Exosomes are nano-sized extracellular vesicles secreted by cells and they have emerged as promising biomarkers for non-invasive clinical diagnostics. However, their small size (30–200 nm) and low abundance in biofluids pose significant challenges for isolation and detection. This review highlights recent advances in electrochemical biosensors for exosome detection, with particular emphasis on immunomagnetic separation (IMS) strategies and electrochemical platforms developed for clinical diagnostics. Within this context, this review discusses a coherent series of exosome biosensing platforms developed within our research group, contextualized with representative contributions from other teams in the field. The reviewed platforms rely on immunological, enzymatic, and nucleic acid–based recognition strategies, and integrate selective exosome capture, typically assisted by magnetic separation, with electrochemical or related readouts for sensitive detection in complex biological matrices. These approaches integrate selective exosome capture, typically assisted by magnetic separation with electrochemical transduction, enabling sensitive detection in complex biological matrices. Methodologically, the platforms exploit different exosomal features, including surface-associated proteins, intrinsic enzymatic activity, and nucleic acid cargo, illustrating the versatility of electrochemical biosensing for exosome analysis. Reported limits of detection range from approximately 10⁵ down to 10² exosomes μL⁻¹, with demonstrated capability to discriminate clinically relevant samples (e.g., cancer versus healthy controls, Alzheimer’s disease versus controls). The integration of magnetic separation with electrochemical readout in portable formats supports sensitive, specific, and rapid analysis, highlighting the potential of these approaches for POC applications and resource-limited settings. In this regard, this article pays tribute to Prof. Kubota’s legacy in biosensing by reflecting his long-standing emphasis on transforming innovative analytical concepts into solutions suited for real diagnostic settings.
Nimesulide (NMS) is a sulfonamide-class non-steroidal anti-inflammatory drug widely used in pharmaceutical formulations, whose reliable quantification is essential for quality control. In this work, an unmodified laser-induced graphene electrode (LIGe), fabricated by direct CO2 laser ablation of polyimide, is proposed for the electrochemical determination of NMS using square-wave voltammetry (SWV). Voltammetric studies revealed that NMS oxidation is an irreversible, diffusion-controlled process that involves the transfer of two protons and two electrons. After systematic optimization of pH and instrumental parameters, the method exhibited a wide linear working range from 11.0 to 131 µmol L-1, with a detection limit of 1.30 µmol L-1. The sensor showed good repeatability (RSD < 5%, n = 10) and was successfully applied to the analysis of pharmaceutical tablets, yielding results consistent with the declared content. Accuracy was further evaluated in synthetic urine, with recovery rates between 97% and 111%. Interference studies demonstrated that common non-electroactive urinary constituents caused only minor signal variations (<3%). Although the detection limit is higher than that reported for heavily modified electrodes, the proposed approach offers simplicity of fabrication, rapid analysis, disposability, and a broad linear range, which are notably advantageous for pharmaceutical quality control applications where analyte concentrations are relatively high. These results highlight unmodified LIG as a practical, cost-effective, and scalable sensing platform for routine nimesulide analysis.
Cobalt (Co) is an industrially important metal, and its determination at low concentrations is essential due to its biological and toxicological relevance, requiring sensitive analytical methods, often involving preconcentration steps. The magnetic solid phase extraction (MSPE) becomes an efficient and sustainable alternative. In this study, a method based on MSPE was developed for the extraction and preconcentration of cobalt ions in aqueous systems, with determination by flame atomic absorption spectrometry, using the calyces of the Hibiscus sabdariffa flower, after a magnetization process. The material was prepared by washing and magnetization with iron oxide nanoparticles. Energy dispersive X-ray fluorescence (EDXRF) characterization confirmed the high incorporation of iron (≈88%), and X-ray diffraction (XRD) indicated the presence of magnetite/hematite. The pH at the point of zero charge (pHPZC) increased from 2.0 (natural hibiscus) to 3.0 (magnetized). The percentage of Co(II) adsorption was significantly higher for the magnetized material at pH 7 and 8 (≈57–58% of extraction). The MSPE conditions were optimized using factorial design and Doehlert matrix, defining the adsorbent mass (≈51 mg), contact time (≈7.5 min), and eluent (0.1 mol L⁻¹ HCl). The developed method showed a linear range of 0.440–10.0 mg L-1 (r = 0.990), with a detection and quantification limit of 0.132 mg L-¹ and 0.440 mg L-¹, respectively. Recovery tests in aqueous samples (coconut water, swimming pool water, tap water, calcium-rich hard water, and cachaça—a Brazilian spirit distilled from sugarcane juice) showed variable recoveries (47.3% to 118%), indicating that accuracy is matrix-dependent, with challenges observed in complex samples (calcium-rich hard water and cachaça). The proposed method, using a low-cost, natural, and magnetized adsorbent, shows potential for the determination of Co(II), particularly in simpler matrices, aligning with the principles of green analytical chemistry.
Studies have shown that organic fertilizers derived from liquid swine waste (LSW) can improve several soil chemical properties, including cation exchange capacity (CEC). The determination of CEC is traditionally carried out using chemical methods based on cation extraction, followed by quantification through titrimetry, flame atomic absorption spectrometry (FAAS), or flame photometry. As an alternative to these classical methods, the present study aimed to evaluate energy-dispersive X-ray fluorescence (EDXRF) combined with chemometrics for the determination of CEC. For this purpose, multiple linear regression (MLR) was employed as the multivariate calibration method. The calibration model was built using X-ray intensities obtained by EDXRF and the CEC values determined by the chemical reference method (CEC at pH 7.0). The CEC values determined by the reference method ranged from 4.92 to 10.71 cmolc kg⁻¹. EDXRF analyses indicated a predominance of SiO₂ and Al₂O₃, reflecting the mineralogy of Cerrado soils, which are characterized by intense weathering and the presence of silicate and metal oxide minerals. The developed MLR model showed an adjusted R² of 0.97, indicating a strong correlation between the CEC values obtained by the reference method and those predicted by the model. The root mean square error (RMSE) of 0.40 cmolc kg⁻¹, approximately ten times lower than the lowest CEC value assessed, and the ratio of performance to deviation (RPD) of 5.97 demonstrate the high accuracy of the model. The characteristics of EDXRF, a rapid, non-destructive technique suitable for the analysis of solid samples, combined with MLR proved to be an efficient and environmentally friendly analytical strategy for determining CEC in Cerrado soils amended with LSW under different crops and application periods, in accordance with the principles of green chemistry.
Gatifloxacin (GAT), a fourth-generation antimicrobial agent, requires stringent quality control measures in human medication. Electroanalysis offers a cost-effective method for quality control, showing promise for drug analyses. In this study, GAT eye drops were analyzed using differential pulse voltammetry (DPV) with a BiVO4-modified carbon paste electrode (CPE). The electrochemical characterization of GAT involved a pH study, determining pH 7.0 as optimal with an anodic oxidation process at 0.88 V. The anodic peak suggested equivalence between protons and electrons, with a Nernstian-like slope of around 59 mV. pH-1 in the Epa vs. pH plot. Subsequent assays using PBS at pH 7.0 showed promising results. The electroanalytical method exhibited precision and accuracy that were suitable for the intended purpose. CPE/BiVO4 provided a low-cost, versatile, and rapid alternative for GAT quantification in pharmaceutical formulations, indicating the potential for analyzing other drugs in complex matrices. Subsequent voltammetric assays were carried out using PBS at pH 7.0. Further tests demonstrated a linear correlation between GAT concentration and current intensity (r2 = 0.99), with an LOD of 0.07 µM and LOQ of 0.23 µM. Precision testing confirmed the method’s reliability, showing a relative standard deviation of 1.28% and 1.48% for intra-day and inter-day precision, respectively. The CPE/BiVO4 method proved accurate, precise, and reproducible, offering a low-cost and versatile alternative for GAT quantification in pharmaceutical formulations. The findings suggest the potential application of this electroanalytical approach for drug analysis in complex biological matrices, showing promise for the quantification of other pharmaceuticals.
Carbon-based nanomaterials, particularly multi-walled carbon nanotubes (MWCNTs), are widely used in electrochemical sensors due to their high conductivity and large surface area. However, their poor dispersion often requires synthetic surfactants. Biopolymers such as cellulose have emerged as promising sustainable dispersing agents. In this context, bacterial nanocellulose (BNC) produced from waste offers a green alternative with potential to stabilize and disperse MWCNTs in aqueous media. Herein, a surfactant-free nanocomposite based on BNC produced from beer waste and MWCNTs modified with silver nanoparticles (AgNPs) was engineered to modify electrochemical sensors for pharmaceutical detection of furosemide. The morphology, composition, and electrochemical properties of the nanocomposite were characterized by scanning electron microscopy (SEM) with Energy-Dispersive X-ray Spectroscopy (EDS), cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), differential pulse voltammetry (DPV). Furosemide oxidation was evaluated in aqueous medium and synthetic urine using a glassy carbon electrode modified with the MWCNTs/BNC-AgNPs film, and voltammetric parameters were optimized to enhance the analytical response. The sensor presented a linear range from 5.0 to 65.0 µmol L⁻¹, with limits of detection and quantification of 0.19 and 0.64 µmol L⁻¹, respectively. These findings demonstrate the high sensitivity and practical applicability of the MWCNTs/BNC-AgNPs-modified electrode for reliable furosemide determination in urine samples.
This study reports a laser-induced graphene (LIG) electrode as a simple platform for the determination of ciprofloxacin (CPX). LIG was fabricated by laser scribing a Kapton® polyimide tape affixed to a ceramic substrate. Under optimized conditions, phosphate buffer as supporting electrolyte (0.10 mol L⁻¹, pH 7.0), and differential pulse voltammetry (pulse amplitude 60 mV, scan rate 30 mV s⁻¹), the method delivered a linear response from 8.3 to 166.7 μmol L⁻¹, with limits of detection and quantification of 2.5 and 8.3 μmol L⁻¹, respectively. The procedure was successfully applied to a pharmaceutical formulation and river water, yielding recoveries of 92.93–110% with good precision, thereby demonstrating the robustness of the method. The proposed LIG sensor is a cost-effective and sustainable alternative for environmental and quality control monitoring of CPX.
A conductively-heated digestion system (CHDS) with closed-vessel and diluted acid was evaluated for the preparation of animal feed for subsequent determination of As, Cd, Mo, Ni, Pb, Se and V by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The analytical performance of the digestor was checked by analyzing fish tissue, tuna and corn bran certified reference materials (CRMs) using 1 mL of 30% (w w-1) hydrogen peroxide plus 2 mL of nitric acid at 14, 7 and 3.5 mol L-1 HNO3. The recoveries of analytes in CRMs digested at different acid concentrations varied from 80 – 116% (3.5 mol L⁻¹ HNO₃) 86 – 118% (7 mol L⁻¹ HNO₃) e 80 – 123% (14 mol L⁻¹ HNO₃). The method was applied to real animal feed samples, and the CHDS results were like those of comparative microwave digestion (MWD). Quantification limits of analytes found by CHDS and MWD were also similar, concerning different acid concentrations. However, the use of diluted nitric acid in the preparation of animal feed samples proved to be a viable and efficient approach, allowing lower residual acidity, reduction in reagent consumption and acid waste generation, in accordance with the principles of Green Chemistry.
Indirect polarography was used to develop a novel analytical approach for prothipendyl quantification in dosage forms, using oxone as an oxidative agent. Prothipendyl is not reducible at a mercury electrode, but it can be determined by differential pulse voltammetry as its initial S-oxidation product, which is obtained by oxidation with KHSO5 in 0.02 mol L–1 HCl solution at 20 °C for 3 min (reduction peak at 0.02 mol L–1 HCl, about –0.6÷–0.7 V vs Ag/AgCl-reference electrode). Using differential pulse voltammetry at a hanging mercury drop electrode, a linear calibration curve was established for prothipendyl over the concentration range of 0.3 to 3.3 µg mL–1 in 0.02 mol L–1 HCl, with a detection limit of 0.17 µg mL–1. The feasibility of quantitatively determining prothipendyl hydrochloride in 40 mg Dominal® tablets was confirmed. A percentage recovery (%Re) of 100.425 and a relative standard deviation of 1.70% (n = 7) were obtained. The validity of the measurement method was examined by determining how closely the mean measured value (x̅) aligned with the established reference value (μ), based on the condition │(x̅ – μ) × 100%/μ│ < tα × RSD / √n, with n = 7 and a 95% confidence level (P = 0.95).
Mycotoxins are toxic substances produced by fungi and can occur in medicinal plants. Among them are aflatoxins, ochratoxins, fumonisins, zearalenone, and deoxynivalenol, which can be quantified by HPLC analysis. The objective of this scoping review was to evaluate HPLC-based analytical methods using fluorescence detection and diode-array detection (HPLC-FLD and HPLC-DAD) for the quantification of mycotoxins in medicinal plants. The study was conducted in accordance with the Joanna Briggs Institute recommendations. 3,939 articles were identified, of which 30 were selected for review. Among the selected papers, 27.6% were published in China, where the use of medicinal plants is common due to Traditional Chinese Medicine. Of the 30 articles, 23 investigated aflatoxins, a group of mycotoxins with the highest health risk. Furthermore, 60% of the publications mention the development of an analytical method for only a single group of mycotoxins, highlighting the difficulty of developing multi-mycotoxin methods with the detector used. Of the articles included in the scoping review, 90% involve solvent extraction followed by immunoaffinity columns. Mobile phases consist mostly of a composition of water, methanol, and acetonitrile in varying proportions. Half of the articles reported performing analytical method validation, with the main parameters evaluated being limit of detection, limit of quantification, precision, and linearity.
Professor José Alberto Fracassi da Silva holds a degree in Chemistry from the University of São Paulo (1995), a PhD in Chemistry (Analytical Chemistry) from the University of São Paulo (2001) and completed postdoctoral degree at the Laboratory of Integrable Systems of the Polytechnic School of the University of São Paulo (2003), as well as The Ralph N. Adams Institute for Bioanalytical Chemistry at The University of Kansas, USA (2011). He has been a professor at the Institute of Chemistry at the State University of Campinas since 2004 and was promoted to Associate Professor in 2019. His research experience lies in the field of chemistry, with an emphasis on analytical instrumentation. He primarily works on topics such as capillary electrophoresis, electrochemical and fluorescence detection, and microanalysis systems (lab-on-a-chip).
Before beginning this text, one point must be made very clear. Vaccination is essential for preventing serious diseases and avoiding outbreaks that threaten public health. Vaccines protect individuals and the community through herd immunity, reducing mortality, hospital costs, and permanent sequelae associated with infections. Vaccination is an act of caring for yourself and others. Thimerosal (TM) is a mercury-containing organic compound widely used as a preservative in various biological and pharmaceutical products, including many vaccines, to prevent the growth of harmful microbes inadvertently introduced into the vaccine during its use. The documented antimicrobial properties of TM contribute to the safe use of vaccines in multi-dose vials, which are less expensive, easier to store, and help reduce waste. TM, which is approximately 50% mercury by weight, has been one of the most widely used preservatives in vaccines. It is metabolized or degraded to ethylmercury (EtHg) and thiosalicylate. In general, a vaccine containing 0.01% (m/v) TM as a preservative contains 50 µg of TM per 0.5 mL dose, corresponding to approximately 25 µg of mercury per 0.5 mL dose.1 The use of TM as a preservative in multi-dose vaccines is controversial because this compound has been abolished in the United States and the European Union, either due to its replacement with other preservatives (free mercury) or the adoption of single-dose formulations. In Brazil, it is somewhat surprising that of the use of TM in cosmetics (topical use) has been suspended,2 partly related to allergic contact dermatitis, but its use in vaccines is still permitted. The World Health Organization (WHO) supports this decision, stating that “ethylmercury is present in thiomersal as a preservative in some vaccines and does not pose a health risk.”3 However, a growing body of scientific evidence has increasingly challenged this assertion. Experimental models for assessing the toxicity of a given species are particularly decisive. Regardless of the model (simple or complex) tested with TM, evidence of this compound’s toxicity consistently emerges to varying degrees (Figure 1). TM has demonstrated the ability to form adducts with cysteine, glutathione, and especially with carrier proteins, binding to free thiol groups and thereby being transported throughout the body, reaching other proteins, enzymes, and organs.4 In this sense, the effect of TM on proteins has been associated with its ability to induce protein fibrillation,4,5 impair hemoglobin’s capacity to bind oxygen, and increase protein glycation.6 The use of electrospray ionization–mass spectrometry (ESI-MS) has confirmed TM’s high affinity for proteins containing free thiol groups, leading to metalation and the formation of stable adducts with cytochrome c, ribonuclease A, carbonic anhydrase I, and superoxide dismutase, thereby compromising the natural activity of these enzymes.7 When used as a cellular model, erythrocytes exposed to TM show alterations in essential functions, particularly in oxygen transport capacity, along with changes in cellular morphology.8 Across different cellular models, TM has consistently demonstrated toxicity, indicating that the doses used to achieve antimicrobial activity cannot be considered safe.9 Different animal models (flies, fish, and rodents) have consistently demonstrated that TM is a toxic compound, even at sublethal doses.10–12 In mouse models, TM compromises vaccine potency through thiol modification, affecting the antigenicity and immunogenicity of the formulation by reducing the binding activity between antigens and antibodies.13 In contrast, a Wistar rat model mimicking TM exposure in infants following childhood vaccination revealed significant damage to bioenergetic pathways within the nervous system, particularly the brain.14 Moreover, in baby monkeys exposed to TM-containing vaccines, researchers found that the fraction of inorganic mercury in the brain ranges from 21% to 86% of total mercury measured, with an average of ≈ 70%.15 In vitro studies comparing EtHg with methyl mercury (MeHg) have shown similar outcomes in cardiovascular, neural, and immune cells. However, under in vivo conditions, evidence indicates distinct toxicokinetic profiles between MeHg and EtHg, with the latter exhibiting a shorter blood half-life, different compartment distribution, and faster elimination. EtHg’s toxicity profile, therefore, differs markedly from that of MeHg, leading to distinct patterns of exposure and associated toxicity risks.16 From another perspective, studies on the environmental fate and risk of mercury have mostly focused on total mercury and the toxic species MeHg. However, EtHg has long been overlooked, partly due to analytical limitations. The occurrence of EtHg and its possible natural sources in the environment provide essential background information and valuable clues for understanding its natural presence and environmental behavior.17 Thus, the distribution and toxicological aspects of EtHg are not solely associated with TM use; they are also related to other environmental and chemical pathways. Therefore, expanding research on TM and EtHg is a strategic priority to achieve a more comprehensive understanding of their biological effects and associated impacts.18 In this context, the continued use of TM in some vaccines reflects less an unavoidable scientific necessity and more a set of regulatory, logistical, and economic barriers. The proven stability of these formulations, the low rate of serious adverse events, and the reduced cost of multidose vials create a scenario in which regulatory agencies are reluctant to require reformulations that would necessitate new stability, safety, and immunogenicity studies. From an industry perspective, the lack of economic incentives to modify products intended primarily for low-return markets reinforces institutional inertia, even in the face of technically feasible alternatives consistent with global efforts to reduce mercury use. In this context, analytical chemistry plays a crucial role in providing evidence that extends beyond traditional safety indicators. Sensitive chemical speciation methods, for example, enable the distinction between EtHg, MeHg, and their inorganic forms, thereby revealing metabolic pathways that in the past could not be assessed with conventional toxicological approaches. These advances enable the characterization not only of the kinetics of systemic elimination, but also of the formation and accumulation of inorganic species in target tissues, providing a more accurate basis for reassessing risks in vulnerable subpopulations. In addition, microbiological monitoring techniques and chemical stability analyses provide robust data to validate formulations without TM or with alternative preservatives, demonstrating that microbiological safety can be preserved through optimized packaging systems or the adoption of single-dose presentations. Based on this evidence, a central conclusion can be drawn: The maintenance of TM today is more a consequence of a regulatory and productive framework that is insufficiently dynamic than it is a result of real scientific limitations. The data generated by analytical chemistry, speciation studies, kinetic analyses, stability evaluations, and post-use surveillance not only allow for a more detailed characterization of the toxicological profile of EtHg, but also provide technical support for transitioning to safer and scientifically sound alternatives. Thus, analytical advances cease to function merely as evaluative tools and become true catalysts for change, providing the scientific basis required for regulators and manufacturers to adopt policies and formulations that progressively reduce dependence on mercury compounds in vaccines. Finally, it is crucial to emphasize that, regardless of whether TM is present, vaccination remains essential. For adolescents and adults, TM-associated risks are typically minimal; however, for infants and newborns, existing uncertainties deserve more careful consideration. Nevertheless, the choice between a TM-containing vaccine and no vaccination at all is unequivocal: Vaccination unquestionably remains the safer and more responsible option.
While the aim of "Quantifying Uncertainty in Analytical Measurement" is to provide a standardized framework and methodology for estimating and expressing uncertainty in quantitative analytical measurements, the Eurachem/CITAC guide for qualitative analysis expands the concept of uncertainty to categorical decisions, allowing qualitative analyses to be evaluated with the same metrological and statistical rigor. Published in 2021, the Eurachem/CITAC Guide on Assessment of Performance and Uncertainty in Qualitative Chemical Analysis is considered as a key methodological reference for assessing the reliability of qualitative outcomes. Such qualitative tests yield categorical results, such as the presence or absence of a substance or the identification of a compound. Although such results do not yield a directly measured numerical value, they are not exempt from uncertainty. The guide proposes quantifying these uncertainties associated with the probability of false-positive or false-negative results to inform users of the analysis about the method's reliability limits.1 In this context, the document provides different methodological approaches that enable qualitative decisions to be treated with the same statistical rigor as quantitative analyses. It presents practical examples illustrating how these approaches can be implemented, recognizing that the outcome of a qualitative method, such as the identification of a compound or the confirmation of a substance’s presence, is subject to errors and uncertainties that must be systematically evaluated and communicated.2 Defining the types of criteria used in qualitative analysis involves distinguishing between quantitative criteria, which entail converting a numerical value into a category (e.g., compliant or non-compliant based on a threshold), and qualitative criteria, such as color change, visual observation, or other indications of presence or absence. Although the guide primarily focuses on binary nominal classification (e.g., yes or no, present or absent), it acknowledges that, in certain categorical cases, classification can be reduced to correct or incorrect to apply the same principles. By explicitly defining the decision criteria and the statistical or probabilistic thresholds that delineate the boundary between categories, the guide ensures metrological traceability and transparency in the interpretation of results. As a central tool for characterizing the performance of qualitative methods, the guide introduces metrics based on false results. Laboratories are advised to collect samples with known or reference results, apply the method under evaluation to these cases, and estimate the frequency with which the process fails to correctly classify an item. From these results, the proportions of true positives, true negatives, false positives, and false negatives are calculated. These proportions allow the estimation of error probabilities and the construction of confidence intervals, thereby expressing statistical uncertainty. Treated as direct measures of uncertainty, these error rates provide qualitative analyses with a quantitative dimension of reliability, which is important when analytical results affect technical, regulatory, or scientific decisions.3,4 A crucial aspect mentioned by the guide is the representativeness and diversity of the cases used in performance estimation. If the test cases are too homogeneous or not representative of real conditions, the estimated error rates may underestimate or overestimate the actual uncertainty. Therefore, it is recommended to use test samples that cover the expected range of conditions, including different matrices, varying levels of interferents, and operational variations, to ensure the reliability estimate is robust. This recommendation becomes especially relevant in methodologies based on spectroscopy and measurement instruments, where spectral variability and signal overlap among similar constituents pose practical challenges. By requiring sample sets that are representative of real analysis conditions, the guide promotes validation that reflects the natural variability of matrices and the presence of interferents, an essential element to realistically estimating the probability of incorrect classifications.5-7 In addition to representativeness, the number of samples used for performance estimation plays a critical role in the reliability and stability of validation results. Although no universally fixed minimum sample size can be defined since this number depends on the analytical objective, system complexity, and data variability, previous validation studies conducted under simple or simulated scenarios have shown that insufficient sample numbers can lead to unstable or overly optimistic estimates of error rates, particularly in qualitative and classification problems. As a general indication, validation studies require sample sizes on the order of several tens per class to achieve stable and convergent performance estimates, with larger sample sizes becoming necessary as model complexity increases.8 Therefore, representativeness and sample size should be considered jointly to ensure that estimated probabilities of incorrect classification realistically reflect the uncertainty associated with real analytical conditions. When it comes to selectivity, the guide allows for a reinterpretation of this traditional concept. Instead of remaining a purely qualitative attribute, selectivity is viewed as a measurable parameter linked to the probability of correctly distinguishing between samples containing similar compounds or interferents. Thus, evaluating selectivity becomes an exercise in pattern recognition and probabilistic classification, where performance is expressed by the method’s ability to assign each sample to its category correctly. This perspective broadens the understanding of selectivity, associating it with the statistical reliability of the response rather than solely the absence of visual or instrumental interference.9-13 The practical application of this methodology is especially relevant in non-targeted analyses, where the goal is to detect complex patterns and identify substances across diverse matrices without predefined targets. In such cases, the method must correctly differentiate signals corresponding to distinct compounds, even in the presence of instrumental noise and spectral overlap. Recent studies illustrate this by developing an automated approach for identifying microplastics using Raman spectroscopy, addressing the challenges of spectral variability and signal overlap among similar polymers. Instead of relying on an arbitrary similarity value, the method used a correlation distribution obtained via bootstrap sampling to determine the practical acceptance threshold, aligning with the guide's recommendation to base qualitative decisions on probabilistic metrics and explicit performance assessments. This statistically controlled approach has been shown to significantly reduce classification errors, providing known confidence levels for each decision and bringing the analytical process closer to a metrologically traceable system where each decision is supported by uncertainty and performance estimates.14 When Raman spectroscopy is applied to quantify species in reactive mixtures, as in studies of urea and thiourea, adherence to the principles outlined in the guide is essential. Although the primary objective of such studies is to estimate concentrations from Raman signals quantitatively, a qualitative component remains inherent in spectral assignment, band identification, and the differentiation of interfering signals, steps that carry a risk of interpretative error. The guide emphasizes that any implicit qualitative decision, for instance, assigning a peak to a specific vibrational mode or determining whether a signal belongs to the analyte or to noise, is subject to uncertainty, and this uncertainty should be expressed in terms of error probabilities. In the urea/thiourea system, this involves evaluating the likelihood of misinterpretations—such as mistaking an interfering band for the analyte or overlooking weak peaks buried in noise—and designing validation experiments that account for varying compound ratios, noise levels, and instrumental conditions.15 Moreover, the guide recommends that qualitative conclusions, such as band assignments or confirmation of analyte presence, be accompanied by a confidence statement or an estimate of the local error probability derived from previously assessed spectral error rates, thereby enhancing transparency and discouraging absolute interpretations of spectroscopic signals. The systematic implementation of these guidelines also fosters a culture of continuous performance monitoring in laboratories. By acknowledging that even seemingly unambiguous spectral assignments may fail, researchers are encouraged to establish controls, retest protocols, and periodically review the criteria used to discriminate signals.16,17 Beyond individual method performance, interlaboratory comparability of qualitative decisions is equally critical to ensure reproducibility across different analytical contexts. In this regard, the joint IUPAC/CITAC (2025) guide enhances metrological capacity by proposing a statistical framework specifically designed for analyzing agreement in categorical results obtained across laboratories, operators, or instruments. Tools such as CATANOVA (Categorical Analysis of Variance) and ORDANOVA (Ordinal Analysis of Variance) enable the treatment of nominal and ordinal variables, respectively, quantifying the degree of agreement among classifications and identifying sources of systematic variability between laboratories. This statistical evaluation is crucial for validating automated decision systems in qualitative methods. Consequently, it ensures that a method not only performs reliably within a single laboratory but also yields equivalent, traceable results across diverse analytical environments — an indispensable requirement for the international recognition of qualitative measurements.18 Furthermore, the consolidation of metrological approaches to qualitative methods has expanded across various domains, reflecting the need to transform descriptive judgments into traceable, comparable decisions. In environmental monitoring systems, the use of portable devices, assay kits, and continuous sensors highlights the importance of incorporating performance and uncertainty considerations into the interpretation of detection or non-detection results.19 In toxicological, forensic, and genetic sequencing contexts, harmonised procedures and the application of statistical metrics enable quantification of interlaboratory variability and reduction of classification errors.20,21 In the assessment of pharmaceutical equivalence and clinical diagnostic testing, risk models and sensitivity and specificity metrics reinforce the need to track the reliability of qualitative decisions.22,23 Finally, studies involving bottom-up uncertainty estimation in complex matrices and interlaboratory comparisons demonstrate that selectivity and agreement among categorical results can be addressed with statistical rigor, thereby consolidating qualitative metrology as a structured discipline applicable across multiple analytical fields.24-26 The relevance of these principles and tools transcends specific domains, extending well beyond laboratory contexts. In sectors such as food and beverages, authentication and origin traceability depend strongly on spectroscopic and multivariate methods, in which trace elements and spectral profiles are used as markers to distinguish regions or products. Despite the high performance of supervised classification models, the natural variability of elements and the overlap of compositional features can lead to false positives or negatives, which are precisely the central metrics discussed in the Eurachem and IUPAC/CITAC guides. The explicit incorporation of performance and uncertainty measures could therefore enhance the validation of these methods, making results more comparable across laboratories and legally more defensible in cases of fraud or commercial dispute.27 A comparison among these contexts generally reveals significant conceptual convergence, with all cases exhibiting a clear transition from descriptive approaches to quantitatively validated systems for qualitative decision-making. The central principle that emerges is that every qualitative decision is, in essence, a statistical inference and must therefore be accompanied by explicit measures of performance, uncertainty, and reproducibility. This perspective establishes a new paradigm for analytical chemistry, in which qualitative methods are no longer merely screening tools but rather integral components of the metrological domain, characterized by traceability, comparability, and transparency. The scope of the guide is broad, reflecting a modern approach to the determination of neurotransmitters. The determination and correct interpretation of the limits of detection (LoD) and quantification (LoQ) are essential aspects for ensuring the reliability and sensitivity of the proposed analytical method. In the study, achieving extremely low LoD and LoQ values demonstrates not only the system's high instrumental performance but also its capability to detect and quantify analytes at trace levels, which is fundamental for biological and neurochemical applications. Thus, defining LoD and LoQ within the context of this work not only confirms the procedure's sensitivity but also reinforces the method's metrological robustness and suitability for its analytical purpose, in accordance with internationally advocated principles of performance and uncertainty.28 Finally, incorporating the recommendations from the guides promotes a culture of quality within laboratories: it does not blindly assume that a qualitative method is infallible. Instead, it acknowledges that it can and should be accompanied by reasonable estimates of reliability. This stance strengthens the credibility of results, supports analytical risk management, guides decisions on confirming or investigating borderline cases, and encourages continuous improvement of procedures, periodic method validation, and quality control strategies adapted to non-targeted analyses. In this way, selectivity ceases to be merely a technical property and becomes a performance metric with direct implications for uncertainty assessment and the reliability of analytical decisions. This consolidation elevates qualitative metrology to the status of a mature scientific discipline, indispensable for ensuring confidence in analytical decisions at both local and international levels.
Selenium (Se) plays a significant role in many physiological processes. During the past years the role of Se has changed, from being considered toxic to the definition of being essential in almost every cell of our body. Furthermore, Se species play a role in mercury (Hg) detoxification suggesting that the protective effect of Se against Hg is related to the amount of Se available. In this work, two methods for Se determination in fish, by HG-AFS and HG-MP AES were developed. Moreover, a green analysis was applied to evaluate them. To both methods, optimization conditions were exhaustively evaluated and validated. Excellent figures of merit were obtained, with LOD of 0.04 mg kg-1 and 0.004 mg kg-1 to HG-MPAES and HG-AFS, respectively. The developed methods fit our purpose, being adequate for the determination of Se in fish and were compared in terms of accordance with the green analytical chemistry principles using AGREE metrics. The HG-MP AES method constitutes a greener alternative (0.60) than AFS (0.46). Borriqueta porgy (Boridia grossidens) fish samples from the Uruguayan coast were analyzed. The levels of Se in the samples were between 0.15-0.40 mg kg-1 determined by HG-MP AES and 0.13-0.35 mg kg-1 with HG-AFS, being the developed methods two alternatives for the Se monitoring. Finally, an ecotoxicological study was conducted to evaluate Se protection against Hg in fish tissue. All samples presented a Se:Hg molar ratio and the Selenium Health Benefit Value above 1, suggesting the protection of Se against mercury toxicity. This work presents two developed analytical methods suitable for the determination of Se in fish samples; in addition, this is the first evaluation that presents HG-MP AES for Se determination in fish. Furthermore, this work constitutes the first to determine Se in Uruguayan coast as well as the first to evaluate the Se protection against Hg in fish tissue.
This study evaluates the concentrations of particulate matter (PM10 and PM2.5) in indoor and outdoor university classrooms using a low-cost particulate matter sensor. Measurements were conducted hourly, daily, and annually in a closed, air-conditioned classroom at the Institute of Biosciences, Letters and Exact Sciences (Ibilce) of S & atilde;o Paulo State University (UNESP) throughout 2022. Results revealed that PM10 levels consistently exceeded the World Health Organization's (WHO) annual guideline of 15 & micro;g m-3, aligning with local CETESB data. Meanwhile, average indoor PM2.5 concentrations (12.5 +/- 11.2 & micro;g m-3) were almost three times the annual WHO limit of 5 & micro;g m-3. Peak values reached 43.75 & micro;g m-3, nearly 900% above the guideline, raising significant health concerns, and the calculated hazard quotient (HQ) approached the reference threshold. Outdoor PM2.5 concentrations showed similar trends, with multiple peaks surpassing recommended thresholds. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) analysis linked high PM levels to wildfires in central and northern Brazil and localized factors, including vehicle traffic and classroom maintenance. Statistical analysis revealed no significant difference between indoor and outdoor PM2.5 levels, emphasizing the influence of external pollution on indoor air quality. These findings show the urgency of implementing targeted interventions, such as regular cleaning of classrooms, curtains, and air conditioning systems, to mitigate PM exposure. The study highlights the need for improved air quality management to ensure a safe learning environment for students and faculty.
In this work, we study two UV filters (octocrylene and octinoxate) and the synthetic fragrance galaxolide in the yellow clam (Amarilladesma mactroides). Two beaches were strategically selected based on their contrasting population density, level of tourism and recreational activity. The clam samples used in this study originate from two distinct sub-populations. Samples from each beach were separated into 4 sizes (<54 mm, 55-56 mm, 57-58 mm and >59 mm); then, a modified citrate buffered QuEChERS method was used for subsequent evaluation in gas chromatography-mass spectrometry (GC-MS). The recoveries for all analytes were in the range between 70 to 102%, the RSD oscillated between 1 to 18%, and the limit of quantification was defined as the lowest recovery level with a value of 50 & micro;g Kg-1 for all three analytes. As a result, the presence of the UV filter octinoxate was detected in the size of 55-56 mm on Barra Puimayen beach, being lower than the limit of quantification (50 & micro;g Kg-1). However, the evaluation indicated the absence of analytes from the individuals belonging to La Maciega beach, which presents a minimal anthropogenic impact.
Calendula officinalis extract was evaluated as a functionalization agent for gold nanoparticles (AuNPs). The resulting nanoconjugate (AuNP-Cale) was thoroughly characterized, and explored as a sensitizer for dye-sensitized solar cells (DSSC). As a starting point, citrate-reduced AuNPs (AuNP-Cit) were synthesized and fully characterized. Comprehensive characterization for both AuNP-Cit and AuNP-Cale included dynamic light scattering (DLS), electrophoretic light scattering (ELS), colloidal and stability assay. Successful functionalization included increased hydrodynamic diameter, reduced zeta potential, and improved colloidal stability. DSSC evaluation demonstrated that while pre-formed AuNP-Cale did not enhance efficiency, improved performance was achieved when AuNP-Cit was added sequentially after the extract on the TiO2 electrode, likely due to better electrode coverage. This result correlated with enhanced light absorption (FORS) and favorable electrochemical impedance spectroscopy parameters.
The lack of appropriate formulations for the pediatric population often requires the manipulation of adult dosage forms in hospital settings, a practice that can lead to errors that impact patient safety. This study aimed to support the quality control of pediatric preparations by developing and validating analytical methods for the quantification of active pharmaceutical ingredients (APIs) in the tablets used for their elaboration. Folic acid and phenobarbital were selected based on a risk analysis as the priorityAPIs to work with. High-performance liquid chromatography (HPLC) and Near-infrared (NIR) spectroscopy methods for the quantification of theseAPIs in commercially available products were developed and validated. NIR methods are presented as a rapid and non-destructive alternative that offers the possibility of determining the content of the same tablets that will be used to prepare the pediatric dilution. This could be considered as a promising analytical tool for drug quality control during the elaboration process of these preparations.
The scientific landscape is undergoing a profound transformation driven by artificial intelligence (AI). While its influence is already evident in disciplines such as bioinformatics, materials science, and drug discovery, analytical chemistry has only now begun to fully embrace its potential.1 Yet, few areas could benefit more from the structured, data-rich nature of AI than the analytical sciences themselves. Analytical chemistry has always been the discipline of signals, patterns, and interpretation. In that sense, analytical chemistry could be regarded as a conceptual precursor to machine learning thinking, long before the modern algorithms existed. AI provides an unprecedented opportunity to enhance analytical workflows, at every stage, from experimental design and method development to data processing, interpretation and decision-making.2 Machine learning models can optimize instrumental conditions, uncover hidden correlations between parameters, and automate complex calibration or validation procedures. For several years now, the trend has been toward the use of miniaturized and more environmentally friendly methodologies, and in this regard the development of AI can be of great help to improve existing greenness-assessment algorithms, providing smarter, more sustainable analytic protocols with lower sample and solvent consumption. In spectroscopic and chromatographic analyses, AI algorithms are increasingly capable of distinguishing genuine analytical signals from background noise or matrix interferences, enabling faster and more reliable quantification and identification.2 Beyond improving performance, AI is redefining the very role of the analytical chemist — from manual operator to data curator and critical interpreter. AI may also facilitate structural elucidation of unknown compounds, possibly offering a cost-effective alternative to expensive commercial spectral libraries or extensive manual interpretation workflows. However, this transformation brings certain challenges. AI systems must be transparent, explainable, and validated according to the same rigorous standards that govern traditional analytical methods.3 The “black box” problem remains one of the greatest barriers to trust and acceptance. It is essential that machine-learned models complement, rather than replace, human expertise — that they become partners in analytical reasoning, not substitutes for it. This balance between automation and understanding is central to the spirit of analytical chemistry. The adoption of AI also requires rethinking education and training. Future analytical chemists will need to navigate not only spectral lines and chromatograms but also fluency in algorithms, datasets, and validation metrics1,3 Integrating AI literacy and data science skills into analytical chemistry curricula is no longer optional: it is a prerequisite for keeping the discipline relevant and forward-looking. Those who understand how to merge chemical intuition with computational power will lead the next generation of analytical breakthroughs. The essence of analytical chemistry has always been about transforming raw data into knowledge. In this new era, artificial intelligence emerges not just as a tool, but as a collaborator in that pursuit. The challenge —and opportunity— lies in ensuring that as machines learn to think, we do not lose our capacity to question. The analytical chemist of tomorrow will not merely measure, they will also design, model, predict, and interpret. Embracing AI is not the end of analytical chemistry as we know it—it is its most exciting reinvention.
Cane syrup, a nutrient-rich byproduct of sugarcane, is valued for its bioactive compounds and mineral content, including phosphorus, a vital macromineral essential for human bone health, enzyme activity, and plant metabolism. Conventional methods for total phosphorus analysis in such viscous matrices face challenges, such as matrix interference, high reagent consumption, and environmental impact. This study optimized and validated an ultrasound-assisted extraction (UAE) method combined with UV-V is spectrophotometry for determining total phosphorus in cane syrup. UAE parameters were optimized using a simplex centroid mixture design to assess the effects of HNO3, HCl, and ultrapure water as extraction solvents. UV-Vis spectra revealed that HCl-rich extraction solvents enhanced pigment production via the Maillard reaction, interfering with spectrophotometric detection. In contrast, ternary acid mixtures minimize these effects. The optimal conditions (1.67 mL HNO3, 2.00 mL HCl, and 1.30 mL H2O) achieved recovery rates of approximately 100%, without significant matrix interference. The validation of UAE combined with UV-Vis spectrophotometry demonstrated excellent selectivity and linearity (R-2 > 98.0%), low limits of detection and quantification (0.296 mu g g(-1) and 0.898 mu g g(-1), respectively), and good precision (RSD < 11%). The method's accuracy was confirmed through a paired t-test comparison with microwave-assisted digestion (MAD), showing no significant differences (p > 0.05). UAE proved to be more environmentally friendly than MAD, with lower energy consumption (4.17 vs. 62.50 Wh/sample) and reduced reagent usage, as indicated by the AGREEprep metrics (scores: 0.41 vs. 0.30). The total phosphorus content in cane syrup samples varied significantly (11.48-129.54 mg kg(-1)), influenced by geographical origin and production processes. The validated UAE method provides a fast, cost-effective, and sustainable alternative for phosphorus analysis in complex food matrices, aligning with the principles of green chemistry.
