its nutritional and functional value. Throughout 63 days of storage at 4 °C, sodium alginate effectively prevented phase separation and supported the retention of antioxidant capacity, as evidenced by stable ferric reducing antioxidant power (FRAP) and total phenolic content, although ABTS radical scavenging activity declined over time. No microbial growth was detected during storage, confirming the adequacy of the applied thermal treatment and aseptic filling procedures applied. The acorn-based pudding, developed by adapting a traditional egg-based recipe, functioned as a proof of concept illustrating the technological versatility of acorns across distinct plant-based matrices, exhibiting a nutritional profile comparable to commercial counterparts and high consumer acceptability. Overall, this work demonstrates the technological feasibility and versatility of incorporating acorns into plant-based food matrices, supporting their potential as sustainable ingredients for the development of innovative value-added foods and contributing to the valorization of forest resources. Full article

The aim of this study was to valorize by-products of blue poppy (Papaver somniferum), a widely used ingredient in the food industry. This study focused on the isolation of bioactive compounds from leaves, stems, roots, capsules and cold-pressed cake. All samples were subjected to conventional solid–liquid extraction (SLE) using ethanol–water solutions of varying concentrations (0, 20, 40, 60, 80 and 96%) as the extraction solvent. The obtained extracts were analyzed for total phenolic content (TP), hydroxycinnamic acids (HCA), flavonols (FL), total flavonoids (TF), condensed tannins (CT) and antioxidant activity. Furthermore, the extracts were subjected to untargeted LC-MS analysis for qualitative characterization. Ethanol concentration significantly influenced the extraction efficiency of bioactive compounds, with the optimal solvent varying depending on the plant part and the specific class of compounds analyzed. Based on TP and TF content, capsule extracts exhibited the highest polyphenol levels. HCAs were detected in extracts from leaves, capsules, and cold-pressed cake. In total, 62 compounds were identified, belonging to various biochemical classes, including organic acids, hydroxycinnamic acids, alkaloids, flavonoids, and fatty acids. Overall, the results indicate that poppy by-products are a valuable source of bioactive components, with promising applications across different industrial sectors. Full article

Aflatoxins are widespread hepatotoxic food contaminants, yet age-specific cumulative exposure to multiple aflatoxins and associated health risks remain poorly characterized. This study assessed cumulative dietary exposure to aflatoxin B₁ (AFB₁), B₂, G₁, and G₂, and hepatocellular carcinoma (HCC) risk across five age groups, evaluating the influence of packaging and retail sources on contamination. Contamination data of 1179 food samples and consumption data were integrated to calculate the margin of exposure (MoE) and annual HCC incidence. AFB₁ was most frequently detected and often co-occurred with other aflatoxins; bulk vegetable oils showed the highest total aflatoxin detection rate. Roasted peanuts contributed most to aflatoxin exposure, particularly among children aged 3–6 (MoE 900–1206). Rice, rice products, and coarse grains were primary contributors to aflatoxin-attributable HCC risk (0.008 cases per 100,000 person-years). Overall contamination was significantly higher in bulk products than in pre-packaged foods (p < 0.05) and in samples from farmers’ markets and grocery stores than in other sites (p < 0.05). These findings reveal non-negligible aflatoxin-related health risks for Guangzhou residents, especially young children and frequent consumers of staple grains and nuts. Targeted monitoring of high-risk foods and retail environments and age-specific dietary guidance are recommended to reduce population-level aflatoxin exposure and HCC risk. Full article

Trifluoroacetic acid (TFA) is an ultrashort-chain perfluoroalkyl substance (PFAS) characterized by environmental persistence, water solubility, and a growing global presence, resulting primarily from the degradation of fluorinated compounds. Evidence suggests that plant-based foods may represent an underestimated exposure route, with wine emerging as a significant dietary source due to accumulation in soils, irrigation water, and plant uptake. This review provides an updated summary of the evidence on the environmental sources and temporal evolution of TFA in wine, its analytical detection, its contribution to dietary exposure, potential implications for human health, and current regulatory attention. A structured but non-systematic literature search was conducted using PubMed and Scopus, supplemented by European reports and monitoring data, and in accordance with SANRA guidelines. Evidence shows that TFA concentrations in wine derive from widespread environmental sources and have increased over time, from negligible levels before the 1970s to a marked increase in recent decades. Reported concentrations range from tens to several hundred µg/L, despite analytical challenges. Exposure estimates indicate that wine may contribute significantly to total dietary TFA intake in regular consumers. Although toxicological data suggest low acute toxicity, uncertainties remain regarding long-term exposure, and regulatory limits for TFA in foods and beverages are lacking. Full article

Electrochemical sensing provides an alternative approach for the trace detection of bioactive substances in fruits. However, the complex matrix in fruit tissues, the coexistence of multiple active components, and the varied pH environments limit the sensing performance and accurate quantitative detection of conventional electrochemical sensors. Herein, a dual-mode electrochemical sensor based on a Co₃O₄@N-MWCNTs modified glassy carbon electrode was developed for the sequential detection of quercetin, rutin, and glucose in fruits under acidic and alkaline conditions. The as-prepared electrode exhibited improved charge transfer efficiency and favorable electrocatalytic activity toward the three target analytes. Under optimal conditions, the sensor displayed wide linear ranges of 0.5~70 μM for quercetin and 0.5~5 μM for rutin in acidic environment, with low detection limits of 0.124 μM and 0.045 μM, respectively. In alkaline environment, the detection limit for glucose was determined to be 8.86 μM. Moreover, four combined machine learning models with feature selection algorithms were established, among which the CARS-RFE+RFR model achieved the best prediction accuracy and robustness for multicomponent quantification. Furthermore, the proposed sensing system was applied to the rapid determination of quercetin, rutin, and glucose in real litchi samples, with recoveries ranging from 98.4% to 105.4%. This study provides a feasible electrochemical strategy for multicomponent detection in complex plant matrices, showing good applicability for rapid on-site analysis in agricultural and food-related applications. Full article

The growing demand for functional foods has stimulated the development of chocolate matrices enriched with bioactive ingredients. This study aimed to evaluate the effect of extraction conditions and formulation strategies on the phenolic profile and antioxidant activity of dark chocolate. Five formulations were evaluated: control chocolate (C), chocolate containing vitamin microcapsules (T1), chocolate with DHA/EPA microcapsules (T2), lipid-modified chocolate with structuring oil (T3), and chocolate combining microcapsules with lipid modification (T4). Phenolic compounds were extracted using hydro-organic solvents of different polarities (50% ethanol, 70% methanol, and 70% acetone). Among the tested solvents, 70% methanol showed the highest extraction efficiency, enabling broader detection of phenolic compounds and alkaloids. HPLC-DAD analysis revealed compounds characteristic of cocoa matrices, including epicatechin, gallic acid, vanillin, and procyanidins, as well as the methylxanthines theobromine and caffeine. Among the formulations, T4 exhibited a greater abundance of extractable compounds and the most complex chromatographic profile. Antioxidant activity was evaluated using DPPH radical scavenging and β-carotene/linoleic acid bleaching assays. T4 also showed the highest antioxidant performance in both assays. These findings suggest that the combination of microencapsulation and lipid phase modification may enhance the extractability and functional expression of bioactive compounds, supporting the development of functional chocolate products with added value. Full article

Bacterial infections pose a persistent global threat to public health, driving the demand for rapid, sensitive, and specific detection technologies applicable to disease diagnosis, food safety, and environmental monitoring. Conventional methods like plate culture and polymerase chain reaction are often hampered by lengthy procedures, dependence on complex instrumentation, and requirements for specialized personnel. The emergence of nanozymes and nanomaterials with enzyme-like catalytic activities has introduced a paradigm shift in biosensing, offering superior stability, cost-effectiveness, and tunable functionality compared to their natural counterparts. This review provides a comprehensive and systematic analysis of the latest advancements in nanozyme-mediated bacterial detection. It is structured around the primary signal transduction modalities: colorimetric, fluorescence, electrochemical, and surface-enhanced Raman scattering (SERS) analyses. For each approach, we outline the fundamental design principles, which commonly integrate a synergistic cascade of specific recognition, catalytic signal amplification, and signal readout, and present representative applications for detecting key pathogens like Staphylococcus aureus, Salmonella, and Listeria monocytogenes in complex samples. We evaluate and contrast the advantages, analytical performance, and appropriateness of these different platforms for various practical scenarios. Finally, we address current challenges, including achieving high specificity in complex matrices, precise modulation of nanozyme activity, and method standardization. Perspectives on future research directions aimed at developing next-generation, high-performance, and potentially portable bacterial detection systems are also provided. Full article

Rising global demand for safe, high-quality foods has accelerated the development of rapid, sensitive, and cost-effective analytical technologies for detecting harmful substances and quality markers. Electrochemical sensors have emerged as promising tools for food safety monitoring due to their high sensitivity, fast response, portability, and affordability compared with conventional laboratory methods. This review highlights recent advances in nanostructured electrochemical sensors for detecting key food analytes, including antioxidants, mycotoxins, allergens, and flavor compounds in diverse food matrices. It examines advanced nanomaterials such as metal oxides, MXenes, doped carbon nitrides, and noble metal-decorated graphene, which enhance sensor performance through improved surface area, conductivity, and electrocatalytic activity. Integrated with screen-printed or glassy carbon electrodes, these materials achieve ultra-low detection limits, wide linear ranges, and strong selectivity in complex food systems. The review also explores next-generation applications such as NFC-enabled smart packaging for continuous, non-invasive monitoring across the supply chain. Emerging trends in miniaturization, multiplex sensing, and artificial intelligence are discussed, along with key challenges in translating laboratory innovations into practical commercial solutions for global food safety. Full article

This research aimed to develop a rapid, non-destructive, and accurate method for detecting adulteration in turmeric using Visible–Near-Infrared (UV/Vis and NIR) spectroscopy combined with machine learning algorithms. Spectral data from the samples were collected and analyzed in two ranges: 170–870 nm (UV/Vis) and 900–2170 nm (NIR). Four supervised learning algorithms, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), the Multilayer Perceptron (MLP) neural network, and Decision Tree, were evaluated for modeling. To quantitatively assess model performance, we employed not only the accuracy metric but also complementary performance indicators including precision, recall, and the F1-score to provide a more comprehensive evaluation of classification effectiveness. The models developed in the 900–2170 nm spectral range demonstrated highly significant performance, with most models achieving 100% accuracy on the independent test set. To reduce data dimensionality and enhance computational efficiency, a hybrid feature selection method combining SVM with five algorithms—League Championship Algorithm (LCA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Imperialist Competitive Algorithm (ICA)—was employed. Upon evaluation of each method, the SVM-LCA was selected as the optimal feature selection technique. This algorithm successfully extracted the most effective wavelengths with the highest correlation and lowest error, which maintained or improved the accuracy of the classification models. This study confirms the high potential of UV/Vis and NIR spectroscopy as rapid, non-destructive, and precise tools for detecting adulteration in turmeric. The findings can pave the way for the development of intelligent quality control systems in the food and pharmaceutical industries, playing a crucial role in ensuring consumer health and safety. Full article

This paper presents a comprehensive review on near-infrared (NIR) spectroscopy applied in food analysis, systematically elaborating its core principles, widespread industrial applications, advanced chemometric strategies, and cutting-edge technological progress. NIR spectroscopy (760–2500 nm), characterized by rapid, non-destructive detection and minimal sample preparation, has been widely implemented in quality evaluation and safety monitoring of grains, meat, fruits and vegetables, dairy, fermented products, tea, coffee, and other processed foods, realizing quantitative analysis of nutrients, freshness assessment, texture prediction, adulteration identification, origin tracing, and rapid preliminary screening of toxin/pesticide residues. A series of chemometric methods, including spectral preprocessing (SNV, MSC, S-G smoothing), feature extraction, and variable selection (CARS, PSO-CMW, ICPA), as well as linear/nonlinear modeling algorithms (PLS, SVM, BP-ANN, fuzzy clustering) significantly boost the accuracy and robustness of spectral analysis. Meanwhile, portable NIR devices and online monitoring systems promote on-site and real-time detection in food supply chains. Despite existing challenges such as calibration transfer, matrix interference, and model generalization, innovations like multimodal data fusion, deep learning integration, and intelligent algorithm optimization offer effective solutions. This review not only summarizes the latest research advances of NIR technology in the food field but also emphasizes its significant advantages as a rapid, non-destructive complementary tool to traditional destructive detection methods, providing theoretical support and technical reference for accelerating the industrial translation and standardized application of NIR spectroscopy, and ultimately safeguarding global food quality and safety. Full article